Pandas Exclude Column

  1. Pandas Select All Except One Column Using the loc Property
  2. Pandas Select All Except One Column Using the drop() Method
  3. Pandas Select All Except One Column Using the difference() Method

This tutorial explains how we can select all columns except one from a particular DataFrame. We will use the below example DataFrame in this article.

import pandas as pd

stocks_df = pd.DataFrame({
    'Stock': ["Amazon","Tesla","Facebook","Boeing"],
    'Price(in $)':  [3180,835,267,209],
    'Sector':["Technology","Technology","Technology","Aircraft"]
})

print("Stocks Dataframe:")
print(stocks_df,"\n")

Output:

Stocks Dataframe:
      Stock  Price(in $)      Sector
0    Amazon         3180  Technology
1     Tesla          835  Technology
2  Facebook          267  Technology
3    Boeing          209    Aircraft

Pandas Select All Except One Column Using the loc Property

import pandas as pd

stocks_df = pd.DataFrame({
    'Stock': ["Amazon","Tesla","Facebook","Boeing"],
    'Price(in $)':  [3180,835,267,209],
    'Sector':["Technology","Technology","Technology","Aircraft"]
})

print("Stocks Dataframe:")
print(stocks_df,"\n")

print("Stocks DataFrame excluding Sector Column:")
filtered_df=stocks_df.loc[:,stocks_df.columns!="Sector"]
print(filtered_df,"\n")

Output:

Stocks Dataframe:
      Stock  Price(in $)      Sector
0    Amazon         3180  Technology
1     Tesla          835  Technology
2  Facebook          267  Technology
3    Boeing          209    Aircraft

Stocks DataFrame excluding Sector Column:
      Stock  Price(in $)
0    Amazon         3180
1     Tesla          835
2  Facebook          267
3    Boeing          209

It selects all except the Sector column from the stocks_df DataFrame, assigns the result to filtered_df, and then displays the content of the filetered_df.

The loc property selects the elements based on specified rows and columns. The : symbol before , in loc property specifies we need to select all the rows. For the columns, we have specified to select only the column whose name is not Sector. Hence, it will select all the columns except the Sector column.

Pandas Select All Except One Column Using the drop() Method

We can drop specified columns from a DataFrame using the drop() method by setting axis=1 in the method.

import pandas as pd

stocks_df = pd.DataFrame({
    'Stock': ["Amazon","Tesla","Facebook","Boeing"],
    'Price(in $)':  [3180,835,267,209],
    'Sector':["Technology","Technology","Technology","Aircraft"]
})

print("Stocks Dataframe:")
print(stocks_df,"\n")

print("Stocks DataFrame excluding Sector Column:")
filtered_df=stocks_df.drop("Sector",axis=1)
print(filtered_df,"\n")

Output:

Stocks Dataframe:
      Stock  Price(in $)      Sector
0    Amazon         3180  Technology
1     Tesla          835  Technology
2  Facebook          267  Technology
3    Boeing          209    Aircraft

Stocks DataFrame excluding Sector Column:
      Stock  Price(in $)
0    Amazon         3180
1     Tesla          835
2  Facebook          267
3    Boeing          209

It drops the Sector column from the stocks_df DataFrame and assigns the result to filtered_df.

We can also exclude multiple columns from a DataFrame by dropping multiple columns using the drop() method. We provide a list of columns’ names to be dropped as an argument to the drop() method.

import pandas as pd

stocks_df = pd.DataFrame({
    'Stock': ["Amazon","Tesla","Facebook","Boeing"],
    'Price(in $)':  [3180,835,267,209],
    'Sector':["Technology","Technology","Technology","Aircraft"]
})

print("Stocks Dataframe:")
print(stocks_df,"\n")

print("Stocks DataFrame excluding Sector and Price Column:")
filtered_df=stocks_df.drop(["Sector","Price(in $)"],axis=1)
print(filtered_df,"\n")

Output:

Stocks Dataframe:
      Stock  Price(in $)      Sector
0    Amazon         3180  Technology
1     Tesla          835  Technology
2  Facebook          267  Technology
3    Boeing          209    Aircraft

Stocks DataFrame excluding Sector and Price Column:
      Stock
0    Amazon
1     Tesla
2  Facebook
3    Boeing

It excludes the columns Price(in $) and Sector from the stocks_df DataFrame.

Pandas Select All Except One Column Using the difference() Method

import pandas as pd

stocks_df = pd.DataFrame({
    'Stock': ["Amazon","Tesla","Facebook","Boeing"],
    'Price(in $)':  [3180,835,267,209],
    'Sector':["Technology","Technology","Technology","Aircraft"]
})

print("Stocks Dataframe:")
print(stocks_df,"\n")

print("Stocks DataFrame excluding Sector Column:")
filtered_df=stocks_df[stocks_df.columns.difference(['Sector'])]
print(filtered_df,"\n")

Output:

Stocks Dataframe:
      Stock  Price(in $)      Sector
0    Amazon         3180  Technology
1     Tesla          835  Technology
2  Facebook          267  Technology
3    Boeing          209    Aircraft

Stocks DataFrame excluding Sector Column:
   Price(in $)     Stock
0         3180    Amazon
1          835     Tesla
2          267  Facebook
3          209    Boeing

It drops the Sector column from the stocks_df DataFrame and assigns the result to filtered_df.

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