python - 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

question from:https://stackoverflow.com/questions/50242968/check-for-duplicate-values-in-pandas-dataframe-column

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

---

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.

---

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='

') # 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='

')

# 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