Check for duplicate values in Pandas dataframe column

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='\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

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

If you want to know how many duplicates & what they are use:

df.pivot_table(index=['ColumnName'], aggfunc='size')

df.pivot_table(index=['ColumnName1',.., 'ColumnNameN'], aggfunc='size')