How To Drop Duplicate Rows From DataFrame?
Table Of Contents:
- Syntax ‘drop_duplicates( )’ Method In Pandas.
- Examples ‘drop_duplicates( )’ Method.
(1) Syntax:
DataFrame.drop_duplicates(subset=None, *, keep='first', inplace=False, ignore_index=False)Description:
-
Return DataFrame with duplicate rows removed.
-
Considering certain columns is optional. Indexes, including time indexes, are ignored.
Parameters:
- subset: column label or sequence of labels, optional –
- Only consider certain columns for identifying duplicates, by default use all of the columns.
- keep: {‘first’, ‘last’, False}, default ‘first’ –
- Determines which duplicates (if any) to keep. –
first: Drop duplicates except for the first occurrence. –last: Drop duplicates except for the last occurrence. – False : Drop all duplicates.
- Determines which duplicates (if any) to keep. –
- in place: bool, default False –
- Whether to modify the DataFrame rather than create a new one.
- ignore_index: bool, default False –
- If True, the resulting axis will be labeled 0, 1, …, n – 1.
Returns:
- DataFrame or None – DataFrame with duplicates removed or None if
inplace=True.
(2) Examples Of drop_duplicates() Method:
Example-1
df = pd.DataFrame({
'brand': ['Yum Yum', 'Yum Yum', 'Indomie', 'Indomie', 'Indomie'],
'style': ['cup', 'cup', 'cup', 'pack', 'pack'],
'rating': [4, 4, 3.5, 15, 5]
})
dfOutput:
# By default, it removes duplicate rows based on all columns.
df.drop_duplicates()Output:
# To remove duplicates on specific column(s), use subset.
df.drop_duplicates(subset=['brand'])Output:
# To remove duplicates on multiple column(s), use subset.
df.drop_duplicates(subset=['brand', 'style'])Output:
# To remove duplicates and keep last occurrences, use keep.
df.drop_duplicates(subset=['brand', 'style'], keep='last')Output:
