How to drop one or multiple columns in Pandas Dataframe

Last Updated : 10 Dec, 2018

Let’s discuss how to drop one or multiple columns in Pandas Dataframe. Drop one or more than one columns from a DataFrame can be achieved in multiple ways.

Create a simple dataframe with dictionary of lists, say column names are A, B, C, D, E.

# Import pandas package 
import pandas as pd
  
# create a dictionary with five fields each
data = {
    'A':['A1', 'A2', 'A3', 'A4', 'A5'], 
    'B':['B1', 'B2', 'B3', 'B4', 'B5'], 
    'C':['C1', 'C2', 'C3', 'C4', 'C5'], 
    'D':['D1', 'D2', 'D3', 'D4', 'D5'], 
    'E':['E1', 'E2', 'E3', 'E4', 'E5'] }
  
# Convert the dictionary into DataFrame 
df = pd.DataFrame(data)
  
df

Output:

Method #1: Drop Columns from a Dataframe using drop() method.

Remove specific single column.

# Import pandas package 
import pandas as pd
  
# create a dictionary with five fields each 
data = {
    'A':['A1', 'A2', 'A3', 'A4', 'A5'], 
    'B':['B1', 'B2', 'B3', 'B4', 'B5'], 
    'C':['C1', 'C2', 'C3', 'C4', 'C5'], 
    'D':['D1', 'D2', 'D3', 'D4', 'D5'], 
    'E':['E1', 'E2', 'E3', 'E4', 'E5'] }
  
# Convert the dictionary into DataFrame 
df = pd.DataFrame(data)
  
# Remove column name 'A'
df.drop(['A'], axis = 1)

Output:

Remove specific multiple columns.

# Import pandas package 
import pandas as pd
  
# create a dictionary with five fields each
data = {
    'A':['A1', 'A2', 'A3', 'A4', 'A5'], 
    'B':['B1', 'B2', 'B3', 'B4', 'B5'], 
    'C':['C1', 'C2', 'C3', 'C4', 'C5'], 
    'D':['D1', 'D2', 'D3', 'D4', 'D5'], 
    'E':['E1', 'E2', 'E3', 'E4', 'E5'] }
  
# Convert the dictionary into DataFrame 
df = pd.DataFrame(data)
  
# Remove two columns name is 'C' and 'D'
df.drop(['C', 'D'], axis = 1)
  
# df.drop(columns =['C', 'D'])

Output:

Remove columns as based on column index.

# Import pandas package 
import pandas as pd
  
# create a dictionary with five fields each
data = {
    'A':['A1', 'A2', 'A3', 'A4', 'A5'], 
    'B':['B1', 'B2', 'B3', 'B4', 'B5'], 
    'C':['C1', 'C2', 'C3', 'C4', 'C5'], 
    'D':['D1', 'D2', 'D3', 'D4', 'D5'], 
    'E':['E1', 'E2', 'E3', 'E4', 'E5'] }
  
# Convert the dictionary into DataFrame 
df = pd.DataFrame(data)
  
# Remove three columns as index base
df.drop(df.columns[[0, 4, 2]], axis = 1, inplace = True)
  
df

Output:

Method #2: Drop Columns from a Dataframe using iloc[] and drop() method.

Remove all columns between a specific column to another columns.

# Import pandas package 
import pandas as pd
# create a dictionary with five fields each 
data = {
    'A':['A1', 'A2', 'A3', 'A4', 'A5'], 
    'B':['B1', 'B2', 'B3', 'B4', 'B5'], 
    'C':['C1', 'C2', 'C3', 'C4', 'C5'], 
    'D':['D1', 'D2', 'D3', 'D4', 'D5'], 
    'E':['E1', 'E2', 'E3', 'E4', 'E5'] }
  
# Convert the dictionary into DataFrame 
df = pd.DataFrame(data)
  
# Remove all columns between column index 1 to 3
df.drop(df.iloc[:, 1:3], inplace = True, axis = 1)
  
df

Output:

Method #3: Drop Columns from a Dataframe using ix() and drop() method.

Remove all columns between a specific column name to another columns name.

# Import pandas package 
import pandas as pd
  
# create a dictionary with five fields each 
data = {
    'A':['A1', 'A2', 'A3', 'A4', 'A5'], 
    'B':['B1', 'B2', 'B3', 'B4', 'B5'], 
    'C':['C1', 'C2', 'C3', 'C4', 'C5'], 
    'D':['D1', 'D2', 'D3', 'D4', 'D5'], 
    'E':['E1', 'E2', 'E3', 'E4', 'E5'] }
  
# Convert the dictionary into DataFrame 
df = pd.DataFrame(data)
  
# Remove all columns between column name 'B' to 'D'
df.drop(df.ix[:, 'B':'D'].columns, axis = 1)

Output:

Method #4: Drop Columns from a Dataframe using loc[] and drop() method.

Remove all columns between a specific column name to another columns name.

# Import pandas package 
import pandas as pd
  
# create a dictionary with five fields each 
data = {
    'A':['A1', 'A2', 'A3', 'A4', 'A5'], 
    'B':['B1', 'B2', 'B3', 'B4', 'B5'], 
    'C':['C1', 'C2', 'C3', 'C4', 'C5'], 
    'D':['D1', 'D2', 'D3', 'D4', 'D5'], 
    'E':['E1', 'E2', 'E3', 'E4', 'E5'] }
  
# Convert the dictionary into DataFrame 
df = pd.DataFrame(data)
  
# Remove all columns between column name 'B' to 'D'
df.drop(df.loc[:, 'B':'D'].columns, axis = 1)

Output:

Note: Different loc() and iloc() is iloc() exclude last column range element.

Method #5: Drop Columns from a Dataframe by iterative way.

Remove all columns between a specific column name to another columns name.

# Import pandas package 
import pandas as pd
  
# create a dictionary with five fields each 
data = {
    'A':['A1', 'A2', 'A3', 'A4', 'A5'], 
    'B':['B1', 'B2', 'B3', 'B4', 'B5'], 
    'C':['C1', 'C2', 'C3', 'C4', 'C5'], 
    'D':['D1', 'D2', 'D3', 'D4', 'D5'], 
    'E':['E1', 'E2', 'E3', 'E4', 'E5'] }
  
# Convert the dictionary into DataFrame 
df = pd.DataFrame(data)
for col in df.columns:
    if 'A' in col:
        del df[col]
  
df

Output:

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