pandas.Index.duplicated

Index.duplicated(keep='first')

Indicate duplicate index values.

Duplicated values are indicated as True values in the resulting array. Either all duplicates, all except the first, or all except the last occurrence of duplicates can be indicated.

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:

np.ndarray[bool]

A numpy array of boolean values indicating duplicate index values.

See also

Series.duplicated

Equivalent method on pandas.Series.

DataFrame.duplicated

Equivalent method on pandas.DataFrame.

Index.drop_duplicates

Remove duplicate values from Index.

Examples

By default, for each set of duplicated values, the first occurrence is set to False and all others to True:

>>> idx = pd.Index(["llama", "cow", "llama", "beetle", "llama"])
>>> idx.duplicated()
array([False, False,  True, False,  True])

which is equivalent to

>>> idx.duplicated(keep="first")
array([False, False,  True, False,  True])

By using ‘last’, the last occurrence of each set of duplicated values is set on False and all others on True:

>>> idx.duplicated(keep="last")
array([ True, False,  True, False, False])

By setting keep on False, all duplicates are True:

>>> idx.duplicated(keep=False)
array([ True, False,  True, False,  True])