Check for duplicate values in Pandas dataframe column

Question

Is there a way in pandas to check if a dataframe column has duplicate values, without actually dropping rows? I have a function that will remove duplicate rows, however, I only want it to run if there are actually duplicates in a specific column.

Currently I compare the number of unique values in the column to the number of rows: if there are less unique values than rows then there are duplicates and the code runs.

 if len(df['Student'].unique()) < len(df.index):     # Code to remove duplicates based on Date column runs 

Is there an easier or more efficient way to check if duplicate values exist in a specific column, using pandas?

Some of the sample data I am working with (only two columns shown). If duplicates are found then another function identifies which row to keep (row with oldest date):

    Student Date 0   Joe     December 2017 1   James   January 2018 2   Bob     April 2018 3   Joe     December 2017 4   Jack    February 2018 5   Jack    March 2018 

Answer 1

Main question

Is there a duplicate value in a column, True/False?

╔═════════╦═══════════════╗ ║ Student ║ Date          ║ ╠═════════╬═══════════════╣ ║ Joe     ║ December 2017 ║ ╠═════════╬═══════════════╣ ║ Bob     ║ April 2018    ║ ╠═════════╬═══════════════╣ ║ Joe     ║ December 2018 ║ ╚═════════╩═══════════════╝ 

Assuming above dataframe (df), we could do a quick check if duplicated in the Student col by:

boolean = not df["Student"].is_unique      # True (credit to @Carsten) boolean = df['Student'].duplicated().any() # True 

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Further reading and references

Above we are using one of the Pandas Series methods. The pandas DataFrame has several useful methods, two of which are:

1. drop_duplicates(self[, subset, keep, inplace]) - Return DataFrame with duplicate rows removed, optionally only considering certain columns.
2. duplicated(self[, subset, keep]) - Return boolean Series denoting duplicate rows, optionally only considering certain columns.

These methods can be applied on the DataFrame as a whole, and not just a Serie (column) as above. The equivalent would be:

boolean = df.duplicated(subset=['Student']).any() # True # We were expecting True, as Joe can be seen twice. 

However, if we are interested in the whole frame we could go ahead and do:

boolean = df.duplicated().any() # False boolean = df.duplicated(subset=['Student','Date']).any() # False # We were expecting False here - no duplicates row-wise  # ie. Joe Dec 2017, Joe Dec 2018 

And a final useful tip. By using the keep paramater we can normally skip a few rows directly accessing what we need:

keep : {‘first’, ‘last’, False}, default ‘first’

• first : Drop duplicates except for the first occurrence.
• last : Drop duplicates except for the last occurrence.
• False : Drop all duplicates.

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Example to play around with

import pandas as pd import io  data = '''\ Student,Date Joe,December 2017 Bob,April 2018 Joe,December 2018'''  df = pd.read_csv(io.StringIO(data), sep=',')  # Approach 1: Simple True/False boolean = df.duplicated(subset=['Student']).any() print(boolean, end='\n\n') # True  # Approach 2: First store boolean array, check then remove duplicate_in_student = df.duplicated(subset=['Student']) if duplicate_in_student.any():     print(df.loc[~duplicate_in_student], end='\n\n')  # Approach 3: Use drop_duplicates method df.drop_duplicates(subset=['Student'], inplace=True) print(df) 

Returns

True    Student           Date 0     Joe  December 2017 1     Bob     April 2018    Student           Date 0     Joe  December 2017 1     Bob     April 2018 

Answer 2

You can use is_unique:

df['Student'].is_unique  # equals true in case of no duplicates 

Older pandas versions required:

pd.Series(df['Student']).is_unique