[Pandas][DataFrame]S2_05_selecting_columns

bank customers.csv

0.65MB

 

 

 

데이터 출처:

https://www.kaggle.com/datasets/mathchi/churn-for-bank-customers?resource=download

 

In [1]:

import pandas as pd

 

In [3]:

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

 

Out[3]:

	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 [5]:

# Return a column
# The output will be a Pandas Series
sample = bank_df["Surname"]; sample

 

Out[5]:

0        Hargrave
1            Hill
2            Onio
3            Boni
4        Mitchell
          ...    
9995     Obijiaku
9996    Johnstone
9997          Liu
9998    Sabbatini
9999       Walker
Name: Surname, Length: 10000, dtype: object

 

In [6]:

# Alternatively, using dot
# 컬럼 이름에 spaces가 있다면 작동되지 않는다.
bank_df.Surname

 

Out[6]:

0        Hargrave
1            Hill
2            Onio
3            Boni
4        Mitchell
          ...    
9995     Obijiaku
9996    Johnstone
9997          Liu
9998    Sabbatini
9999       Walker
Name: Surname, Length: 10000, dtype: object

 

In [7]:

# select multiple columns
# A list containing all column names that you would like to select
sample = bank_df[ ['Surname','Age','Balance'] ]
sample

 

Out[7]:

	Surname	Age	Balance
0	Hargrave	42	0.00
1	Hill	41	83807.86
2	Onio	42	159660.80
3	Boni	39	0.00
4	Mitchell	43	125510.82
...	...	...	...
9995	Obijiaku	39	0.00
9996	Johnstone	35	57369.61
9997	Liu	36	0.00
9998	Sabbatini	42	75075.31
9999	Walker	28	130142.79

10000 rows × 3 columns

 

In [9]:

# Alternatively, A list first and then use it to select columns
selected_columns = ['Surname','Balance']
sample = bank_df[selected_columns]
sample

 

Out[9]:

	Surname	Balance
0	Hargrave	0.00
1	Hill	83807.86
2	Onio	159660.80
3	Boni	0.00
4	Mitchell	125510.82
...	...	...
9995	Obijiaku	0.00
9996	Johnstone	57369.61
9997	Liu	0.00
9998	Sabbatini	75075.31
9999	Walker	130142.79

10000 rows × 2 columns

 

In [10]:

# Accesing a given row
bank_df[0:2]

 

Out[10]:

	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