HOWTO · Pandas
How to Exclude Pandas DataFrame Column
This tutorial explains how we can get all columns of a DataFrame except a particular column.
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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.
