{"id":235870,"date":"2024-05-20T13:01:10","date_gmt":"2024-05-20T13:01:10","guid":{"rendered":"https:\/\/namso-gen.co\/blog\/?p=235870"},"modified":"2024-05-20T13:01:10","modified_gmt":"2024-05-20T13:01:10","slug":"how-to-delete-rows-based-on-column-value-in-pandas","status":"publish","type":"post","link":"https:\/\/namso-gen.co\/blog\/how-to-delete-rows-based-on-column-value-in-pandas\/","title":{"rendered":"How to delete rows based on column value in Pandas?"},"content":{"rendered":"

Deleting rows based on a specific column value in Pandas can be easily achieved using the `.loc` method combined with boolean indexing. By creating a boolean mask that filters out the rows that meet your condition, you can then use `.loc` to exclude those rows from your DataFrame.<\/p>\n

Here is an example of how to delete rows based on a column value in Pandas:<\/p>\n

“`python
\nimport pandas as pd<\/p>\n

# Create a sample DataFrame
\ndata = {‘col1’: [1, 2, 3, 4, 5],
\n ‘col2’: [‘a’, ‘b’, ‘c’, ‘a’, ‘b’]}<\/p>\n

df = pd.DataFrame(data)<\/p>\n

# Delete rows where col2 is equal to ‘a’
\ndf = df.loc[df[‘col2’] != ‘a’]<\/p>\n

print(df)
\n“`<\/p>\n

In this example, the rows where the value in `col2` is equal to ‘a’ are excluded from the DataFrame.<\/p>\n

**To delete rows based on column value in Pandas, you can use the following code:**<\/p>\n

“`python
\ndf = df.loc[df[‘col2’] != ‘a’]
\n“`<\/p>\n

This line of code filters out the rows where the value in the `col2` column is equal to ‘a’ and assigns the resulting DataFrame back to `df`.<\/p>\n

<\/p>\n

\n
\n

Table of Contents<\/p>\nToggle<\/span><\/path><\/svg><\/svg><\/span><\/span><\/span><\/a><\/span><\/div>\n