[Pandas][DataFrame]S2_09_Broadcasting

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 [3]:

# broadcasting : assume that we want to update the all column data
bank_df['Balance'] = bank_df['Balance'] + 1000

 

In [4]:

bank_df

 

Out[4]:

	RowNumber	CustomerId	Surname	CreditScore	Geography	Gender	Age	Tenure	Balance	NumOfProducts	HasCrCard	IsActiveMember	EstimatedSalary	Exited
0	1	15634602	Hargrave	619	France	Female	42	2	1000.00	1	1	1	101348.88	1
1	2	15647311	Hill	608	Spain	Female	41	1	84807.86	1	0	1	112542.58	0
2	3	15619304	Onio	502	France	Female	42	8	160660.80	3	1	0	113931.57	1
3	4	15701354	Boni	699	France	Female	39	1	1000.00	2	0	0	93826.63	0
4	5	15737888	Mitchell	850	Spain	Female	43	2	126510.82	1	1	1	79084.10	0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
9995	9996	15606229	Obijiaku	771	France	Male	39	5	1000.00	2	1	0	96270.64	0
9996	9997	15569892	Johnstone	516	France	Male	35	10	58369.61	1	1	1	101699.77	0
9997	9998	15584532	Liu	709	France	Female	36	7	1000.00	1	0	1	42085.58	1
9998	9999	15682355	Sabbatini	772	Germany	Male	42	3	76075.31	2	1	0	92888.52	1
9999	10000	15628319	Walker	792	France	Female	28	4	131142.79	1	1	0	38190.78	0

10000 rows × 14 columns

 

In [5]:

bank_df['Balance'] = bank_df['Balance'].add(1000); bank_df

 

Out[5]:

	RowNumber	CustomerId	Surname	CreditScore	Geography	Gender	Age	Tenure	Balance	NumOfProducts	HasCrCard	IsActiveMember	EstimatedSalary	Exited
0	1	15634602	Hargrave	619	France	Female	42	2	2000.00	1	1	1	101348.88	1
1	2	15647311	Hill	608	Spain	Female	41	1	85807.86	1	0	1	112542.58	0
2	3	15619304	Onio	502	France	Female	42	8	161660.80	3	1	0	113931.57	1
3	4	15701354	Boni	699	France	Female	39	1	2000.00	2	0	0	93826.63	0
4	5	15737888	Mitchell	850	Spain	Female	43	2	127510.82	1	1	1	79084.10	0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
9995	9996	15606229	Obijiaku	771	France	Male	39	5	2000.00	2	1	0	96270.64	0
9996	9997	15569892	Johnstone	516	France	Male	35	10	59369.61	1	1	1	101699.77	0
9997	9998	15584532	Liu	709	France	Female	36	7	2000.00	1	0	1	42085.58	1
9998	9999	15682355	Sabbatini	772	Germany	Male	42	3	77075.31	2	1	0	92888.52	1
9999	10000	15628319	Walker	792	France	Female	28	4	132142.79	1	1	0	38190.78	0

10000 rows × 14 columns

 

In [6]:

# 1usd = 1240krw
bank_df['Balance (krw)'] = bank_df['Balance'].mul(1240);bank_df

 

Out[6]:

	RowNumber	CustomerId	Surname	CreditScore	Geography	Gender	Age	Tenure	Balance	NumOfProducts	HasCrCard	IsActiveMember	EstimatedSalary	Exited	Balance (krw)
0	1	15634602	Hargrave	619	France	Female	42	2	2000.00	1	1	1	101348.88	1	2480000.0
1	2	15647311	Hill	608	Spain	Female	41	1	85807.86	1	0	1	112542.58	0	106401746.4
2	3	15619304	Onio	502	France	Female	42	8	161660.80	3	1	0	113931.57	1	200459392.0
3	4	15701354	Boni	699	France	Female	39	1	2000.00	2	0	0	93826.63	0	2480000.0
4	5	15737888	Mitchell	850	Spain	Female	43	2	127510.82	1	1	1	79084.10	0	158113416.8
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
9995	9996	15606229	Obijiaku	771	France	Male	39	5	2000.00	2	1	0	96270.64	0	2480000.0
9996	9997	15569892	Johnstone	516	France	Male	35	10	59369.61	1	1	1	101699.77	0	73618316.4
9997	9998	15584532	Liu	709	France	Female	36	7	2000.00	1	0	1	42085.58	1	2480000.0
9998	9999	15682355	Sabbatini	772	Germany	Male	42	3	77075.31	2	1	0	92888.52	1	95573384.4
9999	10000	15628319	Walker	792	France	Female	28	4	132142.79	1	1	0	38190.78	0	163857059.6

10000 rows × 15 columns

 

In [7]:

# update the tenure of a given customer
bank_df.iloc[1,7] = 3
bank_df

 

Out[7]:

	RowNumber	CustomerId	Surname	CreditScore	Geography	Gender	Age	Tenure	Balance	NumOfProducts	HasCrCard	IsActiveMember	EstimatedSalary	Exited	Balance (krw)
0	1	15634602	Hargrave	619	France	Female	42	2	2000.00	1	1	1	101348.88	1	2480000.0
1	2	15647311	Hill	608	Spain	Female	41	3	85807.86	1	0	1	112542.58	0	106401746.4
2	3	15619304	Onio	502	France	Female	42	8	161660.80	3	1	0	113931.57	1	200459392.0
3	4	15701354	Boni	699	France	Female	39	1	2000.00	2	0	0	93826.63	0	2480000.0
4	5	15737888	Mitchell	850	Spain	Female	43	2	127510.82	1	1	1	79084.10	0	158113416.8
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
9995	9996	15606229	Obijiaku	771	France	Male	39	5	2000.00	2	1	0	96270.64	0	2480000.0
9996	9997	15569892	Johnstone	516	France	Male	35	10	59369.61	1	1	1	101699.77	0	73618316.4
9997	9998	15584532	Liu	709	France	Female	36	7	2000.00	1	0	1	42085.58	1	2480000.0
9998	9999	15682355	Sabbatini	772	Germany	Male	42	3	77075.31	2	1	0	92888.52	1	95573384.4
9999	10000	15628319	Walker	792	France	Female	28	4	132142.79	1	1	0	38190.78	0	163857059.6

10000 rows × 15 columns

 

In [8]:

# update the Balance of two clients
bank_df.iloc[[0,3], [8]] = [6000, 12000]
bank_df

 

Out[8]:

	RowNumber	CustomerId	Surname	CreditScore	Geography	Gender	Age	Tenure	Balance	NumOfProducts	HasCrCard	IsActiveMember	EstimatedSalary	Exited	Balance (krw)
0	1	15634602	Hargrave	619	France	Female	42	2	6000.00	1	1	1	101348.88	1	2480000.0
1	2	15647311	Hill	608	Spain	Female	41	3	85807.86	1	0	1	112542.58	0	106401746.4
2	3	15619304	Onio	502	France	Female	42	8	161660.80	3	1	0	113931.57	1	200459392.0
3	4	15701354	Boni	699	France	Female	39	1	12000.00	2	0	0	93826.63	0	2480000.0
4	5	15737888	Mitchell	850	Spain	Female	43	2	127510.82	1	1	1	79084.10	0	158113416.8
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
9995	9996	15606229	Obijiaku	771	France	Male	39	5	2000.00	2	1	0	96270.64	0	2480000.0
9996	9997	15569892	Johnstone	516	France	Male	35	10	59369.61	1	1	1	101699.77	0	73618316.4
9997	9998	15584532	Liu	709	France	Female	36	7	2000.00	1	0	1	42085.58	1	2480000.0
9998	9999	15682355	Sabbatini	772	Germany	Male	42	3	77075.31	2	1	0	92888.52	1	95573384.4
9999	10000	15628319	Walker	792	France	Female	28	4	132142.79	1	1	0	38190.78	0	163857059.6

10000 rows × 15 columns