Selecting columns from Pandas DataFrame

Selecting column or columns from a Pandas DataFrame is one of the most frequently performed tasks while manipulating data. Pandas provides several technique to efficiently retrieve subsets of data from your DataFrame. The Python indexing operators '[]' and attribute operator '.' allows simple and fast access to DataFrame across a wide range of use cases. Following article will discuss different ways to work with a DataFrame that has a large number of columns.

Create a DataFrame with data

 
import pandas as pd
import numpy as np
df = pd.DataFrame()
df['Name'] = ['John', 'Doe', 'Bill','Jim','Harry','Ben']
df['TotalMarks'] = [82, 38, 63,22,55,40]
df['Grade'] = ['A', 'E', 'B','E','C','D']
df['Promoted'] = [True, False,True,False,True,True]

 
    Name  TotalMarks Grade  Promoted
0   John          82     A      True
1    Doe          38     E     False
2   Bill          63     B      True
3    Jim          22     E     False
4  Harry          55     C      True
5    Ben          40     D      True

Selecting single column from Pandas DataFrame

You can apply Python selection filters to the DataFrame itself, to select a single column to work with.

 
 df['Name']

 
0     John
1      Doe
2     Bill
3      Jim
4    Harry
5      Ben

Selecting multiple column from Pandas DataFrame

When you select multiple columns from DataFrame, use a list of column names within the selection brackets [].

 
df[['Name','TotalMarks']]

 
    Name  TotalMarks
0   John          82
1    Doe          38
2   Bill          63
3    Jim          22
4  Harry          55
5    Ben          40

Here the inner square brackets [] define a Python list with column names from DataFrame, whereas the outer brackets[] are used to select the data from a DataFrame .

If you want to get dimensionality of the DataFrame

 
df[['Name','TotalMarks']].shape

 
(6, 2)

Selecting range of columns

 
#select second and third columns with all rows
df[df.columns[1:3]]

 
   TotalMarks Grade
0          82     A
1          38     E
2          63     B
3          22     E
4          55     C
5          40     D

Select two column with first 3 rows

DataFrame.loc access a group of rows and columns by label(s) or a boolean array .

 
df.loc[0:2, 'Name':'TotalMarks']

 
   Name  TotalMarks
0  John          82
1   Doe          38
2  Bill          63

Select all column with first row

 
df.loc[0, :]

 
Name          John
TotalMarks      82
Grade            A
Promoted      True

how to Select columns values from Pandas DataFrame

Select all rows with first three column

 
df.iloc[:, 0:3]

 
    Name  TotalMarks Grade
0   John          82     A
1    Doe          38     E
2   Bill          63     B
3    Jim          22     E
4  Harry          55     C
5    Ben          40     D

Select first three rows with first four column

 
df.iloc[0:3, 0:4]

 
   Name  TotalMarks Grade  Promoted
0  John          82     A      True
1   Doe          38     E     False
2  Bill          63     B      True

Next : Selecting multiple columns in a Pandas dataframe based on condition