Python Pandas – Find unique values from multiple columns

To find unique values from multiple columns, use the unique() method. Let’s say you have Employee Records with “EmpName” and “Zone” in your Pandas DataFrame. The name and zone can get repeated since two employees can have similar names and a zone can have more than one employee. In that case, if you want unique Employee names, then use the unique() for DataFrame.

At first, import the required library. Here, we have set pd as an alias −

import pandas as pd

At first, create a DataFrame. Here, we have two columns −

dataFrame = pd.DataFrame(
   {
      "EmpName": ['John', 'Ted', 'Jacob', 'Scarlett', 'Ami', 'Ted', 'Scarlett'],"Zone": ['North', 'South', 'South', 'East', 'West', 'East', 'North']
   }
)

Fetch unique Employee Names and Zone from the DataFrame column “EmpName” and “Zone” −

{pd.concat([dataFrame['EmpName'],dataFrame['Zone']]).unique()}

Example

Following is the complete code −

import pandas as pd

# Create DataFrame
dataFrame = pd.DataFrame(
   {
      "EmpName": ['John', 'Ted', 'Jacob', 'Scarlett', 'Ami', 'Ted', 'Scarlett'],"Zone": ['North', 'South', 'South', 'East', 'West', 'East', 'North']
   }
)

print("DataFrame ...\n",dataFrame)

# Fetch unique values from multiple columns
print(f"\nFetching unique Values from the two columns and concatenate them:\n \
{pd.concat([dataFrame['EmpName'],dataFrame['Zone']]).unique()}")

Output

This will produce the following output −

DataFrame ...
    EmpName   Zone
0      John  North
1       Ted  South
2     Jacob  South
3  Scarlett   East
4       Ami   West
5       Ted   East
6  Scarlett  North

Fetching unique Values from the two columns and concatenate them:
['John' 'Ted' 'Jacob' 'Scarlett' 'Ami' 'North' 'South' 'East' 'West']