Exploratory data analysis of all variables¶
Goal¶
The goal of this notebook is to explore correlations among variables in a dataset, in order to select those features that merit further exploration.
This is a shotgun kind of analysis, used as the basis for further exploration in quick visualization tools such as Tableau, and as a starting point for modeling decisions: feature selection, further feature engineering, model selection, and so on. See this example of the kind of exploration that stems from the results found here -- like this one there should be several that may not need to end up documented.
Sections¶
Example of how to use the data presented in these tables
Number of notifications vs outcome
Analyses by types
1. Categorical relationships effect size heatmap
2. Numerical relationships effect size heatmap
3. Numerical and categorical relationships effect size heatmap
Automatic analyses done by three functions
Summarize numerical categorical
The goal of these functions is to quickly assess whether there is a relationship between variables. So, for each variable:
- A summary is presented: summary statistics, value counts, contingency table.
- A plot, should the number of categories be small enough (less than 20)
- A p-value for the null hypothesis that there is no difference between the variables: chi^2, correlation, or Kruskal-Wallis H-test for independent samples or paired t-tests.
- A measure of effect size: Cramer's V, Pearson's R, eta^2, or Cohen's d.
Libraries and data
Data¶
The data consists of a dataset of customer lookups for flights in app. Dataset which was cleaned and processed in a sepparate notebook and combined with data from freely available datasets, used mostly to explore markets and market penetration:
- https://ourairports.com/data/airports.csv
- https://population.un.org/wpp/Download/Standard/Population/
- https://data.worldbank.org/indicator/IS.AIR.PSGR
Some feature engineering has been done, mostly separating information contained in a single variable. This process is documented in a separate notebook.
See a sample of the working dataset with all the current variables.
In [1]:
from IPython.core.display import HTML
style = """
<style>
div.output_area {
overflow-y: scroll;
}
div.output_area img {
max-width: unset;
}
</style>
"""
HTML(style)
Out[1]:
In [1]:
# Import resources
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from scipy import stats
from itertools import combinations
%matplotlib inline
import warnings
warnings.filterwarnings("ignore")
# Display all columns in dataframe
pd.set_option('display.max_columns', None)
In [2]:
# Function to summarize categorical variables
def summarize_categorical(df, x, y, plot=True):
"""
Presents contingency table, chi-squared decision, and Cramer's V
for two categorical variables.
"""
plot_adj = False
if plot & (df[x].unique().shape[0] <=20) & (df[y].unique().shape[0] <=20):
plot_adj = True
print("\n"+"\n"+"------------ "+ x + " and " + y + " ------------" +"\n")
# Value counts
print(x + " : " + np.str(df[x].unique().shape[0]) + " unique values.")
print(y + " : " + np.str(df[y].unique().shape[0]) + " unique values.")
# Plot
if plot_adj:
plt.figure()
sns.countplot(y=x, hue=y, data=df,palette='husl',
order=df[x].value_counts().iloc[:10].index);
plt.show()
# Contingency table
contingency_table = pd.crosstab(df[y],df[x],margins = True)
if (contingency_table.shape[0] <= 10) & (contingency_table.shape[1] <= 10):
print(contingency_table)
else:
print("\n" + "Too many values to print contingency table.")
# Chi-squared
contingency_cols = contingency_table.shape[1] - 1
max_val = df[x].unique().shape[0]
contingency_obs = np.array([contingency_table.iloc[0][0:max_val].values])
for i in range(1,contingency_table.shape[0]-1):
contingency_obs = np.vstack((contingency_obs, contingency_table.iloc[i][0:max_val].values))
chi_2, p_v, deg_fr = stats.chi2_contingency(contingency_obs)[0:3]
# Cramer's V
## Get the smallest of (r-1) or (c-1), with r->rows, c->columns, of the
## contingency table observations.
t = np.min([contingency_obs.shape[0] - 1,contingency_obs.shape[1] - 1])
## Get the number of samples
n = df[x].shape[0]
## Calculate Cramer's V
cramers_v = np.sqrt(chi_2/(n * t))
# Print results
print("\n"+"\n"+"X^2 TEST")
print("\n"+ "p-value to reject null: " + np.str(np.round(p_v,4)))
print("X^2: " + np.str(np.round(chi_2,2)))
print("D.f: " + np.str(deg_fr))
print("\n"+"\n"+"Effect size")
print("Cramer's V: " + np.str(np.round(cramers_v,2)))
return p_v, cramers_v
In [3]:
# Function to summarize numerical variables
def summarize_numerical(df, x, y, summary=False, plot=True):
"""
Presents a first glimpse into relationship between two numerical variables: plots
vs each other, Pearson's R correlation, and p-value for it.
"""
print("\n"+"\n"+"------------ "+ x + " and " + y + " ------------" +"\n")
# Summary stats
if summary:
print("\n" + "Summary for " + x)
summary = df[x].describe()
print(summary)
print("\n" + "Summary for " + y)
summary = df[y].describe()
print(summary)
x_org = df[x][~df[x].isnull() & ~df[y].isnull()]
y_org = df[y][~df[x].isnull() & ~df[y].isnull()]
x_log = np.log(x_org + 0.01)
y_log = np.log(y_org + 0.01)
# Plots
if plot:
fig, ax= plt.subplots(2,2, figsize=(14,6))
ax[0,0].scatter(x = x_org, y = y_org, alpha = 0.1)
ax[0,0].set(title=x + ' original and ' + y + ' original', xlabel=x, ylabel=y)
ax[0,1].scatter(x=x_log, y=y_org, alpha = 0.1)
ax[0,1].set(title=x + ' log_transf and ' + y + ' original', xlabel=x +' log_transf', ylabel=y)
ax[1,0].scatter(x=x_org, y=y_log, alpha = 0.1)
ax[1,0].set(title=x + ' original and ' + y + ' log_transf', xlabel=x, ylabel=y +' log_transf')
ax[1,1].scatter(x=x_log, y=y_log, alpha = 0.1)
ax[1,1].set(title=x + ' log_transf and ' + y + ' log_transf', xlabel=x +' log_transf', ylabel=y +' log_transf')
plt.show()
# Correlation
r, p_val = stats.pearsonr(x_org, y_org)
# Print results
print("\n" + " Pearson's R: " + np.str(np.round(r,2)))
print("\n" + " p-value to reject null: " + np.str(np.round(p_val,2)))
return p_val, r
In [4]:
def t_test(tb,x,y):
vals = df[y].unique()
# T-test paired
c1 = tb[x][tb[y]==vals[0]].dropna()
c2 = tb[x][tb[y]==vals[1]].dropna()
t_stat, p_val = stats.ttest_ind(c1,c2)
# Effect size Cohen's d
numerator = np.mean(c1) - np.mean(c2)
denominator = np.sqrt((np.std(c1)**2 + np.std(c2)**2) / 2)
cohens_d = numerator/denominator
# Give absolute value of Cohen's d
cohens_d = np.abs(cohens_d)
if cohens_d > 1:
cohens_d = 1
return p_val, cohens_d, t_stat
def kruskal_wallis(df,x,y):
# Kruskal-Wallis
## Dictionary of numerical arrays for each categorical value
cats = {}
for i,cat in enumerate(df[y].unique()):
cats[cat] = df[x][df[y] == cat].dropna()
H, p_val = stats.kruskal(*cats.values())
# Effect size
k = df[y].unique().shape[0] # Number of groups
n = df[x].dropna().shape[0] # Total number of observations
eta2 = (H - k + 1)/(n - k)
return p_val, eta2, H
In [5]:
# Function to summarize numerical by categorical variables
def summarize_numerical_categorical(df, num, cat='outcome',plot=True, summary=True, hist=False):
"""
x: numerical variable
y: categorical variable
"""
x = num
y = cat
plot = False
if df[y].unique().shape[0] <=20:
plot = True
print("\n"+"\n"+"------------ "+ x + " by " + y + " ------------" +"\n")
# Summary stats
if summary:
summary = df[x].describe()
print(summary)
x_org = df[x][~df[x].isnull() & ~df[y].isnull()]
y_org = df[y][~df[x].isnull() & ~df[y].isnull()]
x_log = np.log(x_org + 0.01)
# Plots
if hist:
# Plot numerical variable distribution
f, axes = plt.subplots(2, 2, sharex=False, gridspec_kw={"height_ratios":(.15, .85)},
figsize = (12, 4))
sns.boxplot(x_org, ax=axes[0,0])
axes[1,0].hist(x_org)
sns.boxplot(x_log, ax=axes[0,1])
axes[1,1].hist(x_log)
plt.show()
if plot:
# Plot boxplots
f, axes = plt.subplots(1, 2, figsize=(12,4))
sns.boxplot(x = x_org, y = y_org , ax=axes[0]).set_title(x)
sns.boxplot(x = x_log, y = y_org, ax=axes[1]).set_title(x + " log transf")
plt.show()
else:
cat_vals = np.str(df[y].unique().shape[0])
print("\n"+ cat_vals + " different categorical values, too many to plot." + "\n")
# Tests: t-test if 2 groups; kruskal_wallis if 3 groups or more.
vals = df[y].unique()
if vals.shape[0] == 2:
p_val,effect_size,statistic = t_test(df,x,y)
test_name = "Paired t-test"
effect_name = "Cohen's d"
stat_name = "t-statistic"
elif vals.shape[0] > 2:
p_val,effect_size,statistic = kruskal_wallis(df,x,y)
test_name = "Anova - Kruskal-Wallis"
effect_name = "Eta^2"
stat_name = "H"
else:
p_val = 1000
print("Check the unique values of your categorical variable")
# Print results
print("\n"+"\n"+" " + test_name)
print("p_val to reject null: " + np.str(np.round(p_val,4)))
print(stat_name + " value: " + np.str(np.round(statistic,2)))
print("\n"+"\n"+"Effect size")
print(effect_name + ": " + np.str(np.round(effect_size,2)))
return p_val,effect_size
Load dataset¶
In [7]:
df = pd.read_csv("df_ordered_filled_out_with_geography_population_and_passengers_carried.csv")
In [8]:
df.sample(20)
Out[8]:
| transaction_id | origin_city | destination_city | user_id | trip_id | trip_type | departure_date | return_date | stay | weekend | filter_no_lcc | filter_non_stop | filter_short_layover | filter_name | status_updates | first_search_dt | watch_added_dt | latest_status_change_dt | status_latest | total_notifs | total_buy_notifs | first_rec | first_total | last_rec | last_total | first_buy_dt | first_buy_total | lowest_total | Use frequency | outcome | ordered | session | diff_day | diff | Search or watch | first_buy - lowest_total | days_to_departure | latitude_deg_origin | longitude_deg_origin | continent_origin | City origin | latitude_deg_destination | longitude_deg_destination | continent_destination | City destination | region_origin | region_destination | country_origin | country_destination | Domestic or international | Adult population | Country name | Capital origin name | Count_uniq_users_per_country | Percent unique users to country adult pop | Country Code | Region | IncomeGroup | Passengers carried Q1 | Percent unique users to passengers carried by country | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 251039 | 501147 | ORL | CLE | 38cd91530c4f7be82c440717b9cf3ea9f5f892ef3d1273... | d4c27f01c5311fa4886dad35d1e4e99a3b9b2bd64d34cf... | round_trip | 2018-01-26 00:00:00 | 2018-01-28 00:00:00 | 2.0 | 1 | 0 | 0 | 0 | NoFilter | 1 | 2018-01-10 10:01:00 | NaN | 2018-01-10 10:01:00 | shopped | 0.0 | 0.0 | buy | 110.0 | buy | 110.0 | 2018-01-10 10:01:00 | 110.0 | 110.0 | 8 | gained | 4 | 2 | 0 | 0 days 12:00:00.000000000 | search | 0.0 | 15 | 28.545500 | -81.332901 | NorthA | Orlando | 41.411701 | -81.849800 | NorthA | Cleveland | FL | OH | US | US | Domestic | 244635911.0 | United States | Washington | 585228 | 0.239224 | USA | North America | High income | 2.222555e+08 | 0.263313 |
| 568128 | 831289 | LHR | DUB | ad861b69926881aa7a806fc5fd57237f56ac9f0223f678... | f2d2ac3035fa7e7480e81d2ca869020a9f029bf421bdf9... | round_trip | 2018-03-24 00:00:00 | 2018-03-25 00:00:00 | 1.0 | 1 | 0 | 0 | 0 | NoFilter | 1 | 2018-02-14 18:19:00 | NaN | 2018-02-14 18:19:00 | shopped | 0.0 | 0.0 | buy | 101.0 | buy | 94.0 | 2018-02-14 18:19:00 | 101.0 | 94.0 | 13 | gained | 10 | 4 | 0 | 0 days 00:01:00.000000000 | search | 7.0 | 37 | 51.470600 | -0.461941 | EU | London | 53.421299 | -6.270070 | EU | Dublin | ENG | D | GB | IE | International | 51563063.0 | United Kingdom | London | 10952 | 0.021240 | GBR | Europe & Central Asia | High income | 4.134715e+07 | 0.026488 |
| 105493 | 985087 | QPH | SZX | 03f7e1c704f178d3150aa48f972569f0141aabda512e4c... | 6ea40289cb286dbcfe2d20d84ef312dd51425df2984f9b... | round_trip | 2018-04-26 00:00:00 | 2018-05-04 00:00:00 | 8.0 | 0 | 0 | 0 | 0 | NoFilter | 1 | 2018-01-01 13:05:00 | NaN | 2018-01-01 13:05:00 | shopped | 0.0 | 0.0 | buy | 795.0 | buy | 795.0 | 2018-01-01 13:05:00 | 795.0 | 795.0 | 13 | gained | 1 | 1 | 0 | 0 days 00:00:00.000000000 | search | 0.0 | 114 | -22.566999 | 27.150000 | AF | Palapye | 22.639299 | 113.810997 | AS | Shenzhen | CE | 44 | BW | CN | International | 1270186.0 | Botswana | Gaborone | 9821 | 0.773194 | BWA | Sub-Saharan Africa | Upper middle income | 6.335425e+04 | 15.501722 |
| 721156 | 568203 | ORD | PHX | e58dbac77fa8861a117496e5505b114d65773ed693640a... | 6c210d671510225fc3ac6818600f0046a2d541281c5ae4... | round_trip | 2018-02-16 00:00:00 | 2018-02-20 00:00:00 | 4.0 | 0 | 0 | 0 | 0 | NoFilter | 1 | 2018-01-09 16:26:00 | NaN | 2018-01-09 16:26:00 | shopped | 0.0 | 0.0 | buy | 337.0 | buy | 337.0 | 2018-01-09 16:26:00 | 337.0 | 337.0 | 19 | gained | 1 | 1 | 0 | 0 days 00:00:00.000000000 | search | 0.0 | 37 | 41.978600 | -87.904800 | NorthA | Chicago | 33.434299 | -112.012001 | NorthA | Phoenix | IL | AZ | US | US | Domestic | 244635911.0 | United States | Washington | 585228 | 0.239224 | USA | North America | High income | 2.222555e+08 | 0.263313 |
| 751511 | 852209 | QPH | SAN | f0c1d358c0e29a673a279f8294c9dc5c85b896a5a7876f... | cc44fc649ecd3af9c177a99e21f6a6e8053fd3cd0c1c1c... | round_trip | 2018-05-12 00:00:00 | 2018-05-19 00:00:00 | 7.0 | 0 | 0 | 0 | 0 | NoFilter | 10 | 2018-01-30 18:02:00 | 2018-01-30 18:02:00 | 2018-02-19 15:35:00 | active | 13.0 | 7.0 | wait | 297.0 | buy | 229.0 | 2018-03-07 16:48:00 | 226.0 | 131.0 | 2 | expected | 1 | 1 | 0 | 0 days 00:00:00.000000000 | watch | 95.0 | 101 | -22.566999 | 27.150000 | AF | Palapye | 32.733601 | -117.190002 | NorthA | San Diego | CE | CA | BW | US | International | 1270186.0 | Botswana | Gaborone | 9821 | 0.773194 | BWA | Sub-Saharan Africa | Upper middle income | 6.335425e+04 | 15.501722 |
| 199309 | 739873 | OAK | DCA | 2591047d624ffb767a6e0143f597f33e9e65bc6a329c5b... | f7478c81dc558ce2bbb6475672427b4db440deefa5f052... | round_trip | 2018-03-23 00:00:00 | 2018-03-25 00:00:00 | 2.0 | 1 | 0 | 0 | 0 | NoFilter | 1 | 2018-02-06 13:11:00 | NaN | 2018-02-06 13:11:00 | shopped | 0.0 | 0.0 | buy | 539.0 | buy | 539.0 | 2018-02-06 13:11:00 | 539.0 | 539.0 | 41 | gained | 13 | 5 | 7 | 7 days 13:58:00.000000000 | search | 0.0 | 44 | 37.721298 | -122.221001 | NorthA | Oakland | 38.852100 | -77.037697 | NorthA | Washington | CA | DC | US | US | Domestic | 244635911.0 | United States | Washington | 585228 | 0.239224 | USA | North America | High income | 2.222555e+08 | 0.263313 |
| 495114 | 65570 | BGI | FDF | 92304efa41b145fb97c39586513a62c9cdb8def46d8445... | 84a1f441dbce58eeb09edad33318ef04363690a7a83ea5... | one_way | 2018-03-15 00:00:00 | NaN | NaN | 0 | 0 | 0 | 0 | NoFilter | 1 | 2018-02-23 23:30:00 | NaN | 2018-02-23 23:30:00 | shopped | 0.0 | 0.0 | buy | 181.0 | buy | 181.0 | 2018-02-23 23:30:00 | 181.0 | 181.0 | 6 | gained | 2 | 1 | 0 | 0 days 00:04:00.000000000 | search | 0.0 | 19 | 13.074600 | -59.492500 | NorthA | Bridgetown | 14.591000 | -61.003201 | NorthA | Fort-de-France | 01 | U-A | BB | MQ | International | 217880.0 | Barbados | Bridgetown | 172 | 0.078943 | BRB | Latin America & Caribbean | High income | NaN | NaN |
| 50355 | 595692 | ICN | JFK | 8bfd9a871faf17f9995c52cfb06a812c827afd7d75e7b3... | 156b1dc84cb35ae762cf3220c78243306aa2411503ec05... | round_trip | 2018-04-01 00:00:00 | 2018-04-08 00:00:00 | 7.0 | 0 | 0 | 0 | 0 | NoFilter | 1 | 2018-02-16 08:28:00 | NaN | 2018-02-16 08:28:00 | shopped | 0.0 | 0.0 | wait | 867.0 | wait | 867.0 | NaN | NaN | 867.0 | 1 | gained | 1 | 1 | 0 | 0 days 00:00:00.000000000 | search | NaN | 43 | 37.469101 | 126.450996 | AS | Seoul | 40.639801 | -73.778900 | NorthA | New York | 28 | NY | KR | US | International | 41781113.0 | South Korea | Seoul | 1038 | 0.002484 | KOR | East Asia & Pacific | High income | 2.203939e+07 | 0.004710 |
| 375599 | 937754 | SEA | SNA | 65f5223957c3530eb458c38ccab975772b5868a992b398... | f0977f5a55a33fbf9f6e7c4aa2389846c4def81f010cce... | round_trip | 2018-04-04 00:00:00 | 2018-04-10 00:00:00 | 6.0 | 0 | 0 | 0 | 0 | NoFilter | 1 | 2018-02-23 02:16:00 | NaN | 2018-02-23 02:16:00 | shopped | 0.0 | 0.0 | wait | 193.0 | wait | 193.0 | NaN | NaN | 193.0 | 7 | gained | 2 | 2 | 37 | 37 days 13:41:00.000000000 | search | NaN | 39 | 47.449001 | -122.308998 | NorthA | Seattle | 33.675701 | -117.867996 | NorthA | Santa Ana | WA | CA | US | US | Domestic | 244635911.0 | United States | Washington | 585228 | 0.239224 | USA | North America | High income | 2.222555e+08 | 0.263313 |
| 245788 | 704702 | COS | DCA | 36c79a69dc84e795f59d95a5fe544c06844c95dedb9ed6... | 0a0d2ce155e90bb079ca2bb3afa55fac8d7fc7e78c3689... | round_trip | 2018-03-29 00:00:00 | 2018-04-02 00:00:00 | 4.0 | 1 | 0 | 0 | 0 | NoFilter | 1 | 2018-01-16 10:47:00 | NaN | 2018-01-16 10:47:00 | shopped | 0.0 | 0.0 | wait | 338.0 | wait | 338.0 | NaN | NaN | 338.0 | 8 | gained | 6 | 3 | 1 | 1 days 00:05:00.000000000 | search | NaN | 71 | 38.805801 | -104.700996 | NorthA | Colorado Springs | 38.852100 | -77.037697 | NorthA | Washington | CO | DC | US | US | Domestic | 244635911.0 | United States | Washington | 585228 | 0.239224 | USA | North America | High income | 2.222555e+08 | 0.263313 |
| 126578 | 724935 | SLC | CUN | 0b467a9ea3e4728b3735abade411bdebd151806b1dde04... | 0acee089116641ff3b2e8723381d908e06348cc8cf8f90... | round_trip | 2018-02-23 00:00:00 | 2018-02-28 00:00:00 | 5.0 | 0 | 0 | 0 | 0 | NoFilter | 1 | 2018-01-12 13:31:00 | NaN | 2018-01-12 13:31:00 | shopped | 0.0 | 0.0 | buy | 288.0 | buy | 288.0 | 2018-01-12 13:31:00 | 288.0 | 288.0 | 4 | gained | 2 | 2 | 6 | 6 days 18:58:00.000000000 | search | 0.0 | 41 | 40.788399 | -111.977997 | NorthA | Salt Lake City | 21.036501 | -86.877098 | NorthA | Cancún | UT | ROO | US | MX | International | 244635911.0 | United States | Washington | 585228 | 0.239224 | USA | North America | High income | 2.222555e+08 | 0.263313 |
| 61894 | 711066 | SCL | LIM | 5ed119f12791922b3f5708a053823c217119277608ca46... | 7f999aa50fed2147ad29321bd51c640e099beba93cebec... | round_trip | 2018-06-27 00:00:00 | 2018-07-16 00:00:00 | 19.0 | 0 | 0 | 1 | 0 | NonStop | 2 | 2018-01-29 10:33:00 | 2018-01-29 10:33:00 | 2018-01-29 10:33:00 | active | 10.0 | 2.0 | wait | 299.0 | wait | 346.0 | 2018-02-15 18:28:00 | 233.0 | 233.0 | 1 | expected | 1 | 1 | 0 | 0 days 00:00:00.000000000 | watch | 0.0 | 148 | -33.393002 | -70.785797 | SA | Santiago | -12.021900 | -77.114305 | SA | Lima | RM | LIM | CL | PE | International | 13749004.0 | Chile | Santiago | 2199 | 0.015994 | CHL | Latin America & Caribbean | High income | 4.879296e+06 | 0.045068 |
| 422455 | 869561 | SJU | EWR | 775dafd552372c2811fcdbe0c9740cf0bf8dda64a11a9c... | 347c10a47b6eb40a41a011d76e45a1e57f347c8b31feb1... | one_way | 2018-02-26 00:00:00 | NaN | NaN | 0 | 0 | 0 | 0 | NoFilter | 1 | 2018-01-10 09:27:00 | NaN | 2018-01-10 09:27:00 | shopped | 0.0 | 0.0 | buy | 124.0 | buy | 148.0 | 2018-01-10 09:27:00 | 124.0 | 124.0 | 24 | gained | 10 | 2 | 0 | 0 days 00:28:00.000000000 | search | 0.0 | 46 | 18.439400 | -66.001801 | NorthA | San Juan | 40.692501 | -74.168701 | NorthA | New York | U-A | NJ | PR | US | International | 2334548.0 | Puerto Rico | San Juan | 12666 | 0.542546 | PRI | Latin America & Caribbean | High income | NaN | NaN |
| 84594 | 919537 | PTY | BOG | 31f20bb021373c92c90997d9c98a76c5f3399acdc87f86... | 5dffb18eeb9158745bd011b50bf637e1991b881c020144... | round_trip | 2018-02-08 00:00:00 | 2018-02-18 00:00:00 | 10.0 | 0 | 0 | 0 | 0 | NoFilter | 1 | 2018-01-23 11:58:00 | NaN | 2018-01-23 11:58:00 | shopped | 0.0 | 0.0 | buy | 215.0 | buy | 215.0 | 2018-01-23 11:58:00 | 215.0 | 215.0 | 1 | gained | 1 | 1 | 0 | 0 days 00:00:00.000000000 | search | 0.0 | 15 | 9.071360 | -79.383499 | NorthA | Tocumen | 4.701590 | -74.146900 | SA | Bogota | 8 | CUN | PA | CO | International | 2694239.0 | Panama | Panama City | 2560 | 0.095018 | PAN | Latin America & Caribbean | High income | 3.234838e+06 | 0.079138 |
| 286026 | 696990 | ATL | MIA | 45c561e2d1d0d7239d9db58313cc842999089601d86d35... | 0d96cd139ff0f66faf8b26931daa96e54d3d4a9cf77357... | round_trip | 2018-03-07 00:00:00 | 2018-03-13 00:00:00 | 6.0 | 0 | 0 | 0 | 0 | NoFilter | 1 | 2018-01-02 04:45:00 | NaN | 2018-01-02 04:45:00 | shopped | 0.0 | 0.0 | buy | 115.0 | buy | 115.0 | 2018-01-02 04:45:00 | 115.0 | 115.0 | 4 | gained | 1 | 1 | 0 | 0 days 00:00:00.000000000 | search | 0.0 | 63 | 33.636700 | -84.428101 | NorthA | Atlanta | 25.793200 | -80.290604 | NorthA | Miami | GA | FL | US | US | Domestic | 244635911.0 | United States | Washington | 585228 | 0.239224 | USA | North America | High income | 2.222555e+08 | 0.263313 |
| 56160 | 654275 | SLC | SEA | d9fc70558ab7e99873b614a8b37005340d123d608f95d2... | 1fa888bfefc52cd18ee170366b6c3b69aafa4844f6d52b... | round_trip | 2018-02-17 00:00:00 | 2018-02-24 00:00:00 | 7.0 | 0 | 0 | 1 | 0 | NonStop | 3 | 2018-01-17 18:56:00 | 2018-01-17 18:57:00 | 2018-02-11 09:25:00 | inactive | 5.0 | 5.0 | buy | 262.0 | buy | 647.0 | 2018-01-17 18:56:00 | 262.0 | 202.0 | 1 | lost | 1 | 1 | 0 | 0 days 00:00:00.000000000 | watch | 60.0 | 30 | 40.788399 | -111.977997 | NorthA | Salt Lake City | 47.449001 | -122.308998 | NorthA | Seattle | UT | WA | US | US | Domestic | 244635911.0 | United States | Washington | 585228 | 0.239224 | USA | North America | High income | 2.222555e+08 | 0.263313 |
| 636635 | 360338 | YQU | HNL | c68053db0cefc1bfcd5bb17d4ee0927aace29436c2a486... | 0dd7f18c57251dfbb665f62f7bfafc679995e5f58b1d59... | round_trip | 2018-02-19 00:00:00 | 2018-03-01 00:00:00 | 10.0 | 0 | 0 | 0 | 0 | NoFilter | 1 | 2018-01-09 10:19:00 | NaN | 2018-01-09 10:19:00 | shopped | 0.0 | 0.0 | wait | 1029.0 | wait | 1029.0 | NaN | NaN | 1029.0 | 3 | gained | 1 | 1 | 0 | 0 days 00:00:00.000000000 | search | NaN | 40 | 55.179699 | -118.885002 | NorthA | Grande Prairie | 21.320620 | -157.924228 | NorthA | Honolulu | AB | HI | CA | US | International | 29156938.0 | Canada | Ottawa | 50409 | 0.172889 | CAN | North America | High income | 2.234500e+07 | 0.225594 |
| 171754 | 785709 | MCO | DCA | 1b86c9e0e6864815ca7ddf25b5876ddee1bda26bbab86e... | 76e4b1ac9947989862b523b1f27b7d486914f4315ea76b... | round_trip | 2018-03-30 00:00:00 | 2018-04-01 00:00:00 | 2.0 | 1 | 0 | 0 | 0 | NoFilter | 3 | 2018-03-20 18:43:00 | 2018-03-20 18:44:00 | 2018-03-28 23:10:00 | inactive | 11.0 | 11.0 | buy | 232.0 | buy | 237.0 | 2018-03-20 18:43:00 | 232.0 | 232.0 | 9 | lost | 4 | 2 | 54 | 54 days 02:18:00.000000000 | watch | 0.0 | 9 | 28.429399 | -81.308998 | NorthA | Orlando | 38.852100 | -77.037697 | NorthA | Washington | FL | DC | US | US | Domestic | 244635911.0 | United States | Washington | 585228 | 0.239224 | USA | North America | High income | 2.222555e+08 | 0.263313 |
| 143497 | 724118 | HNL | LAX | 116cc43b7980f50a9a8c33f75edfb0781d3dbbf7163f26... | a426dd93d10661651e3ad11edfec987a10fc63e4045b6f... | round_trip | 2018-04-10 00:00:00 | 2018-04-18 00:00:00 | 8.0 | 0 | 0 | 0 | 0 | NoFilter | 3 | 2018-01-08 16:22:00 | 2018-01-08 16:22:00 | 2018-03-07 11:20:00 | inactive | 13.0 | 8.0 | wait | 597.0 | buy | 502.0 | 2018-01-10 01:31:00 | 504.0 | 456.0 | 4 | lost | 2 | 1 | 0 | 0 days 00:03:00.000000000 | watch | 48.0 | 91 | 21.320620 | -157.924228 | NorthA | Honolulu | 33.942501 | -118.407997 | NorthA | Los Angeles | HI | CA | US | US | Domestic | 244635911.0 | United States | Washington | 585228 | 0.239224 | USA | North America | High income | 2.222555e+08 | 0.263313 |
| 14684 | 193882 | BRU | ALC | 719f38d90fe60162a921b6df8f026345d7171c10b216db... | c68645efb3bd5147fcae9d94bc93320a250665f07cd345... | round_trip | 2018-04-24 00:00:00 | 2018-04-27 00:00:00 | 3.0 | 0 | 0 | 0 | 0 | NoFilter | 3 | 2018-03-25 07:15:00 | 2018-03-25 07:15:00 | 2018-03-25 07:15:00 | inactive | 0.0 | 0.0 | buy | 174.0 | buy | 174.0 | 2018-03-25 07:15:00 | 174.0 | 174.0 | 1 | lost | 1 | 1 | 0 | 0 days 00:00:00.000000000 | watch | 0.0 | 29 | 50.901402 | 4.484440 | EU | Brussels | 38.282200 | -0.558156 | EU | Alicante | BRU | V | BE | ES | International | 8891916.0 | Belgium | Brussels | 1620 | 0.018219 | BEL | Europe & Central Asia | High income | 3.409872e+06 | 0.047509 |
In [9]:
df['is_session_1'] = df['session'] == 1
print("There are " + np.str(np.sum(df['is_session_1'])) + " entries of session 1")
df['is_US'] = df['Country name'] == 'United States'
print("There are " + np.str(np.sum(df['is_US'])) + " entries of US")
df['is_CA'] = df['Country name'] == 'Canada'
print("There are " + np.str(np.sum(df['is_CA'])) + " entries of CA")
There are 370413 entries of session 1 There are 585228 entries of US There are 50409 entries of CA
Categorical variables¶
In [10]:
categorical = ['origin_city','destination_city','trip_type','weekend','filter_no_lcc','filter_non_stop',
'filter_short_layover', 'filter_name','first_rec','last_rec','is_session_1',
'Search or watch','Use frequency','continent_origin','City origin', 'City destination',
'region_origin','region_destination','country_origin','continent_destination',
'country_destination','Domestic or international','Region','IncomeGroup','outcome']
In [11]:
df[categorical].dtypes
Out[11]:
origin_city object destination_city object trip_type object weekend int64 filter_no_lcc int64 filter_non_stop int64 filter_short_layover int64 filter_name object first_rec object last_rec object is_session_1 bool Search or watch object Use frequency int64 continent_origin object City origin object City destination object region_origin object region_destination object country_origin object continent_destination object country_destination object Domestic or international object Region object IncomeGroup object outcome object dtype: object
Make all NaN values in categorical data, a category itself.¶
In [12]:
df[categorical].dtypes.value_counts()
Out[12]:
object 19 int64 5 bool 1 dtype: int64
In [13]:
for c in categorical:
df[c] = df[c].astype(str).astype('category')
df[categorical].dtypes
Out[13]:
origin_city category destination_city category trip_type category weekend category filter_no_lcc category filter_non_stop category filter_short_layover category filter_name category first_rec category last_rec category is_session_1 category Search or watch category Use frequency category continent_origin category City origin category City destination category region_origin category region_destination category country_origin category continent_destination category country_destination category Domestic or international category Region category IncomeGroup category outcome category dtype: object
Summarizing categorical variables against each other¶
In [14]:
categorical_correlations = pd.DataFrame(index=categorical, columns=categorical)
In [14]:
for i,j in combinations(categorical,2):
p, effect = summarize_categorical(df,i,j)
if p < .05:
categorical_correlations[i][j] = effect
------------ origin_city and destination_city ------------ origin_city : 1324 unique values. destination_city : 1583 unique values. Too many values to print contingency table. X^2 TEST p-value to reject null: 0.0 X^2: 17195192.92 D.f: 2092986 Effect size Cramer's V: 0.11 ------------ origin_city and trip_type ------------ origin_city : 1324 unique values. trip_type : 2 unique values. Too many values to print contingency table. X^2 TEST p-value to reject null: 0.0 X^2: 63454.83 D.f: 1323 Effect size Cramer's V: 0.25 ------------ origin_city and weekend ------------ origin_city : 1324 unique values. weekend : 2 unique values. Too many values to print contingency table. X^2 TEST p-value to reject null: 0.0 X^2: 35868.39 D.f: 1323 Effect size Cramer's V: 0.19 ------------ origin_city and filter_no_lcc ------------ origin_city : 1324 unique values. filter_no_lcc : 2 unique values. Too many values to print contingency table. X^2 TEST p-value to reject null: 0.0 X^2: 3068.51 D.f: 1323 Effect size Cramer's V: 0.06 ------------ origin_city and filter_non_stop ------------ origin_city : 1324 unique values. filter_non_stop : 2 unique values. Too many values to print contingency table. X^2 TEST p-value to reject null: 0.0 X^2: 19925.34 D.f: 1323 Effect size Cramer's V: 0.14 ------------ origin_city and filter_short_layover ------------ origin_city : 1324 unique values. filter_short_layover : 2 unique values. Too many values to print contingency table. X^2 TEST p-value to reject null: 0.0 X^2: 7965.37 D.f: 1323 Effect size Cramer's V: 0.09 ------------ origin_city and filter_name ------------ origin_city : 1324 unique values. filter_name : 6 unique values. Too many values to print contingency table. X^2 TEST p-value to reject null: 0.0 X^2: 32988.16 D.f: 6615 Effect size Cramer's V: 0.08 ------------ origin_city and first_rec ------------ origin_city : 1324 unique values. first_rec : 3 unique values. Too many values to print contingency table. X^2 TEST p-value to reject null: 0.0 X^2: 24478.84 D.f: 2646 Effect size Cramer's V: 0.11 ------------ origin_city and last_rec ------------ origin_city : 1324 unique values. last_rec : 3 unique values. Too many values to print contingency table. X^2 TEST p-value to reject null: 0.0 X^2: 19234.13 D.f: 2646 Effect size Cramer's V: 0.1 ------------ origin_city and is_session_1 ------------ origin_city : 1324 unique values. is_session_1 : 2 unique values. Too many values to print contingency table. X^2 TEST p-value to reject null: 0.0 X^2: 12806.05 D.f: 1323 Effect size Cramer's V: 0.11 ------------ origin_city and Search or watch ------------ origin_city : 1324 unique values. Search or watch : 2 unique values. Too many values to print contingency table. X^2 TEST p-value to reject null: 0.0 X^2: 4972.08 D.f: 1323 Effect size Cramer's V: 0.07 ------------ origin_city and Use frequency ------------ origin_city : 1324 unique values. Use frequency : 2 unique values. Too many values to print contingency table. X^2 TEST p-value to reject null: 0.0 X^2: 8270.52 D.f: 1323 Effect size Cramer's V: 0.09 ------------ origin_city and continent_origin ------------ origin_city : 1324 unique values. continent_origin : 6 unique values. Too many values to print contingency table. X^2 TEST p-value to reject null: 0.0 X^2: 5038460.0 D.f: 6615 Effect size Cramer's V: 1.0 ------------ origin_city and City origin ------------ origin_city : 1324 unique values. City origin : 1237 unique values. Too many values to print contingency table. X^2 TEST p-value to reject null: 0.0 X^2: 1245507312.0 D.f: 1635228 Effect size Cramer's V: 1.0 ------------ origin_city and City destination ------------ origin_city : 1324 unique values. City destination : 1487 unique values. Too many values to print contingency table. X^2 TEST p-value to reject null: 0.0 X^2: 16623566.45 D.f: 1965978 Effect size Cramer's V: 0.11 ------------ origin_city and region_origin ------------ origin_city : 1324 unique values. region_origin : 453 unique values. Too many values to print contingency table. X^2 TEST p-value to reject null: 0.0 X^2: 455476784.0 D.f: 597996 Effect size Cramer's V: 1.0 ------------ origin_city and region_destination ------------ origin_city : 1324 unique values. region_destination : 498 unique values. Too many values to print contingency table. X^2 TEST p-value to reject null: 0.0 X^2: 6146998.61 D.f: 657531 Effect size Cramer's V: 0.11 ------------ origin_city and country_origin ------------ origin_city : 1324 unique values. country_origin : 188 unique values. Too many values to print contingency table. X^2 TEST p-value to reject null: 0.0 X^2: 188438404.0 D.f: 247401 Effect size Cramer's V: 1.0 ------------ origin_city and continent_destination ------------ origin_city : 1324 unique values. continent_destination : 6 unique values. Too many values to print contingency table. X^2 TEST p-value to reject null: 0.0 X^2: 471425.99 D.f: 6615 Effect size Cramer's V: 0.31 ------------ origin_city and country_destination ------------ origin_city : 1324 unique values. country_destination : 209 unique values. Too many values to print contingency table. X^2 TEST p-value to reject null: 0.0 X^2: 2599910.26 D.f: 275184 Effect size Cramer's V: 0.11 ------------ origin_city and Domestic or international ------------ origin_city : 1324 unique values. Domestic or international : 2 unique values. Too many values to print contingency table. X^2 TEST p-value to reject null: 0.0 X^2: 309490.45 D.f: 1323 Effect size Cramer's V: 0.55 ------------ origin_city and Region ------------ origin_city : 1324 unique values. Region : 8 unique values. Too many values to print contingency table. X^2 TEST p-value to reject null: 0.0 X^2: 7053844.0 D.f: 9261 Effect size Cramer's V: 1.0 ------------ origin_city and IncomeGroup ------------ origin_city : 1324 unique values. IncomeGroup : 5 unique values. Too many values to print contingency table. X^2 TEST p-value to reject null: 0.0 X^2: 4030768.0 D.f: 5292 Effect size Cramer's V: 1.0 ------------ origin_city and outcome ------------ origin_city : 1324 unique values. outcome : 3 unique values. Too many values to print contingency table. X^2 TEST p-value to reject null: 0.0 X^2: 8044.5 D.f: 2646 Effect size Cramer's V: 0.06 ------------ destination_city and trip_type ------------ destination_city : 1583 unique values. trip_type : 2 unique values. Too many values to print contingency table. X^2 TEST p-value to reject null: 0.0 X^2: 36138.66 D.f: 1582 Effect size Cramer's V: 0.19 ------------ destination_city and weekend ------------ destination_city : 1583 unique values. weekend : 2 unique values. Too many values to print contingency table. X^2 TEST p-value to reject null: 0.0 X^2: 98031.79 D.f: 1582 Effect size Cramer's V: 0.31 ------------ destination_city and filter_no_lcc ------------ destination_city : 1583 unique values. filter_no_lcc : 2 unique values. Too many values to print contingency table. X^2 TEST p-value to reject null: 0.0 X^2: 3899.21 D.f: 1582 Effect size Cramer's V: 0.06 ------------ destination_city and filter_non_stop ------------ destination_city : 1583 unique values. filter_non_stop : 2 unique values. Too many values to print contingency table. X^2 TEST p-value to reject null: 0.0 X^2: 19731.37 D.f: 1582 Effect size Cramer's V: 0.14 ------------ destination_city and filter_short_layover ------------ destination_city : 1583 unique values. filter_short_layover : 2 unique values. Too many values to print contingency table. X^2 TEST p-value to reject null: 0.0 X^2: 12278.92 D.f: 1582 Effect size Cramer's V: 0.11 ------------ destination_city and filter_name ------------ destination_city : 1583 unique values. filter_name : 6 unique values. Too many values to print contingency table. X^2 TEST p-value to reject null: 0.0 X^2: 37517.73 D.f: 7910 Effect size Cramer's V: 0.09 ------------ destination_city and first_rec ------------ destination_city : 1583 unique values. first_rec : 3 unique values. Too many values to print contingency table. X^2 TEST p-value to reject null: 0.0 X^2: 36545.71 D.f: 3164 Effect size Cramer's V: 0.13 ------------ destination_city and last_rec ------------ destination_city : 1583 unique values. last_rec : 3 unique values. Too many values to print contingency table. X^2 TEST p-value to reject null: 0.0 X^2: 28809.55 D.f: 3164 Effect size Cramer's V: 0.12 ------------ destination_city and is_session_1 ------------ destination_city : 1583 unique values. is_session_1 : 2 unique values. Too many values to print contingency table. X^2 TEST p-value to reject null: 0.0 X^2: 7310.02 D.f: 1582 Effect size Cramer's V: 0.09 ------------ destination_city and Search or watch ------------ destination_city : 1583 unique values. Search or watch : 2 unique values. Too many values to print contingency table. X^2 TEST p-value to reject null: 0.0 X^2: 7644.88 D.f: 1582 Effect size Cramer's V: 0.09 ------------ destination_city and Use frequency ------------ destination_city : 1583 unique values. Use frequency : 2 unique values. Too many values to print contingency table. X^2 TEST p-value to reject null: 0.0 X^2: 5336.47 D.f: 1582 Effect size Cramer's V: 0.07 ------------ destination_city and continent_origin ------------ destination_city : 1583 unique values. continent_origin : 6 unique values. Too many values to print contingency table. X^2 TEST p-value to reject null: 0.0 X^2: 601510.69 D.f: 7910 Effect size Cramer's V: 0.35 ------------ destination_city and City origin ------------ destination_city : 1583 unique values. City origin : 1237 unique values. Too many values to print contingency table. X^2 TEST p-value to reject null: 0.0 X^2: 15049133.53 D.f: 1955352 Effect size Cramer's V: 0.11 ------------ destination_city and City destination ------------ destination_city : 1583 unique values. City destination : 1487 unique values. Too many values to print contingency table. X^2 TEST p-value to reject null: 0.0 X^2: 1497430312.0 D.f: 2350852 Effect size Cramer's V: 1.0 ------------ destination_city and region_origin ------------ destination_city : 1583 unique values. region_origin : 453 unique values. Too many values to print contingency table. X^2 TEST p-value to reject null: 0.0 X^2: 6941738.56 D.f: 715064 Effect size Cramer's V: 0.12 ------------ destination_city and region_destination ------------ destination_city : 1583 unique values. region_destination : 498 unique values. Too many values to print contingency table. X^2 TEST p-value to reject null: 0.0 X^2: 500822924.0 D.f: 786254 Effect size Cramer's V: 1.0 ------------ destination_city and country_origin ------------ destination_city : 1583 unique values. country_origin : 188 unique values. Too many values to print contingency table. X^2 TEST p-value to reject null: 0.0 X^2: 3322527.7 D.f: 295834 Effect size Cramer's V: 0.13 ------------ destination_city and continent_destination ------------ destination_city : 1583 unique values. continent_destination : 6 unique values. Too many values to print contingency table. X^2 TEST p-value to reject null: 0.0 X^2: 5038460.0 D.f: 7910 Effect size Cramer's V: 1.0 ------------ destination_city and country_destination ------------ destination_city : 1583 unique values. country_destination : 209 unique values. Too many values to print contingency table. X^2 TEST p-value to reject null: 0.0 X^2: 209599936.0 D.f: 329056 Effect size Cramer's V: 1.0 ------------ destination_city and Domestic or international ------------ destination_city : 1583 unique values. Domestic or international : 2 unique values. Too many values to print contingency table. X^2 TEST p-value to reject null: 0.0 X^2: 639073.56 D.f: 1582 Effect size Cramer's V: 0.8 ------------ destination_city and Region ------------ destination_city : 1583 unique values. Region : 8 unique values. Too many values to print contingency table. X^2 TEST p-value to reject null: 0.0 X^2: 621780.44 D.f: 11074 Effect size Cramer's V: 0.3 ------------ destination_city and IncomeGroup ------------ destination_city : 1583 unique values. IncomeGroup : 5 unique values. Too many values to print contingency table. X^2 TEST p-value to reject null: 0.0 X^2: 175014.28 D.f: 6328 Effect size Cramer's V: 0.21 ------------ destination_city and outcome ------------ destination_city : 1583 unique values. outcome : 3 unique values. Too many values to print contingency table. X^2 TEST p-value to reject null: 0.0 X^2: 13412.18 D.f: 3164 Effect size Cramer's V: 0.08 ------------ trip_type and weekend ------------ trip_type : 2 unique values. weekend : 2 unique values.
trip_type one_way round_trip All weekend 0 168772 639008 807780 1 0 199912 199912 All 168772 838920 1007692 X^2 TEST p-value to reject null: 0.0 X^2: 50169.58 D.f: 1 Effect size Cramer's V: 0.22 ------------ trip_type and filter_no_lcc ------------ trip_type : 2 unique values. filter_no_lcc : 2 unique values.
trip_type one_way round_trip All filter_no_lcc 0 166696 827712 994408 1 2076 11208 13284 All 168772 838920 1007692 X^2 TEST p-value to reject null: 0.0005 X^2: 12.04 D.f: 1 Effect size Cramer's V: 0.0 ------------ trip_type and filter_non_stop ------------ trip_type : 2 unique values. filter_non_stop : 2 unique values.
trip_type one_way round_trip All filter_non_stop 0 151320 752554 903874 1 17452 86366 103818 All 168772 838920 1007692 X^2 TEST p-value to reject null: 0.5763 X^2: 0.31 D.f: 1 Effect size Cramer's V: 0.0 ------------ trip_type and filter_short_layover ------------ trip_type : 2 unique values. filter_short_layover : 2 unique values.
trip_type one_way round_trip All filter_short_layover 0 163829 813095 976924 1 4943 25825 30768 All 168772 838920 1007692 X^2 TEST p-value to reject null: 0.0012 X^2: 10.57 D.f: 1 Effect size Cramer's V: 0.0 ------------ trip_type and filter_name ------------ trip_type : 2 unique values. filter_name : 6 unique values.
trip_type one_way round_trip All filter_name And(NonStop,NoLCC) 1030 5218 6248 And(ShortLayover,NoLCC) 383 1838 2221 NoFilter 145714 722577 868291 NoLCC 663 4152 4815 NonStop 16422 81148 97570 ShortLayover 4560 23987 28547 All 168772 838920 1007692 X^2 TEST p-value to reject null: 0.0 X^2: 44.8 D.f: 5 Effect size Cramer's V: 0.01 ------------ trip_type and first_rec ------------ trip_type : 2 unique values. first_rec : 3 unique values.
trip_type one_way round_trip All first_rec buy 87405 420652 508057 nan 10126 48037 58163 wait 71241 370231 441472 All 168772 838920 1007692 X^2 TEST p-value to reject null: 0.0 X^2: 212.1 D.f: 2 Effect size Cramer's V: 0.01 ------------ trip_type and last_rec ------------ trip_type : 2 unique values. last_rec : 3 unique values.
trip_type one_way round_trip All last_rec buy 93766 457818 551584 nan 10126 48037 58163 wait 64880 333065 397945 All 168772 838920 1007692 X^2 TEST p-value to reject null: 0.0 X^2: 99.59 D.f: 2 Effect size Cramer's V: 0.01 ------------ trip_type and is_session_1 ------------ trip_type : 2 unique values. is_session_1 : 2 unique values.
trip_type one_way round_trip All is_session_1 False 100681 436527 537208 True 68091 402393 470484 All 168772 838920 1007692 X^2 TEST p-value to reject null: 0.0 X^2: 3277.96 D.f: 1 Effect size Cramer's V: 0.06 ------------ trip_type and Search or watch ------------ trip_type : 2 unique values. Search or watch : 2 unique values.
trip_type one_way round_trip All Search or watch search 112488 543962 656450 watch 56284 294958 351242 All 168772 838920 1007692 X^2 TEST p-value to reject null: 0.0 X^2: 202.67 D.f: 1 Effect size Cramer's V: 0.01 ------------ trip_type and Use frequency ------------ trip_type : 2 unique values. Use frequency : 2 unique values.
trip_type one_way round_trip All Use frequency more than once 160670 774176 934846 once 8102 64744 72846 All 168772 838920 1007692 X^2 TEST p-value to reject null: 0.0 X^2: 1782.23 D.f: 1 Effect size Cramer's V: 0.04 ------------ trip_type and continent_origin ------------ trip_type : 2 unique values. continent_origin : 6 unique values.
trip_type one_way round_trip All continent_origin AF 2746 13781 16527 AS 9176 20647 29823 EU 18452 41096 59548 NorthA 131286 728017 859303 OC 2064 5311 7375 SA 5048 30068 35116 All 168772 838920 1007692 X^2 TEST p-value to reject null: 0.0 X^2: 15004.31 D.f: 5 Effect size Cramer's V: 0.12 ------------ trip_type and City origin ------------ trip_type : 2 unique values. City origin : 1237 unique values. Too many values to print contingency table. X^2 TEST p-value to reject null: 0.0 X^2: 62089.14 D.f: 1236 Effect size Cramer's V: 0.25 ------------ trip_type and City destination ------------ trip_type : 2 unique values. City destination : 1487 unique values. Too many values to print contingency table. X^2 TEST p-value to reject null: 0.0 X^2: 34557.81 D.f: 1486 Effect size Cramer's V: 0.19 ------------ trip_type and region_origin ------------ trip_type : 2 unique values. region_origin : 453 unique values. Too many values to print contingency table. X^2 TEST p-value to reject null: 0.0 X^2: 50249.05 D.f: 452 Effect size Cramer's V: 0.22 ------------ trip_type and region_destination ------------ trip_type : 2 unique values. region_destination : 498 unique values. Too many values to print contingency table. X^2 TEST p-value to reject null: 0.0 X^2: 23830.36 D.f: 497 Effect size Cramer's V: 0.15 ------------ trip_type and country_origin ------------ trip_type : 2 unique values. country_origin : 188 unique values. Too many values to print contingency table. X^2 TEST p-value to reject null: 0.0 X^2: 33086.84 D.f: 187 Effect size Cramer's V: 0.18 ------------ trip_type and continent_destination ------------ trip_type : 2 unique values. continent_destination : 6 unique values.
trip_type one_way round_trip All continent_destination AF 2589 12203 14792 AS 10942 70998 81940 EU 23749 121176 144925 NorthA 124350 595385 719735 OC 2651 9522 12173 SA 4491 29636 34127 All 168772 838920 1007692 X^2 TEST p-value to reject null: 0.0 X^2: 1377.22 D.f: 5 Effect size Cramer's V: 0.04 ------------ trip_type and country_destination ------------ trip_type : 2 unique values. country_destination : 209 unique values. Too many values to print contingency table. X^2 TEST p-value to reject null: 0.0 X^2: 13649.61 D.f: 208 Effect size Cramer's V: 0.12 ------------ trip_type and Domestic or international ------------ trip_type : 2 unique values. Domestic or international : 2 unique values.
trip_type one_way round_trip All Domestic or international Domestic 88508 428244 516752 International 80264 410676 490940 All 168772 838920 1007692 X^2 TEST p-value to reject null: 0.0 X^2: 109.43 D.f: 1 Effect size Cramer's V: 0.01 ------------ trip_type and Region ------------ trip_type : 2 unique values. Region : 8 unique values.
trip_type one_way round_trip All Region East Asia & Pacific 7840 15965 23805 Europe & Central Asia 18686 41584 60270 Latin America & Caribbean 16961 70081 87042 Middle East & North Africa 1851 6807 8658 North America 119331 687931 807262 South Asia 1511 2691 4202 Sub-Saharan Africa 2375 13289 15664 nan 217 572 789 All 168772 838920 1007692 X^2 TEST p-value to reject null: 0.0 X^2: 17301.96 D.f: 7 Effect size Cramer's V: 0.13 ------------ trip_type and IncomeGroup ------------ trip_type : 2 unique values. IncomeGroup : 5 unique values.
trip_type one_way round_trip All IncomeGroup High income 147720 762929 910649 Low income 356 336 692 Lower middle income 4972 10995 15967 Upper middle income 15507 64088 79595 nan 217 572 789 All 168772 838920 1007692 X^2 TEST p-value to reject null: 0.0 X^2: 3642.53 D.f: 4 Effect size Cramer's V: 0.06 ------------ trip_type and outcome ------------ trip_type : 2 unique values. outcome : 3 unique values.
trip_type one_way round_trip All outcome expected 18788 117181 135969 gained 113299 545831 659130 lost 36685 175908 212593 All 168772 838920 1007692 X^2 TEST p-value to reject null: 0.0 X^2: 968.6 D.f: 2 Effect size Cramer's V: 0.03 ------------ weekend and filter_no_lcc ------------ weekend : 2 unique values. filter_no_lcc : 2 unique values.
weekend 0 1 All filter_no_lcc 0 797592 196816 994408 1 10188 3096 13284 All 807780 199912 1007692 X^2 TEST p-value to reject null: 0.0 X^2: 101.56 D.f: 1 Effect size Cramer's V: 0.01 ------------ weekend and filter_non_stop ------------ weekend : 2 unique values. filter_non_stop : 2 unique values.
weekend 0 1 All filter_non_stop 0 730919 172955 903874 1 76861 26957 103818 All 807780 199912 1007692 X^2 TEST p-value to reject null: 0.0 X^2: 2731.79 D.f: 1 Effect size Cramer's V: 0.05 ------------ weekend and filter_short_layover ------------ weekend : 2 unique values. filter_short_layover : 2 unique values.
weekend 0 1 All filter_short_layover 0 781189 195735 976924 1 26591 4177 30768 All 807780 199912 1007692 X^2 TEST p-value to reject null: 0.0 X^2: 782.35 D.f: 1 Effect size Cramer's V: 0.03 ------------ weekend and filter_name ------------ weekend : 2 unique values. filter_name : 6 unique values.
weekend 0 1 All filter_name And(NonStop,NoLCC) 4459 1789 6248 And(ShortLayover,NoLCC) 1929 292 2221 NoFilter 700528 167763 868291 NoLCC 3800 1015 4815 NonStop 72402 25168 97570 ShortLayover 24662 3885 28547 All 807780 199912 1007692 X^2 TEST p-value to reject null: 0.0 X^2: 3390.53 D.f: 5 Effect size Cramer's V: 0.06 ------------ weekend and first_rec ------------ weekend : 2 unique values. first_rec : 3 unique values.
weekend 0 1 All first_rec buy 413500 94557 508057 nan 47178 10985 58163 wait 347102 94370 441472 All 807780 199912 1007692 X^2 TEST p-value to reject null: 0.0 X^2: 1170.54 D.f: 2 Effect size Cramer's V: 0.03 ------------ weekend and last_rec ------------ weekend : 2 unique values. last_rec : 3 unique values.
weekend 0 1 All last_rec buy 443205 108379 551584 nan 47178 10985 58163 wait 317397 80548 397945 All 807780 199912 1007692 X^2 TEST p-value to reject null: 0.0 X^2: 86.18 D.f: 2 Effect size Cramer's V: 0.01 ------------ weekend and is_session_1 ------------ weekend : 2 unique values. is_session_1 : 2 unique values.
weekend 0 1 All is_session_1 False 429789 107419 537208 True 377991 92493 470484 All 807780 199912 1007692 X^2 TEST p-value to reject null: 0.0 X^2: 17.86 D.f: 1 Effect size Cramer's V: 0.0 ------------ weekend and Search or watch ------------ weekend : 2 unique values. Search or watch : 2 unique values.
weekend 0 1 All Search or watch search 535289 121161 656450 watch 272491 78751 351242 All 807780 199912 1007692 X^2 TEST p-value to reject null: 0.0 X^2: 2260.28 D.f: 1 Effect size Cramer's V: 0.05 ------------ weekend and Use frequency ------------ weekend : 2 unique values. Use frequency : 2 unique values.
weekend 0 1 All Use frequency more than once 750034 184812 934846 once 57746 15100 72846 All 807780 199912 1007692 X^2 TEST p-value to reject null: 0.0 X^2: 39.06 D.f: 1 Effect size Cramer's V: 0.01 ------------ weekend and continent_origin ------------ weekend : 2 unique values. continent_origin : 6 unique values.
weekend 0 1 All continent_origin AF 12810 3717 16527 AS 28074 1749 29823 EU 53276 6272 59548 NorthA 673872 185431 859303 OC 6800 575 7375 SA 32948 2168 35116 All 807780 199912 1007692 X^2 TEST p-value to reject null: 0.0 X^2: 13410.6 D.f: 5 Effect size Cramer's V: 0.12 ------------ weekend and City origin ------------ weekend : 2 unique values. City origin : 1237 unique values. Too many values to print contingency table. X^2 TEST p-value to reject null: 0.0 X^2: 33993.9 D.f: 1236 Effect size Cramer's V: 0.18 ------------ weekend and City destination ------------ weekend : 2 unique values. City destination : 1487 unique values. Too many values to print contingency table. X^2 TEST p-value to reject null: 0.0 X^2: 96642.12 D.f: 1486 Effect size Cramer's V: 0.31 ------------ weekend and region_origin ------------ weekend : 2 unique values. region_origin : 453 unique values. Too many values to print contingency table. X^2 TEST p-value to reject null: 0.0 X^2: 29300.47 D.f: 452 Effect size Cramer's V: 0.17 ------------ weekend and region_destination ------------ weekend : 2 unique values. region_destination : 498 unique values. Too many values to print contingency table. X^2 TEST p-value to reject null: 0.0 X^2: 87320.91 D.f: 497 Effect size Cramer's V: 0.29 ------------ weekend and country_origin ------------ weekend : 2 unique values. country_origin : 188 unique values. Too many values to print contingency table. X^2 TEST p-value to reject null: 0.0 X^2: 25459.28 D.f: 187 Effect size Cramer's V: 0.16 ------------ weekend and continent_destination ------------ weekend : 2 unique values. continent_destination : 6 unique values.
weekend 0 1 All continent_destination AF 13234 1558 14792 AS 79912 2028 81940 EU 136057 8868 144925 NorthA 535831 183904 719735 OC 11580 593 12173 SA 31166 2961 34127 All 807780 199912 1007692 X^2 TEST p-value to reject null: 0.0 X^2: 52653.56 D.f: 5 Effect size Cramer's V: 0.23 ------------ weekend and country_destination ------------ weekend : 2 unique values. country_destination : 209 unique values. Too many values to print contingency table. X^2 TEST p-value to reject null: 0.0 X^2: 66628.21 D.f: 208 Effect size Cramer's V: 0.26 ------------ weekend and Domestic or international ------------ weekend : 2 unique values. Domestic or international : 2 unique values.
weekend 0 1 All Domestic or international Domestic 359725 157027 516752 International 448055 42885 490940 All 807780 199912 1007692 X^2 TEST p-value to reject null: 0.0 X^2: 74215.62 D.f: 1 Effect size Cramer's V: 0.27 ------------ weekend and Region ------------ weekend : 2 unique values. Region : 8 unique values.
weekend 0 1 All Region East Asia & Pacific 22227 1578 23805 Europe & Central Asia 53944 6326 60270 Latin America & Caribbean 79350 7692 87042 Middle East & North Africa 8100 558 8658 North America 627365 179897 807262 South Asia 4108 94 4202 Sub-Saharan Africa 11968 3696 15664 nan 718 71 789 All 807780 199912 1007692 X^2 TEST p-value to reject null: 0.0 X^2: 17574.69 D.f: 7 Effect size Cramer's V: 0.13 ------------ weekend and IncomeGroup ------------ weekend : 2 unique values. IncomeGroup : 5 unique values.
weekend 0 1 All IncomeGroup High income 719813 190836 910649 Low income 681 11 692 Lower middle income 15331 636 15967 Upper middle income 71237 8358 79595 nan 718 71 789 All 807780 199912 1007692 X^2 TEST p-value to reject null: 0.0 X^2: 7806.58 D.f: 4 Effect size Cramer's V: 0.09 ------------ weekend and outcome ------------ weekend : 2 unique values. outcome : 3 unique values.
weekend 0 1 All outcome expected 107201 28768 135969 gained 537374 121756 659130 lost 163205 49388 212593 All 807780 199912 1007692 X^2 TEST p-value to reject null: 0.0 X^2: 2461.28 D.f: 2 Effect size Cramer's V: 0.05 ------------ filter_no_lcc and filter_non_stop ------------ filter_no_lcc : 2 unique values. filter_non_stop : 2 unique values.
filter_no_lcc 0 1 All filter_non_stop 0 896838 7036 903874 1 97570 6248 103818 All 994408 13284 1007692 X^2 TEST p-value to reject null: 0.0 X^2: 19649.65 D.f: 1 Effect size Cramer's V: 0.14 ------------ filter_no_lcc and filter_short_layover ------------ filter_no_lcc : 2 unique values. filter_short_layover : 2 unique values.
filter_no_lcc 0 1 All filter_short_layover 0 965861 11063 976924 1 28547 2221 30768 All 994408 13284 1007692 X^2 TEST p-value to reject null: 0.0 X^2: 8488.56 D.f: 1 Effect size Cramer's V: 0.09 ------------ filter_no_lcc and filter_name ------------ filter_no_lcc : 2 unique values. filter_name : 6 unique values.
filter_no_lcc 0 1 All filter_name And(NonStop,NoLCC) 0 6248 6248 And(ShortLayover,NoLCC) 0 2221 2221 NoFilter 868291 0 868291 NoLCC 0 4815 4815 NonStop 97570 0 97570 ShortLayover 28547 0 28547 All 994408 13284 1007692 X^2 TEST p-value to reject null: 0.0 X^2: 1007692.0 D.f: 5 Effect size Cramer's V: 1.0 ------------ filter_no_lcc and first_rec ------------ filter_no_lcc : 2 unique values. first_rec : 3 unique values.
filter_no_lcc 0 1 All first_rec buy 503667 4390 508057 nan 57523 640 58163 wait 433218 8254 441472 All 994408 13284 1007692 X^2 TEST p-value to reject null: 0.0 X^2: 1858.65 D.f: 2 Effect size Cramer's V: 0.04 ------------ filter_no_lcc and last_rec ------------ filter_no_lcc : 2 unique values. last_rec : 3 unique values.
filter_no_lcc 0 1 All last_rec buy 546315 5269 551584 nan 57523 640 58163 wait 390570 7375 397945 All 994408 13284 1007692 X^2 TEST p-value to reject null: 0.0 X^2: 1455.58 D.f: 2 Effect size Cramer's V: 0.04 ------------ filter_no_lcc and is_session_1 ------------ filter_no_lcc : 2 unique values. is_session_1 : 2 unique values.
filter_no_lcc 0 1 All is_session_1 False 529927 7281 537208 True 464481 6003 470484 All 994408 13284 1007692 X^2 TEST p-value to reject null: 0.0005 X^2: 12.1 D.f: 1 Effect size Cramer's V: 0.0 ------------ filter_no_lcc and Search or watch ------------ filter_no_lcc : 2 unique values. Search or watch : 2 unique values.
filter_no_lcc 0 1 All Search or watch search 650508 5942 656450 watch 343900 7342 351242 All 994408 13284 1007692 X^2 TEST p-value to reject null: 0.0 X^2: 2469.51 D.f: 1 Effect size Cramer's V: 0.05 ------------ filter_no_lcc and Use frequency ------------ filter_no_lcc : 2 unique values. Use frequency : 2 unique values.
filter_no_lcc 0 1 All Use frequency more than once 922212 12634 934846 once 72196 650 72846 All 994408 13284 1007692 X^2 TEST p-value to reject null: 0.0 X^2: 109.17 D.f: 1 Effect size Cramer's V: 0.01 ------------ filter_no_lcc and continent_origin ------------ filter_no_lcc : 2 unique values. continent_origin : 6 unique values.
filter_no_lcc 0 1 All continent_origin AF 16242 285 16527 AS 29553 270 29823 EU 58765 783 59548 NorthA 847638 11665 859303 OC 7267 108 7375 SA 34943 173 35116 All 994408 13284 1007692 X^2 TEST p-value to reject null: 0.0 X^2: 255.43 D.f: 5 Effect size Cramer's V: 0.02 ------------ filter_no_lcc and City origin ------------ filter_no_lcc : 2 unique values. City origin : 1237 unique values. Too many values to print contingency table. X^2 TEST p-value to reject null: 0.0 X^2: 2826.64 D.f: 1236 Effect size Cramer's V: 0.05 ------------ filter_no_lcc and City destination ------------ filter_no_lcc : 2 unique values. City destination : 1487 unique values. Too many values to print contingency table. X^2 TEST p-value to reject null: 0.0 X^2: 3741.93 D.f: 1486 Effect size Cramer's V: 0.06 ------------ filter_no_lcc and region_origin ------------ filter_no_lcc : 2 unique values. region_origin : 453 unique values. Too many values to print contingency table. X^2 TEST p-value to reject null: 0.0 X^2: 1897.52 D.f: 452 Effect size Cramer's V: 0.04 ------------ filter_no_lcc and region_destination ------------ filter_no_lcc : 2 unique values. region_destination : 498 unique values. Too many values to print contingency table. X^2 TEST p-value to reject null: 0.0 X^2: 2458.01 D.f: 497 Effect size Cramer's V: 0.05 ------------ filter_no_lcc and country_origin ------------ filter_no_lcc : 2 unique values. country_origin : 188 unique values. Too many values to print contingency table. X^2 TEST p-value to reject null: 0.0 X^2: 1205.64 D.f: 187 Effect size Cramer's V: 0.03 ------------ filter_no_lcc and continent_destination ------------ filter_no_lcc : 2 unique values. continent_destination : 6 unique values.
filter_no_lcc 0 1 All continent_destination AF 14652 140 14792 AS 81365 575 81940 EU 143204 1721 144925 NorthA 709198 10537 719735 OC 12031 142 12173 SA 33958 169 34127 All 994408 13284 1007692 X^2 TEST p-value to reject null: 0.0 X^2: 571.58 D.f: 5 Effect size Cramer's V: 0.02 ------------ filter_no_lcc and country_destination ------------ filter_no_lcc : 2 unique values. country_destination : 209 unique values. Too many values to print contingency table. X^2 TEST p-value to reject null: 0.0 X^2: 1573.82 D.f: 208 Effect size Cramer's V: 0.04 ------------ filter_no_lcc and Domestic or international ------------ filter_no_lcc : 2 unique values. Domestic or international : 2 unique values.
filter_no_lcc 0 1 All Domestic or international Domestic 508007 8745 516752 International 486401 4539 490940 All 994408 13284 1007692 X^2 TEST p-value to reject null: 0.0 X^2: 1140.14 D.f: 1 Effect size Cramer's V: 0.03 ------------ filter_no_lcc and Region ------------ filter_no_lcc : 2 unique values. Region : 8 unique values.
filter_no_lcc 0 1 All Region East Asia & Pacific 23526 279 23805 Europe & Central Asia 59482 788 60270 Latin America & Caribbean 86457 585 87042 Middle East & North Africa 8594 64 8658 North America 796009 11253 807262 South Asia 4178 24 4202 Sub-Saharan Africa 15387 277 15664 nan 775 14 789 All 994408 13284 1007692 X^2 TEST p-value to reject null: 0.0 X^2: 384.92 D.f: 7 Effect size Cramer's V: 0.02 ------------ filter_no_lcc and IncomeGroup ------------ filter_no_lcc : 2 unique values. IncomeGroup : 5 unique values.
filter_no_lcc 0 1 All IncomeGroup High income 898161 12488 910649 Low income 687 5 692 Lower middle income 15857 110 15967 Upper middle income 78928 667 79595 nan 775 14 789 All 994408 13284 1007692 X^2 TEST p-value to reject null: 0.0 X^2: 212.61 D.f: 4 Effect size Cramer's V: 0.01 ------------ filter_no_lcc and outcome ------------ filter_no_lcc : 2 unique values. outcome : 3 unique values.
filter_no_lcc 0 1 All outcome expected 133837 2132 135969 gained 653138 5992 659130 lost 207433 5160 212593 All 994408 13284 1007692 X^2 TEST p-value to reject null: 0.0 X^2: 2923.11 D.f: 2 Effect size Cramer's V: 0.05 ------------ filter_non_stop and filter_short_layover ------------ filter_non_stop : 2 unique values. filter_short_layover : 2 unique values.
filter_non_stop 0 1 All filter_short_layover 0 873106 103818 976924 1 30768 0 30768 All 903874 103818 1007692 X^2 TEST p-value to reject null: 0.0 X^2: 3644.13 D.f: 1 Effect size Cramer's V: 0.06 ------------ filter_non_stop and filter_name ------------ filter_non_stop : 2 unique values. filter_name : 6 unique values.
filter_non_stop 0 1 All filter_name And(NonStop,NoLCC) 0 6248 6248 And(ShortLayover,NoLCC) 2221 0 2221 NoFilter 868291 0 868291 NoLCC 4815 0 4815 NonStop 0 97570 97570 ShortLayover 28547 0 28547 All 903874 103818 1007692 X^2 TEST p-value to reject null: 0.0 X^2: 1007692.0 D.f: 5 Effect size Cramer's V: 1.0 ------------ filter_non_stop and first_rec ------------ filter_non_stop : 2 unique values. first_rec : 3 unique values.
filter_non_stop 0 1 All first_rec buy 462317 45740 508057 nan 53285 4878 58163 wait 388272 53200 441472 All 903874 103818 1007692 X^2 TEST p-value to reject null: 0.0 X^2: 2619.35 D.f: 2 Effect size Cramer's V: 0.05 ------------ filter_non_stop and last_rec ------------ filter_non_stop : 2 unique values. last_rec : 3 unique values.
filter_non_stop 0 1 All last_rec buy 497245 54339 551584 nan 53285 4878 58163 wait 353344 44601 397945 All 903874 103818 1007692 X^2 TEST p-value to reject null: 0.0 X^2: 705.37 D.f: 2 Effect size Cramer's V: 0.03 ------------ filter_non_stop and is_session_1 ------------ filter_non_stop : 2 unique values. is_session_1 : 2 unique values.
filter_non_stop 0 1 All is_session_1 False 483151 54057 537208 True 420723 49761 470484 All 903874 103818 1007692 X^2 TEST p-value to reject null: 0.0 X^2: 71.64 D.f: 1 Effect size Cramer's V: 0.01 ------------ filter_non_stop and Search or watch ------------ filter_non_stop : 2 unique values. Search or watch : 2 unique values.
filter_non_stop 0 1 All Search or watch search 610300 46150 656450 watch 293574 57668 351242 All 903874 103818 1007692 X^2 TEST p-value to reject null: 0.0 X^2: 21821.67 D.f: 1 Effect size Cramer's V: 0.15 ------------ filter_non_stop and Use frequency ------------ filter_non_stop : 2 unique values. Use frequency : 2 unique values.
filter_non_stop 0 1 All Use frequency more than once 837360 97486 934846 once 66514 6332 72846 All 903874 103818 1007692 X^2 TEST p-value to reject null: 0.0 X^2: 220.13 D.f: 1 Effect size Cramer's V: 0.01 ------------ filter_non_stop and continent_origin ------------ filter_non_stop : 2 unique values. continent_origin : 6 unique values.
filter_non_stop 0 1 All continent_origin AF 14311 2216 16527 AS 27100 2723 29823 EU 54194 5354 59548 NorthA 768105 91198 859303 OC 6874 501 7375 SA 33290 1826 35116 All 903874 103818 1007692 X^2 TEST p-value to reject null: 0.0 X^2: 1504.99 D.f: 5 Effect size Cramer's V: 0.04 ------------ filter_non_stop and City origin ------------ filter_non_stop : 2 unique values. City origin : 1237 unique values. Too many values to print contingency table. X^2 TEST p-value to reject null: 0.0 X^2: 19141.85 D.f: 1236 Effect size Cramer's V: 0.14 ------------ filter_non_stop and City destination ------------ filter_non_stop : 2 unique values. City destination : 1487 unique values. Too many values to print contingency table. X^2 TEST p-value to reject null: 0.0 X^2: 18944.79 D.f: 1486 Effect size Cramer's V: 0.14 ------------ filter_non_stop and region_origin ------------ filter_non_stop : 2 unique values. region_origin : 453 unique values. Too many values to print contingency table. X^2 TEST p-value to reject null: 0.0 X^2: 13218.61 D.f: 452 Effect size Cramer's V: 0.11 ------------ filter_non_stop and region_destination ------------ filter_non_stop : 2 unique values. region_destination : 498 unique values. Too many values to print contingency table. X^2 TEST p-value to reject null: 0.0 X^2: 13335.57 D.f: 497 Effect size Cramer's V: 0.12 ------------ filter_non_stop and country_origin ------------ filter_non_stop : 2 unique values. country_origin : 188 unique values. Too many values to print contingency table. X^2 TEST p-value to reject null: 0.0 X^2: 4061.28 D.f: 187 Effect size Cramer's V: 0.06 ------------ filter_non_stop and continent_destination ------------ filter_non_stop : 2 unique values. continent_destination : 6 unique values.
filter_non_stop 0 1 All continent_destination AF 13875 917 14792 AS 77013 4927 81940 EU 133273 11652 144925 NorthA 635921 83814 719735 OC 11552 621 12173 SA 32240 1887 34127 All 903874 103818 1007692 X^2 TEST p-value to reject null: 0.0 X^2: 5305.45 D.f: 5 Effect size Cramer's V: 0.07 ------------ filter_non_stop and country_destination ------------ filter_non_stop : 2 unique values. country_destination : 209 unique values. Too many values to print contingency table. X^2 TEST p-value to reject null: 0.0 X^2: 9090.51 D.f: 208 Effect size Cramer's V: 0.09 ------------ filter_non_stop and Domestic or international ------------ filter_non_stop : 2 unique values. Domestic or international : 2 unique values.
filter_non_stop 0 1 All Domestic or international Domestic 452960 63792 516752 International 450914 40026 490940 All 903874 103818 1007692 X^2 TEST p-value to reject null: 0.0 X^2: 4786.65 D.f: 1 Effect size Cramer's V: 0.07 ------------ filter_non_stop and Region ------------ filter_non_stop : 2 unique values. Region : 8 unique values.
filter_non_stop 0 1 All Region East Asia & Pacific 21876 1929 23805 Europe & Central Asia 54819 5451 60270 Latin America & Caribbean 81322 5720 87042 Middle East & North Africa 7745 913 8658 North America 719959 87303 807262 South Asia 3934 268 4202 Sub-Saharan Africa 13497 2167 15664 nan 722 67 789 All 903874 103818 1007692 X^2 TEST p-value to reject null: 0.0 X^2: 2052.95 D.f: 7 Effect size Cramer's V: 0.05 ------------ filter_non_stop and IncomeGroup ------------ filter_non_stop : 2 unique values. IncomeGroup : 5 unique values.
filter_non_stop 0 1 All IncomeGroup High income 814061 96588 910649 Low income 631 61 692 Lower middle income 15038 929 15967 Upper middle income 73422 6173 79595 nan 722 67 789 All 903874 103818 1007692 X^2 TEST p-value to reject null: 0.0 X^2: 1001.71 D.f: 4 Effect size Cramer's V: 0.03 ------------ filter_non_stop and outcome ------------ filter_non_stop : 2 unique values. outcome : 3 unique values.
filter_non_stop 0 1 All outcome expected 114521 21448 135969 gained 612378 46752 659130 lost 176975 35618 212593 All 903874 103818 1007692 X^2 TEST p-value to reject null: 0.0 X^2: 21327.74 D.f: 2 Effect size Cramer's V: 0.15 ------------ filter_short_layover and filter_name ------------ filter_short_layover : 2 unique values. filter_name : 6 unique values.
filter_short_layover 0 1 All filter_name And(NonStop,NoLCC) 6248 0 6248 And(ShortLayover,NoLCC) 0 2221 2221 NoFilter 868291 0 868291 NoLCC 4815 0 4815 NonStop 97570 0 97570 ShortLayover 0 28547 28547 All 976924 30768 1007692 X^2 TEST p-value to reject null: 0.0 X^2: 1007692.0 D.f: 5 Effect size Cramer's V: 1.0 ------------ filter_short_layover and first_rec ------------ filter_short_layover : 2 unique values. first_rec : 3 unique values.
filter_short_layover 0 1 All first_rec buy 493843 14214 508057 nan 56755 1408 58163 wait 426326 15146 441472 All 976924 30768 1007692 X^2 TEST p-value to reject null: 0.0 X^2: 403.26 D.f: 2 Effect size Cramer's V: 0.02 ------------ filter_short_layover and last_rec ------------ filter_short_layover : 2 unique values. last_rec : 3 unique values.
filter_short_layover 0 1 All last_rec buy 535643 15941 551584 nan 56755 1408 58163 wait 384526 13419 397945 All 976924 30768 1007692 X^2 TEST p-value to reject null: 0.0 X^2: 264.89 D.f: 2 Effect size Cramer's V: 0.02 ------------ filter_short_layover and is_session_1 ------------ filter_short_layover : 2 unique values. is_session_1 : 2 unique values.
filter_short_layover 0 1 All is_session_1 False 521846 15362 537208 True 455078 15406 470484 All 976924 30768 1007692 X^2 TEST p-value to reject null: 0.0 X^2: 145.72 D.f: 1 Effect size Cramer's V: 0.01 ------------ filter_short_layover and Search or watch ------------ filter_short_layover : 2 unique values. Search or watch : 2 unique values.
filter_short_layover 0 1 All Search or watch search 643303 13147 656450 watch 333621 17621 351242 All 976924 30768 1007692 X^2 TEST p-value to reject null: 0.0 X^2: 7021.13 D.f: 1 Effect size Cramer's V: 0.08 ------------ filter_short_layover and Use frequency ------------ filter_short_layover : 2 unique values. Use frequency : 2 unique values.
filter_short_layover 0 1 All Use frequency more than once 905973 28873 934846 once 70951 1895 72846 All 976924 30768 1007692 X^2 TEST p-value to reject null: 0.0 X^2: 54.02 D.f: 1 Effect size Cramer's V: 0.01 ------------ filter_short_layover and continent_origin ------------ filter_short_layover : 2 unique values. continent_origin : 6 unique values.
filter_short_layover 0 1 All continent_origin AF 16101 426 16527 AS 28816 1007 29823 EU 57534 2014 59548 NorthA 833221 26082 859303 OC 7048 327 7375 SA 34204 912 35116 All 976924 30768 1007692 X^2 TEST p-value to reject null: 0.0 X^2: 118.04 D.f: 5 Effect size Cramer's V: 0.01 ------------ filter_short_layover and City origin ------------ filter_short_layover : 2 unique values. City origin : 1237 unique values. Too many values to print contingency table. X^2 TEST p-value to reject null: 0.0 X^2: 7511.6 D.f: 1236 Effect size Cramer's V: 0.09 ------------ filter_short_layover and City destination ------------ filter_short_layover : 2 unique values. City destination : 1487 unique values. Too many values to print contingency table. X^2 TEST p-value to reject null: 0.0 X^2: 11872.04 D.f: 1486 Effect size Cramer's V: 0.11 ------------ filter_short_layover and region_origin ------------ filter_short_layover : 2 unique values. region_origin : 453 unique values. Too many values to print contingency table. X^2 TEST p-value to reject null: 0.0 X^2: 3419.67 D.f: 452 Effect size Cramer's V: 0.06 ------------ filter_short_layover and region_destination ------------ filter_short_layover : 2 unique values. region_destination : 498 unique values. Too many values to print contingency table. X^2 TEST p-value to reject null: 0.0 X^2: 6937.82 D.f: 497 Effect size Cramer's V: 0.08 ------------ filter_short_layover and country_origin ------------ filter_short_layover : 2 unique values. country_origin : 188 unique values. Too many values to print contingency table. X^2 TEST p-value to reject null: 0.0 X^2: 1243.59 D.f: 187 Effect size Cramer's V: 0.04 ------------ filter_short_layover and continent_destination ------------ filter_short_layover : 2 unique values. continent_destination : 6 unique values.
filter_short_layover 0 1 All continent_destination AF 14206 586 14792 AS 78490 3450 81940 EU 139960 4965 144925 NorthA 699787 19948 719735 OC 11574 599 12173 SA 32907 1220 34127 All 976924 30768 1007692 X^2 TEST p-value to reject null: 0.0 X^2: 847.58 D.f: 5 Effect size Cramer's V: 0.03 ------------ filter_short_layover and country_destination ------------ filter_short_layover : 2 unique values. country_destination : 209 unique values. Too many values to print contingency table. X^2 TEST p-value to reject null: 0.0 X^2: 4236.05 D.f: 208 Effect size Cramer's V: 0.06 ------------ filter_short_layover and Domestic or international ------------ filter_short_layover : 2 unique values. Domestic or international : 2 unique values.
filter_short_layover 0 1 All Domestic or international Domestic 505031 11721 516752 International 471893 19047 490940 All 976924 30768 1007692 X^2 TEST p-value to reject null: 0.0 X^2: 2208.15 D.f: 1 Effect size Cramer's V: 0.05 ------------ filter_short_layover and Region ------------ filter_short_layover : 2 unique values. Region : 8 unique values.
filter_short_layover 0 1 All Region East Asia & Pacific 23019 786 23805 Europe & Central Asia 58235 2035 60270 Latin America & Caribbean 84828 2214 87042 Middle East & North Africa 8399 259 8658 North America 782483 24779 807262 South Asia 3944 258 4202 Sub-Saharan Africa 15254 410 15664 nan 762 27 789 All 976924 30768 1007692 X^2 TEST p-value to reject null: 0.0 X^2: 249.11 D.f: 7 Effect size Cramer's V: 0.02 ------------ filter_short_layover and IncomeGroup ------------ filter_short_layover : 2 unique values. IncomeGroup : 5 unique values.
filter_short_layover 0 1 All IncomeGroup High income 882632 28017 910649 Low income 664 28 692 Lower middle income 15382 585 15967 Upper middle income 77484 2111 79595 nan 762 27 789 All 976924 30768 1007692 X^2 TEST p-value to reject null: 0.0 X^2: 67.71 D.f: 4 Effect size Cramer's V: 0.01 ------------ filter_short_layover and outcome ------------ filter_short_layover : 2 unique values. outcome : 3 unique values.
filter_short_layover 0 1 All outcome expected 129198 6771 135969 gained 645837 13293 659130 lost 201889 10704 212593 All 976924 30768 1007692 X^2 TEST p-value to reject null: 0.0 X^2: 6917.68 D.f: 2 Effect size Cramer's V: 0.08 ------------ filter_name and first_rec ------------ filter_name : 6 unique values. first_rec : 3 unique values.
filter_name And(NonStop,NoLCC) And(ShortLayover,NoLCC) NoFilter NoLCC \ first_rec buy 1788 762 446263 1840 nan 268 88 51593 284 wait 4192 1371 370435 2691 All 6248 2221 868291 4815 filter_name NonStop ShortLayover All first_rec buy 43952 13452 508057 nan 4610 1320 58163 wait 49008 13775 441472 All 97570 28547 1007692 X^2 TEST p-value to reject null: 0.0 X^2: 4363.87 D.f: 10 Effect size Cramer's V: 0.05 ------------ filter_name and last_rec ------------ filter_name : 6 unique values. last_rec : 3 unique values.
filter_name And(NonStop,NoLCC) And(ShortLayover,NoLCC) NoFilter NoLCC \ last_rec buy 2384 896 479315 1989 nan 268 88 51593 284 wait 3596 1237 337383 2542 All 6248 2221 868291 4815 filter_name NonStop ShortLayover All last_rec buy 51955 15045 551584 nan 4610 1320 58163 wait 41005 12182 397945 All 97570 28547 1007692 X^2 TEST p-value to reject null: 0.0 X^2: 2182.75 D.f: 10 Effect size Cramer's V: 0.03 ------------ filter_name and is_session_1 ------------ filter_name : 6 unique values. is_session_1 : 2 unique values.
filter_name And(NonStop,NoLCC) And(ShortLayover,NoLCC) NoFilter NoLCC \ is_session_1 False 3494 1204 465206 2583 True 2754 1017 403085 2232 All 6248 2221 868291 4815 filter_name NonStop ShortLayover All is_session_1 False 50563 14158 537208 True 47007 14389 470484 All 97570 28547 1007692 X^2 TEST p-value to reject null: 0.0 X^2: 287.98 D.f: 5 Effect size Cramer's V: 0.02 ------------ filter_name and Search or watch ------------ filter_name : 6 unique values. Search or watch : 2 unique values.
filter_name And(NonStop,NoLCC) And(ShortLayover,NoLCC) NoFilter NoLCC \ Search or watch search 2189 841 594241 2912 watch 4059 1380 274050 1903 All 6248 2221 868291 4815 filter_name NonStop ShortLayover All Search or watch search 43961 12306 656450 watch 53609 16241 351242 All 97570 28547 1007692 X^2 TEST p-value to reject null: 0.0 X^2: 30862.29 D.f: 5 Effect size Cramer's V: 0.18 ------------ filter_name and Use frequency ------------ filter_name : 6 unique values. Use frequency : 2 unique values.
filter_name And(NonStop,NoLCC) And(ShortLayover,NoLCC) NoFilter NoLCC \ Use frequency more than once 5952 2127 803932 4555 once 296 94 64359 260 All 6248 2221 868291 4815 filter_name NonStop ShortLayover All Use frequency more than once 91534 26746 934846 once 6036 1801 72846 All 97570 28547 1007692 X^2 TEST p-value to reject null: 0.0 X^2: 349.23 D.f: 5 Effect size Cramer's V: 0.02 ------------ filter_name and continent_origin ------------ filter_name : 6 unique values. continent_origin : 6 unique values.
filter_name And(NonStop,NoLCC) And(ShortLayover,NoLCC) NoFilter \ continent_origin AF 152 27 13779 AS 99 39 25961 EU 288 204 51889 NorthA 5627 1876 737861 OC 34 30 6503 SA 48 45 32298 All 6248 2221 868291 filter_name NoLCC NonStop ShortLayover All continent_origin AF 106 2064 399 16527 AS 132 2624 968 29823 EU 291 5066 1810 59548 NorthA 4162 85571 24206 859303 OC 44 467 297 7375 SA 80 1778 867 35116 All 4815 97570 28547 1007692 X^2 TEST p-value to reject null: 0.0 X^2: 1807.63 D.f: 25 Effect size Cramer's V: 0.02 ------------ filter_name and City origin ------------ filter_name : 6 unique values. City origin : 1237 unique values. Too many values to print contingency table. X^2 TEST p-value to reject null: 0.0 X^2: 31279.43 D.f: 6180 Effect size Cramer's V: 0.08 ------------ filter_name and City destination ------------ filter_name : 6 unique values. City destination : 1487 unique values. Too many values to print contingency table. X^2 TEST p-value to reject null: 0.0 X^2: 35963.15 D.f: 7430 Effect size Cramer's V: 0.08 ------------ filter_name and region_origin ------------ filter_name : 6 unique values. region_origin : 453 unique values. Too many values to print contingency table. X^2 TEST p-value to reject null: 0.0 X^2: 19205.47 D.f: 2260 Effect size Cramer's V: 0.06 ------------ filter_name and region_destination ------------ filter_name : 6 unique values. region_destination : 498 unique values. Too many values to print contingency table. X^2 TEST p-value to reject null: 0.0 X^2: 23010.64 D.f: 2485 Effect size Cramer's V: 0.07 ------------ filter_name and country_origin ------------ filter_name : 6 unique values. country_origin : 188 unique values. Too many values to print contingency table. X^2 TEST p-value to reject null: 0.0 X^2: 6675.51 D.f: 935 Effect size Cramer's V: 0.04 ------------ filter_name and continent_destination ------------ filter_name : 6 unique values. continent_destination : 6 unique values.
filter_name And(NonStop,NoLCC) And(ShortLayover,NoLCC) NoFilter \ continent_destination AF 50 16 13215 AS 140 156 73284 EU 645 383 127615 NorthA 5343 1551 612330 OC 38 37 10886 SA 32 78 30961 All 6248 2221 868291 filter_name NoLCC NonStop ShortLayover All continent_destination AF 74 867 570 14792 AS 279 4787 3294 81940 EU 693 11007 4582 144925 NorthA 3643 78471 18397 719735 OC 67 583 562 12173 SA 59 1855 1142 34127 All 4815 97570 28547 1007692 X^2 TEST p-value to reject null: 0.0 X^2: 6266.34 D.f: 25 Effect size Cramer's V: 0.04 ------------ filter_name and country_destination ------------ filter_name : 6 unique values. country_destination : 209 unique values. Too many values to print contingency table. X^2 TEST p-value to reject null: 0.0 X^2: 14879.42 D.f: 1040 Effect size Cramer's V: 0.05 ------------ filter_name and Domestic or international ------------ filter_name : 6 unique values. Domestic or international : 2 unique values.
filter_name And(NonStop,NoLCC) And(ShortLayover,NoLCC) \ Domestic or international Domestic 4714 1057 International 1534 1164 All 6248 2221 filter_name NoFilter NoLCC NonStop ShortLayover All Domestic or international Domestic 438265 2974 59078 10664 516752 International 430026 1841 38492 17883 490940 All 868291 4815 97570 28547 1007692 X^2 TEST p-value to reject null: 0.0 X^2: 7481.1 D.f: 5 Effect size Cramer's V: 0.09 ------------ filter_name and Region ------------ filter_name : 6 unique values. Region : 8 unique values.
filter_name And(NonStop,NoLCC) And(ShortLayover,NoLCC) \ Region East Asia & Pacific 104 47 Europe & Central Asia 292 203 Latin America & Caribbean 201 144 Middle East & North Africa 23 7 North America 5474 1777 South Asia 4 13 Sub-Saharan Africa 149 27 nan 1 3 All 6248 2221 filter_name NoFilter NoLCC NonStop ShortLayover All Region East Asia & Pacific 20962 128 1825 739 23805 Europe & Central Asia 52491 293 5159 1832 60270 Latin America & Caribbean 78868 240 5519 2070 87042 Middle East & North Africa 7452 34 890 252 8658 North America 691178 4002 81829 23002 807262 South Asia 3669 7 264 245 4202 Sub-Saharan Africa 12986 101 2018 383 15664 nan 685 10 66 24 789 All 868291 4815 97570 28547 1007692 X^2 TEST p-value to reject null: 0.0 X^2: 2585.99 D.f: 35 Effect size Cramer's V: 0.02 ------------ filter_name and IncomeGroup ------------ filter_name : 6 unique values. IncomeGroup : 5 unique values.
filter_name And(NonStop,NoLCC) And(ShortLayover,NoLCC) NoFilter \ IncomeGroup High income 5948 2060 781564 Low income 1 2 601 Lower middle income 31 18 14392 Upper middle income 267 138 71049 nan 1 3 685 All 6248 2221 868291 filter_name NoLCC NonStop ShortLayover All IncomeGroup High income 4480 90640 25957 910649 Low income 2 60 26 692 Lower middle income 61 898 567 15967 Upper middle income 262 5906 1973 79595 nan 10 66 24 789 All 4815 97570 28547 1007692 X^2 TEST p-value to reject null: 0.0 X^2: 1206.94 D.f: 20 Effect size Cramer's V: 0.02 ------------ filter_name and outcome ------------ filter_name : 6 unique values. outcome : 3 unique values.
filter_name And(NonStop,NoLCC) And(ShortLayover,NoLCC) NoFilter NoLCC \ outcome expected 1221 465 107304 446 gained 2206 857 596156 2929 lost 2821 899 164831 1440 All 6248 2221 868291 4815 filter_name NonStop ShortLayover All outcome expected 20227 6306 135969 gained 44546 12436 659130 lost 32797 9805 212593 All 97570 28547 1007692 X^2 TEST p-value to reject null: 0.0 X^2: 30681.31 D.f: 10 Effect size Cramer's V: 0.12 ------------ first_rec and last_rec ------------ first_rec : 3 unique values. last_rec : 3 unique values.
first_rec buy nan wait All last_rec buy 483188 0 68396 551584 nan 0 58163 0 58163 wait 24869 0 373076 397945 All 508057 58163 441472 1007692 X^2 TEST p-value to reject null: 0.0 X^2: 1660326.03 D.f: 4 Effect size Cramer's V: 0.91 ------------ first_rec and is_session_1 ------------ first_rec : 3 unique values. is_session_1 : 2 unique values.
first_rec buy nan wait All is_session_1 False 270248 37007 229953 537208 True 237809 21156 211519 470484 All 508057 58163 441472 1007692 X^2 TEST p-value to reject null: 0.0 X^2: 2754.73 D.f: 2 Effect size Cramer's V: 0.05 ------------ first_rec and Search or watch ------------ first_rec : 3 unique values. Search or watch : 2 unique values.
first_rec buy nan wait All Search or watch search 358436 49929 248085 656450 watch 149621 8234 193387 351242 All 508057 58163 441472 1007692 X^2 TEST p-value to reject null: 0.0 X^2: 33085.37 D.f: 2 Effect size Cramer's V: 0.18 ------------ first_rec and Use frequency ------------ first_rec : 3 unique values. Use frequency : 2 unique values.
first_rec buy nan wait All Use frequency more than once 470524 54557 409765 934846 once 37533 3606 31707 72846 All 508057 58163 441472 1007692 X^2 TEST p-value to reject null: 0.0 X^2: 112.36 D.f: 2 Effect size Cramer's V: 0.01 ------------ first_rec and continent_origin ------------ first_rec : 3 unique values. continent_origin : 6 unique values.
first_rec buy nan wait All continent_origin AF 7808 917 7802 16527 AS 18258 1868 9697 29823 EU 35347 3122 21079 59548 NorthA 421576 50113 387614 859303 OC 5343 359 1673 7375 SA 19725 1784 13607 35116 All 508057 58163 441472 1007692 X^2 TEST p-value to reject null: 0.0 X^2: 6170.56 D.f: 10 Effect size Cramer's V: 0.06 ------------ first_rec and City origin ------------ first_rec : 3 unique values. City origin : 1237 unique values. Too many values to print contingency table. X^2 TEST p-value to reject null: 0.0 X^2: 23529.67 D.f: 2472 Effect size Cramer's V: 0.11 ------------ first_rec and City destination ------------ first_rec : 3 unique values. City destination : 1487 unique values. Too many values to print contingency table. X^2 TEST p-value to reject null: 0.0 X^2: 35538.38 D.f: 2972 Effect size Cramer's V: 0.13 ------------ first_rec and region_origin ------------ first_rec : 3 unique values. region_origin : 453 unique values. Too many values to print contingency table. X^2 TEST p-value to reject null: 0.0 X^2: 15923.13 D.f: 904 Effect size Cramer's V: 0.09 ------------ first_rec and region_destination ------------ first_rec : 3 unique values. region_destination : 498 unique values. Too many values to print contingency table. X^2 TEST p-value to reject null: 0.0 X^2: 23043.34 D.f: 994 Effect size Cramer's V: 0.11 ------------ first_rec and country_origin ------------ first_rec : 3 unique values. country_origin : 188 unique values. Too many values to print contingency table. X^2 TEST p-value to reject null: 0.0 X^2: 10971.02 D.f: 374 Effect size Cramer's V: 0.07 ------------ first_rec and continent_destination ------------ first_rec : 3 unique values. continent_destination : 6 unique values.
first_rec buy nan wait All continent_destination AF 8323 782 5687 14792 AS 42990 4798 34152 81940 EU 84523 8327 52075 144925 NorthA 347614 41679 330442 719735 OC 6860 681 4632 12173 SA 17747 1896 14484 34127 All 508057 58163 441472 1007692 X^2 TEST p-value to reject null: 0.0 X^2: 5786.51 D.f: 10 Effect size Cramer's V: 0.05 ------------ first_rec and country_destination ------------ first_rec : 3 unique values. country_destination : 209 unique values. Too many values to print contingency table. X^2 TEST p-value to reject null: 0.0 X^2: 13368.17 D.f: 416 Effect size Cramer's V: 0.08 ------------ first_rec and Domestic or international ------------ first_rec : 3 unique values. Domestic or international : 2 unique values.
first_rec buy nan wait All Domestic or international Domestic 248344 30229 238179 516752 International 259713 27934 203293 490940 All 508057 58163 441472 1007692 X^2 TEST p-value to reject null: 0.0 X^2: 2442.16 D.f: 2 Effect size Cramer's V: 0.05 ------------ first_rec and Region ------------ first_rec : 3 unique values. Region : 8 unique values.
first_rec buy nan wait All Region East Asia & Pacific 15070 1429 7306 23805 Europe & Central Asia 35730 3161 21379 60270 Latin America & Caribbean 49365 4676 33001 87042 Middle East & North Africa 5253 549 2856 8658 North America 391877 47217 368168 807262 South Asia 2984 232 986 4202 Sub-Saharan Africa 7310 859 7495 15664 nan 468 40 281 789 All 508057 58163 441472 1007692 X^2 TEST p-value to reject null: 0.0 X^2: 7508.71 D.f: 14 Effect size Cramer's V: 0.06 ------------ first_rec and IncomeGroup ------------ first_rec : 3 unique values. IncomeGroup : 5 unique values.
first_rec buy nan wait All IncomeGroup High income 454380 52788 403481 910649 Low income 424 41 227 692 Lower middle income 9662 945 5360 15967 Upper middle income 43123 4349 32123 79595 nan 468 40 281 789 All 508057 58163 441472 1007692 X^2 TEST p-value to reject null: 0.0 X^2: 1320.24 D.f: 8 Effect size Cramer's V: 0.03 ------------ first_rec and outcome ------------ first_rec : 3 unique values. outcome : 3 unique values.
first_rec buy nan wait All outcome expected 48028 2725 85216 135969 gained 360065 49990 249075 659130 lost 99964 5448 107181 212593 All 508057 58163 441472 1007692 X^2 TEST p-value to reject null: 0.0 X^2: 38452.58 D.f: 4 Effect size Cramer's V: 0.14 ------------ last_rec and is_session_1 ------------ last_rec : 3 unique values. is_session_1 : 2 unique values.
last_rec buy nan wait All is_session_1 False 287699 37007 212502 537208 True 263885 21156 185443 470484 All 551584 58163 397945 1007692 X^2 TEST p-value to reject null: 0.0 X^2: 2781.99 D.f: 2 Effect size Cramer's V: 0.05 ------------ last_rec and Search or watch ------------ last_rec : 3 unique values. Search or watch : 2 unique values.
last_rec buy nan wait All Search or watch search 359156 49929 247365 656450 watch 192428 8234 150580 351242 All 551584 58163 397945 1007692 X^2 TEST p-value to reject null: 0.0 X^2: 12535.07 D.f: 2 Effect size Cramer's V: 0.11 ------------ last_rec and Use frequency ------------ last_rec : 3 unique values. Use frequency : 2 unique values.
last_rec buy nan wait All Use frequency more than once 509313 54557 370976 934846 once 42271 3606 26969 72846 All 551584 58163 397945 1007692 X^2 TEST p-value to reject null: 0.0 X^2: 368.38 D.f: 2 Effect size Cramer's V: 0.02 ------------ last_rec and continent_origin ------------ last_rec : 3 unique values. continent_origin : 6 unique values.
last_rec buy nan wait All continent_origin AF 8587 917 7023 16527 AS 18874 1868 9081 29823 EU 36165 3122 20261 59548 NorthA 461860 50113 347330 859303 OC 5339 359 1677 7375 SA 20759 1784 12573 35116 All 551584 58163 397945 1007692 X^2 TEST p-value to reject null: 0.0 X^2: 3535.22 D.f: 10 Effect size Cramer's V: 0.04 ------------ last_rec and City origin ------------ last_rec : 3 unique values. City origin : 1237 unique values. Too many values to print contingency table. X^2 TEST p-value to reject null: 0.0 X^2: 18409.82 D.f: 2472 Effect size Cramer's V: 0.1 ------------ last_rec and City destination ------------ last_rec : 3 unique values. City destination : 1487 unique values. Too many values to print contingency table. X^2 TEST p-value to reject null: 0.0 X^2: 27990.5 D.f: 2972 Effect size Cramer's V: 0.12 ------------ last_rec and region_origin ------------ last_rec : 3 unique values. region_origin : 453 unique values. Too many values to print contingency table. X^2 TEST p-value to reject null: 0.0 X^2: 11703.89 D.f: 904 Effect size Cramer's V: 0.08 ------------ last_rec and region_destination ------------ last_rec : 3 unique values. region_destination : 498 unique values. Too many values to print contingency table. X^2 TEST p-value to reject null: 0.0 X^2: 16414.34 D.f: 994 Effect size Cramer's V: 0.09 ------------ last_rec and country_origin ------------ last_rec : 3 unique values. country_origin : 188 unique values. Too many values to print contingency table. X^2 TEST p-value to reject null: 0.0 X^2: 7499.31 D.f: 374 Effect size Cramer's V: 0.06 ------------ last_rec and continent_destination ------------ last_rec : 3 unique values. continent_destination : 6 unique values.
last_rec buy nan wait All continent_destination AF 8789 782 5221 14792 AS 44276 4798 32866 81940 EU 86410 8327 50188 144925 NorthA 386060 41679 291996 719735 OC 6988 681 4504 12173 SA 19061 1896 13170 34127 All 551584 58163 397945 1007692 X^2 TEST p-value to reject null: 0.0 X^2: 2071.24 D.f: 10 Effect size Cramer's V: 0.03 ------------ last_rec and country_destination ------------ last_rec : 3 unique values. country_destination : 209 unique values. Too many values to print contingency table. X^2 TEST p-value to reject null: 0.0 X^2: 8650.63 D.f: 416 Effect size Cramer's V: 0.07 ------------ last_rec and Domestic or international ------------ last_rec : 3 unique values. Domestic or international : 2 unique values.
last_rec buy nan wait All Domestic or international Domestic 278530 30229 207993 516752 International 273054 27934 189952 490940 All 551584 58163 397945 1007692 X^2 TEST p-value to reject null: 0.0 X^2: 301.84 D.f: 2 Effect size Cramer's V: 0.02 ------------ last_rec and Region ------------ last_rec : 3 unique values. Region : 8 unique values.
last_rec buy nan wait All Region East Asia & Pacific 15311 1429 7065 23805 Europe & Central Asia 36565 3161 20544 60270 Latin America & Caribbean 52026 4676 30340 87042 Middle East & North Africa 5474 549 2635 8658 North America 430531 47217 329514 807262 South Asia 3128 232 842 4202 Sub-Saharan Africa 8065 859 6740 15664 nan 484 40 265 789 All 551584 58163 397945 1007692 X^2 TEST p-value to reject null: 0.0 X^2: 4500.01 D.f: 14 Effect size Cramer's V: 0.05 ------------ last_rec and IncomeGroup ------------ last_rec : 3 unique values. IncomeGroup : 5 unique values.
last_rec buy nan wait All IncomeGroup High income 494802 52788 363059 910649 Low income 450 41 201 692 Lower middle income 10130 945 4892 15967 Upper middle income 45718 4349 29528 79595 nan 484 40 265 789 All 551584 58163 397945 1007692 X^2 TEST p-value to reject null: 0.0 X^2: 880.32 D.f: 8 Effect size Cramer's V: 0.02 ------------ last_rec and outcome ------------ last_rec : 3 unique values. outcome : 3 unique values.
last_rec buy nan wait All outcome expected 61426 2725 71818 135969 gained 361246 49990 247894 659130 lost 128912 5448 78233 212593 All 551584 58163 397945 1007692 X^2 TEST p-value to reject null: 0.0 X^2: 21609.47 D.f: 4 Effect size Cramer's V: 0.1 ------------ is_session_1 and Search or watch ------------ is_session_1 : 2 unique values. Search or watch : 2 unique values.
is_session_1 False True All Search or watch search 384170 272280 656450 watch 153038 198204 351242 All 537208 470484 1007692 X^2 TEST p-value to reject null: 0.0 X^2: 20550.59 D.f: 1 Effect size Cramer's V: 0.14 ------------ is_session_1 and Use frequency ------------ is_session_1 : 2 unique values. Use frequency : 2 unique values.
is_session_1 False True All Use frequency more than once 537208 397638 934846 once 0 72846 72846 All 537208 470484 1007692 X^2 TEST p-value to reject null: 0.0 X^2: 89656.11 D.f: 1 Effect size Cramer's V: 0.3 ------------ is_session_1 and continent_origin ------------ is_session_1 : 2 unique values. continent_origin : 6 unique values.
is_session_1 False True All continent_origin AF 8909 7618 16527 AS 14677 15146 29823 EU 28344 31204 59548 NorthA 466042 393261 859303 OC 3108 4267 7375 SA 16128 18988 35116 All 537208 470484 1007692 X^2 TEST p-value to reject null: 0.0 X^2: 2417.53 D.f: 5 Effect size Cramer's V: 0.05 ------------ is_session_1 and City origin ------------ is_session_1 : 2 unique values. City origin : 1237 unique values. Too many values to print contingency table. X^2 TEST p-value to reject null: 0.0 X^2: 12218.15 D.f: 1236 Effect size Cramer's V: 0.11 ------------ is_session_1 and City destination ------------ is_session_1 : 2 unique values. City destination : 1487 unique values. Too many values to print contingency table. X^2 TEST p-value to reject null: 0.0 X^2: 6663.13 D.f: 1486 Effect size Cramer's V: 0.08 ------------ is_session_1 and region_origin ------------ is_session_1 : 2 unique values. region_origin : 453 unique values. Too many values to print contingency table. X^2 TEST p-value to reject null: 0.0 X^2: 9036.59 D.f: 452 Effect size Cramer's V: 0.09 ------------ is_session_1 and region_destination ------------ is_session_1 : 2 unique values. region_destination : 498 unique values. Too many values to print contingency table. X^2 TEST p-value to reject null: 0.0 X^2: 3947.76 D.f: 497 Effect size Cramer's V: 0.06 ------------ is_session_1 and country_origin ------------ is_session_1 : 2 unique values. country_origin : 188 unique values. Too many values to print contingency table. X^2 TEST p-value to reject null: 0.0 X^2: 5814.61 D.f: 187 Effect size Cramer's V: 0.08 ------------ is_session_1 and continent_destination ------------ is_session_1 : 2 unique values. continent_destination : 6 unique values.
is_session_1 False True All continent_destination AF 7719 7073 14792 AS 41975 39965 81940 EU 75527 69398 144925 NorthA 388201 331534 719735 OC 6092 6081 12173 SA 17694 16433 34127 All 537208 470484 1007692 X^2 TEST p-value to reject null: 0.0 X^2: 428.67 D.f: 5 Effect size Cramer's V: 0.02 ------------ is_session_1 and country_destination ------------ is_session_1 : 2 unique values. country_destination : 209 unique values. Too many values to print contingency table. X^2 TEST p-value to reject null: 0.0 X^2: 2180.14 D.f: 208 Effect size Cramer's V: 0.05 ------------ is_session_1 and Domestic or international ------------ is_session_1 : 2 unique values. Domestic or international : 2 unique values.
is_session_1 False True All Domestic or international Domestic 278840 237912 516752 International 258368 232572 490940 All 537208 470484 1007692 X^2 TEST p-value to reject null: 0.0 X^2: 179.65 D.f: 1 Effect size Cramer's V: 0.01 ------------ is_session_1 and Region ------------ is_session_1 : 2 unique values. Region : 8 unique values.
is_session_1 False True All Region East Asia & Pacific 11541 12264 23805 Europe & Central Asia 28661 31609 60270 Latin America & Caribbean 43681 43361 87042 Middle East & North Africa 4113 4545 8658 North America 438418 368844 807262 South Asia 1936 2266 4202 Sub-Saharan Africa 8468 7196 15664 nan 390 399 789 All 537208 470484 1007692 X^2 TEST p-value to reject null: 0.0 X^2: 1904.69 D.f: 7 Effect size Cramer's V: 0.04 ------------ is_session_1 and IncomeGroup ------------ is_session_1 : 2 unique values. IncomeGroup : 5 unique values.
is_session_1 False True All IncomeGroup High income 488371 422278 910649 Low income 401 291 692 Lower middle income 8672 7295 15967 Upper middle income 39374 40221 79595 nan 390 399 789 All 537208 470484 1007692 X^2 TEST p-value to reject null: 0.0 X^2: 526.45 D.f: 4 Effect size Cramer's V: 0.02 ------------ is_session_1 and outcome ------------ is_session_1 : 2 unique values. outcome : 3 unique values.
is_session_1 False True All outcome expected 63587 72382 135969 gained 385611 273519 659130 lost 88010 124583 212593 All 537208 470484 1007692 X^2 TEST p-value to reject null: 0.0 X^2: 21599.67 D.f: 2 Effect size Cramer's V: 0.15 ------------ Search or watch and Use frequency ------------ Search or watch : 2 unique values. Use frequency : 2 unique values.
Search or watch search watch All Use frequency more than once 618755 316091 934846 once 37695 35151 72846 All 656450 351242 1007692 X^2 TEST p-value to reject null: 0.0 X^2: 6206.7 D.f: 1 Effect size Cramer's V: 0.08 ------------ Search or watch and continent_origin ------------ Search or watch : 2 unique values. continent_origin : 6 unique values.
Search or watch search watch All continent_origin AF 10456 6071 16527 AS 20661 9162 29823 EU 37735 21813 59548 NorthA 559779 299524 859303 OC 4718 2657 7375 SA 23101 12015 35116 All 656450 351242 1007692 X^2 TEST p-value to reject null: 0.0 X^2: 343.63 D.f: 5 Effect size Cramer's V: 0.02 ------------ Search or watch and City origin ------------ Search or watch : 2 unique values. City origin : 1237 unique values. Too many values to print contingency table. X^2 TEST p-value to reject null: 0.0 X^2: 4252.77 D.f: 1236 Effect size Cramer's V: 0.06 ------------ Search or watch and City destination ------------ Search or watch : 2 unique values. City destination : 1487 unique values. Too many values to print contingency table. X^2 TEST p-value to reject null: 0.0 X^2: 7138.07 D.f: 1486 Effect size Cramer's V: 0.08 ------------ Search or watch and region_origin ------------ Search or watch : 2 unique values. region_origin : 453 unique values. Too many values to print contingency table. X^2 TEST p-value to reject null: 0.0 X^2: 2459.85 D.f: 452 Effect size Cramer's V: 0.05 ------------ Search or watch and region_destination ------------ Search or watch : 2 unique values. region_destination : 498 unique values. Too many values to print contingency table. X^2 TEST p-value to reject null: 0.0 X^2: 4526.19 D.f: 497 Effect size Cramer's V: 0.07 ------------ Search or watch and country_origin ------------ Search or watch : 2 unique values. country_origin : 188 unique values. Too many values to print contingency table. X^2 TEST p-value to reject null: 0.0 X^2: 1581.51 D.f: 187 Effect size Cramer's V: 0.04 ------------ Search or watch and continent_destination ------------ Search or watch : 2 unique values. continent_destination : 6 unique values.
Search or watch search watch All continent_destination AF 9685 5107 14792 AS 57538 24402 81940 EU 96303 48622 144925 NorthA 462078 257657 719735 OC 8162 4011 12173 SA 22684 11443 34127 All 656450 351242 1007692 X^2 TEST p-value to reject null: 0.0 X^2: 1366.95 D.f: 5 Effect size Cramer's V: 0.04 ------------ Search or watch and country_destination ------------ Search or watch : 2 unique values. country_destination : 209 unique values. Too many values to print contingency table. X^2 TEST p-value to reject null: 0.0 X^2: 3266.86 D.f: 208 Effect size Cramer's V: 0.06 ------------ Search or watch and Domestic or international ------------ Search or watch : 2 unique values. Domestic or international : 2 unique values.
Search or watch search watch All Domestic or international Domestic 328611 188141 516752 International 327839 163101 490940 All 656450 351242 1007692 X^2 TEST p-value to reject null: 0.0 X^2: 1125.43 D.f: 1 Effect size Cramer's V: 0.03 ------------ Search or watch and Region ------------ Search or watch : 2 unique values. Region : 8 unique values.
Search or watch search watch All Region East Asia & Pacific 16016 7789 23805 Europe & Central Asia 38190 22080 60270 Latin America & Caribbean 57283 29759 87042 Middle East & North Africa 6150 2508 8658 North America 525521 281741 807262 South Asia 2935 1267 4202 Sub-Saharan Africa 9825 5839 15664 nan 530 259 789 All 656450 351242 1007692 X^2 TEST p-value to reject null: 0.0 X^2: 364.61 D.f: 7 Effect size Cramer's V: 0.02 ------------ Search or watch and IncomeGroup ------------ Search or watch : 2 unique values. IncomeGroup : 5 unique values.
Search or watch search watch All IncomeGroup High income 592373 318276 910649 Low income 473 219 692 Lower middle income 11231 4736 15967 Upper middle income 51843 27752 79595 nan 530 259 789 All 656450 351242 1007692 X^2 TEST p-value to reject null: 0.0 X^2: 197.91 D.f: 4 Effect size Cramer's V: 0.01 ------------ Search or watch and outcome ------------ Search or watch : 2 unique values. outcome : 3 unique values.
Search or watch search watch All outcome expected 0 135969 135969 gained 656320 2810 659130 lost 130 212463 212593 All 656450 351242 1007692 X^2 TEST p-value to reject null: 0.0 X^2: 994797.34 D.f: 2 Effect size Cramer's V: 0.99 ------------ Use frequency and continent_origin ------------ Use frequency : 2 unique values. continent_origin : 6 unique values.
Use frequency more than once once All continent_origin AF 15353 1174 16527 AS 27087 2736 29823 EU 53770 5778 59548 NorthA 800685 58618 859303 OC 6538 837 7375 SA 31413 3703 35116 All 934846 72846 1007692 X^2 TEST p-value to reject null: 0.0 X^2: 1687.31 D.f: 5 Effect size Cramer's V: 0.04 ------------ Use frequency and City origin ------------ Use frequency : 2 unique values. City origin : 1237 unique values. Too many values to print contingency table. X^2 TEST p-value to reject null: 0.0 X^2: 7880.69 D.f: 1236 Effect size Cramer's V: 0.09 ------------ Use frequency and City destination ------------ Use frequency : 2 unique values. City destination : 1487 unique values. Too many values to print contingency table. X^2 TEST p-value to reject null: 0.0 X^2: 5023.61 D.f: 1486 Effect size Cramer's V: 0.07 ------------ Use frequency and region_origin ------------ Use frequency : 2 unique values. region_origin : 453 unique values. Too many values to print contingency table. X^2 TEST p-value to reject null: 0.0 X^2: 5458.12 D.f: 452 Effect size Cramer's V: 0.07 ------------ Use frequency and region_destination ------------ Use frequency : 2 unique values. region_destination : 498 unique values. Too many values to print contingency table. X^2 TEST p-value to reject null: 0.0 X^2: 2487.29 D.f: 497 Effect size Cramer's V: 0.05 ------------ Use frequency and country_origin ------------ Use frequency : 2 unique values. country_origin : 188 unique values. Too many values to print contingency table. X^2 TEST p-value to reject null: 0.0 X^2: 3570.3 D.f: 187 Effect size Cramer's V: 0.06 ------------ Use frequency and continent_destination ------------ Use frequency : 2 unique values. continent_destination : 6 unique values.
Use frequency more than once once All continent_destination AF 13604 1188 14792 AS 75356 6584 81940 EU 134517 10408 144925 NorthA 669171 50564 719735 OC 11124 1049 12173 SA 31074 3053 34127 All 934846 72846 1007692 X^2 TEST p-value to reject null: 0.0 X^2: 323.6 D.f: 5 Effect size Cramer's V: 0.02 ------------ Use frequency and country_destination ------------ Use frequency : 2 unique values. country_destination : 209 unique values. Too many values to print contingency table. X^2 TEST p-value to reject null: 0.0 X^2: 1346.82 D.f: 208 Effect size Cramer's V: 0.04 ------------ Use frequency and Domestic or international ------------ Use frequency : 2 unique values. Domestic or international : 2 unique values.
Use frequency more than once once All Domestic or international Domestic 480010 36742 516752 International 454836 36104 490940 All 934846 72846 1007692 X^2 TEST p-value to reject null: 0.0 X^2: 22.29 D.f: 1 Effect size Cramer's V: 0.0 ------------ Use frequency and Region ------------ Use frequency : 2 unique values. Region : 8 unique values.
Use frequency more than once once All Region East Asia & Pacific 21599 2206 23805 Europe & Central Asia 54416 5854 60270 Latin America & Caribbean 79076 7966 87042 Middle East & North Africa 7797 861 8658 North America 752910 54352 807262 South Asia 3773 429 4202 Sub-Saharan Africa 14557 1107 15664 nan 718 71 789 All 934846 72846 1007692 X^2 TEST p-value to reject null: 0.0 X^2: 1633.26 D.f: 7 Effect size Cramer's V: 0.04 ------------ Use frequency and IncomeGroup ------------ Use frequency : 2 unique values. IncomeGroup : 5 unique values.
Use frequency more than once once All IncomeGroup High income 846629 64020 910649 Low income 660 32 692 Lower middle income 14729 1238 15967 Upper middle income 72110 7485 79595 nan 718 71 789 All 934846 72846 1007692 X^2 TEST p-value to reject null: 0.0 X^2: 632.31 D.f: 4 Effect size Cramer's V: 0.03 ------------ Use frequency and outcome ------------ Use frequency : 2 unique values. outcome : 3 unique values.
Use frequency more than once once All outcome expected 119565 16404 135969 gained 621259 37871 659130 lost 194022 18571 212593 All 934846 72846 1007692 X^2 TEST p-value to reject null: 0.0 X^2: 7622.73 D.f: 2 Effect size Cramer's V: 0.09 ------------ continent_origin and City origin ------------ continent_origin : 6 unique values. City origin : 1237 unique values. Too many values to print contingency table. X^2 TEST p-value to reject null: 0.0 X^2: 4966131.02 D.f: 6180 Effect size Cramer's V: 0.99 ------------ continent_origin and City destination ------------ continent_origin : 6 unique values. City destination : 1487 unique values. Too many values to print contingency table. X^2 TEST p-value to reject null: 0.0 X^2: 584427.65 D.f: 7430 Effect size Cramer's V: 0.34 ------------ continent_origin and region_origin ------------ continent_origin : 6 unique values. region_origin : 453 unique values. Too many values to print contingency table. X^2 TEST p-value to reject null: 0.0 X^2: 4339419.2 D.f: 2260 Effect size Cramer's V: 0.93 ------------ continent_origin and region_destination ------------ continent_origin : 6 unique values. region_destination : 498 unique values. Too many values to print contingency table. X^2 TEST p-value to reject null: 0.0 X^2: 434996.58 D.f: 2485 Effect size Cramer's V: 0.29 ------------ continent_origin and country_origin ------------ continent_origin : 6 unique values. country_origin : 188 unique values. Too many values to print contingency table. X^2 TEST p-value to reject null: 0.0 X^2: 5036566.37 D.f: 935 Effect size Cramer's V: 1.0 ------------ continent_origin and continent_destination ------------ continent_origin : 6 unique values. continent_destination : 6 unique values.
continent_origin AF AS EU NorthA OC SA All continent_destination AF 1011 774 2656 10089 100 162 14792 AS 993 13534 7456 56735 2030 1192 81940 EU 2111 4775 28792 100851 1093 7303 144925 NorthA 12096 9364 17575 663988 1559 15153 719735 OC 174 1139 1047 6893 2449 471 12173 SA 142 237 2022 20747 144 10835 34127 All 16527 29823 59548 859303 7375 35116 1007692 X^2 TEST p-value to reject null: 0.0 X^2: 298463.39 D.f: 25 Effect size Cramer's V: 0.24 ------------ continent_origin and country_destination ------------ continent_origin : 6 unique values. country_destination : 209 unique values. Too many values to print contingency table. X^2 TEST p-value to reject null: 0.0 X^2: 398941.06 D.f: 1040 Effect size Cramer's V: 0.28 ------------ continent_origin and Domestic or international ------------ continent_origin : 6 unique values. Domestic or international : 2 unique values.
continent_origin AF AS EU NorthA OC SA All Domestic or international Domestic 473 2155 2947 504576 1579 5022 516752 International 16054 27668 56601 354727 5796 30094 490940 All 16527 29823 59548 859303 7375 35116 1007692 X^2 TEST p-value to reject null: 0.0 X^2: 130726.42 D.f: 5 Effect size Cramer's V: 0.36 ------------ continent_origin and Region ------------ continent_origin : 6 unique values. Region : 8 unique values.
continent_origin AF AS EU NorthA OC SA All Region East Asia & Pacific 0 16433 0 0 7372 0 23805 Europe & Central Asia 0 830 59440 0 0 0 60270 Latin America & Caribbean 0 0 0 51928 0 35114 87042 Middle East & North Africa 816 7740 102 0 0 0 8658 North America 0 0 0 807262 0 0 807262 South Asia 0 4202 0 0 0 0 4202 Sub-Saharan Africa 15664 0 0 0 0 0 15664 nan 47 618 6 113 3 2 789 All 16527 29823 59548 859303 7375 35116 1007692 X^2 TEST p-value to reject null: 0.0 X^2: 3421545.28 D.f: 35 Effect size Cramer's V: 0.82 ------------ continent_origin and IncomeGroup ------------ continent_origin : 6 unique values. IncomeGroup : 5 unique values.
continent_origin AF AS EU NorthA OC SA All IncomeGroup High income 5 13549 57734 828433 7316 3612 910649 Low income 292 122 0 278 0 0 692 Lower middle income 1484 9480 311 4116 14 562 15967 Upper middle income 14699 6054 1497 26363 42 30940 79595 nan 47 618 6 113 3 2 789 All 16527 29823 59548 859303 7375 35116 1007692 X^2 TEST p-value to reject null: 0.0 X^2: 715623.92 D.f: 20 Effect size Cramer's V: 0.42 ------------ continent_origin and outcome ------------ continent_origin : 6 unique values. outcome : 3 unique values.
continent_origin AF AS EU NorthA OC SA All outcome expected 2329 3504 8691 115556 1085 4804 135969 gained 10493 20713 37869 562185 4752 23118 659130 lost 3705 5606 12988 181562 1538 7194 212593 All 16527 29823 59548 859303 7375 35116 1007692 X^2 TEST p-value to reject null: 0.0 X^2: 361.08 D.f: 10 Effect size Cramer's V: 0.01 ------------ City origin and City destination ------------ City origin : 1237 unique values. City destination : 1487 unique values. Too many values to print contingency table. X^2 TEST p-value to reject null: 0.0 X^2: 14552724.99 D.f: 1836696 Effect size Cramer's V: 0.11 ------------ City origin and region_origin ------------ City origin : 1237 unique values. region_origin : 453 unique values. Too many values to print contingency table. X^2 TEST p-value to reject null: 0.0 X^2: 437927069.37 D.f: 558672 Effect size Cramer's V: 0.98 ------------ City origin and region_destination ------------ City origin : 1237 unique values. region_destination : 498 unique values. Too many values to print contingency table. X^2 TEST p-value to reject null: 0.0 X^2: 5541398.34 D.f: 614292 Effect size Cramer's V: 0.11 ------------ City origin and country_origin ------------ City origin : 1237 unique values. country_origin : 188 unique values. Too many values to print contingency table. X^2 TEST p-value to reject null: 0.0 X^2: 185354945.85 D.f: 231132 Effect size Cramer's V: 0.99 ------------ City origin and continent_destination ------------ City origin : 1237 unique values. continent_destination : 6 unique values. Too many values to print contingency table. X^2 TEST p-value to reject null: 0.0 X^2: 452932.9 D.f: 6180 Effect size Cramer's V: 0.3 ------------ City origin and country_destination ------------ City origin : 1237 unique values. country_destination : 209 unique values. Too many values to print contingency table. X^2 TEST p-value to reject null: 0.0 X^2: 2447098.69 D.f: 257088 Effect size Cramer's V: 0.11 ------------ City origin and Domestic or international ------------ City origin : 1237 unique values. Domestic or international : 2 unique values. Too many values to print contingency table. X^2 TEST p-value to reject null: 0.0 X^2: 295496.29 D.f: 1236 Effect size Cramer's V: 0.54 ------------ City origin and Region ------------ City origin : 1237 unique values. Region : 8 unique values. Too many values to print contingency table. X^2 TEST p-value to reject null: 0.0 X^2: 6976614.73 D.f: 8652 Effect size Cramer's V: 0.99 ------------ City origin and IncomeGroup ------------ City origin : 1237 unique values. IncomeGroup : 5 unique values. Too many values to print contingency table. X^2 TEST p-value to reject null: 0.0 X^2: 3978232.18 D.f: 4944 Effect size Cramer's V: 0.99 ------------ City origin and outcome ------------ City origin : 1237 unique values. outcome : 3 unique values. Too many values to print contingency table. X^2 TEST p-value to reject null: 0.0 X^2: 7142.62 D.f: 2472 Effect size Cramer's V: 0.06 ------------ City destination and region_origin ------------ City destination : 1487 unique values. region_origin : 453 unique values. Too many values to print contingency table. X^2 TEST p-value to reject null: 0.0 X^2: 6673185.65 D.f: 671672 Effect size Cramer's V: 0.12 ------------ City destination and region_destination ------------ City destination : 1487 unique values. region_destination : 498 unique values. Too many values to print contingency table. X^2 TEST p-value to reject null: 0.0 X^2: 482080166.23 D.f: 738542 Effect size Cramer's V: 0.98 ------------ City destination and country_origin ------------ City destination : 1487 unique values. country_origin : 188 unique values. Too many values to print contingency table. X^2 TEST p-value to reject null: 0.0 X^2: 3213619.6 D.f: 277882 Effect size Cramer's V: 0.13 ------------ City destination and continent_destination ------------ City destination : 1487 unique values. continent_destination : 6 unique values. Too many values to print contingency table. X^2 TEST p-value to reject null: 0.0 X^2: 4960803.6 D.f: 7430 Effect size Cramer's V: 0.99 ------------ City destination and country_destination ------------ City destination : 1487 unique values. country_destination : 209 unique values. Too many values to print contingency table. X^2 TEST p-value to reject null: 0.0 X^2: 206909300.84 D.f: 309088 Effect size Cramer's V: 0.99 ------------ City destination and Domestic or international ------------ City destination : 1487 unique values. Domestic or international : 2 unique values. Too many values to print contingency table. X^2 TEST p-value to reject null: 0.0 X^2: 628387.01 D.f: 1486 Effect size Cramer's V: 0.79 ------------ City destination and Region ------------ City destination : 1487 unique values. Region : 8 unique values. Too many values to print contingency table. X^2 TEST p-value to reject null: 0.0 X^2: 604117.78 D.f: 10402 Effect size Cramer's V: 0.29 ------------ City destination and IncomeGroup ------------ City destination : 1487 unique values. IncomeGroup : 5 unique values. Too many values to print contingency table. X^2 TEST p-value to reject null: 0.0 X^2: 166858.98 D.f: 5944 Effect size Cramer's V: 0.2 ------------ City destination and outcome ------------ City destination : 1487 unique values. outcome : 3 unique values. Too many values to print contingency table. X^2 TEST p-value to reject null: 0.0 X^2: 12739.85 D.f: 2972 Effect size Cramer's V: 0.08 ------------ region_origin and region_destination ------------ region_origin : 453 unique values. region_destination : 498 unique values. Too many values to print contingency table. X^2 TEST p-value to reject null: 0.0 X^2: 3825269.06 D.f: 224644 Effect size Cramer's V: 0.09 ------------ region_origin and country_origin ------------ region_origin : 453 unique values. country_origin : 188 unique values. Too many values to print contingency table. X^2 TEST p-value to reject null: 0.0 X^2: 99553016.33 D.f: 84524 Effect size Cramer's V: 0.73 ------------ region_origin and continent_destination ------------ region_origin : 453 unique values. continent_destination : 6 unique values. Too many values to print contingency table. X^2 TEST p-value to reject null: 0.0 X^2: 365684.32 D.f: 2260 Effect size Cramer's V: 0.27 ------------ region_origin and country_destination ------------ region_origin : 453 unique values. country_destination : 209 unique values. Too many values to print contingency table. X^2 TEST p-value to reject null: 0.0 X^2: 1525303.61 D.f: 94016 Effect size Cramer's V: 0.09 ------------ region_origin and Domestic or international ------------ region_origin : 453 unique values. Domestic or international : 2 unique values. Too many values to print contingency table. X^2 TEST p-value to reject null: 0.0 X^2: 266668.75 D.f: 452 Effect size Cramer's V: 0.51 ------------ region_origin and Region ------------ region_origin : 453 unique values. Region : 8 unique values. Too many values to print contingency table. X^2 TEST p-value to reject null: 0.0 X^2: 5679553.71 D.f: 3164 Effect size Cramer's V: 0.9 ------------ region_origin and IncomeGroup ------------ region_origin : 453 unique values. IncomeGroup : 5 unique values. Too many values to print contingency table. X^2 TEST p-value to reject null: 0.0 X^2: 2832378.21 D.f: 1808 Effect size Cramer's V: 0.84 ------------ region_origin and outcome ------------ region_origin : 453 unique values. outcome : 3 unique values. Too many values to print contingency table. X^2 TEST p-value to reject null: 0.0 X^2: 3925.7 D.f: 904 Effect size Cramer's V: 0.04 ------------ region_destination and country_origin ------------ region_destination : 498 unique values. country_origin : 188 unique values. Too many values to print contingency table. X^2 TEST p-value to reject null: 0.0 X^2: 1790597.54 D.f: 92939 Effect size Cramer's V: 0.1 ------------ region_destination and continent_destination ------------ region_destination : 498 unique values. continent_destination : 6 unique values. Too many values to print contingency table. X^2 TEST p-value to reject null: 0.0 X^2: 4209379.45 D.f: 2485 Effect size Cramer's V: 0.91 ------------ region_destination and country_destination ------------ region_destination : 498 unique values. country_destination : 209 unique values. Too many values to print contingency table. X^2 TEST p-value to reject null: 0.0 X^2: 107420517.61 D.f: 103376 Effect size Cramer's V: 0.72 ------------ region_destination and Domestic or international ------------ region_destination : 498 unique values. Domestic or international : 2 unique values. Too many values to print contingency table. X^2 TEST p-value to reject null: 0.0 X^2: 601591.11 D.f: 497 Effect size Cramer's V: 0.77 ------------ region_destination and Region ------------ region_destination : 498 unique values. Region : 8 unique values. Too many values to print contingency table. X^2 TEST p-value to reject null: 0.0 X^2: 434033.35 D.f: 3479 Effect size Cramer's V: 0.25 ------------ region_destination and IncomeGroup ------------ region_destination : 498 unique values. IncomeGroup : 5 unique values. Too many values to print contingency table. X^2 TEST p-value to reject null: 0.0 X^2: 115182.45 D.f: 1988 Effect size Cramer's V: 0.17 ------------ region_destination and outcome ------------ region_destination : 498 unique values. outcome : 3 unique values. Too many values to print contingency table. X^2 TEST p-value to reject null: 0.0 X^2: 7970.65 D.f: 994 Effect size Cramer's V: 0.06 ------------ country_origin and continent_destination ------------ country_origin : 188 unique values. continent_destination : 6 unique values. Too many values to print contingency table. X^2 TEST p-value to reject null: 0.0 X^2: 368861.0 D.f: 935 Effect size Cramer's V: 0.27 ------------ country_origin and country_destination ------------ country_origin : 188 unique values. country_destination : 209 unique values. Too many values to print contingency table. X^2 TEST p-value to reject null: 0.0 X^2: 1364649.57 D.f: 38896 Effect size Cramer's V: 0.09 ------------ country_origin and Domestic or international ------------ country_origin : 188 unique values. Domestic or international : 2 unique values. Too many values to print contingency table. X^2 TEST p-value to reject null: 0.0 X^2: 254119.97 D.f: 187 Effect size Cramer's V: 0.5 ------------ country_origin and Region ------------ country_origin : 188 unique values. Region : 8 unique values. Too many values to print contingency table. X^2 TEST p-value to reject null: 0.0 X^2: 7053844.0 D.f: 1309 Effect size Cramer's V: 1.0 ------------ country_origin and IncomeGroup ------------ country_origin : 188 unique values. IncomeGroup : 5 unique values. Too many values to print contingency table. X^2 TEST p-value to reject null: 0.0 X^2: 4030768.0 D.f: 748 Effect size Cramer's V: 1.0 ------------ country_origin and outcome ------------ country_origin : 188 unique values. outcome : 3 unique values. Too many values to print contingency table. X^2 TEST p-value to reject null: 0.0 X^2: 2014.3 D.f: 374 Effect size Cramer's V: 0.03 ------------ continent_destination and country_destination ------------ continent_destination : 6 unique values. country_destination : 209 unique values. Too many values to print contingency table. X^2 TEST p-value to reject null: 0.0 X^2: 5033241.42 D.f: 1040 Effect size Cramer's V: 1.0 ------------ continent_destination and Domestic or international ------------ continent_destination : 6 unique values. Domestic or international : 2 unique values.
continent_destination AF AS EU NorthA OC SA All Domestic or international Domestic 476 2156 2943 504576 1579 5022 516752 International 14316 79784 141982 215159 10594 29105 490940 All 14792 81940 144925 719735 12173 34127 1007692 X^2 TEST p-value to reject null: 0.0 X^2: 359509.51 D.f: 5 Effect size Cramer's V: 0.6 ------------ continent_destination and Region ------------ continent_destination : 6 unique values. Region : 8 unique values.
continent_destination AF AS EU NorthA OC SA \ Region East Asia & Pacific 239 10383 2734 6830 3350 269 Europe & Central Asia 2687 7644 29077 17759 1060 2043 Latin America & Caribbean 631 2439 15098 53955 655 14264 Middle East & North Africa 658 3405 2723 1693 88 91 North America 9620 55486 93049 625081 6709 17317 South Asia 21 1391 422 2272 93 3 Sub-Saharan Africa 918 819 1757 11864 172 134 nan 18 373 65 281 46 6 All 14792 81940 144925 719735 12173 34127 continent_destination All Region East Asia & Pacific 23805 Europe & Central Asia 60270 Latin America & Caribbean 87042 Middle East & North Africa 8658 North America 807262 South Asia 4202 Sub-Saharan Africa 15664 nan 789 All 1007692 X^2 TEST p-value to reject null: 0.0 X^2: 234076.83 D.f: 35 Effect size Cramer's V: 0.22 ------------ continent_destination and IncomeGroup ------------ continent_destination : 6 unique values. IncomeGroup : 5 unique values.
continent_destination AF AS EU NorthA OC SA All IncomeGroup High income 13254 71872 127569 665139 10839 21976 910649 Low income 103 85 69 415 1 19 692 Lower middle income 266 4131 2060 8520 444 546 15967 Upper middle income 1151 5479 15162 45380 843 11580 79595 nan 18 373 65 281 46 6 789 All 14792 81940 144925 719735 12173 34127 1007692 X^2 TEST p-value to reject null: 0.0 X^2: 45971.34 D.f: 20 Effect size Cramer's V: 0.11 ------------ continent_destination and outcome ------------ continent_destination : 6 unique values. outcome : 3 unique values.
continent_destination AF AS EU NorthA OC SA All outcome expected 2103 10557 21643 95337 1792 4537 135969 gained 9722 57634 96639 464188 8203 22744 659130 lost 2967 13749 26643 160210 2178 6846 212593 All 14792 81940 144925 719735 12173 34127 1007692 X^2 TEST p-value to reject null: 0.0 X^2: 2521.05 D.f: 10 Effect size Cramer's V: 0.04 ------------ country_destination and Domestic or international ------------ country_destination : 209 unique values. Domestic or international : 2 unique values. Too many values to print contingency table. X^2 TEST p-value to reject null: 0.0 X^2: 606139.21 D.f: 208 Effect size Cramer's V: 0.78 ------------ country_destination and Region ------------ country_destination : 209 unique values. Region : 8 unique values. Too many values to print contingency table. X^2 TEST p-value to reject null: 0.0 X^2: 390561.05 D.f: 1456 Effect size Cramer's V: 0.24 ------------ country_destination and IncomeGroup ------------ country_destination : 209 unique values. IncomeGroup : 5 unique values. Too many values to print contingency table. X^2 TEST p-value to reject null: 0.0 X^2: 88945.59 D.f: 832 Effect size Cramer's V: 0.15 ------------ country_destination and outcome ------------ country_destination : 209 unique values. outcome : 3 unique values. Too many values to print contingency table. X^2 TEST p-value to reject null: 0.0 X^2: 5351.55 D.f: 416 Effect size Cramer's V: 0.05 ------------ Domestic or international and Region ------------ Domestic or international : 2 unique values. Region : 8 unique values.
Domestic or international Domestic International All Region East Asia & Pacific 3051 20754 23805 Europe & Central Asia 2993 57277 60270 Latin America & Caribbean 8565 78477 87042 Middle East & North Africa 114 8544 8658 North America 501033 306229 807262 South Asia 547 3655 4202 Sub-Saharan Africa 449 15215 15664 nan 0 789 789 All 516752 490940 1007692 X^2 TEST p-value to reject null: 0.0 X^2: 189898.64 D.f: 7 Effect size Cramer's V: 0.43 ------------ Domestic or international and IncomeGroup ------------ Domestic or international : 2 unique values. IncomeGroup : 5 unique values.
Domestic or international Domestic International All IncomeGroup High income 506093 404556 910649 Low income 9 683 692 Lower middle income 1240 14727 15967 Upper middle income 9410 70185 79595 nan 0 789 789 All 516752 490940 1007692 X^2 TEST p-value to reject null: 0.0 X^2: 69948.65 D.f: 4 Effect size Cramer's V: 0.26 ------------ Domestic or international and outcome ------------ Domestic or international : 2 unique values. outcome : 3 unique values.
Domestic or international Domestic International All outcome expected 67770 68199 135969 gained 330349 328781 659130 lost 118633 93960 212593 All 516752 490940 1007692 X^2 TEST p-value to reject null: 0.0 X^2: 2208.84 D.f: 2 Effect size Cramer's V: 0.05 ------------ Region and IncomeGroup ------------ Region : 8 unique values. IncomeGroup : 5 unique values.
Region East Asia & Pacific Europe & Central Asia \ IncomeGroup High income 14011 57720 Low income 0 0 Lower middle income 5531 323 Upper middle income 4263 2227 nan 0 0 All 23805 60270 Region Latin America & Caribbean Middle East & North Africa \ IncomeGroup High income 24783 6868 Low income 278 0 Lower middle income 4678 802 Upper middle income 57303 988 nan 0 0 All 87042 8658 Region North America South Asia Sub-Saharan Africa nan \ IncomeGroup High income 807262 0 5 0 Low income 0 122 292 0 Lower middle income 0 3938 695 0 Upper middle income 0 142 14672 0 nan 0 0 0 789 All 807262 4202 15664 789 Region All IncomeGroup High income 910649 Low income 692 Lower middle income 15967 Upper middle income 79595 nan 789 All 1007692 X^2 TEST p-value to reject null: 0.0 X^2: 1992213.8 D.f: 28 Effect size Cramer's V: 0.7 ------------ Region and outcome ------------ Region : 8 unique values. outcome : 3 unique values.
Region East Asia & Pacific Europe & Central Asia \ outcome expected 3115 8803 gained 16086 38324 lost 4604 13143 All 23805 60270 Region Latin America & Caribbean Middle East & North Africa \ outcome expected 11578 907 gained 57430 6162 lost 18034 1589 All 87042 8658 Region North America South Asia Sub-Saharan Africa nan All outcome expected 108763 473 2232 98 135969 gained 527797 2940 9861 530 659130 lost 170702 789 3571 161 212593 All 807262 4202 15664 789 1007692 X^2 TEST p-value to reject null: 0.0 X^2: 385.63 D.f: 14 Effect size Cramer's V: 0.01 ------------ IncomeGroup and outcome ------------ IncomeGroup : 5 unique values. outcome : 3 unique values.
IncomeGroup High income Low income Lower middle income \ outcome expected 123050 73 1840 gained 594888 477 11257 lost 192711 142 2870 All 910649 692 15967 IncomeGroup Upper middle income nan All outcome expected 10908 98 135969 gained 51978 530 659130 lost 16709 161 212593 All 79595 789 1007692 X^2 TEST p-value to reject null: 0.0 X^2: 196.3 D.f: 8 Effect size Cramer's V: 0.01
In [15]:
categorical_correlations = categorical_correlations.fillna(0)
categorical_correlations.style.background_gradient(cmap='Blues', axis=None)
Out[15]:
| origin_city | destination_city | trip_type | weekend | filter_no_lcc | filter_non_stop | filter_short_layover | filter_name | first_rec | last_rec | is_session_1 | Search or watch | Use frequency | continent_origin | City origin | City destination | region_origin | region_destination | country_origin | continent_destination | country_destination | Domestic or international | Region | IncomeGroup | outcome | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| origin_city | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| destination_city | 0.113569 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| trip_type | 0.250939 | 0.189375 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| weekend | 0.188665 | 0.311903 | 0.223129 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| filter_no_lcc | 0.0551823 | 0.0622049 | 0.00345676 | 0.0100393 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| filter_non_stop | 0.140617 | 0.139931 | 0 | 0.0520667 | 0.139641 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| filter_short_layover | 0.0889076 | 0.110387 | 0.00323824 | 0.0278636 | 0.0917811 | 0.0601358 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| filter_name | 0.0809152 | 0.0862918 | 0.00666794 | 0.0580056 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| first_rec | 0.110209 | 0.13466 | 0.0145079 | 0.0340823 | 0.0429472 | 0.0509839 | 0.0200045 | 0.0465326 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| last_rec | 0.0976916 | 0.119561 | 0.00994136 | 0.00924758 | 0.0380062 | 0.0264573 | 0.0162132 | 0.0329097 | 0.907649 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| is_session_1 | 0.112731 | 0.0851717 | 0.0570345 | 0.00420952 | 0.00346529 | 0.00843189 | 0.0120254 | 0.0169052 | 0.0522848 | 0.0525428 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Search or watch | 0.0702433 | 0.0871007 | 0.0141817 | 0.0473606 | 0.0495041 | 0.147157 | 0.0834718 | 0.175005 | 0.181198 | 0.111532 | 0.142807 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Use frequency | 0.0905946 | 0.0727718 | 0.0420551 | 0.00622556 | 0.0104085 | 0.0147801 | 0.00732145 | 0.0186162 | 0.0105593 | 0.0191198 | 0.298281 | 0.0784814 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| continent_origin | 1 | 0.34552 | 0.122024 | 0.115361 | 0.0159211 | 0.0386458 | 0.0108232 | 0.0189411 | 0.0553329 | 0.0418822 | 0.0489804 | 0.0184665 | 0.0409197 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| City origin | 1 | 0.109921 | 0.248224 | 0.183669 | 0.0529629 | 0.137825 | 0.0863381 | 0.0787917 | 0.108051 | 0.0955754 | 0.110113 | 0.0649639 | 0.0884338 | 0.992796 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| City destination | 0.111665 | 1 | 0.185186 | 0.309684 | 0.0609374 | 0.137114 | 0.108542 | 0.0844851 | 0.132791 | 0.117849 | 0.0813158 | 0.084164 | 0.0706064 | 0.340578 | 0.108093 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| region_origin | 1 | 0.123453 | 0.223306 | 0.170519 | 0.0433939 | 0.114533 | 0.0582544 | 0.0617396 | 0.0888864 | 0.0762055 | 0.0946975 | 0.0494072 | 0.0735966 | 0.92804 | 0.980546 | 0.121041 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| region_destination | 0.110787 | 1 | 0.153781 | 0.294371 | 0.0493888 | 0.115038 | 0.0829751 | 0.0675796 | 0.106929 | 0.090247 | 0.062591 | 0.0670197 | 0.049682 | 0.293829 | 0.105188 | 0.98111 | 0.0916427 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| country_origin | 1 | 0.132785 | 0.181202 | 0.158949 | 0.0345896 | 0.0634845 | 0.0351297 | 0.0363993 | 0.073781 | 0.0610003 | 0.075962 | 0.0396161 | 0.0595235 | 0.999812 | 0.991785 | 0.130591 | 0.726846 | 0.0974797 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| continent_destination | 0.305885 | 1 | 0.036969 | 0.228586 | 0.0238164 | 0.07256 | 0.0290019 | 0.0352661 | 0.0535833 | 0.032058 | 0.0206251 | 0.0368309 | 0.0179201 | 0.243387 | 0.299825 | 0.992264 | 0.269404 | 0.914029 | 0.270572 | 0 | 0 | 0 | 0 | 0 | 0 |
| country_destination | 0.111374 | 1 | 0.116385 | 0.257137 | 0.0395197 | 0.0949796 | 0.0648361 | 0.0543431 | 0.0814436 | 0.0655156 | 0.0465135 | 0.0569379 | 0.0365587 | 0.281388 | 0.108051 | 0.993561 | 0.0853066 | 0.715893 | 0.0850993 | 0.999482 | 0 | 0 | 0 | 0 | 0 |
| Domestic or international | 0.554191 | 0.796364 | 0.010421 | 0.271384 | 0.0336368 | 0.0689211 | 0.0468113 | 0.0861626 | 0.0492292 | 0.0173071 | 0.0133521 | 0.0334192 | 0.00470323 | 0.360178 | 0.541517 | 0.789677 | 0.514425 | 0.772657 | 0.502175 | 0.597298 | 0.775572 | 0 | 0 | 0 | 0 |
| Region | 1 | 0.296897 | 0.131034 | 0.132063 | 0.0195443 | 0.0451362 | 0.015723 | 0.022655 | 0.0610385 | 0.0472528 | 0.0434758 | 0.0190218 | 0.040259 | 0.824066 | 0.994511 | 0.29265 | 0.897313 | 0.248055 | 1 | 0.215541 | 0.235305 | 0.434107 | 0 | 0 | 0 |
| IncomeGroup | 1 | 0.208374 | 0.0601226 | 0.088017 | 0.0145253 | 0.0315288 | 0.00819696 | 0.0173041 | 0.0255945 | 0.0208998 | 0.0228567 | 0.0140144 | 0.0250496 | 0.421355 | 0.993462 | 0.203461 | 0.838266 | 0.169044 | 1 | 0.106795 | 0.148549 | 0.263467 | 0.70303 | 0 | 0 |
| outcome | 0.0631787 | 0.0815776 | 0.0310033 | 0.0494215 | 0.0538591 | 0.145482 | 0.0828546 | 0.123384 | 0.138129 | 0.103548 | 0.146406 | 0.993581 | 0.0869744 | 0.0133852 | 0.0595319 | 0.0795066 | 0.0441346 | 0.062888 | 0.0316143 | 0.0353681 | 0.0515301 | 0.0468186 | 0.0138327 | 0.00986921 | 0 |
Numerical variables¶
In [15]:
df.head(4)
Out[15]:
| transaction_id | origin_city | destination_city | user_id | trip_id | trip_type | departure_date | return_date | stay | weekend | filter_no_lcc | filter_non_stop | filter_short_layover | filter_name | status_updates | first_search_dt | watch_added_dt | latest_status_change_dt | status_latest | total_notifs | total_buy_notifs | first_rec | first_total | last_rec | last_total | first_buy_dt | first_buy_total | lowest_total | Use frequency | outcome | ordered | session | diff_day | diff | Search or watch | first_buy - lowest_total | days_to_departure | latitude_deg_origin | longitude_deg_origin | continent_origin | City origin | latitude_deg_destination | longitude_deg_destination | continent_destination | City destination | region_origin | region_destination | country_origin | country_destination | Domestic or international | Adult population | Country name | Capital origin name | Count_uniq_users_per_country | Percent unique users to country adult pop | Country Code | Region | IncomeGroup | Passengers carried Q1 | Percent unique users to passengers carried by country | is_session_1 | is_US | is_CA | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 17 | MEX | YUL | e42e7c15cde08c19905ee12200fad7cb5af36d1fe3a331... | 05d59806e67fa9a5b2747bc1b24842189bba0c45e49d37... | round_trip | 2018-04-06 00:00:00 | 2018-04-27 00:00:00 | 21.0 | 0 | 0 | 1 | 0 | NonStop | 5 | 2018-03-15 21:29:00 | 2018-03-16 18:02:00 | 2018-04-07 05:02:00 | expired | 6.0 | 5.0 | buy | 455.0 | buy | 566.0 | 2018-03-16 18:00:00 | 455.0 | 455.0 | 1 | lost | 1 | 1 | 0 | 0 days 00:00:00.000000000 | watch | 0.0 | 21 | 19.436300 | -99.072098 | NorthA | Mexico City | 45.470600 | -73.740799 | NorthA | Montréal | DIF | QC | MX | CA | International | 81522558.0 | Mexico | Mexico City | 10662 | 0.013079 | MEX | Latin America & Caribbean | Upper middle income | 16142410.0 | 0.066050 | True | False | False |
| 1 | 27 | ALB | FLL | 4eb7c43c5afdaf75d8ac5f3f92b5e02e4cd4ab716e7e4a... | 0a44613d689f7fafc7c5ab6fae7cb24ff2fd8b0fb162f6... | round_trip | 2018-03-09 00:00:00 | 2018-03-16 00:00:00 | 7.0 | 0 | 0 | 0 | 1 | ShortLayover | 1 | 2018-02-24 14:36:00 | NaN | 2018-02-24 14:36:00 | shopped | 0.0 | 0.0 | buy | 438.0 | buy | 438.0 | 2018-02-24 14:36:00 | 438.0 | 438.0 | 1 | gained | 1 | 1 | 0 | 0 days 00:00:00.000000000 | search | 0.0 | 12 | 42.748299 | -73.801697 | NorthA | Albany | 26.072599 | -80.152702 | NorthA | Fort Lauderdale | NY | FL | US | US | Domestic | 244635911.0 | United States | Washington | 585228 | 0.239224 | USA | North America | High income | 222255500.0 | 0.263313 | True | True | False |
| 2 | 74 | GEG | BZE | 74ad865b8d1fc01eb27c98ac66db0eb27d8a25c4c04b3a... | a5ad5dc889433c1642318fc8b5233c87bf10f5ed19f906... | round_trip | 2018-05-22 00:00:00 | 2018-06-05 00:00:00 | 14.0 | 0 | 0 | 0 | 0 | NoFilter | 4 | 2018-03-19 23:14:00 | 2018-03-19 23:14:00 | 2018-03-19 23:14:00 | inactive | 0.0 | 0.0 | buy | 615.0 | buy | 615.0 | 2018-03-19 23:14:00 | 615.0 | 615.0 | 1 | lost | 1 | 1 | 0 | 0 days 00:00:00.000000000 | watch | 0.0 | 63 | 47.619900 | -117.533997 | NorthA | Spokane | 17.539101 | -88.308197 | NorthA | Belize City | WA | BZ | US | BZ | International | 244635911.0 | United States | Washington | 585228 | 0.239224 | USA | North America | High income | 222255500.0 | 0.263313 | True | True | False |
| 3 | 82 | YUL | FLL | a0b7a4d6c6ddbbc14980b98c2ee74d29e6ede0141fdf4b... | 72b0cf11dd4baeb987a3a7d95916b5eb87e699cfe82e85... | round_trip | 2018-04-14 00:00:00 | 2018-04-20 00:00:00 | 6.0 | 0 | 0 | 0 | 0 | NoFilter | 1 | 2018-04-07 19:36:00 | NaN | 2018-04-07 19:36:00 | shopped | 0.0 | 0.0 | buy | 358.0 | buy | 358.0 | 2018-04-07 19:36:00 | 358.0 | 358.0 | 1 | gained | 1 | 1 | 0 | 0 days 00:00:00.000000000 | search | 0.0 | 6 | 45.470600 | -73.740799 | NorthA | Montréal | 26.072599 | -80.152702 | NorthA | Fort Lauderdale | QC | FL | CA | US | International | 29156938.0 | Canada | Ottawa | 50409 | 0.172889 | CAN | North America | High income | 22345000.0 | 0.225594 | True | False | True |
In [ ]:
df['Days_to_departure'] = df['departure_date']
In [16]:
numerical = ["stay","status_updates","total_notifs","total_buy_notifs", "first_total",
"first_buy_total","lowest_total","session","first_buy - lowest_total",
"Adult population","Count_uniq_users_per_country","Passengers carried Q1"]
In [17]:
numerical_correlations = pd.DataFrame(index=numerical, columns=numerical)
Summarizing numerical variables against each other¶
In [18]:
for i,j in combinations(numerical,2):
p, effect = summarize_numerical(df,i,j)
if p < .05:
numerical_correlations[i][j] = effect
------------ stay and status_updates ------------
Pearson's R: -0.0 p-value to reject null: 0.13 ------------ stay and total_notifs ------------
Pearson's R: -0.02 p-value to reject null: 0.0 ------------ stay and total_buy_notifs ------------
Pearson's R: -0.01 p-value to reject null: 0.0 ------------ stay and first_total ------------
Pearson's R: 0.3 p-value to reject null: 0.0 ------------ stay and first_buy_total ------------
Pearson's R: 0.3 p-value to reject null: 0.0 ------------ stay and lowest_total ------------
Pearson's R: 0.3 p-value to reject null: 0.0 ------------ stay and session ------------
Pearson's R: -0.0 p-value to reject null: 0.23 ------------ stay and first_buy - lowest_total ------------
Pearson's R: 0.03 p-value to reject null: 0.0 ------------ stay and Adult population ------------
Pearson's R: -0.09 p-value to reject null: 0.0 ------------ stay and Count_uniq_users_per_country ------------
Pearson's R: -0.19 p-value to reject null: 0.0 ------------ stay and Passengers carried Q1 ------------
Pearson's R: -0.19 p-value to reject null: 0.0 ------------ status_updates and total_notifs ------------
Pearson's R: 0.37 p-value to reject null: 0.0 ------------ status_updates and total_buy_notifs ------------
Pearson's R: 0.33 p-value to reject null: 0.0 ------------ status_updates and first_total ------------
Pearson's R: -0.03 p-value to reject null: 0.0 ------------ status_updates and first_buy_total ------------
Pearson's R: -0.03 p-value to reject null: 0.0 ------------ status_updates and lowest_total ------------
Pearson's R: -0.06 p-value to reject null: 0.0 ------------ status_updates and session ------------
Pearson's R: -0.12 p-value to reject null: 0.0 ------------ status_updates and first_buy - lowest_total ------------
Pearson's R: 0.14 p-value to reject null: 0.0 ------------ status_updates and Adult population ------------
Pearson's R: -0.01 p-value to reject null: 0.0 ------------ status_updates and Count_uniq_users_per_country ------------
Pearson's R: -0.0 p-value to reject null: 0.0 ------------ status_updates and Passengers carried Q1 ------------
Pearson's R: -0.0 p-value to reject null: 0.03 ------------ total_notifs and total_buy_notifs ------------
Pearson's R: 0.89 p-value to reject null: 0.0 ------------ total_notifs and first_total ------------
Pearson's R: -0.01 p-value to reject null: 0.0 ------------ total_notifs and first_buy_total ------------
Pearson's R: -0.01 p-value to reject null: 0.0 ------------ total_notifs and lowest_total ------------
Pearson's R: -0.06 p-value to reject null: 0.0 ------------ total_notifs and session ------------
Pearson's R: -0.12 p-value to reject null: 0.0 ------------ total_notifs and first_buy - lowest_total ------------
Pearson's R: 0.24 p-value to reject null: 0.0 ------------ total_notifs and Adult population ------------
Pearson's R: 0.01 p-value to reject null: 0.0 ------------ total_notifs and Count_uniq_users_per_country ------------
Pearson's R: 0.01 p-value to reject null: 0.0 ------------ total_notifs and Passengers carried Q1 ------------
Pearson's R: 0.0 p-value to reject null: 0.0 ------------ total_buy_notifs and first_total ------------
Pearson's R: -0.01 p-value to reject null: 0.0 ------------ total_buy_notifs and first_buy_total ------------
Pearson's R: -0.0 p-value to reject null: 0.0 ------------ total_buy_notifs and lowest_total ------------
Pearson's R: -0.05 p-value to reject null: 0.0 ------------ total_buy_notifs and session ------------
Pearson's R: -0.1 p-value to reject null: 0.0 ------------ total_buy_notifs and first_buy - lowest_total ------------
Pearson's R: 0.24 p-value to reject null: 0.0 ------------ total_buy_notifs and Adult population ------------
Pearson's R: 0.0 p-value to reject null: 0.58 ------------ total_buy_notifs and Count_uniq_users_per_country ------------
Pearson's R: -0.0 p-value to reject null: 0.0 ------------ total_buy_notifs and Passengers carried Q1 ------------
Pearson's R: -0.0 p-value to reject null: 0.0 ------------ first_total and first_buy_total ------------
Pearson's R: 0.99 p-value to reject null: 0.0 ------------ first_total and lowest_total ------------
Pearson's R: 0.99 p-value to reject null: 0.0 ------------ first_total and session ------------
Pearson's R: -0.02 p-value to reject null: 0.0 ------------ first_total and first_buy - lowest_total ------------
Pearson's R: 0.21 p-value to reject null: 0.0 ------------ first_total and Adult population ------------
Pearson's R: -0.12 p-value to reject null: 0.0 ------------ first_total and Count_uniq_users_per_country ------------
Pearson's R: -0.17 p-value to reject null: 0.0 ------------ first_total and Passengers carried Q1 ------------
Pearson's R: -0.18 p-value to reject null: 0.0 ------------ first_buy_total and lowest_total ------------
Pearson's R: 0.99 p-value to reject null: 0.0 ------------ first_buy_total and session ------------
Pearson's R: -0.02 p-value to reject null: 0.0 ------------ first_buy_total and first_buy - lowest_total ------------
Pearson's R: 0.23 p-value to reject null: 0.0 ------------ first_buy_total and Adult population ------------
Pearson's R: -0.1 p-value to reject null: 0.0 ------------ first_buy_total and Count_uniq_users_per_country ------------
Pearson's R: -0.15 p-value to reject null: 0.0 ------------ first_buy_total and Passengers carried Q1 ------------
Pearson's R: -0.17 p-value to reject null: 0.0 ------------ lowest_total and session ------------
Pearson's R: -0.01 p-value to reject null: 0.0 ------------ lowest_total and first_buy - lowest_total ------------
Pearson's R: 0.06 p-value to reject null: 0.0 ------------ lowest_total and Adult population ------------
Pearson's R: -0.12 p-value to reject null: 0.0 ------------ lowest_total and Count_uniq_users_per_country ------------
Pearson's R: -0.17 p-value to reject null: 0.0 ------------ lowest_total and Passengers carried Q1 ------------
Pearson's R: -0.18 p-value to reject null: 0.0 ------------ session and first_buy - lowest_total ------------
Pearson's R: -0.04 p-value to reject null: 0.0 ------------ session and Adult population ------------
Pearson's R: 0.02 p-value to reject null: 0.0 ------------ session and Count_uniq_users_per_country ------------
Pearson's R: 0.04 p-value to reject null: 0.0 ------------ session and Passengers carried Q1 ------------
Pearson's R: 0.04 p-value to reject null: 0.0 ------------ first_buy - lowest_total and Adult population ------------
Pearson's R: -0.0 p-value to reject null: 0.59 ------------ first_buy - lowest_total and Count_uniq_users_per_country ------------
Pearson's R: -0.0 p-value to reject null: 0.02 ------------ first_buy - lowest_total and Passengers carried Q1 ------------
Pearson's R: -0.01 p-value to reject null: 0.0 ------------ Adult population and Count_uniq_users_per_country ------------
Pearson's R: 0.78 p-value to reject null: 0.0 ------------ Adult population and Passengers carried Q1 ------------
Pearson's R: 0.81 p-value to reject null: 0.0 ------------ Count_uniq_users_per_country and Passengers carried Q1 ------------
Pearson's R: 1.0 p-value to reject null: 0.0
In [19]:
numerical_correlations = numerical_correlations.fillna(0)
numerical_correlations.style.background_gradient(cmap='Blues', axis=None)
Out[19]:
| stay | status_updates | total_notifs | total_buy_notifs | first_total | first_buy_total | lowest_total | session | first_buy - lowest_total | Adult population | Count_uniq_users_per_country | Passengers carried Q1 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| stay | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| status_updates | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| total_notifs | -0.0154445 | 0.372897 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| total_buy_notifs | -0.0101269 | 0.333896 | 0.887629 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| first_total | 0.297157 | -0.0291507 | -0.00730957 | -0.00953292 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| first_buy_total | 0.299473 | -0.0262065 | -0.00955931 | -0.00423984 | 0.990864 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| lowest_total | 0.298025 | -0.0571499 | -0.057589 | -0.0513747 | 0.987753 | 0.986177 | 0 | 0 | 0 | 0 | 0 | 0 |
| session | 0 | -0.115988 | -0.120906 | -0.100057 | -0.0193853 | -0.0181638 | -0.0129198 | 0 | 0 | 0 | 0 | 0 |
| first_buy - lowest_total | 0.0261217 | 0.141343 | 0.240642 | 0.244105 | 0.205797 | 0.226673 | 0.0621552 | -0.038378 | 0 | 0 | 0 | 0 |
| Adult population | -0.0853436 | -0.00573711 | 0.00508623 | 0 | -0.117438 | -0.095133 | -0.119279 | 0.0238836 | 0 | 0 | 0 | 0 |
| Count_uniq_users_per_country | -0.190553 | -0.00434965 | 0.00583649 | -0.00449199 | -0.165684 | -0.149245 | -0.168337 | 0.0393756 | -0.00307738 | 0.784671 | 0 | 0 |
| Passengers carried Q1 | -0.193221 | -0.00220959 | 0.00427519 | -0.00460289 | -0.18065 | -0.167473 | -0.183184 | 0.0405854 | -0.00579244 | 0.811608 | 0.995722 | 0 |
Summarizing categorical vs. numerical variables¶
In [20]:
numcat_correlations = pd.DataFrame(index=categorical, columns=numerical)
In [29]:
for i in numerical:
for j in categorical:
p,effect = summarize_numerical_categorical(df, num = i, cat = j, plot=True,summary=True,hist=True)
if p < .05:
numcat_correlations[i][j] = effect
------------ stay by origin_city ------------ count 838920.000000 mean 8.862229 std 14.654277 min 0.000000 25% 3.000000 50% 5.000000 75% 9.000000 max 351.000000 Name: stay, dtype: float64
1324 different categorical values, too many to plot. Anova - Kruskal-Wallis p_val to reject null: nan H value: nan Effect size Eta^2: nan ------------ stay by destination_city ------------ count 838920.000000 mean 8.862229 std 14.654277 min 0.000000 25% 3.000000 50% 5.000000 75% 9.000000 max 351.000000 Name: stay, dtype: float64
1583 different categorical values, too many to plot. Anova - Kruskal-Wallis p_val to reject null: nan H value: nan Effect size Eta^2: nan ------------ stay by trip_type ------------ count 838920.000000 mean 8.862229 std 14.654277 min 0.000000 25% 3.000000 50% 5.000000 75% 9.000000 max 351.000000 Name: stay, dtype: float64
Paired t-test p_val to reject null: nan t-statistic value: nan Effect size Cohen's d: nan ------------ stay by weekend ------------ count 838920.000000 mean 8.862229 std 14.654277 min 0.000000 25% 3.000000 50% 5.000000 75% 9.000000 max 351.000000 Name: stay, dtype: float64
Paired t-test p_val to reject null: 0.0 t-statistic value: 216.63 Effect size Cohen's d: 0.68 ------------ stay by filter_no_lcc ------------ count 838920.000000 mean 8.862229 std 14.654277 min 0.000000 25% 3.000000 50% 5.000000 75% 9.000000 max 351.000000 Name: stay, dtype: float64
Paired t-test p_val to reject null: 0.0 t-statistic value: 10.23 Effect size Cohen's d: 0.11 ------------ stay by filter_non_stop ------------ count 838920.000000 mean 8.862229 std 14.654277 min 0.000000 25% 3.000000 50% 5.000000 75% 9.000000 max 351.000000 Name: stay, dtype: float64
Paired t-test p_val to reject null: 0.0 t-statistic value: -34.99 Effect size Cohen's d: 0.14 ------------ stay by filter_short_layover ------------ count 838920.000000 mean 8.862229 std 14.654277 min 0.000000 25% 3.000000 50% 5.000000 75% 9.000000 max 351.000000 Name: stay, dtype: float64
Paired t-test p_val to reject null: 0.0 t-statistic value: -29.98 Effect size Cohen's d: 0.17 ------------ stay by filter_name ------------ count 838920.000000 mean 8.862229 std 14.654277 min 0.000000 25% 3.000000 50% 5.000000 75% 9.000000 max 351.000000 Name: stay, dtype: float64
Anova - Kruskal-Wallis p_val to reject null: 0.0 H value: 5824.96 Effect size Eta^2: 0.01 ------------ stay by first_rec ------------ count 838920.000000 mean 8.862229 std 14.654277 min 0.000000 25% 3.000000 50% 5.000000 75% 9.000000 max 351.000000 Name: stay, dtype: float64
Anova - Kruskal-Wallis p_val to reject null: 0.0 H value: 146.92 Effect size Eta^2: 0.0 ------------ stay by last_rec ------------ count 838920.000000 mean 8.862229 std 14.654277 min 0.000000 25% 3.000000 50% 5.000000 75% 9.000000 max 351.000000 Name: stay, dtype: float64
Anova - Kruskal-Wallis p_val to reject null: 0.0 H value: 268.56 Effect size Eta^2: 0.0 ------------ stay by is_session_1 ------------ count 838920.000000 mean 8.862229 std 14.654277 min 0.000000 25% 3.000000 50% 5.000000 75% 9.000000 max 351.000000 Name: stay, dtype: float64
Paired t-test p_val to reject null: 0.0 t-statistic value: 8.54 Effect size Cohen's d: 0.02 ------------ stay by Search or watch ------------ count 838920.000000 mean 8.862229 std 14.654277 min 0.000000 25% 3.000000 50% 5.000000 75% 9.000000 max 351.000000 Name: stay, dtype: float64
Paired t-test p_val to reject null: 0.0024 t-statistic value: -3.03 Effect size Cohen's d: 0.01 ------------ stay by Use frequency ------------ count 838920.000000 mean 8.862229 std 14.654277 min 0.000000 25% 3.000000 50% 5.000000 75% 9.000000 max 351.000000 Name: stay, dtype: float64
Paired t-test p_val to reject null: 0.0 t-statistic value: 5.95 Effect size Cohen's d: 0.02 ------------ stay by continent_origin ------------ count 838920.000000 mean 8.862229 std 14.654277 min 0.000000 25% 3.000000 50% 5.000000 75% 9.000000 max 351.000000 Name: stay, dtype: float64
Anova - Kruskal-Wallis p_val to reject null: 0.0 H value: 28526.37 Effect size Eta^2: 0.03 ------------ stay by City origin ------------ count 838920.000000 mean 8.862229 std 14.654277 min 0.000000 25% 3.000000 50% 5.000000 75% 9.000000 max 351.000000 Name: stay, dtype: float64
1237 different categorical values, too many to plot. Anova - Kruskal-Wallis p_val to reject null: nan H value: nan Effect size Eta^2: nan ------------ stay by City destination ------------ count 838920.000000 mean 8.862229 std 14.654277 min 0.000000 25% 3.000000 50% 5.000000 75% 9.000000 max 351.000000 Name: stay, dtype: float64
1487 different categorical values, too many to plot. Anova - Kruskal-Wallis p_val to reject null: nan H value: nan Effect size Eta^2: nan ------------ stay by region_origin ------------ count 838920.000000 mean 8.862229 std 14.654277 min 0.000000 25% 3.000000 50% 5.000000 75% 9.000000 max 351.000000 Name: stay, dtype: float64
453 different categorical values, too many to plot. Anova - Kruskal-Wallis p_val to reject null: nan H value: nan Effect size Eta^2: nan ------------ stay by region_destination ------------ count 838920.000000 mean 8.862229 std 14.654277 min 0.000000 25% 3.000000 50% 5.000000 75% 9.000000 max 351.000000 Name: stay, dtype: float64
498 different categorical values, too many to plot. Anova - Kruskal-Wallis p_val to reject null: nan H value: nan Effect size Eta^2: nan ------------ stay by country_origin ------------ count 838920.000000 mean 8.862229 std 14.654277 min 0.000000 25% 3.000000 50% 5.000000 75% 9.000000 max 351.000000 Name: stay, dtype: float64
188 different categorical values, too many to plot. Anova - Kruskal-Wallis p_val to reject null: nan H value: nan Effect size Eta^2: nan ------------ stay by continent_destination ------------ count 838920.000000 mean 8.862229 std 14.654277 min 0.000000 25% 3.000000 50% 5.000000 75% 9.000000 max 351.000000 Name: stay, dtype: float64
Anova - Kruskal-Wallis p_val to reject null: 0.0 H value: 166144.58 Effect size Eta^2: 0.2 ------------ stay by country_destination ------------ count 838920.000000 mean 8.862229 std 14.654277 min 0.000000 25% 3.000000 50% 5.000000 75% 9.000000 max 351.000000 Name: stay, dtype: float64
209 different categorical values, too many to plot. Anova - Kruskal-Wallis p_val to reject null: 0.0 H value: 201683.66 Effect size Eta^2: 0.24 ------------ stay by Domestic or international ------------ count 838920.000000 mean 8.862229 std 14.654277 min 0.000000 25% 3.000000 50% 5.000000 75% 9.000000 max 351.000000 Name: stay, dtype: float64
Paired t-test p_val to reject null: 0.0 t-statistic value: 264.28 Effect size Cohen's d: 0.57 ------------ stay by Region ------------ count 838920.000000 mean 8.862229 std 14.654277 min 0.000000 25% 3.000000 50% 5.000000 75% 9.000000 max 351.000000 Name: stay, dtype: float64
Anova - Kruskal-Wallis p_val to reject null: 0.0 H value: 34309.42 Effect size Eta^2: 0.04 ------------ stay by IncomeGroup ------------ count 838920.000000 mean 8.862229 std 14.654277 min 0.000000 25% 3.000000 50% 5.000000 75% 9.000000 max 351.000000 Name: stay, dtype: float64
Anova - Kruskal-Wallis p_val to reject null: 0.0 H value: 19325.03 Effect size Eta^2: 0.02 ------------ stay by outcome ------------ count 838920.000000 mean 8.862229 std 14.654277 min 0.000000 25% 3.000000 50% 5.000000 75% 9.000000 max 351.000000 Name: stay, dtype: float64
Anova - Kruskal-Wallis p_val to reject null: 0.0 H value: 2100.55 Effect size Eta^2: 0.0 ------------ status_updates by origin_city ------------ count 1.007692e+06 mean 1.710792e+00 std 1.205219e+00 min 1.000000e+00 25% 1.000000e+00 50% 1.000000e+00 75% 2.000000e+00 max 1.050000e+02 Name: status_updates, dtype: float64
1324 different categorical values, too many to plot. Anova - Kruskal-Wallis p_val to reject null: 0.0 H value: 4665.43 Effect size Eta^2: 0.0 ------------ status_updates by destination_city ------------ count 1.007692e+06 mean 1.710792e+00 std 1.205219e+00 min 1.000000e+00 25% 1.000000e+00 50% 1.000000e+00 75% 2.000000e+00 max 1.050000e+02 Name: status_updates, dtype: float64
1583 different categorical values, too many to plot. Anova - Kruskal-Wallis p_val to reject null: 0.0 H value: 7607.39 Effect size Eta^2: 0.01 ------------ status_updates by trip_type ------------ count 1.007692e+06 mean 1.710792e+00 std 1.205219e+00 min 1.000000e+00 25% 1.000000e+00 50% 1.000000e+00 75% 2.000000e+00 max 1.050000e+02 Name: status_updates, dtype: float64
Paired t-test p_val to reject null: 0.9089 t-statistic value: 0.11 Effect size Cohen's d: 0.0 ------------ status_updates by weekend ------------ count 1.007692e+06 mean 1.710792e+00 std 1.205219e+00 min 1.000000e+00 25% 1.000000e+00 50% 1.000000e+00 75% 2.000000e+00 max 1.050000e+02 Name: status_updates, dtype: float64
Paired t-test p_val to reject null: 0.0 t-statistic value: -33.04 Effect size Cohen's d: 0.08 ------------ status_updates by filter_no_lcc ------------ count 1.007692e+06 mean 1.710792e+00 std 1.205219e+00 min 1.000000e+00 25% 1.000000e+00 50% 1.000000e+00 75% 2.000000e+00 max 1.050000e+02 Name: status_updates, dtype: float64
Paired t-test p_val to reject null: 0.0 t-statistic value: -46.57 Effect size Cohen's d: 0.37 ------------ status_updates by filter_non_stop ------------ count 1.007692e+06 mean 1.710792e+00 std 1.205219e+00 min 1.000000e+00 25% 1.000000e+00 50% 1.000000e+00 75% 2.000000e+00 max 1.050000e+02 Name: status_updates, dtype: float64
Paired t-test p_val to reject null: 0.0 t-statistic value: 135.33 Effect size Cohen's d: 0.39 ------------ status_updates by filter_short_layover ------------ count 1.007692e+06 mean 1.710792e+00 std 1.205219e+00 min 1.000000e+00 25% 1.000000e+00 50% 1.000000e+00 75% 2.000000e+00 max 1.050000e+02 Name: status_updates, dtype: float64
Paired t-test p_val to reject null: 0.0 t-statistic value: -66.18 Effect size Cohen's d: 0.36 ------------ status_updates by filter_name ------------ count 1.007692e+06 mean 1.710792e+00 std 1.205219e+00 min 1.000000e+00 25% 1.000000e+00 50% 1.000000e+00 75% 2.000000e+00 max 1.050000e+02 Name: status_updates, dtype: float64
Anova - Kruskal-Wallis p_val to reject null: 0.0 H value: 29990.24 Effect size Eta^2: 0.03 ------------ status_updates by first_rec ------------ count 1.007692e+06 mean 1.710792e+00 std 1.205219e+00 min 1.000000e+00 25% 1.000000e+00 50% 1.000000e+00 75% 2.000000e+00 max 1.050000e+02 Name: status_updates, dtype: float64
Anova - Kruskal-Wallis p_val to reject null: 0.0 H value: 26561.27 Effect size Eta^2: 0.03 ------------ status_updates by last_rec ------------ count 1.007692e+06 mean 1.710792e+00 std 1.205219e+00 min 1.000000e+00 25% 1.000000e+00 50% 1.000000e+00 75% 2.000000e+00 max 1.050000e+02 Name: status_updates, dtype: float64
Anova - Kruskal-Wallis p_val to reject null: 0.0 H value: 10650.08 Effect size Eta^2: 0.01 ------------ status_updates by is_session_1 ------------ count 1.007692e+06 mean 1.710792e+00 std 1.205219e+00 min 1.000000e+00 25% 1.000000e+00 50% 1.000000e+00 75% 2.000000e+00 max 1.050000e+02 Name: status_updates, dtype: float64
Paired t-test p_val to reject null: 0.0 t-statistic value: 139.79 Effect size Cohen's d: 0.28 ------------ status_updates by Search or watch ------------ count 1.007692e+06 mean 1.710792e+00 std 1.205219e+00 min 1.000000e+00 25% 1.000000e+00 50% 1.000000e+00 75% 2.000000e+00 max 1.050000e+02 Name: status_updates, dtype: float64
Paired t-test p_val to reject null: 0.0 t-statistic value: 1217.04 Effect size Cohen's d: 1 ------------ status_updates by Use frequency ------------ count 1.007692e+06 mean 1.710792e+00 std 1.205219e+00 min 1.000000e+00 25% 1.000000e+00 50% 1.000000e+00 75% 2.000000e+00 max 1.050000e+02 Name: status_updates, dtype: float64
Paired t-test p_val to reject null: 0.0 t-statistic value: 50.49 Effect size Cohen's d: 0.19 ------------ status_updates by continent_origin ------------ count 1.007692e+06 mean 1.710792e+00 std 1.205219e+00 min 1.000000e+00 25% 1.000000e+00 50% 1.000000e+00 75% 2.000000e+00 max 1.050000e+02 Name: status_updates, dtype: float64
Anova - Kruskal-Wallis p_val to reject null: 0.0 H value: 306.43 Effect size Eta^2: 0.0 ------------ status_updates by City origin ------------ count 1.007692e+06 mean 1.710792e+00 std 1.205219e+00 min 1.000000e+00 25% 1.000000e+00 50% 1.000000e+00 75% 2.000000e+00 max 1.050000e+02 Name: status_updates, dtype: float64
1237 different categorical values, too many to plot. Anova - Kruskal-Wallis p_val to reject null: 0.0 H value: 4003.86 Effect size Eta^2: 0.0 ------------ status_updates by City destination ------------ count 1.007692e+06 mean 1.710792e+00 std 1.205219e+00 min 1.000000e+00 25% 1.000000e+00 50% 1.000000e+00 75% 2.000000e+00 max 1.050000e+02 Name: status_updates, dtype: float64
1487 different categorical values, too many to plot. Anova - Kruskal-Wallis p_val to reject null: 0.0 H value: 7184.58 Effect size Eta^2: 0.01 ------------ status_updates by region_origin ------------ count 1.007692e+06 mean 1.710792e+00 std 1.205219e+00 min 1.000000e+00 25% 1.000000e+00 50% 1.000000e+00 75% 2.000000e+00 max 1.050000e+02 Name: status_updates, dtype: float64
453 different categorical values, too many to plot. Anova - Kruskal-Wallis p_val to reject null: 0.0 H value: 2223.07 Effect size Eta^2: 0.0 ------------ status_updates by region_destination ------------ count 1.007692e+06 mean 1.710792e+00 std 1.205219e+00 min 1.000000e+00 25% 1.000000e+00 50% 1.000000e+00 75% 2.000000e+00 max 1.050000e+02 Name: status_updates, dtype: float64
498 different categorical values, too many to plot. Anova - Kruskal-Wallis p_val to reject null: 0.0 H value: 4646.33 Effect size Eta^2: 0.0 ------------ status_updates by country_origin ------------ count 1.007692e+06 mean 1.710792e+00 std 1.205219e+00 min 1.000000e+00 25% 1.000000e+00 50% 1.000000e+00 75% 2.000000e+00 max 1.050000e+02 Name: status_updates, dtype: float64
188 different categorical values, too many to plot. Anova - Kruskal-Wallis p_val to reject null: 0.0 H value: 1501.8 Effect size Eta^2: 0.0 ------------ status_updates by continent_destination ------------ count 1.007692e+06 mean 1.710792e+00 std 1.205219e+00 min 1.000000e+00 25% 1.000000e+00 50% 1.000000e+00 75% 2.000000e+00 max 1.050000e+02 Name: status_updates, dtype: float64
Anova - Kruskal-Wallis p_val to reject null: 0.0 H value: 1663.96 Effect size Eta^2: 0.0 ------------ status_updates by country_destination ------------ count 1.007692e+06 mean 1.710792e+00 std 1.205219e+00 min 1.000000e+00 25% 1.000000e+00 50% 1.000000e+00 75% 2.000000e+00 max 1.050000e+02 Name: status_updates, dtype: float64
209 different categorical values, too many to plot. Anova - Kruskal-Wallis p_val to reject null: 0.0 H value: 3450.18 Effect size Eta^2: 0.0 ------------ status_updates by Domestic or international ------------ count 1.007692e+06 mean 1.710792e+00 std 1.205219e+00 min 1.000000e+00 25% 1.000000e+00 50% 1.000000e+00 75% 2.000000e+00 max 1.050000e+02 Name: status_updates, dtype: float64
Paired t-test p_val to reject null: 0.0 t-statistic value: -27.99 Effect size Cohen's d: 0.06 ------------ status_updates by Region ------------ count 1.007692e+06 mean 1.710792e+00 std 1.205219e+00 min 1.000000e+00 25% 1.000000e+00 50% 1.000000e+00 75% 2.000000e+00 max 1.050000e+02 Name: status_updates, dtype: float64
Anova - Kruskal-Wallis p_val to reject null: 0.0 H value: 304.81 Effect size Eta^2: 0.0 ------------ status_updates by IncomeGroup ------------ count 1.007692e+06 mean 1.710792e+00 std 1.205219e+00 min 1.000000e+00 25% 1.000000e+00 50% 1.000000e+00 75% 2.000000e+00 max 1.050000e+02 Name: status_updates, dtype: float64
Anova - Kruskal-Wallis p_val to reject null: 0.0 H value: 164.86 Effect size Eta^2: 0.0 ------------ status_updates by outcome ------------ count 1.007692e+06 mean 1.710792e+00 std 1.205219e+00 min 1.000000e+00 25% 1.000000e+00 50% 1.000000e+00 75% 2.000000e+00 max 1.050000e+02 Name: status_updates, dtype: float64
Anova - Kruskal-Wallis p_val to reject null: 0.0 H value: 924733.87 Effect size Eta^2: 0.92 ------------ total_notifs by origin_city ------------ count 949529.000000 mean 1.644821 std 3.872667 min 0.000000 25% 0.000000 50% 0.000000 75% 1.000000 max 65.000000 Name: total_notifs, dtype: float64
1324 different categorical values, too many to plot. Anova - Kruskal-Wallis p_val to reject null: nan H value: nan Effect size Eta^2: nan ------------ total_notifs by destination_city ------------ count 949529.000000 mean 1.644821 std 3.872667 min 0.000000 25% 0.000000 50% 0.000000 75% 1.000000 max 65.000000 Name: total_notifs, dtype: float64
1583 different categorical values, too many to plot. Anova - Kruskal-Wallis p_val to reject null: nan H value: nan Effect size Eta^2: nan ------------ total_notifs by trip_type ------------ count 949529.000000 mean 1.644821 std 3.872667 min 0.000000 25% 0.000000 50% 0.000000 75% 1.000000 max 65.000000 Name: total_notifs, dtype: float64
Paired t-test p_val to reject null: 0.0 t-statistic value: 38.83 Effect size Cohen's d: 0.11 ------------ total_notifs by weekend ------------ count 949529.000000 mean 1.644821 std 3.872667 min 0.000000 25% 0.000000 50% 0.000000 75% 1.000000 max 65.000000 Name: total_notifs, dtype: float64
Paired t-test p_val to reject null: 0.0 t-statistic value: -44.97 Effect size Cohen's d: 0.11 ------------ total_notifs by filter_no_lcc ------------ count 949529.000000 mean 1.644821 std 3.872667 min 0.000000 25% 0.000000 50% 0.000000 75% 1.000000 max 65.000000 Name: total_notifs, dtype: float64
Paired t-test p_val to reject null: 0.0 t-statistic value: -6.41 Effect size Cohen's d: 0.06 ------------ total_notifs by filter_non_stop ------------ count 949529.000000 mean 1.644821 std 3.872667 min 0.000000 25% 0.000000 50% 0.000000 75% 1.000000 max 65.000000 Name: total_notifs, dtype: float64
Paired t-test p_val to reject null: 0.0 t-statistic value: 76.55 Effect size Cohen's d: 0.24 ------------ total_notifs by filter_short_layover ------------ count 949529.000000 mean 1.644821 std 3.872667 min 0.000000 25% 0.000000 50% 0.000000 75% 1.000000 max 65.000000 Name: total_notifs, dtype: float64
Paired t-test p_val to reject null: 0.0 t-statistic value: -27.74 Effect size Cohen's d: 0.16 ------------ total_notifs by filter_name ------------ count 949529.000000 mean 1.644821 std 3.872667 min 0.000000 25% 0.000000 50% 0.000000 75% 1.000000 max 65.000000 Name: total_notifs, dtype: float64
Anova - Kruskal-Wallis p_val to reject null: 0.0 H value: 15709.82 Effect size Eta^2: 0.02 ------------ total_notifs by first_rec ------------ count 949529.000000 mean 1.644821 std 3.872667 min 0.000000 25% 0.000000 50% 0.000000 75% 1.000000 max 65.000000 Name: total_notifs, dtype: float64
Anova - Kruskal-Wallis p_val to reject null: nan H value: nan Effect size Eta^2: nan ------------ total_notifs by last_rec ------------ count 949529.000000 mean 1.644821 std 3.872667 min 0.000000 25% 0.000000 50% 0.000000 75% 1.000000 max 65.000000 Name: total_notifs, dtype: float64
Anova - Kruskal-Wallis p_val to reject null: nan H value: nan Effect size Eta^2: nan ------------ total_notifs by is_session_1 ------------ count 949529.000000 mean 1.644821 std 3.872667 min 0.000000 25% 0.000000 50% 0.000000 75% 1.000000 max 65.000000 Name: total_notifs, dtype: float64
Paired t-test p_val to reject null: 0.0 t-statistic value: 128.51 Effect size Cohen's d: 0.26 ------------ total_notifs by Search or watch ------------ count 949529.000000 mean 1.644821 std 3.872667 min 0.000000 25% 0.000000 50% 0.000000 75% 1.000000 max 65.000000 Name: total_notifs, dtype: float64
Paired t-test p_val to reject null: 0.0 t-statistic value: 666.89 Effect size Cohen's d: 1 ------------ total_notifs by Use frequency ------------ count 949529.000000 mean 1.644821 std 3.872667 min 0.000000 25% 0.000000 50% 0.000000 75% 1.000000 max 65.000000 Name: total_notifs, dtype: float64
Paired t-test p_val to reject null: 0.0 t-statistic value: 73.11 Effect size Cohen's d: 0.25 ------------ total_notifs by continent_origin ------------ count 949529.000000 mean 1.644821 std 3.872667 min 0.000000 25% 0.000000 50% 0.000000 75% 1.000000 max 65.000000 Name: total_notifs, dtype: float64
Anova - Kruskal-Wallis p_val to reject null: 0.0 H value: 201.76 Effect size Eta^2: 0.0 ------------ total_notifs by City origin ------------ count 949529.000000 mean 1.644821 std 3.872667 min 0.000000 25% 0.000000 50% 0.000000 75% 1.000000 max 65.000000 Name: total_notifs, dtype: float64
1237 different categorical values, too many to plot. Anova - Kruskal-Wallis p_val to reject null: nan H value: nan Effect size Eta^2: nan ------------ total_notifs by City destination ------------ count 949529.000000 mean 1.644821 std 3.872667 min 0.000000 25% 0.000000 50% 0.000000 75% 1.000000 max 65.000000 Name: total_notifs, dtype: float64
1487 different categorical values, too many to plot. Anova - Kruskal-Wallis p_val to reject null: nan H value: nan Effect size Eta^2: nan ------------ total_notifs by region_origin ------------ count 949529.000000 mean 1.644821 std 3.872667 min 0.000000 25% 0.000000 50% 0.000000 75% 1.000000 max 65.000000 Name: total_notifs, dtype: float64
453 different categorical values, too many to plot. Anova - Kruskal-Wallis p_val to reject null: nan H value: nan Effect size Eta^2: nan ------------ total_notifs by region_destination ------------ count 949529.000000 mean 1.644821 std 3.872667 min 0.000000 25% 0.000000 50% 0.000000 75% 1.000000 max 65.000000 Name: total_notifs, dtype: float64
498 different categorical values, too many to plot. Anova - Kruskal-Wallis p_val to reject null: 0.0 H value: 3068.02 Effect size Eta^2: 0.0 ------------ total_notifs by country_origin ------------ count 949529.000000 mean 1.644821 std 3.872667 min 0.000000 25% 0.000000 50% 0.000000 75% 1.000000 max 65.000000 Name: total_notifs, dtype: float64
188 different categorical values, too many to plot. Anova - Kruskal-Wallis p_val to reject null: nan H value: nan Effect size Eta^2: nan ------------ total_notifs by continent_destination ------------ count 949529.000000 mean 1.644821 std 3.872667 min 0.000000 25% 0.000000 50% 0.000000 75% 1.000000 max 65.000000 Name: total_notifs, dtype: float64
Anova - Kruskal-Wallis p_val to reject null: 0.0 H value: 614.39 Effect size Eta^2: 0.0 ------------ total_notifs by country_destination ------------ count 949529.000000 mean 1.644821 std 3.872667 min 0.000000 25% 0.000000 50% 0.000000 75% 1.000000 max 65.000000 Name: total_notifs, dtype: float64
209 different categorical values, too many to plot. Anova - Kruskal-Wallis p_val to reject null: 0.0 H value: 2056.57 Effect size Eta^2: 0.0 ------------ total_notifs by Domestic or international ------------ count 949529.000000 mean 1.644821 std 3.872667 min 0.000000 25% 0.000000 50% 0.000000 75% 1.000000 max 65.000000 Name: total_notifs, dtype: float64
Paired t-test p_val to reject null: 0.0 t-statistic value: -21.96 Effect size Cohen's d: 0.05 ------------ total_notifs by Region ------------ count 949529.000000 mean 1.644821 std 3.872667 min 0.000000 25% 0.000000 50% 0.000000 75% 1.000000 max 65.000000 Name: total_notifs, dtype: float64
Anova - Kruskal-Wallis p_val to reject null: 0.0 H value: 280.32 Effect size Eta^2: 0.0 ------------ total_notifs by IncomeGroup ------------ count 949529.000000 mean 1.644821 std 3.872667 min 0.000000 25% 0.000000 50% 0.000000 75% 1.000000 max 65.000000 Name: total_notifs, dtype: float64
Anova - Kruskal-Wallis p_val to reject null: 0.0 H value: 114.83 Effect size Eta^2: 0.0 ------------ total_notifs by outcome ------------ count 949529.000000 mean 1.644821 std 3.872667 min 0.000000 25% 0.000000 50% 0.000000 75% 1.000000 max 65.000000 Name: total_notifs, dtype: float64
Anova - Kruskal-Wallis p_val to reject null: 0.0 H value: 602301.52 Effect size Eta^2: 0.63 ------------ total_buy_notifs by origin_city ------------ count 949529.000000 mean 0.947830 std 2.790264 min 0.000000 25% 0.000000 50% 0.000000 75% 0.000000 max 65.000000 Name: total_buy_notifs, dtype: float64
1324 different categorical values, too many to plot. Anova - Kruskal-Wallis p_val to reject null: nan H value: nan Effect size Eta^2: nan ------------ total_buy_notifs by destination_city ------------ count 949529.000000 mean 0.947830 std 2.790264 min 0.000000 25% 0.000000 50% 0.000000 75% 0.000000 max 65.000000 Name: total_buy_notifs, dtype: float64
1583 different categorical values, too many to plot. Anova - Kruskal-Wallis p_val to reject null: nan H value: nan Effect size Eta^2: nan ------------ total_buy_notifs by trip_type ------------ count 949529.000000 mean 0.947830 std 2.790264 min 0.000000 25% 0.000000 50% 0.000000 75% 0.000000 max 65.000000 Name: total_buy_notifs, dtype: float64
Paired t-test p_val to reject null: 0.0 t-statistic value: 37.01 Effect size Cohen's d: 0.11 ------------ total_buy_notifs by weekend ------------ count 949529.000000 mean 0.947830 std 2.790264 min 0.000000 25% 0.000000 50% 0.000000 75% 0.000000 max 65.000000 Name: total_buy_notifs, dtype: float64
Paired t-test p_val to reject null: 0.0 t-statistic value: -42.34 Effect size Cohen's d: 0.1 ------------ total_buy_notifs by filter_no_lcc ------------ count 949529.000000 mean 0.947830 std 2.790264 min 0.000000 25% 0.000000 50% 0.000000 75% 0.000000 max 65.000000 Name: total_buy_notifs, dtype: float64
Paired t-test p_val to reject null: 0.0107 t-statistic value: 2.55 Effect size Cohen's d: 0.02 ------------ total_buy_notifs by filter_non_stop ------------ count 949529.000000 mean 0.947830 std 2.790264 min 0.000000 25% 0.000000 50% 0.000000 75% 0.000000 max 65.000000 Name: total_buy_notifs, dtype: float64
Paired t-test p_val to reject null: 0.0 t-statistic value: 57.45 Effect size Cohen's d: 0.18 ------------ total_buy_notifs by filter_short_layover ------------ count 949529.000000 mean 0.947830 std 2.790264 min 0.000000 25% 0.000000 50% 0.000000 75% 0.000000 max 65.000000 Name: total_buy_notifs, dtype: float64
Paired t-test p_val to reject null: 0.0 t-statistic value: -16.74 Effect size Cohen's d: 0.1 ------------ total_buy_notifs by filter_name ------------ count 949529.000000 mean 0.947830 std 2.790264 min 0.000000 25% 0.000000 50% 0.000000 75% 0.000000 max 65.000000 Name: total_buy_notifs, dtype: float64
Anova - Kruskal-Wallis p_val to reject null: 0.0 H value: 8801.53 Effect size Eta^2: 0.01 ------------ total_buy_notifs by first_rec ------------ count 949529.000000 mean 0.947830 std 2.790264 min 0.000000 25% 0.000000 50% 0.000000 75% 0.000000 max 65.000000 Name: total_buy_notifs, dtype: float64
Anova - Kruskal-Wallis p_val to reject null: nan H value: nan Effect size Eta^2: nan ------------ total_buy_notifs by last_rec ------------ count 949529.000000 mean 0.947830 std 2.790264 min 0.000000 25% 0.000000 50% 0.000000 75% 0.000000 max 65.000000 Name: total_buy_notifs, dtype: float64
Anova - Kruskal-Wallis p_val to reject null: nan H value: nan Effect size Eta^2: nan ------------ total_buy_notifs by is_session_1 ------------ count 949529.000000 mean 0.947830 std 2.790264 min 0.000000 25% 0.000000 50% 0.000000 75% 0.000000 max 65.000000 Name: total_buy_notifs, dtype: float64
Paired t-test p_val to reject null: 0.0 t-statistic value: 109.34 Effect size Cohen's d: 0.22 ------------ total_buy_notifs by Search or watch ------------ count 949529.000000 mean 0.947830 std 2.790264 min 0.000000 25% 0.000000 50% 0.000000 75% 0.000000 max 65.000000 Name: total_buy_notifs, dtype: float64
Paired t-test p_val to reject null: 0.0 t-statistic value: 493.36 Effect size Cohen's d: 0.9 ------------ total_buy_notifs by Use frequency ------------ count 949529.000000 mean 0.947830 std 2.790264 min 0.000000 25% 0.000000 50% 0.000000 75% 0.000000 max 65.000000 Name: total_buy_notifs, dtype: float64
Paired t-test p_val to reject null: 0.0 t-statistic value: 66.42 Effect size Cohen's d: 0.23 ------------ total_buy_notifs by continent_origin ------------ count 949529.000000 mean 0.947830 std 2.790264 min 0.000000 25% 0.000000 50% 0.000000 75% 0.000000 max 65.000000 Name: total_buy_notifs, dtype: float64
Anova - Kruskal-Wallis p_val to reject null: 0.0 H value: 146.96 Effect size Eta^2: 0.0 ------------ total_buy_notifs by City origin ------------ count 949529.000000 mean 0.947830 std 2.790264 min 0.000000 25% 0.000000 50% 0.000000 75% 0.000000 max 65.000000 Name: total_buy_notifs, dtype: float64
1237 different categorical values, too many to plot. Anova - Kruskal-Wallis p_val to reject null: nan H value: nan Effect size Eta^2: nan ------------ total_buy_notifs by City destination ------------ count 949529.000000 mean 0.947830 std 2.790264 min 0.000000 25% 0.000000 50% 0.000000 75% 0.000000 max 65.000000 Name: total_buy_notifs, dtype: float64
1487 different categorical values, too many to plot. Anova - Kruskal-Wallis p_val to reject null: nan H value: nan Effect size Eta^2: nan ------------ total_buy_notifs by region_origin ------------ count 949529.000000 mean 0.947830 std 2.790264 min 0.000000 25% 0.000000 50% 0.000000 75% 0.000000 max 65.000000 Name: total_buy_notifs, dtype: float64
453 different categorical values, too many to plot. Anova - Kruskal-Wallis p_val to reject null: nan H value: nan Effect size Eta^2: nan ------------ total_buy_notifs by region_destination ------------ count 949529.000000 mean 0.947830 std 2.790264 min 0.000000 25% 0.000000 50% 0.000000 75% 0.000000 max 65.000000 Name: total_buy_notifs, dtype: float64
498 different categorical values, too many to plot. Anova - Kruskal-Wallis p_val to reject null: 0.0 H value: 2358.23 Effect size Eta^2: 0.0 ------------ total_buy_notifs by country_origin ------------ count 949529.000000 mean 0.947830 std 2.790264 min 0.000000 25% 0.000000 50% 0.000000 75% 0.000000 max 65.000000 Name: total_buy_notifs, dtype: float64
188 different categorical values, too many to plot. Anova - Kruskal-Wallis p_val to reject null: nan H value: nan Effect size Eta^2: nan ------------ total_buy_notifs by continent_destination ------------ count 949529.000000 mean 0.947830 std 2.790264 min 0.000000 25% 0.000000 50% 0.000000 75% 0.000000 max 65.000000 Name: total_buy_notifs, dtype: float64
Anova - Kruskal-Wallis p_val to reject null: 0.0 H value: 475.93 Effect size Eta^2: 0.0 ------------ total_buy_notifs by country_destination ------------ count 949529.000000 mean 0.947830 std 2.790264 min 0.000000 25% 0.000000 50% 0.000000 75% 0.000000 max 65.000000 Name: total_buy_notifs, dtype: float64
209 different categorical values, too many to plot. Anova - Kruskal-Wallis p_val to reject null: 0.0 H value: 1625.59 Effect size Eta^2: 0.0 ------------ total_buy_notifs by Domestic or international ------------ count 949529.000000 mean 0.947830 std 2.790264 min 0.000000 25% 0.000000 50% 0.000000 75% 0.000000 max 65.000000 Name: total_buy_notifs, dtype: float64
Paired t-test p_val to reject null: 0.0 t-statistic value: -20.78 Effect size Cohen's d: 0.04 ------------ total_buy_notifs by Region ------------ count 949529.000000 mean 0.947830 std 2.790264 min 0.000000 25% 0.000000 50% 0.000000 75% 0.000000 max 65.000000 Name: total_buy_notifs, dtype: float64
Anova - Kruskal-Wallis p_val to reject null: 0.0 H value: 77.25 Effect size Eta^2: 0.0 ------------ total_buy_notifs by IncomeGroup ------------ count 949529.000000 mean 0.947830 std 2.790264 min 0.000000 25% 0.000000 50% 0.000000 75% 0.000000 max 65.000000 Name: total_buy_notifs, dtype: float64
Anova - Kruskal-Wallis p_val to reject null: 0.0 H value: 37.12 Effect size Eta^2: 0.0 ------------ total_buy_notifs by outcome ------------ count 949529.000000 mean 0.947830 std 2.790264 min 0.000000 25% 0.000000 50% 0.000000 75% 0.000000 max 65.000000 Name: total_buy_notifs, dtype: float64
Anova - Kruskal-Wallis p_val to reject null: 0.0 H value: 385367.86 Effect size Eta^2: 0.41 ------------ first_total by origin_city ------------ count 949529.000000 mean 484.516101 std 393.182042 min 9.000000 25% 229.000000 50% 372.000000 75% 627.000000 max 21539.000000 Name: first_total, dtype: float64
1324 different categorical values, too many to plot. Anova - Kruskal-Wallis p_val to reject null: nan H value: nan Effect size Eta^2: nan ------------ first_total by destination_city ------------ count 949529.000000 mean 484.516101 std 393.182042 min 9.000000 25% 229.000000 50% 372.000000 75% 627.000000 max 21539.000000 Name: first_total, dtype: float64
1583 different categorical values, too many to plot. Anova - Kruskal-Wallis p_val to reject null: nan H value: nan Effect size Eta^2: nan ------------ first_total by trip_type ------------ count 949529.000000 mean 484.516101 std 393.182042 min 9.000000 25% 229.000000 50% 372.000000 75% 627.000000 max 21539.000000 Name: first_total, dtype: float64
Paired t-test p_val to reject null: 0.0 t-statistic value: 199.0 Effect size Cohen's d: 0.58 ------------ first_total by weekend ------------ count 949529.000000 mean 484.516101 std 393.182042 min 9.000000 25% 229.000000 50% 372.000000 75% 627.000000 max 21539.000000 Name: first_total, dtype: float64
Paired t-test p_val to reject null: 0.0 t-statistic value: 178.82 Effect size Cohen's d: 0.54 ------------ first_total by filter_no_lcc ------------ count 949529.000000 mean 484.516101 std 393.182042 min 9.000000 25% 229.000000 50% 372.000000 75% 627.000000 max 21539.000000 Name: first_total, dtype: float64
Paired t-test p_val to reject null: 0.0 t-statistic value: 10.06 Effect size Cohen's d: 0.09 ------------ first_total by filter_non_stop ------------ count 949529.000000 mean 484.516101 std 393.182042 min 9.000000 25% 229.000000 50% 372.000000 75% 627.000000 max 21539.000000 Name: first_total, dtype: float64
Paired t-test p_val to reject null: 0.0 t-statistic value: -45.12 Effect size Cohen's d: 0.15 ------------ first_total by filter_short_layover ------------ count 949529.000000 mean 484.516101 std 393.182042 min 9.000000 25% 229.000000 50% 372.000000 75% 627.000000 max 21539.000000 Name: first_total, dtype: float64
Paired t-test p_val to reject null: 0.0 t-statistic value: -88.63 Effect size Cohen's d: 0.46 ------------ first_total by filter_name ------------ count 949529.000000 mean 484.516101 std 393.182042 min 9.000000 25% 229.000000 50% 372.000000 75% 627.000000 max 21539.000000 Name: first_total, dtype: float64
Anova - Kruskal-Wallis p_val to reject null: 0.0 H value: 12364.14 Effect size Eta^2: 0.01 ------------ first_total by first_rec ------------ count 949529.000000 mean 484.516101 std 393.182042 min 9.000000 25% 229.000000 50% 372.000000 75% 627.000000 max 21539.000000 Name: first_total, dtype: float64
Anova - Kruskal-Wallis p_val to reject null: nan H value: nan Effect size Eta^2: nan ------------ first_total by last_rec ------------ count 949529.000000 mean 484.516101 std 393.182042 min 9.000000 25% 229.000000 50% 372.000000 75% 627.000000 max 21539.000000 Name: first_total, dtype: float64
Anova - Kruskal-Wallis p_val to reject null: nan H value: nan Effect size Eta^2: nan ------------ first_total by is_session_1 ------------ count 949529.000000 mean 484.516101 std 393.182042 min 9.000000 25% 229.000000 50% 372.000000 75% 627.000000 max 21539.000000 Name: first_total, dtype: float64
Paired t-test p_val to reject null: 0.0 t-statistic value: 27.99 Effect size Cohen's d: 0.06 ------------ first_total by Search or watch ------------ count 949529.000000 mean 484.516101 std 393.182042 min 9.000000 25% 229.000000 50% 372.000000 75% 627.000000 max 21539.000000 Name: first_total, dtype: float64
Paired t-test p_val to reject null: 0.0 t-statistic value: -17.95 Effect size Cohen's d: 0.04 ------------ first_total by Use frequency ------------ count 949529.000000 mean 484.516101 std 393.182042 min 9.000000 25% 229.000000 50% 372.000000 75% 627.000000 max 21539.000000 Name: first_total, dtype: float64
Paired t-test p_val to reject null: 0.0 t-statistic value: 14.18 Effect size Cohen's d: 0.06 ------------ first_total by continent_origin ------------ count 949529.000000 mean 484.516101 std 393.182042 min 9.000000 25% 229.000000 50% 372.000000 75% 627.000000 max 21539.000000 Name: first_total, dtype: float64
Anova - Kruskal-Wallis p_val to reject null: 0.0 H value: 21929.51 Effect size Eta^2: 0.02 ------------ first_total by City origin ------------ count 949529.000000 mean 484.516101 std 393.182042 min 9.000000 25% 229.000000 50% 372.000000 75% 627.000000 max 21539.000000 Name: first_total, dtype: float64
1237 different categorical values, too many to plot. Anova - Kruskal-Wallis p_val to reject null: nan H value: nan Effect size Eta^2: nan ------------ first_total by City destination ------------ count 949529.000000 mean 484.516101 std 393.182042 min 9.000000 25% 229.000000 50% 372.000000 75% 627.000000 max 21539.000000 Name: first_total, dtype: float64
1487 different categorical values, too many to plot. Anova - Kruskal-Wallis p_val to reject null: nan H value: nan Effect size Eta^2: nan ------------ first_total by region_origin ------------ count 949529.000000 mean 484.516101 std 393.182042 min 9.000000 25% 229.000000 50% 372.000000 75% 627.000000 max 21539.000000 Name: first_total, dtype: float64
453 different categorical values, too many to plot. Anova - Kruskal-Wallis p_val to reject null: nan H value: nan Effect size Eta^2: nan ------------ first_total by region_destination ------------ count 949529.000000 mean 484.516101 std 393.182042 min 9.000000 25% 229.000000 50% 372.000000 75% 627.000000 max 21539.000000 Name: first_total, dtype: float64
498 different categorical values, too many to plot. Anova - Kruskal-Wallis p_val to reject null: 0.0 H value: 269291.13 Effect size Eta^2: 0.28 ------------ first_total by country_origin ------------ count 949529.000000 mean 484.516101 std 393.182042 min 9.000000 25% 229.000000 50% 372.000000 75% 627.000000 max 21539.000000 Name: first_total, dtype: float64
188 different categorical values, too many to plot. Anova - Kruskal-Wallis p_val to reject null: nan H value: nan Effect size Eta^2: nan ------------ first_total by continent_destination ------------ count 949529.000000 mean 484.516101 std 393.182042 min 9.000000 25% 229.000000 50% 372.000000 75% 627.000000 max 21539.000000 Name: first_total, dtype: float64
Anova - Kruskal-Wallis p_val to reject null: 0.0 H value: 174460.05 Effect size Eta^2: 0.18 ------------ first_total by country_destination ------------ count 949529.000000 mean 484.516101 std 393.182042 min 9.000000 25% 229.000000 50% 372.000000 75% 627.000000 max 21539.000000 Name: first_total, dtype: float64
209 different categorical values, too many to plot. Anova - Kruskal-Wallis p_val to reject null: 0.0 H value: 235544.03 Effect size Eta^2: 0.25 ------------ first_total by Domestic or international ------------ count 949529.000000 mean 484.516101 std 393.182042 min 9.000000 25% 229.000000 50% 372.000000 75% 627.000000 max 21539.000000 Name: first_total, dtype: float64
Paired t-test p_val to reject null: 0.0 t-statistic value: 502.59 Effect size Cohen's d: 1 ------------ first_total by Region ------------ count 949529.000000 mean 484.516101 std 393.182042 min 9.000000 25% 229.000000 50% 372.000000 75% 627.000000 max 21539.000000 Name: first_total, dtype: float64
Anova - Kruskal-Wallis p_val to reject null: 0.0 H value: 16344.92 Effect size Eta^2: 0.02 ------------ first_total by IncomeGroup ------------ count 949529.000000 mean 484.516101 std 393.182042 min 9.000000 25% 229.000000 50% 372.000000 75% 627.000000 max 21539.000000 Name: first_total, dtype: float64
Anova - Kruskal-Wallis p_val to reject null: 0.0 H value: 12645.92 Effect size Eta^2: 0.01 ------------ first_total by outcome ------------ count 949529.000000 mean 484.516101 std 393.182042 min 9.000000 25% 229.000000 50% 372.000000 75% 627.000000 max 21539.000000 Name: first_total, dtype: float64
Anova - Kruskal-Wallis p_val to reject null: 0.0 H value: 2640.89 Effect size Eta^2: 0.0 ------------ first_buy_total by origin_city ------------ count 597414.000000 mean 461.218400 std 365.240937 min 9.000000 25% 217.000000 50% 361.000000 75% 602.000000 max 20103.000000 Name: first_buy_total, dtype: float64
1324 different categorical values, too many to plot. Anova - Kruskal-Wallis p_val to reject null: nan H value: nan Effect size Eta^2: nan ------------ first_buy_total by destination_city ------------ count 597414.000000 mean 461.218400 std 365.240937 min 9.000000 25% 217.000000 50% 361.000000 75% 602.000000 max 20103.000000 Name: first_buy_total, dtype: float64
1583 different categorical values, too many to plot. Anova - Kruskal-Wallis p_val to reject null: nan H value: nan Effect size Eta^2: nan ------------ first_buy_total by trip_type ------------ count 597414.000000 mean 461.218400 std 365.240937 min 9.000000 25% 217.000000 50% 361.000000 75% 602.000000 max 20103.000000 Name: first_buy_total, dtype: float64
Paired t-test p_val to reject null: 0.0 t-statistic value: 170.35 Effect size Cohen's d: 0.64 ------------ first_buy_total by weekend ------------ count 597414.000000 mean 461.218400 std 365.240937 min 9.000000 25% 217.000000 50% 361.000000 75% 602.000000 max 20103.000000 Name: first_buy_total, dtype: float64
Paired t-test p_val to reject null: 0.0 t-statistic value: 135.64 Effect size Cohen's d: 0.51 ------------ first_buy_total by filter_no_lcc ------------ count 597414.000000 mean 461.218400 std 365.240937 min 9.000000 25% 217.000000 50% 361.000000 75% 602.000000 max 20103.000000 Name: first_buy_total, dtype: float64
Paired t-test p_val to reject null: 0.905 t-statistic value: -0.12 Effect size Cohen's d: 0.0 ------------ first_buy_total by filter_non_stop ------------ count 597414.000000 mean 461.218400 std 365.240937 min 9.000000 25% 217.000000 50% 361.000000 75% 602.000000 max 20103.000000 Name: first_buy_total, dtype: float64
Paired t-test p_val to reject null: 0.0 t-statistic value: -32.36 Effect size Cohen's d: 0.14 ------------ first_buy_total by filter_short_layover ------------ count 597414.000000 mean 461.218400 std 365.240937 min 9.000000 25% 217.000000 50% 361.000000 75% 602.000000 max 20103.000000 Name: first_buy_total, dtype: float64
Paired t-test p_val to reject null: 0.0 t-statistic value: -68.26 Effect size Cohen's d: 0.46 ------------ first_buy_total by filter_name ------------ count 597414.000000 mean 461.218400 std 365.240937 min 9.000000 25% 217.000000 50% 361.000000 75% 602.000000 max 20103.000000 Name: first_buy_total, dtype: float64
Anova - Kruskal-Wallis p_val to reject null: 0.0 H value: 7039.74 Effect size Eta^2: 0.01 ------------ first_buy_total by first_rec ------------ count 597414.000000 mean 461.218400 std 365.240937 min 9.000000 25% 217.000000 50% 361.000000 75% 602.000000 max 20103.000000 Name: first_buy_total, dtype: float64
Anova - Kruskal-Wallis p_val to reject null: nan H value: nan Effect size Eta^2: nan ------------ first_buy_total by last_rec ------------ count 597414.000000 mean 461.218400 std 365.240937 min 9.000000 25% 217.000000 50% 361.000000 75% 602.000000 max 20103.000000 Name: first_buy_total, dtype: float64
Anova - Kruskal-Wallis p_val to reject null: nan H value: nan Effect size Eta^2: nan ------------ first_buy_total by is_session_1 ------------ count 597414.000000 mean 461.218400 std 365.240937 min 9.000000 25% 217.000000 50% 361.000000 75% 602.000000 max 20103.000000 Name: first_buy_total, dtype: float64
Paired t-test p_val to reject null: 0.0 t-statistic value: 21.97 Effect size Cohen's d: 0.06 ------------ first_buy_total by Search or watch ------------ count 597414.000000 mean 461.218400 std 365.240937 min 9.000000 25% 217.000000 50% 361.000000 75% 602.000000 max 20103.000000 Name: first_buy_total, dtype: float64
Paired t-test p_val to reject null: 0.0 t-statistic value: -9.73 Effect size Cohen's d: 0.03 ------------ first_buy_total by Use frequency ------------ count 597414.000000 mean 461.218400 std 365.240937 min 9.000000 25% 217.000000 50% 361.000000 75% 602.000000 max 20103.000000 Name: first_buy_total, dtype: float64
Paired t-test p_val to reject null: 0.0 t-statistic value: 10.93 Effect size Cohen's d: 0.05 ------------ first_buy_total by continent_origin ------------ count 597414.000000 mean 461.218400 std 365.240937 min 9.000000 25% 217.000000 50% 361.000000 75% 602.000000 max 20103.000000 Name: first_buy_total, dtype: float64
Anova - Kruskal-Wallis p_val to reject null: 0.0 H value: 17713.82 Effect size Eta^2: 0.03 ------------ first_buy_total by City origin ------------ count 597414.000000 mean 461.218400 std 365.240937 min 9.000000 25% 217.000000 50% 361.000000 75% 602.000000 max 20103.000000 Name: first_buy_total, dtype: float64
1237 different categorical values, too many to plot. Anova - Kruskal-Wallis p_val to reject null: nan H value: nan Effect size Eta^2: nan ------------ first_buy_total by City destination ------------ count 597414.000000 mean 461.218400 std 365.240937 min 9.000000 25% 217.000000 50% 361.000000 75% 602.000000 max 20103.000000 Name: first_buy_total, dtype: float64
1487 different categorical values, too many to plot. Anova - Kruskal-Wallis p_val to reject null: nan H value: nan Effect size Eta^2: nan ------------ first_buy_total by region_origin ------------ count 597414.000000 mean 461.218400 std 365.240937 min 9.000000 25% 217.000000 50% 361.000000 75% 602.000000 max 20103.000000 Name: first_buy_total, dtype: float64
453 different categorical values, too many to plot. Anova - Kruskal-Wallis p_val to reject null: nan H value: nan Effect size Eta^2: nan ------------ first_buy_total by region_destination ------------ count 597414.000000 mean 461.218400 std 365.240937 min 9.000000 25% 217.000000 50% 361.000000 75% 602.000000 max 20103.000000 Name: first_buy_total, dtype: float64
498 different categorical values, too many to plot. Anova - Kruskal-Wallis p_val to reject null: nan H value: nan Effect size Eta^2: nan ------------ first_buy_total by country_origin ------------ count 597414.000000 mean 461.218400 std 365.240937 min 9.000000 25% 217.000000 50% 361.000000 75% 602.000000 max 20103.000000 Name: first_buy_total, dtype: float64
188 different categorical values, too many to plot. Anova - Kruskal-Wallis p_val to reject null: nan H value: nan Effect size Eta^2: nan ------------ first_buy_total by continent_destination ------------ count 597414.000000 mean 461.218400 std 365.240937 min 9.000000 25% 217.000000 50% 361.000000 75% 602.000000 max 20103.000000 Name: first_buy_total, dtype: float64
Anova - Kruskal-Wallis p_val to reject null: 0.0 H value: 92026.55 Effect size Eta^2: 0.15 ------------ first_buy_total by country_destination ------------ count 597414.000000 mean 461.218400 std 365.240937 min 9.000000 25% 217.000000 50% 361.000000 75% 602.000000 max 20103.000000 Name: first_buy_total, dtype: float64
209 different categorical values, too many to plot. Anova - Kruskal-Wallis p_val to reject null: nan H value: nan Effect size Eta^2: nan ------------ first_buy_total by Domestic or international ------------ count 597414.000000 mean 461.218400 std 365.240937 min 9.000000 25% 217.000000 50% 361.000000 75% 602.000000 max 20103.000000 Name: first_buy_total, dtype: float64
Paired t-test p_val to reject null: 0.0 t-statistic value: 381.21 Effect size Cohen's d: 0.98 ------------ first_buy_total by Region ------------ count 597414.000000 mean 461.218400 std 365.240937 min 9.000000 25% 217.000000 50% 361.000000 75% 602.000000 max 20103.000000 Name: first_buy_total, dtype: float64
Anova - Kruskal-Wallis p_val to reject null: 0.0 H value: 12996.82 Effect size Eta^2: 0.02 ------------ first_buy_total by IncomeGroup ------------ count 597414.000000 mean 461.218400 std 365.240937 min 9.000000 25% 217.000000 50% 361.000000 75% 602.000000 max 20103.000000 Name: first_buy_total, dtype: float64
Anova - Kruskal-Wallis p_val to reject null: 0.0 H value: 7545.73 Effect size Eta^2: 0.01 ------------ first_buy_total by outcome ------------ count 597414.000000 mean 461.218400 std 365.240937 min 9.000000 25% 217.000000 50% 361.000000 75% 602.000000 max 20103.000000 Name: first_buy_total, dtype: float64
Anova - Kruskal-Wallis p_val to reject null: 0.0 H value: 1697.59 Effect size Eta^2: 0.0 ------------ lowest_total by origin_city ------------ count 949529.000000 mean 472.297550 std 384.511628 min 9.000000 25% 222.000000 50% 362.000000 75% 612.000000 max 21539.000000 Name: lowest_total, dtype: float64
1324 different categorical values, too many to plot. Anova - Kruskal-Wallis p_val to reject null: nan H value: nan Effect size Eta^2: nan ------------ lowest_total by destination_city ------------ count 949529.000000 mean 472.297550 std 384.511628 min 9.000000 25% 222.000000 50% 362.000000 75% 612.000000 max 21539.000000 Name: lowest_total, dtype: float64
1583 different categorical values, too many to plot. Anova - Kruskal-Wallis p_val to reject null: nan H value: nan Effect size Eta^2: nan ------------ lowest_total by trip_type ------------ count 949529.000000 mean 472.297550 std 384.511628 min 9.000000 25% 222.000000 50% 362.000000 75% 612.000000 max 21539.000000 Name: lowest_total, dtype: float64
Paired t-test p_val to reject null: 0.0 t-statistic value: 197.48 Effect size Cohen's d: 0.58 ------------ lowest_total by weekend ------------ count 949529.000000 mean 472.297550 std 384.511628 min 9.000000 25% 222.000000 50% 362.000000 75% 612.000000 max 21539.000000 Name: lowest_total, dtype: float64
Paired t-test p_val to reject null: 0.0 t-statistic value: 180.91 Effect size Cohen's d: 0.54 ------------ lowest_total by filter_no_lcc ------------ count 949529.000000 mean 472.297550 std 384.511628 min 9.000000 25% 222.000000 50% 362.000000 75% 612.000000 max 21539.000000 Name: lowest_total, dtype: float64
Paired t-test p_val to reject null: 0.0 t-statistic value: 10.95 Effect size Cohen's d: 0.1 ------------ lowest_total by filter_non_stop ------------ count 949529.000000 mean 472.297550 std 384.511628 min 9.000000 25% 222.000000 50% 362.000000 75% 612.000000 max 21539.000000 Name: lowest_total, dtype: float64
Paired t-test p_val to reject null: 0.0 t-statistic value: -50.05 Effect size Cohen's d: 0.17 ------------ lowest_total by filter_short_layover ------------ count 949529.000000 mean 472.297550 std 384.511628 min 9.000000 25% 222.000000 50% 362.000000 75% 612.000000 max 21539.000000 Name: lowest_total, dtype: float64
Paired t-test p_val to reject null: 0.0 t-statistic value: -85.44 Effect size Cohen's d: 0.44 ------------ lowest_total by filter_name ------------ count 949529.000000 mean 472.297550 std 384.511628 min 9.000000 25% 222.000000 50% 362.000000 75% 612.000000 max 21539.000000 Name: lowest_total, dtype: float64
Anova - Kruskal-Wallis p_val to reject null: 0.0 H value: 12749.45 Effect size Eta^2: 0.01 ------------ lowest_total by first_rec ------------ count 949529.000000 mean 472.297550 std 384.511628 min 9.000000 25% 222.000000 50% 362.000000 75% 612.000000 max 21539.000000 Name: lowest_total, dtype: float64
Anova - Kruskal-Wallis p_val to reject null: nan H value: nan Effect size Eta^2: nan ------------ lowest_total by last_rec ------------ count 949529.000000 mean 472.297550 std 384.511628 min 9.000000 25% 222.000000 50% 362.000000 75% 612.000000 max 21539.000000 Name: lowest_total, dtype: float64
Anova - Kruskal-Wallis p_val to reject null: nan H value: nan Effect size Eta^2: nan ------------ lowest_total by is_session_1 ------------ count 949529.000000 mean 472.297550 std 384.511628 min 9.000000 25% 222.000000 50% 362.000000 75% 612.000000 max 21539.000000 Name: lowest_total, dtype: float64
Paired t-test p_val to reject null: 0.0 t-statistic value: 21.12 Effect size Cohen's d: 0.04 ------------ lowest_total by Search or watch ------------ count 949529.000000 mean 472.297550 std 384.511628 min 9.000000 25% 222.000000 50% 362.000000 75% 612.000000 max 21539.000000 Name: lowest_total, dtype: float64
Paired t-test p_val to reject null: 0.0 t-statistic value: -57.16 Effect size Cohen's d: 0.12 ------------ lowest_total by Use frequency ------------ count 949529.000000 mean 472.297550 std 384.511628 min 9.000000 25% 222.000000 50% 362.000000 75% 612.000000 max 21539.000000 Name: lowest_total, dtype: float64
Paired t-test p_val to reject null: 0.0 t-statistic value: 10.56 Effect size Cohen's d: 0.04 ------------ lowest_total by continent_origin ------------ count 949529.000000 mean 472.297550 std 384.511628 min 9.000000 25% 222.000000 50% 362.000000 75% 612.000000 max 21539.000000 Name: lowest_total, dtype: float64
Anova - Kruskal-Wallis p_val to reject null: 0.0 H value: 22158.96 Effect size Eta^2: 0.02 ------------ lowest_total by City origin ------------ count 949529.000000 mean 472.297550 std 384.511628 min 9.000000 25% 222.000000 50% 362.000000 75% 612.000000 max 21539.000000 Name: lowest_total, dtype: float64
1237 different categorical values, too many to plot. Anova - Kruskal-Wallis p_val to reject null: nan H value: nan Effect size Eta^2: nan ------------ lowest_total by City destination ------------ count 949529.000000 mean 472.297550 std 384.511628 min 9.000000 25% 222.000000 50% 362.000000 75% 612.000000 max 21539.000000 Name: lowest_total, dtype: float64
1487 different categorical values, too many to plot. Anova - Kruskal-Wallis p_val to reject null: nan H value: nan Effect size Eta^2: nan ------------ lowest_total by region_origin ------------ count 949529.000000 mean 472.297550 std 384.511628 min 9.000000 25% 222.000000 50% 362.000000 75% 612.000000 max 21539.000000 Name: lowest_total, dtype: float64
453 different categorical values, too many to plot. Anova - Kruskal-Wallis p_val to reject null: nan H value: nan Effect size Eta^2: nan ------------ lowest_total by region_destination ------------ count 949529.000000 mean 472.297550 std 384.511628 min 9.000000 25% 222.000000 50% 362.000000 75% 612.000000 max 21539.000000 Name: lowest_total, dtype: float64
498 different categorical values, too many to plot. Anova - Kruskal-Wallis p_val to reject null: 0.0 H value: 272364.97 Effect size Eta^2: 0.29 ------------ lowest_total by country_origin ------------ count 949529.000000 mean 472.297550 std 384.511628 min 9.000000 25% 222.000000 50% 362.000000 75% 612.000000 max 21539.000000 Name: lowest_total, dtype: float64
188 different categorical values, too many to plot. Anova - Kruskal-Wallis p_val to reject null: nan H value: nan Effect size Eta^2: nan ------------ lowest_total by continent_destination ------------ count 949529.000000 mean 472.297550 std 384.511628 min 9.000000 25% 222.000000 50% 362.000000 75% 612.000000 max 21539.000000 Name: lowest_total, dtype: float64
Anova - Kruskal-Wallis p_val to reject null: 0.0 H value: 176933.01 Effect size Eta^2: 0.19 ------------ lowest_total by country_destination ------------ count 949529.000000 mean 472.297550 std 384.511628 min 9.000000 25% 222.000000 50% 362.000000 75% 612.000000 max 21539.000000 Name: lowest_total, dtype: float64
209 different categorical values, too many to plot. Anova - Kruskal-Wallis p_val to reject null: 0.0 H value: 238448.8 Effect size Eta^2: 0.25 ------------ lowest_total by Domestic or international ------------ count 949529.000000 mean 472.297550 std 384.511628 min 9.000000 25% 222.000000 50% 362.000000 75% 612.000000 max 21539.000000 Name: lowest_total, dtype: float64
Paired t-test p_val to reject null: 0.0 t-statistic value: 505.76 Effect size Cohen's d: 1 ------------ lowest_total by Region ------------ count 949529.000000 mean 472.297550 std 384.511628 min 9.000000 25% 222.000000 50% 362.000000 75% 612.000000 max 21539.000000 Name: lowest_total, dtype: float64
Anova - Kruskal-Wallis p_val to reject null: 0.0 H value: 16657.08 Effect size Eta^2: 0.02 ------------ lowest_total by IncomeGroup ------------ count 949529.000000 mean 472.297550 std 384.511628 min 9.000000 25% 222.000000 50% 362.000000 75% 612.000000 max 21539.000000 Name: lowest_total, dtype: float64
Anova - Kruskal-Wallis p_val to reject null: 0.0 H value: 12825.1 Effect size Eta^2: 0.01 ------------ lowest_total by outcome ------------ count 949529.000000 mean 472.297550 std 384.511628 min 9.000000 25% 222.000000 50% 362.000000 75% 612.000000 max 21539.000000 Name: lowest_total, dtype: float64
Anova - Kruskal-Wallis p_val to reject null: 0.0 H value: 4393.07 Effect size Eta^2: 0.0 ------------ session by origin_city ------------ count 1.007692e+06 mean 2.616324e+00 std 2.560719e+00 min 1.000000e+00 25% 1.000000e+00 50% 2.000000e+00 75% 3.000000e+00 max 3.000000e+01 Name: session, dtype: float64
1324 different categorical values, too many to plot. Anova - Kruskal-Wallis p_val to reject null: 0.0 H value: 16421.34 Effect size Eta^2: 0.02 ------------ session by destination_city ------------ count 1.007692e+06 mean 2.616324e+00 std 2.560719e+00 min 1.000000e+00 25% 1.000000e+00 50% 2.000000e+00 75% 3.000000e+00 max 3.000000e+01 Name: session, dtype: float64
1583 different categorical values, too many to plot. Anova - Kruskal-Wallis p_val to reject null: 0.0 H value: 8771.51 Effect size Eta^2: 0.01 ------------ session by trip_type ------------ count 1.007692e+06 mean 2.616324e+00 std 2.560719e+00 min 1.000000e+00 25% 1.000000e+00 50% 2.000000e+00 75% 3.000000e+00 max 3.000000e+01 Name: session, dtype: float64
Paired t-test p_val to reject null: 0.0 t-statistic value: -62.87 Effect size Cohen's d: 0.16 ------------ session by weekend ------------ count 1.007692e+06 mean 2.616324e+00 std 2.560719e+00 min 1.000000e+00 25% 1.000000e+00 50% 2.000000e+00 75% 3.000000e+00 max 3.000000e+01 Name: session, dtype: float64
Paired t-test p_val to reject null: 0.0 t-statistic value: 6.83 Effect size Cohen's d: 0.02 ------------ session by filter_no_lcc ------------ count 1.007692e+06 mean 2.616324e+00 std 2.560719e+00 min 1.000000e+00 25% 1.000000e+00 50% 2.000000e+00 75% 3.000000e+00 max 3.000000e+01 Name: session, dtype: float64
Paired t-test p_val to reject null: 0.0004 t-statistic value: 3.54 Effect size Cohen's d: 0.03 ------------ session by filter_non_stop ------------ count 1.007692e+06 mean 2.616324e+00 std 2.560719e+00 min 1.000000e+00 25% 1.000000e+00 50% 2.000000e+00 75% 3.000000e+00 max 3.000000e+01 Name: session, dtype: float64
Paired t-test p_val to reject null: 0.0 t-statistic value: -10.5 Effect size Cohen's d: 0.03 ------------ session by filter_short_layover ------------ count 1.007692e+06 mean 2.616324e+00 std 2.560719e+00 min 1.000000e+00 25% 1.000000e+00 50% 2.000000e+00 75% 3.000000e+00 max 3.000000e+01 Name: session, dtype: float64
Paired t-test p_val to reject null: 0.0 t-statistic value: 13.59 Effect size Cohen's d: 0.08 ------------ session by filter_name ------------ count 1.007692e+06 mean 2.616324e+00 std 2.560719e+00 min 1.000000e+00 25% 1.000000e+00 50% 2.000000e+00 75% 3.000000e+00 max 3.000000e+01 Name: session, dtype: float64
Anova - Kruskal-Wallis p_val to reject null: 0.0 H value: 442.38 Effect size Eta^2: 0.0 ------------ session by first_rec ------------ count 1.007692e+06 mean 2.616324e+00 std 2.560719e+00 min 1.000000e+00 25% 1.000000e+00 50% 2.000000e+00 75% 3.000000e+00 max 3.000000e+01 Name: session, dtype: float64
Anova - Kruskal-Wallis p_val to reject null: 0.0 H value: 3692.19 Effect size Eta^2: 0.0 ------------ session by last_rec ------------ count 1.007692e+06 mean 2.616324e+00 std 2.560719e+00 min 1.000000e+00 25% 1.000000e+00 50% 2.000000e+00 75% 3.000000e+00 max 3.000000e+01 Name: session, dtype: float64
Anova - Kruskal-Wallis p_val to reject null: 0.0 H value: 3686.38 Effect size Eta^2: 0.0 ------------ session by is_session_1 ------------ count 1.007692e+06 mean 2.616324e+00 std 2.560719e+00 min 1.000000e+00 25% 1.000000e+00 50% 2.000000e+00 75% 3.000000e+00 max 3.000000e+01 Name: session, dtype: float64
Paired t-test p_val to reject null: 0.0 t-statistic value: -734.88 Effect size Cohen's d: 1 ------------ session by Search or watch ------------ count 1.007692e+06 mean 2.616324e+00 std 2.560719e+00 min 1.000000e+00 25% 1.000000e+00 50% 2.000000e+00 75% 3.000000e+00 max 3.000000e+01 Name: session, dtype: float64
Paired t-test p_val to reject null: 0.0 t-statistic value: -127.0 Effect size Cohen's d: 0.28 ------------ session by Use frequency ------------ count 1.007692e+06 mean 2.616324e+00 std 2.560719e+00 min 1.000000e+00 25% 1.000000e+00 50% 2.000000e+00 75% 3.000000e+00 max 3.000000e+01 Name: session, dtype: float64
Paired t-test p_val to reject null: 0.0 t-statistic value: -179.68 Effect size Cohen's d: 0.94 ------------ session by continent_origin ------------ count 1.007692e+06 mean 2.616324e+00 std 2.560719e+00 min 1.000000e+00 25% 1.000000e+00 50% 2.000000e+00 75% 3.000000e+00 max 3.000000e+01 Name: session, dtype: float64
Anova - Kruskal-Wallis p_val to reject null: 0.0 H value: 2797.35 Effect size Eta^2: 0.0 ------------ session by City origin ------------ count 1.007692e+06 mean 2.616324e+00 std 2.560719e+00 min 1.000000e+00 25% 1.000000e+00 50% 2.000000e+00 75% 3.000000e+00 max 3.000000e+01 Name: session, dtype: float64
1237 different categorical values, too many to plot. Anova - Kruskal-Wallis p_val to reject null: 0.0 H value: 15651.9 Effect size Eta^2: 0.01 ------------ session by City destination ------------ count 1.007692e+06 mean 2.616324e+00 std 2.560719e+00 min 1.000000e+00 25% 1.000000e+00 50% 2.000000e+00 75% 3.000000e+00 max 3.000000e+01 Name: session, dtype: float64
1487 different categorical values, too many to plot. Anova - Kruskal-Wallis p_val to reject null: 0.0 H value: 8025.67 Effect size Eta^2: 0.01 ------------ session by region_origin ------------ count 1.007692e+06 mean 2.616324e+00 std 2.560719e+00 min 1.000000e+00 25% 1.000000e+00 50% 2.000000e+00 75% 3.000000e+00 max 3.000000e+01 Name: session, dtype: float64
453 different categorical values, too many to plot. Anova - Kruskal-Wallis p_val to reject null: 0.0 H value: 11715.2 Effect size Eta^2: 0.01 ------------ session by region_destination ------------ count 1.007692e+06 mean 2.616324e+00 std 2.560719e+00 min 1.000000e+00 25% 1.000000e+00 50% 2.000000e+00 75% 3.000000e+00 max 3.000000e+01 Name: session, dtype: float64
498 different categorical values, too many to plot. Anova - Kruskal-Wallis p_val to reject null: 0.0 H value: 4828.28 Effect size Eta^2: 0.0 ------------ session by country_origin ------------ count 1.007692e+06 mean 2.616324e+00 std 2.560719e+00 min 1.000000e+00 25% 1.000000e+00 50% 2.000000e+00 75% 3.000000e+00 max 3.000000e+01 Name: session, dtype: float64
188 different categorical values, too many to plot. Anova - Kruskal-Wallis p_val to reject null: 0.0 H value: 7360.07 Effect size Eta^2: 0.01 ------------ session by continent_destination ------------ count 1.007692e+06 mean 2.616324e+00 std 2.560719e+00 min 1.000000e+00 25% 1.000000e+00 50% 2.000000e+00 75% 3.000000e+00 max 3.000000e+01 Name: session, dtype: float64
Anova - Kruskal-Wallis p_val to reject null: 0.0 H value: 425.24 Effect size Eta^2: 0.0 ------------ session by country_destination ------------ count 1.007692e+06 mean 2.616324e+00 std 2.560719e+00 min 1.000000e+00 25% 1.000000e+00 50% 2.000000e+00 75% 3.000000e+00 max 3.000000e+01 Name: session, dtype: float64
209 different categorical values, too many to plot. Anova - Kruskal-Wallis p_val to reject null: 0.0 H value: 2742.74 Effect size Eta^2: 0.0 ------------ session by Domestic or international ------------ count 1.007692e+06 mean 2.616324e+00 std 2.560719e+00 min 1.000000e+00 25% 1.000000e+00 50% 2.000000e+00 75% 3.000000e+00 max 3.000000e+01 Name: session, dtype: float64
Paired t-test p_val to reject null: 0.0 t-statistic value: 5.12 Effect size Cohen's d: 0.01 ------------ session by Region ------------ count 1.007692e+06 mean 2.616324e+00 std 2.560719e+00 min 1.000000e+00 25% 1.000000e+00 50% 2.000000e+00 75% 3.000000e+00 max 3.000000e+01 Name: session, dtype: float64
Anova - Kruskal-Wallis p_val to reject null: 0.0 H value: 2071.13 Effect size Eta^2: 0.0 ------------ session by IncomeGroup ------------ count 1.007692e+06 mean 2.616324e+00 std 2.560719e+00 min 1.000000e+00 25% 1.000000e+00 50% 2.000000e+00 75% 3.000000e+00 max 3.000000e+01 Name: session, dtype: float64
Anova - Kruskal-Wallis p_val to reject null: 0.0 H value: 556.74 Effect size Eta^2: 0.0 ------------ session by outcome ------------ count 1.007692e+06 mean 2.616324e+00 std 2.560719e+00 min 1.000000e+00 25% 1.000000e+00 50% 2.000000e+00 75% 3.000000e+00 max 3.000000e+01 Name: session, dtype: float64
Anova - Kruskal-Wallis p_val to reject null: 0.0 H value: 24281.92 Effect size Eta^2: 0.02 ------------ first_buy - lowest_total by origin_city ------------ count 597414.000000 mean 10.216287 std 60.636663 min 0.000000 25% 0.000000 50% 0.000000 75% 0.000000 max 18166.000000 Name: first_buy - lowest_total, dtype: float64
1324 different categorical values, too many to plot. Anova - Kruskal-Wallis p_val to reject null: nan H value: nan Effect size Eta^2: nan ------------ first_buy - lowest_total by destination_city ------------ count 597414.000000 mean 10.216287 std 60.636663 min 0.000000 25% 0.000000 50% 0.000000 75% 0.000000 max 18166.000000 Name: first_buy - lowest_total, dtype: float64
1583 different categorical values, too many to plot. Anova - Kruskal-Wallis p_val to reject null: nan H value: nan Effect size Eta^2: nan ------------ first_buy - lowest_total by trip_type ------------ count 597414.000000 mean 10.216287 std 60.636663 min 0.000000 25% 0.000000 50% 0.000000 75% 0.000000 max 18166.000000 Name: first_buy - lowest_total, dtype: float64
Paired t-test p_val to reject null: 0.0 t-statistic value: 20.63 Effect size Cohen's d: 0.08 ------------ first_buy - lowest_total by weekend ------------ count 597414.000000 mean 10.216287 std 60.636663 min 0.000000 25% 0.000000 50% 0.000000 75% 0.000000 max 18166.000000 Name: first_buy - lowest_total, dtype: float64
Paired t-test p_val to reject null: 0.0001 t-statistic value: 3.93 Effect size Cohen's d: 0.01 ------------ first_buy - lowest_total by filter_no_lcc ------------ count 597414.000000 mean 10.216287 std 60.636663 min 0.000000 25% 0.000000 50% 0.000000 75% 0.000000 max 18166.000000 Name: first_buy - lowest_total, dtype: float64
Paired t-test p_val to reject null: 0.0 t-statistic value: -6.78 Effect size Cohen's d: 0.08 ------------ first_buy - lowest_total by filter_non_stop ------------ count 597414.000000 mean 10.216287 std 60.636663 min 0.000000 25% 0.000000 50% 0.000000 75% 0.000000 max 18166.000000 Name: first_buy - lowest_total, dtype: float64
Paired t-test p_val to reject null: 0.0 t-statistic value: 20.64 Effect size Cohen's d: 0.09 ------------ first_buy - lowest_total by filter_short_layover ------------ count 597414.000000 mean 10.216287 std 60.636663 min 0.000000 25% 0.000000 50% 0.000000 75% 0.000000 max 18166.000000 Name: first_buy - lowest_total, dtype: float64
Paired t-test p_val to reject null: 0.0 t-statistic value: -19.99 Effect size Cohen's d: 0.14 ------------ first_buy - lowest_total by filter_name ------------ count 597414.000000 mean 10.216287 std 60.636663 min 0.000000 25% 0.000000 50% 0.000000 75% 0.000000 max 18166.000000 Name: first_buy - lowest_total, dtype: float64
Anova - Kruskal-Wallis p_val to reject null: 0.0 H value: 3462.32 Effect size Eta^2: 0.01 ------------ first_buy - lowest_total by first_rec ------------ count 597414.000000 mean 10.216287 std 60.636663 min 0.000000 25% 0.000000 50% 0.000000 75% 0.000000 max 18166.000000 Name: first_buy - lowest_total, dtype: float64
Anova - Kruskal-Wallis p_val to reject null: nan H value: nan Effect size Eta^2: nan ------------ first_buy - lowest_total by last_rec ------------ count 597414.000000 mean 10.216287 std 60.636663 min 0.000000 25% 0.000000 50% 0.000000 75% 0.000000 max 18166.000000 Name: first_buy - lowest_total, dtype: float64
Anova - Kruskal-Wallis p_val to reject null: nan H value: nan Effect size Eta^2: nan ------------ first_buy - lowest_total by is_session_1 ------------ count 597414.000000 mean 10.216287 std 60.636663 min 0.000000 25% 0.000000 50% 0.000000 75% 0.000000 max 18166.000000 Name: first_buy - lowest_total, dtype: float64
Paired t-test p_val to reject null: 0.0 t-statistic value: 34.96 Effect size Cohen's d: 0.09 ------------ first_buy - lowest_total by Search or watch ------------ count 597414.000000 mean 10.216287 std 60.636663 min 0.000000 25% 0.000000 50% 0.000000 75% 0.000000 max 18166.000000 Name: first_buy - lowest_total, dtype: float64
Paired t-test p_val to reject null: 0.0 t-statistic value: 152.41 Effect size Cohen's d: 0.36 ------------ first_buy - lowest_total by Use frequency ------------ count 597414.000000 mean 10.216287 std 60.636663 min 0.000000 25% 0.000000 50% 0.000000 75% 0.000000 max 18166.000000 Name: first_buy - lowest_total, dtype: float64
Paired t-test p_val to reject null: 0.0 t-statistic value: 16.69 Effect size Cohen's d: 0.08 ------------ first_buy - lowest_total by continent_origin ------------ count 597414.000000 mean 10.216287 std 60.636663 min 0.000000 25% 0.000000 50% 0.000000 75% 0.000000 max 18166.000000 Name: first_buy - lowest_total, dtype: float64
Anova - Kruskal-Wallis p_val to reject null: 0.0 H value: 404.11 Effect size Eta^2: 0.0 ------------ first_buy - lowest_total by City origin ------------ count 597414.000000 mean 10.216287 std 60.636663 min 0.000000 25% 0.000000 50% 0.000000 75% 0.000000 max 18166.000000 Name: first_buy - lowest_total, dtype: float64
1237 different categorical values, too many to plot. Anova - Kruskal-Wallis p_val to reject null: nan H value: nan Effect size Eta^2: nan ------------ first_buy - lowest_total by City destination ------------ count 597414.000000 mean 10.216287 std 60.636663 min 0.000000 25% 0.000000 50% 0.000000 75% 0.000000 max 18166.000000 Name: first_buy - lowest_total, dtype: float64
1487 different categorical values, too many to plot. Anova - Kruskal-Wallis p_val to reject null: nan H value: nan Effect size Eta^2: nan ------------ first_buy - lowest_total by region_origin ------------ count 597414.000000 mean 10.216287 std 60.636663 min 0.000000 25% 0.000000 50% 0.000000 75% 0.000000 max 18166.000000 Name: first_buy - lowest_total, dtype: float64
453 different categorical values, too many to plot. Anova - Kruskal-Wallis p_val to reject null: nan H value: nan Effect size Eta^2: nan ------------ first_buy - lowest_total by region_destination ------------ count 597414.000000 mean 10.216287 std 60.636663 min 0.000000 25% 0.000000 50% 0.000000 75% 0.000000 max 18166.000000 Name: first_buy - lowest_total, dtype: float64
498 different categorical values, too many to plot. Anova - Kruskal-Wallis p_val to reject null: nan H value: nan Effect size Eta^2: nan ------------ first_buy - lowest_total by country_origin ------------ count 597414.000000 mean 10.216287 std 60.636663 min 0.000000 25% 0.000000 50% 0.000000 75% 0.000000 max 18166.000000 Name: first_buy - lowest_total, dtype: float64
188 different categorical values, too many to plot. Anova - Kruskal-Wallis p_val to reject null: nan H value: nan Effect size Eta^2: nan ------------ first_buy - lowest_total by continent_destination ------------ count 597414.000000 mean 10.216287 std 60.636663 min 0.000000 25% 0.000000 50% 0.000000 75% 0.000000 max 18166.000000 Name: first_buy - lowest_total, dtype: float64
Anova - Kruskal-Wallis p_val to reject null: 0.0 H value: 167.34 Effect size Eta^2: 0.0 ------------ first_buy - lowest_total by country_destination ------------ count 597414.000000 mean 10.216287 std 60.636663 min 0.000000 25% 0.000000 50% 0.000000 75% 0.000000 max 18166.000000 Name: first_buy - lowest_total, dtype: float64
209 different categorical values, too many to plot. Anova - Kruskal-Wallis p_val to reject null: nan H value: nan Effect size Eta^2: nan ------------ first_buy - lowest_total by Domestic or international ------------ count 597414.000000 mean 10.216287 std 60.636663 min 0.000000 25% 0.000000 50% 0.000000 75% 0.000000 max 18166.000000 Name: first_buy - lowest_total, dtype: float64
Paired t-test p_val to reject null: 0.0 t-statistic value: 30.3 Effect size Cohen's d: 0.08 ------------ first_buy - lowest_total by Region ------------ count 597414.000000 mean 10.216287 std 60.636663 min 0.000000 25% 0.000000 50% 0.000000 75% 0.000000 max 18166.000000 Name: first_buy - lowest_total, dtype: float64
Anova - Kruskal-Wallis p_val to reject null: 0.0 H value: 449.33 Effect size Eta^2: 0.0 ------------ first_buy - lowest_total by IncomeGroup ------------ count 597414.000000 mean 10.216287 std 60.636663 min 0.000000 25% 0.000000 50% 0.000000 75% 0.000000 max 18166.000000 Name: first_buy - lowest_total, dtype: float64
Anova - Kruskal-Wallis p_val to reject null: 0.0 H value: 53.17 Effect size Eta^2: 0.0 ------------ first_buy - lowest_total by outcome ------------ count 597414.000000 mean 10.216287 std 60.636663 min 0.000000 25% 0.000000 50% 0.000000 75% 0.000000 max 18166.000000 Name: first_buy - lowest_total, dtype: float64
Anova - Kruskal-Wallis p_val to reject null: 0.0 H value: 152072.97 Effect size Eta^2: 0.25 ------------ Adult population by origin_city ------------ count 1.007113e+06 mean 1.935103e+08 std 1.088621e+08 min 5.559800e+04 25% 1.129580e+08 50% 2.446359e+08 75% 2.446359e+08 max 1.088715e+09 Name: Adult population, dtype: float64
1324 different categorical values, too many to plot. Anova - Kruskal-Wallis p_val to reject null: nan H value: nan Effect size Eta^2: nan ------------ Adult population by destination_city ------------ count 1.007113e+06 mean 1.935103e+08 std 1.088621e+08 min 5.559800e+04 25% 1.129580e+08 50% 2.446359e+08 75% 2.446359e+08 max 1.088715e+09 Name: Adult population, dtype: float64
1583 different categorical values, too many to plot. Anova - Kruskal-Wallis p_val to reject null: nan H value: nan Effect size Eta^2: nan ------------ Adult population by trip_type ------------ count 1.007113e+06 mean 1.935103e+08 std 1.088621e+08 min 5.559800e+04 25% 1.129580e+08 50% 2.446359e+08 75% 2.446359e+08 max 1.088715e+09 Name: Adult population, dtype: float64
Paired t-test p_val to reject null: 0.0 t-statistic value: 49.48 Effect size Cohen's d: 0.12 ------------ Adult population by weekend ------------ count 1.007113e+06 mean 1.935103e+08 std 1.088621e+08 min 5.559800e+04 25% 1.129580e+08 50% 2.446359e+08 75% 2.446359e+08 max 1.088715e+09 Name: Adult population, dtype: float64
Paired t-test p_val to reject null: 0.0 t-statistic value: -105.33 Effect size Cohen's d: 0.29 ------------ Adult population by filter_no_lcc ------------ count 1.007113e+06 mean 1.935103e+08 std 1.088621e+08 min 5.559800e+04 25% 1.129580e+08 50% 2.446359e+08 75% 2.446359e+08 max 1.088715e+09 Name: Adult population, dtype: float64
Paired t-test p_val to reject null: 0.0 t-statistic value: -17.12 Effect size Cohen's d: 0.16 ------------ Adult population by filter_non_stop ------------ count 1.007113e+06 mean 1.935103e+08 std 1.088621e+08 min 5.559800e+04 25% 1.129580e+08 50% 2.446359e+08 75% 2.446359e+08 max 1.088715e+09 Name: Adult population, dtype: float64
Paired t-test p_val to reject null: 0.0 t-statistic value: 25.77 Effect size Cohen's d: 0.09 ------------ Adult population by filter_short_layover ------------ count 1.007113e+06 mean 1.935103e+08 std 1.088621e+08 min 5.559800e+04 25% 1.129580e+08 50% 2.446359e+08 75% 2.446359e+08 max 1.088715e+09 Name: Adult population, dtype: float64
Paired t-test p_val to reject null: 0.8635 t-statistic value: 0.17 Effect size Cohen's d: 0.0 ------------ Adult population by filter_name ------------ count 1.007113e+06 mean 1.935103e+08 std 1.088621e+08 min 5.559800e+04 25% 1.129580e+08 50% 2.446359e+08 75% 2.446359e+08 max 1.088715e+09 Name: Adult population, dtype: float64
Anova - Kruskal-Wallis p_val to reject null: 0.0 H value: 1289.57 Effect size Eta^2: 0.0 ------------ Adult population by first_rec ------------ count 1.007113e+06 mean 1.935103e+08 std 1.088621e+08 min 5.559800e+04 25% 1.129580e+08 50% 2.446359e+08 75% 2.446359e+08 max 1.088715e+09 Name: Adult population, dtype: float64
Anova - Kruskal-Wallis p_val to reject null: 0.0 H value: 2993.03 Effect size Eta^2: 0.0 ------------ Adult population by last_rec ------------ count 1.007113e+06 mean 1.935103e+08 std 1.088621e+08 min 5.559800e+04 25% 1.129580e+08 50% 2.446359e+08 75% 2.446359e+08 max 1.088715e+09 Name: Adult population, dtype: float64
Anova - Kruskal-Wallis p_val to reject null: 0.0 H value: 1623.93 Effect size Eta^2: 0.0 ------------ Adult population by is_session_1 ------------ count 1.007113e+06 mean 1.935103e+08 std 1.088621e+08 min 5.559800e+04 25% 1.129580e+08 50% 2.446359e+08 75% 2.446359e+08 max 1.088715e+09 Name: Adult population, dtype: float64
Paired t-test p_val to reject null: 0.0 t-statistic value: -34.19 Effect size Cohen's d: 0.07 ------------ Adult population by Search or watch ------------ count 1.007113e+06 mean 1.935103e+08 std 1.088621e+08 min 5.559800e+04 25% 1.129580e+08 50% 2.446359e+08 75% 2.446359e+08 max 1.088715e+09 Name: Adult population, dtype: float64
Paired t-test p_val to reject null: 0.0055 t-statistic value: 2.77 Effect size Cohen's d: 0.01 ------------ Adult population by Use frequency ------------ count 1.007113e+06 mean 1.935103e+08 std 1.088621e+08 min 5.559800e+04 25% 1.129580e+08 50% 2.446359e+08 75% 2.446359e+08 max 1.088715e+09 Name: Adult population, dtype: float64
Paired t-test p_val to reject null: 0.0 t-statistic value: -29.61 Effect size Cohen's d: 0.11 ------------ Adult population by continent_origin ------------ count 1.007113e+06 mean 1.935103e+08 std 1.088621e+08 min 5.559800e+04 25% 1.129580e+08 50% 2.446359e+08 75% 2.446359e+08 max 1.088715e+09 Name: Adult population, dtype: float64
Anova - Kruskal-Wallis p_val to reject null: 0.0 H value: 398363.35 Effect size Eta^2: 0.4 ------------ Adult population by City origin ------------ count 1.007113e+06 mean 1.935103e+08 std 1.088621e+08 min 5.559800e+04 25% 1.129580e+08 50% 2.446359e+08 75% 2.446359e+08 max 1.088715e+09 Name: Adult population, dtype: float64
1237 different categorical values, too many to plot. Anova - Kruskal-Wallis p_val to reject null: nan H value: nan Effect size Eta^2: nan ------------ Adult population by City destination ------------ count 1.007113e+06 mean 1.935103e+08 std 1.088621e+08 min 5.559800e+04 25% 1.129580e+08 50% 2.446359e+08 75% 2.446359e+08 max 1.088715e+09 Name: Adult population, dtype: float64
1487 different categorical values, too many to plot. Anova - Kruskal-Wallis p_val to reject null: nan H value: nan Effect size Eta^2: nan ------------ Adult population by region_origin ------------ count 1.007113e+06 mean 1.935103e+08 std 1.088621e+08 min 5.559800e+04 25% 1.129580e+08 50% 2.446359e+08 75% 2.446359e+08 max 1.088715e+09 Name: Adult population, dtype: float64
453 different categorical values, too many to plot. Anova - Kruskal-Wallis p_val to reject null: 0.0 H value: 946871.69 Effect size Eta^2: 0.94 ------------ Adult population by region_destination ------------ count 1.007113e+06 mean 1.935103e+08 std 1.088621e+08 min 5.559800e+04 25% 1.129580e+08 50% 2.446359e+08 75% 2.446359e+08 max 1.088715e+09 Name: Adult population, dtype: float64
498 different categorical values, too many to plot. Anova - Kruskal-Wallis p_val to reject null: 0.0 H value: 115214.42 Effect size Eta^2: 0.11 ------------ Adult population by country_origin ------------ count 1.007113e+06 mean 1.935103e+08 std 1.088621e+08 min 5.559800e+04 25% 1.129580e+08 50% 2.446359e+08 75% 2.446359e+08 max 1.088715e+09 Name: Adult population, dtype: float64
188 different categorical values, too many to plot. Anova - Kruskal-Wallis p_val to reject null: nan H value: nan Effect size Eta^2: nan ------------ Adult population by continent_destination ------------ count 1.007113e+06 mean 1.935103e+08 std 1.088621e+08 min 5.559800e+04 25% 1.129580e+08 50% 2.446359e+08 75% 2.446359e+08 max 1.088715e+09 Name: Adult population, dtype: float64
Anova - Kruskal-Wallis p_val to reject null: 0.0 H value: 59581.96 Effect size Eta^2: 0.06 ------------ Adult population by country_destination ------------ count 1.007113e+06 mean 1.935103e+08 std 1.088621e+08 min 5.559800e+04 25% 1.129580e+08 50% 2.446359e+08 75% 2.446359e+08 max 1.088715e+09 Name: Adult population, dtype: float64
209 different categorical values, too many to plot. Anova - Kruskal-Wallis p_val to reject null: 0.0 H value: 104364.18 Effect size Eta^2: 0.1 ------------ Adult population by Domestic or international ------------ count 1.007113e+06 mean 1.935103e+08 std 1.088621e+08 min 5.559800e+04 25% 1.129580e+08 50% 2.446359e+08 75% 2.446359e+08 max 1.088715e+09 Name: Adult population, dtype: float64
Paired t-test p_val to reject null: 0.0 t-statistic value: -434.63 Effect size Cohen's d: 0.86 ------------ Adult population by Region ------------ count 1.007113e+06 mean 1.935103e+08 std 1.088621e+08 min 5.559800e+04 25% 1.129580e+08 50% 2.446359e+08 75% 2.446359e+08 max 1.088715e+09 Name: Adult population, dtype: float64
Anova - Kruskal-Wallis p_val to reject null: 0.0 H value: 668332.15 Effect size Eta^2: 0.66 ------------ Adult population by IncomeGroup ------------ count 1.007113e+06 mean 1.935103e+08 std 1.088621e+08 min 5.559800e+04 25% 1.129580e+08 50% 2.446359e+08 75% 2.446359e+08 max 1.088715e+09 Name: Adult population, dtype: float64
Anova - Kruskal-Wallis p_val to reject null: 0.0 H value: 227648.52 Effect size Eta^2: 0.23 ------------ Adult population by outcome ------------ count 1.007113e+06 mean 1.935103e+08 std 1.088621e+08 min 5.559800e+04 25% 1.129580e+08 50% 2.446359e+08 75% 2.446359e+08 max 1.088715e+09 Name: Adult population, dtype: float64
Anova - Kruskal-Wallis p_val to reject null: 0.0 H value: 21.83 Effect size Eta^2: 0.0 ------------ Count_uniq_users_per_country by origin_city ------------ count 1.007692e+06 mean 5.534014e+05 std 3.181662e+05 min 1.000000e+00 25% 6.405800e+04 50% 7.431010e+05 75% 7.431010e+05 max 7.431010e+05 Name: Count_uniq_users_per_country, dtype: float64
1324 different categorical values, too many to plot. Anova - Kruskal-Wallis p_val to reject null: 0.0 H value: 1007691.0 Effect size Eta^2: 1.0 ------------ Count_uniq_users_per_country by destination_city ------------ count 1.007692e+06 mean 5.534014e+05 std 3.181662e+05 min 1.000000e+00 25% 6.405800e+04 50% 7.431010e+05 75% 7.431010e+05 max 7.431010e+05 Name: Count_uniq_users_per_country, dtype: float64
1583 different categorical values, too many to plot. Anova - Kruskal-Wallis p_val to reject null: 0.0 H value: 181732.34 Effect size Eta^2: 0.18 ------------ Count_uniq_users_per_country by trip_type ------------ count 1.007692e+06 mean 5.534014e+05 std 3.181662e+05 min 1.000000e+00 25% 6.405800e+04 50% 7.431010e+05 75% 7.431010e+05 max 7.431010e+05 Name: Count_uniq_users_per_country, dtype: float64
Paired t-test p_val to reject null: 0.0 t-statistic value: 83.95 Effect size Cohen's d: 0.22 ------------ Count_uniq_users_per_country by weekend ------------ count 1.007692e+06 mean 5.534014e+05 std 3.181662e+05 min 1.000000e+00 25% 6.405800e+04 50% 7.431010e+05 75% 7.431010e+05 max 7.431010e+05 Name: Count_uniq_users_per_country, dtype: float64
Paired t-test p_val to reject null: 0.0 t-statistic value: -146.64 Effect size Cohen's d: 0.4 ------------ Count_uniq_users_per_country by filter_no_lcc ------------ count 1.007692e+06 mean 5.534014e+05 std 3.181662e+05 min 1.000000e+00 25% 6.405800e+04 50% 7.431010e+05 75% 7.431010e+05 max 7.431010e+05 Name: Count_uniq_users_per_country, dtype: float64
Paired t-test p_val to reject null: 0.0 t-statistic value: -22.92 Effect size Cohen's d: 0.21 ------------ Count_uniq_users_per_country by filter_non_stop ------------ count 1.007692e+06 mean 5.534014e+05 std 3.181662e+05 min 1.000000e+00 25% 6.405800e+04 50% 7.431010e+05 75% 7.431010e+05 max 7.431010e+05 Name: Count_uniq_users_per_country, dtype: float64
Paired t-test p_val to reject null: 0.0 t-statistic value: 35.9 Effect size Cohen's d: 0.12 ------------ Count_uniq_users_per_country by filter_short_layover ------------ count 1.007692e+06 mean 5.534014e+05 std 3.181662e+05 min 1.000000e+00 25% 6.405800e+04 50% 7.431010e+05 75% 7.431010e+05 max 7.431010e+05 Name: Count_uniq_users_per_country, dtype: float64
Paired t-test p_val to reject null: 0.0 t-statistic value: 6.52 Effect size Cohen's d: 0.04 ------------ Count_uniq_users_per_country by filter_name ------------ count 1.007692e+06 mean 5.534014e+05 std 3.181662e+05 min 1.000000e+00 25% 6.405800e+04 50% 7.431010e+05 75% 7.431010e+05 max 7.431010e+05 Name: Count_uniq_users_per_country, dtype: float64
Anova - Kruskal-Wallis p_val to reject null: 0.0 H value: 1711.06 Effect size Eta^2: 0.0 ------------ Count_uniq_users_per_country by first_rec ------------ count 1.007692e+06 mean 5.534014e+05 std 3.181662e+05 min 1.000000e+00 25% 6.405800e+04 50% 7.431010e+05 75% 7.431010e+05 max 7.431010e+05 Name: Count_uniq_users_per_country, dtype: float64
Anova - Kruskal-Wallis p_val to reject null: 0.0 H value: 4744.52 Effect size Eta^2: 0.0 ------------ Count_uniq_users_per_country by last_rec ------------ count 1.007692e+06 mean 5.534014e+05 std 3.181662e+05 min 1.000000e+00 25% 6.405800e+04 50% 7.431010e+05 75% 7.431010e+05 max 7.431010e+05 Name: Count_uniq_users_per_country, dtype: float64
Anova - Kruskal-Wallis p_val to reject null: 0.0 H value: 2715.34 Effect size Eta^2: 0.0 ------------ Count_uniq_users_per_country by is_session_1 ------------ count 1.007692e+06 mean 5.534014e+05 std 3.181662e+05 min 1.000000e+00 25% 6.405800e+04 50% 7.431010e+05 75% 7.431010e+05 max 7.431010e+05 Name: Count_uniq_users_per_country, dtype: float64
Paired t-test p_val to reject null: 0.0 t-statistic value: -50.19 Effect size Cohen's d: 0.1 ------------ Count_uniq_users_per_country by Search or watch ------------ count 1.007692e+06 mean 5.534014e+05 std 3.181662e+05 min 1.000000e+00 25% 6.405800e+04 50% 7.431010e+05 75% 7.431010e+05 max 7.431010e+05 Name: Count_uniq_users_per_country, dtype: float64
Paired t-test p_val to reject null: 0.0 t-statistic value: 5.28 Effect size Cohen's d: 0.01 ------------ Count_uniq_users_per_country by Use frequency ------------ count 1.007692e+06 mean 5.534014e+05 std 3.181662e+05 min 1.000000e+00 25% 6.405800e+04 50% 7.431010e+05 75% 7.431010e+05 max 7.431010e+05 Name: Count_uniq_users_per_country, dtype: float64
Paired t-test p_val to reject null: 0.0 t-statistic value: -42.39 Effect size Cohen's d: 0.16 ------------ Count_uniq_users_per_country by continent_origin ------------ count 1.007692e+06 mean 5.534014e+05 std 3.181662e+05 min 1.000000e+00 25% 6.405800e+04 50% 7.431010e+05 75% 7.431010e+05 max 7.431010e+05 Name: Count_uniq_users_per_country, dtype: float64
Anova - Kruskal-Wallis p_val to reject null: 0.0 H value: 590989.9 Effect size Eta^2: 0.59 ------------ Count_uniq_users_per_country by City origin ------------ count 1.007692e+06 mean 5.534014e+05 std 3.181662e+05 min 1.000000e+00 25% 6.405800e+04 50% 7.431010e+05 75% 7.431010e+05 max 7.431010e+05 Name: Count_uniq_users_per_country, dtype: float64
1237 different categorical values, too many to plot. Anova - Kruskal-Wallis p_val to reject null: 0.0 H value: 988774.79 Effect size Eta^2: 0.98 ------------ Count_uniq_users_per_country by City destination ------------ count 1.007692e+06 mean 5.534014e+05 std 3.181662e+05 min 1.000000e+00 25% 6.405800e+04 50% 7.431010e+05 75% 7.431010e+05 max 7.431010e+05 Name: Count_uniq_users_per_country, dtype: float64
1487 different categorical values, too many to plot. Anova - Kruskal-Wallis p_val to reject null: 0.0 H value: 174779.99 Effect size Eta^2: 0.17 ------------ Count_uniq_users_per_country by region_origin ------------ count 1.007692e+06 mean 5.534014e+05 std 3.181662e+05 min 1.000000e+00 25% 6.405800e+04 50% 7.431010e+05 75% 7.431010e+05 max 7.431010e+05 Name: Count_uniq_users_per_country, dtype: float64
453 different categorical values, too many to plot. Anova - Kruskal-Wallis p_val to reject null: 0.0 H value: 952334.7 Effect size Eta^2: 0.95 ------------ Count_uniq_users_per_country by region_destination ------------ count 1.007692e+06 mean 5.534014e+05 std 3.181662e+05 min 1.000000e+00 25% 6.405800e+04 50% 7.431010e+05 75% 7.431010e+05 max 7.431010e+05 Name: Count_uniq_users_per_country, dtype: float64
498 different categorical values, too many to plot. Anova - Kruskal-Wallis p_val to reject null: 0.0 H value: 146437.8 Effect size Eta^2: 0.14 ------------ Count_uniq_users_per_country by country_origin ------------ count 1.007692e+06 mean 5.534014e+05 std 3.181662e+05 min 1.000000e+00 25% 6.405800e+04 50% 7.431010e+05 75% 7.431010e+05 max 7.431010e+05 Name: Count_uniq_users_per_country, dtype: float64
188 different categorical values, too many to plot. Anova - Kruskal-Wallis p_val to reject null: 0.0 H value: 1007691.0 Effect size Eta^2: 1.0 ------------ Count_uniq_users_per_country by continent_destination ------------ count 1.007692e+06 mean 5.534014e+05 std 3.181662e+05 min 1.000000e+00 25% 6.405800e+04 50% 7.431010e+05 75% 7.431010e+05 max 7.431010e+05 Name: Count_uniq_users_per_country, dtype: float64
Anova - Kruskal-Wallis p_val to reject null: 0.0 H value: 84037.0 Effect size Eta^2: 0.08 ------------ Count_uniq_users_per_country by country_destination ------------ count 1.007692e+06 mean 5.534014e+05 std 3.181662e+05 min 1.000000e+00 25% 6.405800e+04 50% 7.431010e+05 75% 7.431010e+05 max 7.431010e+05 Name: Count_uniq_users_per_country, dtype: float64
209 different categorical values, too many to plot. Anova - Kruskal-Wallis p_val to reject null: 0.0 H value: 136805.64 Effect size Eta^2: 0.14 ------------ Count_uniq_users_per_country by Domestic or international ------------ count 1.007692e+06 mean 5.534014e+05 std 3.181662e+05 min 1.000000e+00 25% 6.405800e+04 50% 7.431010e+05 75% 7.431010e+05 max 7.431010e+05 Name: Count_uniq_users_per_country, dtype: float64
Paired t-test p_val to reject null: 0.0 t-statistic value: -572.35 Effect size Cohen's d: 1 ------------ Count_uniq_users_per_country by Region ------------ count 1.007692e+06 mean 5.534014e+05 std 3.181662e+05 min 1.000000e+00 25% 6.405800e+04 50% 7.431010e+05 75% 7.431010e+05 max 7.431010e+05 Name: Count_uniq_users_per_country, dtype: float64
Anova - Kruskal-Wallis p_val to reject null: 0.0 H value: 806833.27 Effect size Eta^2: 0.8 ------------ Count_uniq_users_per_country by IncomeGroup ------------ count 1.007692e+06 mean 5.534014e+05 std 3.181662e+05 min 1.000000e+00 25% 6.405800e+04 50% 7.431010e+05 75% 7.431010e+05 max 7.431010e+05 Name: Count_uniq_users_per_country, dtype: float64
Anova - Kruskal-Wallis p_val to reject null: 0.0 H value: 351544.97 Effect size Eta^2: 0.35 ------------ Count_uniq_users_per_country by outcome ------------ count 1.007692e+06 mean 5.534014e+05 std 3.181662e+05 min 1.000000e+00 25% 6.405800e+04 50% 7.431010e+05 75% 7.431010e+05 max 7.431010e+05 Name: Count_uniq_users_per_country, dtype: float64
Anova - Kruskal-Wallis p_val to reject null: 0.0 H value: 33.8 Effect size Eta^2: 0.0 ------------ Passengers carried Q1 by origin_city ------------ count 9.892330e+05 mean 1.710664e+08 std 8.932979e+07 min 1.121500e+03 25% 2.222555e+08 50% 2.222555e+08 75% 2.222555e+08 max 2.222555e+08 Name: Passengers carried Q1, dtype: float64
1324 different categorical values, too many to plot. Anova - Kruskal-Wallis p_val to reject null: nan H value: nan Effect size Eta^2: nan ------------ Passengers carried Q1 by destination_city ------------ count 9.892330e+05 mean 1.710664e+08 std 8.932979e+07 min 1.121500e+03 25% 2.222555e+08 50% 2.222555e+08 75% 2.222555e+08 max 2.222555e+08 Name: Passengers carried Q1, dtype: float64
1583 different categorical values, too many to plot. Anova - Kruskal-Wallis p_val to reject null: nan H value: nan Effect size Eta^2: nan ------------ Passengers carried Q1 by trip_type ------------ count 9.892330e+05 mean 1.710664e+08 std 8.932979e+07 min 1.121500e+03 25% 2.222555e+08 50% 2.222555e+08 75% 2.222555e+08 max 2.222555e+08 Name: Passengers carried Q1, dtype: float64
Paired t-test p_val to reject null: 0.0 t-statistic value: 74.17 Effect size Cohen's d: 0.19 ------------ Passengers carried Q1 by weekend ------------ count 9.892330e+05 mean 1.710664e+08 std 8.932979e+07 min 1.121500e+03 25% 2.222555e+08 50% 2.222555e+08 75% 2.222555e+08 max 2.222555e+08 Name: Passengers carried Q1, dtype: float64
Paired t-test p_val to reject null: 0.0 t-statistic value: -144.67 Effect size Cohen's d: 0.4 ------------ Passengers carried Q1 by filter_no_lcc ------------ count 9.892330e+05 mean 1.710664e+08 std 8.932979e+07 min 1.121500e+03 25% 2.222555e+08 50% 2.222555e+08 75% 2.222555e+08 max 2.222555e+08 Name: Passengers carried Q1, dtype: float64
Paired t-test p_val to reject null: 0.0 t-statistic value: -22.59 Effect size Cohen's d: 0.21 ------------ Passengers carried Q1 by filter_non_stop ------------ count 9.892330e+05 mean 1.710664e+08 std 8.932979e+07 min 1.121500e+03 25% 2.222555e+08 50% 2.222555e+08 75% 2.222555e+08 max 2.222555e+08 Name: Passengers carried Q1, dtype: float64
Paired t-test p_val to reject null: 0.0 t-statistic value: 34.38 Effect size Cohen's d: 0.12 ------------ Passengers carried Q1 by filter_short_layover ------------ count 9.892330e+05 mean 1.710664e+08 std 8.932979e+07 min 1.121500e+03 25% 2.222555e+08 50% 2.222555e+08 75% 2.222555e+08 max 2.222555e+08 Name: Passengers carried Q1, dtype: float64
Paired t-test p_val to reject null: 0.0 t-statistic value: 7.66 Effect size Cohen's d: 0.04 ------------ Passengers carried Q1 by filter_name ------------ count 9.892330e+05 mean 1.710664e+08 std 8.932979e+07 min 1.121500e+03 25% 2.222555e+08 50% 2.222555e+08 75% 2.222555e+08 max 2.222555e+08 Name: Passengers carried Q1, dtype: float64
Anova - Kruskal-Wallis p_val to reject null: 0.0 H value: 1559.59 Effect size Eta^2: 0.0 ------------ Passengers carried Q1 by first_rec ------------ count 9.892330e+05 mean 1.710664e+08 std 8.932979e+07 min 1.121500e+03 25% 2.222555e+08 50% 2.222555e+08 75% 2.222555e+08 max 2.222555e+08 Name: Passengers carried Q1, dtype: float64
Anova - Kruskal-Wallis p_val to reject null: 0.0 H value: 3260.83 Effect size Eta^2: 0.0 ------------ Passengers carried Q1 by last_rec ------------ count 9.892330e+05 mean 1.710664e+08 std 8.932979e+07 min 1.121500e+03 25% 2.222555e+08 50% 2.222555e+08 75% 2.222555e+08 max 2.222555e+08 Name: Passengers carried Q1, dtype: float64
Anova - Kruskal-Wallis p_val to reject null: 0.0 H value: 1799.56 Effect size Eta^2: 0.0 ------------ Passengers carried Q1 by is_session_1 ------------ count 9.892330e+05 mean 1.710664e+08 std 8.932979e+07 min 1.121500e+03 25% 2.222555e+08 50% 2.222555e+08 75% 2.222555e+08 max 2.222555e+08 Name: Passengers carried Q1, dtype: float64
Paired t-test p_val to reject null: 0.0 t-statistic value: -50.1 Effect size Cohen's d: 0.1 ------------ Passengers carried Q1 by Search or watch ------------ count 9.892330e+05 mean 1.710664e+08 std 8.932979e+07 min 1.121500e+03 25% 2.222555e+08 50% 2.222555e+08 75% 2.222555e+08 max 2.222555e+08 Name: Passengers carried Q1, dtype: float64
Paired t-test p_val to reject null: 0.0 t-statistic value: 5.85 Effect size Cohen's d: 0.01 ------------ Passengers carried Q1 by Use frequency ------------ count 9.892330e+05 mean 1.710664e+08 std 8.932979e+07 min 1.121500e+03 25% 2.222555e+08 50% 2.222555e+08 75% 2.222555e+08 max 2.222555e+08 Name: Passengers carried Q1, dtype: float64
Paired t-test p_val to reject null: 0.0 t-statistic value: -42.36 Effect size Cohen's d: 0.16 ------------ Passengers carried Q1 by continent_origin ------------ count 9.892330e+05 mean 1.710664e+08 std 8.932979e+07 min 1.121500e+03 25% 2.222555e+08 50% 2.222555e+08 75% 2.222555e+08 max 2.222555e+08 Name: Passengers carried Q1, dtype: float64
Anova - Kruskal-Wallis p_val to reject null: 0.0 H value: 535341.13 Effect size Eta^2: 0.54 ------------ Passengers carried Q1 by City origin ------------ count 9.892330e+05 mean 1.710664e+08 std 8.932979e+07 min 1.121500e+03 25% 2.222555e+08 50% 2.222555e+08 75% 2.222555e+08 max 2.222555e+08 Name: Passengers carried Q1, dtype: float64
1237 different categorical values, too many to plot. Anova - Kruskal-Wallis p_val to reject null: nan H value: nan Effect size Eta^2: nan ------------ Passengers carried Q1 by City destination ------------ count 9.892330e+05 mean 1.710664e+08 std 8.932979e+07 min 1.121500e+03 25% 2.222555e+08 50% 2.222555e+08 75% 2.222555e+08 max 2.222555e+08 Name: Passengers carried Q1, dtype: float64
1487 different categorical values, too many to plot. Anova - Kruskal-Wallis p_val to reject null: nan H value: nan Effect size Eta^2: nan ------------ Passengers carried Q1 by region_origin ------------ count 9.892330e+05 mean 1.710664e+08 std 8.932979e+07 min 1.121500e+03 25% 2.222555e+08 50% 2.222555e+08 75% 2.222555e+08 max 2.222555e+08 Name: Passengers carried Q1, dtype: float64
453 different categorical values, too many to plot. Anova - Kruskal-Wallis p_val to reject null: nan H value: nan Effect size Eta^2: nan ------------ Passengers carried Q1 by region_destination ------------ count 9.892330e+05 mean 1.710664e+08 std 8.932979e+07 min 1.121500e+03 25% 2.222555e+08 50% 2.222555e+08 75% 2.222555e+08 max 2.222555e+08 Name: Passengers carried Q1, dtype: float64
498 different categorical values, too many to plot. Anova - Kruskal-Wallis p_val to reject null: 0.0 H value: 139782.78 Effect size Eta^2: 0.14 ------------ Passengers carried Q1 by country_origin ------------ count 9.892330e+05 mean 1.710664e+08 std 8.932979e+07 min 1.121500e+03 25% 2.222555e+08 50% 2.222555e+08 75% 2.222555e+08 max 2.222555e+08 Name: Passengers carried Q1, dtype: float64
188 different categorical values, too many to plot. Anova - Kruskal-Wallis p_val to reject null: nan H value: nan Effect size Eta^2: nan ------------ Passengers carried Q1 by continent_destination ------------ count 9.892330e+05 mean 1.710664e+08 std 8.932979e+07 min 1.121500e+03 25% 2.222555e+08 50% 2.222555e+08 75% 2.222555e+08 max 2.222555e+08 Name: Passengers carried Q1, dtype: float64
Anova - Kruskal-Wallis p_val to reject null: 0.0 H value: 77861.72 Effect size Eta^2: 0.08 ------------ Passengers carried Q1 by country_destination ------------ count 9.892330e+05 mean 1.710664e+08 std 8.932979e+07 min 1.121500e+03 25% 2.222555e+08 50% 2.222555e+08 75% 2.222555e+08 max 2.222555e+08 Name: Passengers carried Q1, dtype: float64
209 different categorical values, too many to plot. Anova - Kruskal-Wallis p_val to reject null: 0.0 H value: 132743.64 Effect size Eta^2: 0.13 ------------ Passengers carried Q1 by Domestic or international ------------ count 9.892330e+05 mean 1.710664e+08 std 8.932979e+07 min 1.121500e+03 25% 2.222555e+08 50% 2.222555e+08 75% 2.222555e+08 max 2.222555e+08 Name: Passengers carried Q1, dtype: float64
Paired t-test p_val to reject null: 0.0 t-statistic value: -542.62 Effect size Cohen's d: 1 ------------ Passengers carried Q1 by Region ------------ count 9.892330e+05 mean 1.710664e+08 std 8.932979e+07 min 1.121500e+03 25% 2.222555e+08 50% 2.222555e+08 75% 2.222555e+08 max 2.222555e+08 Name: Passengers carried Q1, dtype: float64
Anova - Kruskal-Wallis p_val to reject null: nan H value: nan Effect size Eta^2: nan ------------ Passengers carried Q1 by IncomeGroup ------------ count 9.892330e+05 mean 1.710664e+08 std 8.932979e+07 min 1.121500e+03 25% 2.222555e+08 50% 2.222555e+08 75% 2.222555e+08 max 2.222555e+08 Name: Passengers carried Q1, dtype: float64
Anova - Kruskal-Wallis p_val to reject null: nan H value: nan Effect size Eta^2: nan ------------ Passengers carried Q1 by outcome ------------ count 9.892330e+05 mean 1.710664e+08 std 8.932979e+07 min 1.121500e+03 25% 2.222555e+08 50% 2.222555e+08 75% 2.222555e+08 max 2.222555e+08 Name: Passengers carried Q1, dtype: float64
Anova - Kruskal-Wallis p_val to reject null: 0.0 H value: 34.93 Effect size Eta^2: 0.0
In [30]:
numcat_correlations = numcat_correlations.fillna(0)
numcat_correlations.style.background_gradient(cmap='Blues', axis=None)
Out[30]:
| stay | status_updates | total_notifs | total_buy_notifs | first_total | first_buy_total | lowest_total | session | first_buy - lowest_total | Adult population | Count_uniq_users_per_country | Passengers carried Q1 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| origin_city | 0 | 0.00332128 | 0 | 0 | 0 | 0 | 0 | 0.0150028 | 0 | 0 | 1 | 0 |
| destination_city | 0 | 0.0059888 | 0 | 0 | 0 | 0 | 0 | 0.00714585 | 0 | 0 | 0.179056 | 0 |
| trip_type | 0 | 0 | 0.112452 | 0.106473 | 0.583929 | 0.637136 | 0.57819 | 0.159936 | 0.0812652 | 0.123811 | 0.215932 | 0.193932 |
| weekend | 0.684679 | 0.0824399 | 0.110737 | 0.103419 | 0.538623 | 0.51105 | 0.544071 | 0.0173271 | 0.014708 | 0.290729 | 0.39639 | 0.395655 |
| filter_no_lcc | 0.111479 | 0.372551 | 0.057119 | 0.0233623 | 0.0926168 | 0 | 0.100905 | 0.0317537 | 0.083062 | 0.163234 | 0.213423 | 0.212988 |
| filter_non_stop | 0.139014 | 0.390681 | 0.237666 | 0.176829 | 0.154903 | 0.139313 | 0.17182 | 0.0346863 | 0.0881347 | 0.0872697 | 0.121072 | 0.116993 |
| filter_short_layover | 0.171566 | 0.356703 | 0.160511 | 0.0976049 | 0.456049 | 0.459056 | 0.439535 | 0.0810463 | 0.136973 | 0 | 0.0374698 | 0.0442471 |
| filter_name | 0.00693749 | 0.0297565 | 0.0165397 | 0.00926416 | 0.0130162 | 0.0117754 | 0.013422 | 0.000434047 | 0.0057872 | 0.0012755 | 0.00169305 | 0.00157152 |
| first_rec | 0.000172745 | 0.0263566 | 0 | 0 | 0 | 0 | 0 | 0.00366203 | 0 | 0.00296992 | 0.00470633 | 0.00329431 |
| last_rec | 0.000317748 | 0.0105668 | 0 | 0 | 0 | 0 | 0 | 0.00365626 | 0 | 0.00161048 | 0.00269264 | 0.00181713 |
| is_session_1 | 0.018642 | 0.277057 | 0.261587 | 0.222544 | 0.0574585 | 0.0568244 | 0.0433786 | 1 | 0.0895381 | 0.0681421 | 0.100039 | 0.100783 |
| Search or watch | 0.00695598 | 1 | 1 | 0.895893 | 0.0386801 | 0.0259027 | 0.124391 | 0.275585 | 0.360249 | 0.00582045 | 0.0110455 | 0.0123568 |
| Use frequency | 0.024059 | 0.190557 | 0.254328 | 0.227433 | 0.0552827 | 0.0525285 | 0.0413538 | 0.941508 | 0.0832179 | 0.109622 | 0.1579 | 0.15901 |
| continent_origin | 0.033998 | 0.000299127 | 0.000207222 | 0.000149507 | 0.02309 | 0.0296428 | 0.0233317 | 0.00277105 | 0.000668066 | 0.395547 | 0.586477 | 0.541166 |
| City origin | 0 | 0.00275011 | 0 | 0 | 0 | 0 | 0 | 0.0143234 | 0 | 0 | 0.981205 | 0 |
| City destination | 0 | 0.00566344 | 0 | 0 | 0 | 0 | 0 | 0.00649934 | 0 | 0 | 0.172225 | 0 |
| region_origin | 0 | 0.00175834 | 0 | 0 | 0 | 0 | 0 | 0.0111823 | 0 | 0.940158 | 0.945042 | 0 |
| region_destination | 0 | 0.0041197 | 0.0027091 | 0.00196118 | 0.28323 | 0 | 0.286469 | 0.00430035 | 0 | 0.113964 | 0.144898 | 0.140873 |
| country_origin | 0 | 0.001305 | 0 | 0 | 0 | 0 | 0 | 0.00711964 | 0 | 0 | 1 | 0 |
| continent_destination | 0.198041 | 0.00164631 | 0.000641784 | 0.00049596 | 0.183729 | 0.154035 | 0.186334 | 0.000417032 | 0.000271742 | 0.0591565 | 0.0833911 | 0.0787046 |
| country_destination | 0.240221 | 0.0032181 | 0.00194726 | 0.00149327 | 0.2479 | 0 | 0.250959 | 0.00251591 | 0 | 0.103442 | 0.135583 | 0.134006 |
| Domestic or international | 0.572023 | 0.0557807 | 0.0451278 | 0.0427133 | 1 | 0.984622 | 1 | 0.0102012 | 0.0782495 | 0.857869 | 1 | 1 |
| Region | 0.0408892 | 0.000295541 | 0.000287851 | 7.39861e-05 | 0.0172065 | 0.0217437 | 0.0175352 | 0.00204839 | 0.00074042 | 0.66361 | 0.800674 | 0 |
| IncomeGroup | 0.023031 | 0.000159634 | 0.00011672 | 3.48849e-05 | 0.013314 | 0.0126241 | 0.0135027 | 0.000548522 | 8.22999e-05 | 0.226038 | 0.348859 | 0 |
| outcome | 0.0025015 | 0.917676 | 0.634316 | 0.405851 | 0.00277916 | 0.00283824 | 0.00462448 | 0.0240947 | 0.25455 | 1.96906e-05 | 3.15623e-05 | 3.32909e-05 |
Example of how to use the data presented in these tables¶
total_notifs vs outcome
In the table of numerical vs categorical variables, we see that for:
- outcome vs. total_notifs, eta\^2 = 0.63
This is fairly high, so we investigate what may be causing it.
In [31]:
numcat_correlations.iloc[24:, 0:5].style.background_gradient(cmap='Blues', axis=None)
Out[31]:
| stay | status_updates | total_notifs | total_buy_notifs | first_total | |
|---|---|---|---|---|---|
| outcome | 0.0025015 | 0.917676 | 0.634316 | 0.405851 | 0.00277916 |
a) Subset only "watches"
Since we know that 99% of the transactions that are *Search* are also *gained*, there is little information to be gained from the *search* rows. Test restricting the analysis to the subset of entries that are *Watch* and leave *Search* out.
b) Subset only "gained" or "lost"
Also, since we are interested in **outcome** that is only either *gained* or *lost*, we can also filter out those transactions for which the outcome is still unkown, coded as *expected*.
c) Subset only "watches" AND Subset only "gained" or "lost"
In [32]:
# Conditions
watch = df['Search or watch']=='watch'
gain_lost = (df['outcome'] == 'gained') | (df['outcome'] == 'lost')
In [33]:
summarize_numerical_categorical(df[watch], num = "total_notifs", cat = "outcome", summary=False, hist=False)
------------ total_notifs by outcome ------------
Anova - Kruskal-Wallis p_val to reject null: 0.0 H value: 32606.13 Effect size Eta^2: 0.1
Out[33]:
(0.0, 0.09505439705830593)
Eta^2 has now reduced from 0.63 to 0.1.
Unsurprising, since search rows always have 0 total_notifs.
In [34]:
df['total_notifs'][df['Search or watch']=='search'].describe()
Out[34]:
count 606521.0 mean 0.0 std 0.0 min 0.0 25% 0.0 50% 0.0 75% 0.0 max 0.0 Name: total_notifs, dtype: float64
b) Only gained and lost.
In [35]:
summarize_numerical_categorical(df[gain_lost], num = "total_notifs", cat = "outcome", summary=False, hist=False)
------------ total_notifs by outcome ------------
Paired t-test p_val to reject null: 0.0 t-statistic value: 538.21 Effect size Cohen's d: 0.98
Out[35]:
(0.0, 0.9758774311114617)
This is a remarkable effect size, when we exclude expected we see that total_notifs has a big impact on whether the outcome is gained or lost.
HOWEVER, this is being caused by the fact that search and watch entries are combined. The fact that total_notifs is always 0 for the search entries and that most gained entries are also search, pulls the mean of gained down, thus creating a larger difference than what is owed exclusively to any effects of total_notfs on outcome.
c) Subset only "watches" AND Subset only "gained" or "lost"
In [36]:
summarize_numerical_categorical(df[gain_lost & watch], num = "total_notifs", cat = "outcome", summary=False, hist=False)
------------ total_notifs by outcome ------------
Paired t-test p_val to reject null: 0.0 t-statistic value: 26.75 Effect size Cohen's d: 0.69
Out[36]:
(2.4723858106533087e-157, 0.6888764109407535)
In [37]:
df['Search or watch'][gain_lost & watch].value_counts()
Out[37]:
watch 215273 search 0 Name: Search or watch, dtype: int64
In [38]:
df['outcome'][gain_lost & watch].value_counts()
Out[38]:
lost 212463 gained 2810 expected 0 Name: outcome, dtype: int64