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DataFrames can be very large and can contain hundreds of rows and columns. It is necessary to master the basic maintenance operations of DataFrames, such as deleting multiple columns. We can use dataframe.drop()the method to delete columns or rows from a DataFrame based on the specified axis, 0 for rows and 1 for columns. It determines the element to be deleted based on some labels. For example, we will delete columns from the following DataFrame 'a'.

import pandas as pd

df = pd.DataFrame(
    [[10, 6, 7, 8], [1, 9, 12, 14], [5, 8, 10, 6]], columns=["a", "b", "c", "d"]
)

print(df)

df.drop(["a"], axis=1, inplace=True)

print(df)

Output:

    a  b   c   d
0  10  6   7   8
1   1  9  12  14
2   5  8  10   6
   b   c   d
0  6   7   8
1  9  12  14
2  8  10   6

Note inplacethe use of the parameter in the drop function. When inplacethe parameter is set to True, the columns will be dropped from the original DataFrame; otherwise, a copy of the original DataFrame will be returned.

In our case, we have already deleted the columns 'a', but we need to pass their label names to dataframe.drop()the function. When working with large datasets, we should handle such tasks for many columns at once and use column indices instead of their names.

We can dataframe.columns()do this by using the method, which returns all the columns of the DataFrame, and passing the required column labels to the function using their indices dataframe.drop(). The following code snippet explains how we can do this.

import pandas as pd

df = pd.DataFrame(
    [[10, 6, 7, 8], [1, 9, 12, 14], [5, 8, 10, 6]], columns=["a", "b", "c", "d"]
)

df.drop(df.columns[[1, 2]], axis=1, inplace=True)

print(df)

Output:

    a   d
0  10   8
1   1  14
2   5   6

It will drop columns indexed by 1or .2

We can also avoid using axisthe argument and just dataframe.drop()mention columnsthe parameter in the function and it will automatically indicate the columns to be deleted.

import pandas as pd

df = pd.DataFrame(
    [[10, 6, 7, 8], [1, 9, 12, 14], [5, 8, 10, 6]], columns=["a", "b", "c", "d"]
)

df.drop(columns=df.columns[[1, 2]], inplace=True)

print(df)

Output:

    a   d
0  10   8
1   1  14
2   5   6