Pandas: Dropping Rows by Label or Index

Dropping Rows by Label

Dropping Rows by Label in Pandas allows you to remove data that is no longer needed from your dataframe. In order to drop a row, you will need to know its index or label.

To drop a single row by label, you can use the drop() method and specify the row label. For example, if you want to drop row 5, you can use the following code:

df.drop(5, inplace=True)

The inplace=True parameter ensures that the changes are made to the dataframe itself.

To drop a single row by index, you can use the drop() method and specify the row index. For example, if you want to drop the row at index position 2, you can use the following code:

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

Again, the inplace=True parameter ensures that the changes are made to the dataframe itself.

If you want to drop multiple rows, you can pass a list of labels or indices to the drop() method. For example, if you want to drop rows with labels 5 and 6, you can use the following code:

df.drop([5, 6], inplace=True)

If you want to drop rows based on a condition, you can use the drop() method with a boolean expression. For example, if you want to drop all rows where the value in the “Status” column is “Inactive”, you can use the following code:

df.drop(df[df['Status'] == 'Inactive'].index, inplace=True)

This code first selects all rows where the value in the “Status” column is “Inactive” using boolean indexing, and then drops those rows.

Dropping Rows by Label is a useful feature in pandas, and allows you to manipulate your dataframes in powerful and flexible ways.

Removing Rows by Index

Removing Rows by Index in Pandas is similar to dropping rows by label, except that you identify the rows to remove by their position in the dataframe rather than their label.

Pandas provides a drop() method to remove a single row by its index position. For example, if you want to remove the row at the second index position you can use the following code:

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

The inplace=True parameter ensures that the changes are made to the dataframe itself.

To remove multiple rows by index, you can pass a list of index values to the drop() method. For example, to remove rows at the second and third index positions, you can use the following code:

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

Again, the inplace=True parameter ensures that the changes are made to the dataframe itself.

It is also possible to remove rows based on a conditional expression, using boolean indexing. For example, if you want to remove all rows where a particular column value is less than a certain threshold, you can use the following code:

df.drop(df[df['Age'] < 18].index, inplace=True)

This code first selects all rows where the value in the “Age” column is less than 18 using boolean indexing, and then drops those rows.

Removing Rows by Index is a powerful feature in Pandas, and allows you to remove unwanted data from your dataframes in a flexible and efficient way.

Drop Rows with Specific Values

In Pandas, you can drop rows with specific values using the drop() method combined with boolean indexing.

For example, if you have a dataframe containing information about people, and you want to drop all rows where the “gender” column has a value of “male”, you can use the following code:

df.drop(df[df['gender'] == 'male'].index, inplace=True)

This code first selects all rows where the value in the “gender” column is “male” using boolean indexing, and then drops those rows.

Similarly, you can drop rows with specific values in multiple columns using boolean indexing. For example, if you want to drop all rows where the value in the “gender” column is “male” and the value in the “age” column is less than 30, you can use the following code:

df.drop(df[(df['gender'] == 'male') & (df['age'] < 30)].index, inplace=True)

This code first filters the dataframe for rows where both conditions are true using the & operator, and then drops those rows.

You can also drop rows based on a list of specific values in a column. For example, if you want to drop all rows where the “state” column has a value of either “NY” or “CA”, you can use the following code:

df.drop(df[df['state'].isin(['NY', 'CA'])].index, inplace=True)

This code first selects all rows where the value in the “state” column is either “NY” or “CA” using the isin() method, and then drops those rows.

Dropping rows with specific values is a useful feature in Pandas, and allows you to selectively remove rows that do not meet your criteria.

Summary

In this article, we learned how to drop rows in Pandas by label and index. We also learned how to drop rows with specific values using boolean indexing. Dropping rows is a powerful feature in Pandas that allows you to remove unwanted data from your dataframes. Whether you are working with large datasets or just need to remove a few rows, these techniques can help you clean up and filter your data more effectively. Make sure to practice these concepts with sample data to fully understand how they work.