Python | Pandas Index.duplicated ()

Index.duplicated() Pandas Index.duplicated() Specifies duplicate index values. Duplicate values ​​are denoted as True values ​​in the resulting array. All duplicates can be specified, all but the first or all but the last occurrence of duplicates.

Syntax: Index.duplicated (keep = ’first’)

Parameters:
keep: {’first’, ’last’, False}, default ’first’
The value or values ​​in a set of duplicates to mark as missing.
-"  ’first’: Mark duplicates as True except for the first occurrence.
-"  ’last’: Mark duplicates as True except for the last occurrence.
-"  False: Mark all duplicates as True.

Returns: numpy.ndarray

Example # 1: Use Index.duplicated () to specify all duplicate values ​​in the index except the first.

# import pandas as pd import pandas as pd   # Create index idx = pd.Index ([ ’Labrador’ , ’Beagle’ , ’ Labrador’ ,  ’Lhasa’ , ’ Husky’ , ’Beagle’ ])   # Print index idx

Output:

Let’s find out if whether the value present in the Index is duplicate or unique.

# Define duplicate values ​​other than the first idx.duplicated (keep = ’first’ )

Output:

As we can see in the output, Index.duplicated () all occurrences of the duplicate value are True except for the first occurrence.

Example # 2: Use Index.duplicated () to identify all duplicate values. here all duplicate values ​​will be marked as True

# import pandas as pd import pandas as pd   # Create index idx = pd.Index ([ 100 , 50 , 45 , 100 , 12 , 50 , None ])   # Print index idx

Output:

Let’s define all duplicate values ​​in the index.

Note: we have NaN values ​​in the index.

Output:

The function marked all duplicate values ​​as True. He also considered a single occurrence of the NaN value as unique and flagged it as false.

# Determine all duplicate value occurrences idx.duplicated (keep = False )