Deleting DataFrame row in Pandas based on column value

In Pandas, you can delete a row in a DataFrame based on a certain column value by using the drop() method and passing the index label of the row you want to delete. For example, if you have a DataFrame named df and you want to delete a row where the value in the 'Age' column is 30, you can use the following code:

Delete a row in a DataFrame based on a certain column value in Python's Pandas library

df = df[df.Age != 30]

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Alternatively, you can also use boolean indexing

Delete a row in a DataFrame based on a certain column value in Python's Pandas library using boolean indexing

df = df[df['Age'] != 30]

Both of the above lines of code will create a new DataFrame that doesn't include the rows where the 'Age' column is equal to 30.

Another way is to use the DataFrame.query() method. Here's an example:

Delete a row in a DataFrame based on a certain column value in Python's Pandas library the query method

df = df.query('Age != 30')

This method creates a copy of the original dataframe with the query passed as a string

Another way to remove the rows with certain value based on column is using drop() method with boolean indexing

Delete a row in a DataFrame based on a certain column value in Python's Pandas library using the drop method

df.drop(df[df['Age'] == 30].index, inplace=True)

You can also pass axis=0 while calling drop method and condition in the same line

Delete a row in a DataFrame based on a certain column value in Python's Pandas library by passing the axis=0 argument

df = df.drop(df[df['Age'] == 30].index, axis=0)

inplace=True will make the changes to the existing DataFrame, if you don't pass it, it will return a copy of the DataFrame with the changes.