Cross-tabulation (or crosstab)

- Crosstab -> pandas.crosstab(index, columns, values=None, rownames=None, 
                         colnames=None, aggfunc=None, margins=False, dropna=True)


- A frequency table of the factors unless an array of values and an aggregation function are passed

Crosstab

import pandas as pd
import numpy as np

df = pd.read_csv('tele_customers')

df.head()

	customerID	gender	SeniorCitizen	Partner	Dependents	tenure	PhoneService	MultipleLines	InternetService	OnlineSecurity	...	DeviceProtection	TechSupport	StreamingTV	StreamingMovies	Contract	PaperlessBilling	PaymentMethod	MonthlyCharges	TotalCharges	Churn
0	7590-VHVEG	Female	0	Yes	No	1	No	No phone service	DSL	No	...	No	No	No	No	Month-to-month	Yes	Electronic check	29.85	29.85	No
1	5575-GNVDE	Male	0	No	No	34	Yes	No	DSL	Yes	...	Yes	No	No	No	One year	No	Mailed check	56.95	1889.5	No
2	3668-QPYBK	Male	0	No	No	2	Yes	No	DSL	Yes	...	No	No	No	No	Month-to-month	Yes	Mailed check	53.85	108.15	Yes
3	7795-CFOCW	Male	0	No	No	45	No	No phone service	DSL	Yes	...	Yes	Yes	No	No	One year	No	Bank transfer (automatic)	42.30	1840.75	No
4	9237-HQITU	Female	0	No	No	2	Yes	No	Fiber optic	No	...	No	No	No	No	Month-to-month	Yes	Electronic check	70.70	151.65	Yes

5 rows × 21 columns

df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 7043 entries, 0 to 7042
Data columns (total 21 columns):
 #   Column            Non-Null Count  Dtype  
---  ------            --------------  -----  
 0   customerID        7043 non-null   object 
 1   gender            7043 non-null   object 
 2   SeniorCitizen     7043 non-null   int64  
 3   Partner           7043 non-null   object 
 4   Dependents        7043 non-null   object 
 5   tenure            7043 non-null   int64  
 6   PhoneService      7043 non-null   object 
 7   MultipleLines     7043 non-null   object 
 8   InternetService   7043 non-null   object 
 9   OnlineSecurity    7043 non-null   object 
 10  OnlineBackup      7043 non-null   object 
 11  DeviceProtection  7043 non-null   object 
 12  TechSupport       7043 non-null   object 
 13  StreamingTV       7043 non-null   object 
 14  StreamingMovies   7043 non-null   object 
 15  Contract          7043 non-null   object 
 16  PaperlessBilling  7043 non-null   object 
 17  PaymentMethod     7043 non-null   object 
 18  MonthlyCharges    7043 non-null   float64
 19  TotalCharges      7043 non-null   object 
 20  Churn             7043 non-null   object 
dtypes: float64(1), int64(2), object(18)
memory usage: 1.1+ MB

Removing Numeric Columns

df.drop(['customerID','MonthlyCharges', 'TotalCharges', 'tenure'], axis=1, inplace=True)

df.head()

	gender	SeniorCitizen	Partner	Dependents	PhoneService	MultipleLines	InternetService	OnlineSecurity	OnlineBackup	DeviceProtection	TechSupport	StreamingTV	StreamingMovies	Contract	PaperlessBilling	PaymentMethod	Churn
0	Female	0	Yes	No	No	No phone service	DSL	No	Yes	No	No	No	No	Month-to-month	Yes	Electronic check	No
1	Male	0	No	No	Yes	No	DSL	Yes	No	Yes	No	No	No	One year	No	Mailed check	No
2	Male	0	No	No	Yes	No	DSL	Yes	Yes	No	No	No	No	Month-to-month	Yes	Mailed check	Yes
3	Male	0	No	No	No	No phone service	DSL	Yes	No	Yes	Yes	No	No	One year	No	Bank transfer (automatic)	No
4	Female	0	No	No	Yes	No	Fiber optic	No	No	No	No	No	No	Month-to-month	Yes	Electronic check	Yes

Crosstab

crosstab = pd.concat([pd.crosstab(df[x], df.Churn) for x in df.columns[:-1]], keys = df.columns[:-1])

crosstab

	Churn	No	Yes
gender	Female	2549	939
	Male	2625	930
SeniorCitizen	0	4508	1393
	1	666	476
Partner	No	2441	1200
	Yes	2733	669
Dependents	No	3390	1543
	Yes	1784	326
PhoneService	No	512	170
	Yes	4662	1699
MultipleLines	No	2541	849
	No phone service	512	170
	Yes	2121	850
InternetService	DSL	1962	459
	Fiber optic	1799	1297
	No	1413	113
OnlineSecurity	No	2037	1461
	No internet service	1413	113
	Yes	1724	295
OnlineBackup	No	1855	1233
	No internet service	1413	113
	Yes	1906	523
DeviceProtection	No	1884	1211
	No internet service	1413	113
	Yes	1877	545
TechSupport	No	2027	1446
	No internet service	1413	113
	Yes	1734	310
StreamingTV	No	1868	942
	No internet service	1413	113
	Yes	1893	814
StreamingMovies	No	1847	938
	No internet service	1413	113
	Yes	1914	818
Contract	Month-to-month	2220	1655
	One year	1307	166
	Two year	1647	48
PaperlessBilling	No	2403	469
	Yes	2771	1400
PaymentMethod	Bank transfer (automatic)	1286	258
	Credit card (automatic)	1290	232
	Electronic check	1294	1071
	Mailed check	1304	308

Adding a Percentage Column

crosstab['Churn_Percentage'] = crosstab['Yes']*100 / (crosstab['No'] + crosstab['Yes'])

crosstab

	Churn	No	Yes	Churn_Percentage
gender	Female	2549	939	26.920872
	Male	2625	930	26.160338
SeniorCitizen	0	4508	1393	23.606168
	1	666	476	41.681261
Partner	No	2441	1200	32.957979
	Yes	2733	669	19.664903
Dependents	No	3390	1543	31.279140
	Yes	1784	326	15.450237
PhoneService	No	512	170	24.926686
	Yes	4662	1699	26.709637
MultipleLines	No	2541	849	25.044248
	No phone service	512	170	24.926686
	Yes	2121	850	28.609896
InternetService	DSL	1962	459	18.959108
	Fiber optic	1799	1297	41.892765
	No	1413	113	7.404980
OnlineSecurity	No	2037	1461	41.766724
	No internet service	1413	113	7.404980
	Yes	1724	295	14.611194
OnlineBackup	No	1855	1233	39.928756
	No internet service	1413	113	7.404980
	Yes	1906	523	21.531494
DeviceProtection	No	1884	1211	39.127625
	No internet service	1413	113	7.404980
	Yes	1877	545	22.502064
TechSupport	No	2027	1446	41.635474
	No internet service	1413	113	7.404980
	Yes	1734	310	15.166341
StreamingTV	No	1868	942	33.523132
	No internet service	1413	113	7.404980
	Yes	1893	814	30.070188
StreamingMovies	No	1847	938	33.680431
	No internet service	1413	113	7.404980
	Yes	1914	818	29.941435
Contract	Month-to-month	2220	1655	42.709677
	One year	1307	166	11.269518
	Two year	1647	48	2.831858
PaperlessBilling	No	2403	469	16.330084
	Yes	2771	1400	33.565092
PaymentMethod	Bank transfer (automatic)	1286	258	16.709845
	Credit card (automatic)	1290	232	15.243101
	Electronic check	1294	1071	45.285412
	Mailed check	1304	308	19.106700