How to drop one or multiple columns in Pandas Dataframe

In this tutorial we will discuss how to drop one or multiple columns in Pandas Dataframe. Drop one or more than one columns from a DataFrame can be done in multiple ways. Let’s see multiple examples to drop one or multiple columns in Pandas Dataframe.

Dropping a Single Column in Pandas

To drop a single column from a Pandas Dataframe, you can use the drop() function. The syntax for dropping a single column is as follows:

df.drop('column_name', axis=1, inplace=True)

The 'column_name' parameter specifies the name of the column you want to drop. The axis=1 parameter specifies that the column is to be dropped. The inplace=True parameter specifies that the changes made to the Dataframe are to be saved.

Dropping Multiple Columns in Pandas

To drop multiple columns from a Pandas Dataframe, you can use the drop() function with a list of column names. The syntax for dropping multiple columns is as follows:

df.drop(['column_name1', 'column_name2', 'column_name3'], axis=1, inplace=True)

The list ['column_name1', 'column_name2', 'column_name3'] specifies the names of the columns you want to drop. The axis=1 parameter specifies that the columns are to be dropped. The inplace=True parameter specifies that the changes made to the Dataframe are to be saved.

Dropping columns based on a specific condition

import pandas as pd

# create a sample dataframe
data = {'name': ['John', 'Jane', 'Peter', 'Mary'],
        'age': [30, 25, 45, 20],
        'gender': ['M', 'F', 'M', 'F']}
df = pd.DataFrame(data)

# drop columns where age is less than 30
df.drop(df[df['age'] < 30].index, axis=1, inplace=True)
print(df)

Output:

    name  age
0   John   30
2  Peter   45

Dropping columns with a specific prefix

import pandas as pd

# create a sample dataframe
data = {'A_col1': [1, 2, 3, 4],
        'A_col2': [5, 6, 7, 8],
        'B_col1': [9, 10, 11, 12],
        'B_col2': [13, 14, 15, 16]}
df = pd.DataFrame(data)

# drop columns that start with 'B_'
df = df[df.columns.drop(list(df.filter(regex='^B_')))]
print(df)

Output:

   A_col1  A_col2
0       1       5
1       2       6
2       3       7
3       4       8

Dropping multiple columns by position

import pandas as pd

# create a sample dataframe
data = {'name': ['John', 'Jane', 'Peter', 'Mary'],
        'age': [30, 25, 45, 20],
        'gender': ['M', 'F', 'M', 'F']}
df = pd.DataFrame(data)

# drop the 1st and 3rd columns
df.drop(df.columns[[0, 2]], axis=1, inplace=True)
print(df)

Output:

   age
0   30
1   25
2   45
3   20

Drop columns based on a list of values

df = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6], 'C': [7, 8, 9]})

# Drop columns B and C
df.drop(['B', 'C'], axis=1, inplace=True)

Drop columns based on the type of data

df = pd.DataFrame({'A': [1, 2, 3], 'B': ['four', 'five', 'six'], 'C': [7.0, 8.0, 9.0]})

# Drop columns with non-numeric data
df.select_dtypes(include=[np.number]).head()

This will only keep columns with numeric data and drop the other columns.

Last Updated on May 11, 2023 by admin