[Pandas][DataFrame]S2_11_define_functions

bank customers.csv

0.65MB

 

 

In [1]:

import pandas as pd

 

In [2]:

bank_df = pd.read_csv('bank customers.csv')
bank_df

 

Out[2]:

	RowNumber	CustomerId	Surname	CreditScore	Geography	Gender	Age	Tenure	Balance	NumOfProducts	HasCrCard	IsActiveMember	EstimatedSalary	Exited
0	1	15634602	Hargrave	619	France	Female	42	2	0.00	1	1	1	101348.88	1
1	2	15647311	Hill	608	Spain	Female	41	1	83807.86	1	0	1	112542.58	0
2	3	15619304	Onio	502	France	Female	42	8	159660.80	3	1	0	113931.57	1
3	4	15701354	Boni	699	France	Female	39	1	0.00	2	0	0	93826.63	0
4	5	15737888	Mitchell	850	Spain	Female	43	2	125510.82	1	1	1	79084.10	0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
9995	9996	15606229	Obijiaku	771	France	Male	39	5	0.00	2	1	0	96270.64	0
9996	9997	15569892	Johnstone	516	France	Male	35	10	57369.61	1	1	1	101699.77	0
9997	9998	15584532	Liu	709	France	Female	36	7	0.00	1	0	1	42085.58	1
9998	9999	15682355	Sabbatini	772	Germany	Male	42	3	75075.31	2	1	0	92888.52	1
9999	10000	15628319	Walker	792	France	Female	28	4	130142.79	1	1	0	38190.78	0

10000 rows × 14 columns

 

In [9]:

# A function that increases all clients balance by a fixed value of 10%
def Balance_update(balance):
  return balance * 1.1

 

In [10]:

bank_df['Balance'] = bank_df['Balance'].apply(Balance_update)
bank_df

 

Out[10]:

	RowNumber	CustomerId	Surname	CreditScore	Geography	Gender	Age	Tenure	Balance	NumOfProducts	HasCrCard	IsActiveMember	EstimatedSalary	Exited	Rank
0	1	15634602	Hargrave	619	France	Female	42	2	0.000	1	1	1	101348.88	1	1
1	2	15647311	Hill	608	Spain	Female	41	1	92188.646	1	0	1	112542.58	0	744
2	3	15619304	Onio	502	France	Female	42	8	175626.880	3	1	0	113931.57	1	5794
3	4	15701354	Boni	699	France	Female	39	1	0.000	2	0	0	93826.63	0	1
4	5	15737888	Mitchell	850	Spain	Female	43	2	138061.902	1	1	1	79084.10	0	3697
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
9995	9996	15606229	Obijiaku	771	France	Male	39	5	0.000	2	1	0	96270.64	0	1
9996	9997	15569892	Johnstone	516	France	Male	35	10	63106.571	1	1	1	101699.77	0	125
9997	9998	15584532	Liu	709	France	Female	36	7	0.000	1	0	1	42085.58	1	1
9998	9999	15682355	Sabbatini	772	Germany	Male	42	3	82582.841	2	1	0	92888.52	1	428
9999	10000	15628319	Walker	792	France	Female	28	4	143157.069	1	1	0	38190.78	0	4113

10000 rows × 15 columns

 

In [11]:

bank_df['Name Length'] = bank_df['Surname'].apply(len)
bank_df

 

Out[11]:

	RowNumber	CustomerId	Surname	CreditScore	Geography	Gender	Age	Tenure	Balance	NumOfProducts	HasCrCard	IsActiveMember	EstimatedSalary	Exited	Rank	Name Length
0	1	15634602	Hargrave	619	France	Female	42	2	0.000	1	1	1	101348.88	1	1	8
1	2	15647311	Hill	608	Spain	Female	41	1	92188.646	1	0	1	112542.58	0	744	4
2	3	15619304	Onio	502	France	Female	42	8	175626.880	3	1	0	113931.57	1	5794	4
3	4	15701354	Boni	699	France	Female	39	1	0.000	2	0	0	93826.63	0	1	4
4	5	15737888	Mitchell	850	Spain	Female	43	2	138061.902	1	1	1	79084.10	0	3697	8
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
9995	9996	15606229	Obijiaku	771	France	Male	39	5	0.000	2	1	0	96270.64	0	1	8
9996	9997	15569892	Johnstone	516	France	Male	35	10	63106.571	1	1	1	101699.77	0	125	9
9997	9998	15584532	Liu	709	France	Female	36	7	0.000	1	0	1	42085.58	1	1	3
9998	9999	15682355	Sabbatini	772	Germany	Male	42	3	82582.841	2	1	0	92888.52	1	428	9
9999	10000	15628319	Walker	792	France	Female	28	4	143157.069	1	1	0	38190.78	0	4113	6

10000 rows × 16 columns