Pandas provide data analysts a way to delete and filter data frame using
.drop() method. Rows can be removed using index label or column name using this method.
DataFrame.drop(labels=None, axis=0, index=None, columns=None, level=None, inplace=False, errors=’raise’)
labels: String or list of strings referring row or column name.
axis: int or string value, 0 ‘index’ for Rows and 1 ‘columns’ for Columns.
index or columns: Single label or list. index or columns are an alternative to axis and cannot be used together.
level: Used to specify level in case data frame is having multiple level index.
inplace: Makes changes in original Data Frame if True.
errors: Ignores error if any value from the list doesn’t exists and drops rest of the values when errors = ‘ignore’
Return type: Dataframe with dropped values
Now, Let’s create a sample dataframe
Example #1: Delete a single Row in DataFrame by Row Index Label
Example #2: Delete Multiple Rows in DataFrame by Index Labels
Example #3: Delete a Multiple Rows by Index Position in DataFrame
Example #4: Delete rows from dataFrame in Place
- Python | Delete rows/columns from DataFrame using Pandas.drop()
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