Pandas : Get unique values in columns of a Dataframe in Python

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How to get unique values in columns of a Dataframe in Python ?

To find the Unique values in a Dataframe we can use-

  1. series.unique(self)- Returns a numpy array of Unique values
  2. series.nunique(self, axis=0, dropna=True )- Returns the count of Unique values along different axis.(If axis = 0 i.e. default value, it checks along the columns.If axis = 1, it checks along the rows)

To test these functions let’s use the following data-

     Name      Age       City          Experience

a     jack       34.0     Sydney             5
b     Riti        31.0      Delhi               7
c     Aadi      16.0       NaN               11
d    Mohit    31.0       Delhi               7
e    Veena    NaN      Delhi               4
f   Shaunak  35.0     Mumbai           5
g    Shaun    35.0    Colombo          11

Finding unique values in a single column :

To get the unique value(here age) we use the unique( ) function on the column

CODE:-

#Program :

import numpy as np
import pandas as pd
# Data list
emp = [('jack', 34, 'Sydney', 5) ,
         ('Riti', 31, 'Delhi' , 7) ,
         ('Aadi', 16, np.NaN, 11) ,
         ('Mohit', 31,'Delhi' , 7) ,
         ('Veena', np.NaN, 'Delhi' , 4) ,
         ('Shaunak', 35, 'Mumbai', 5 ),
         ('Shaun', 35, 'Colombo', 11)
          ]
# Object of Dataframe class created
empObj = pd.DataFrame(emp, columns=['Name', 'Age', 'City', 'Experience'], index=['a', 'b', 'c', 'd', 'e', 'f', 'g'])
# Obtain the unique values in column 'Age' of the dataframe
uValues = empObj['Age'].unique()
# empObj[‘Age’] returns a series object of the column ‘Age’
print('The unique values in column "Age" are ')
print(uValues)
Output :
The unique values in column "Age" are
[34. 31. 16. nan 35.]

Counting unique values in a single column :

If we want to calculate the number of Unique values rather than the unique values, we can use the .nunique( ) function.

CODE:-

#Program :

import numpy as np
import pandas as pd
# Data list
emp = [('jack', 34, 'Sydney', 5) ,
('Riti', 31, 'Delhi' , 7) ,
('Aadi', 16, np.NaN, 11) ,
('Mohit', 31,'Delhi' , 7) ,
('Veena', np.NaN, 'Delhi' , 4) ,
('Shaunak', 35, 'Mumbai', 5 ),
('Shaun', 35, 'Colombo', 11)
]
# Object of Dataframe class created
empObj = pd.DataFrame(emp, columns=['Name', 'Age', 'City', 'Experience'], index=['a', 'b', 'c', 'd', 'e', 'f', 'g'])
# Counting the  unique values in column 'Age' of the dataframe
uValues = empObj['Age'].nunique()
print('Number of unique values in 'Age' column :')
print(uValues)
Output :
Number of unique values in 'Age' column :
4

Including NaN while counting the Unique values in a column :

NaN’s are not counted by default in the .nunique( ) function. To also include NaN we have to pass the dropna argument

CODE:-

#Program :

import numpy as np
import pandas as pd
# Data list
emp = [('jack', 34, 'Sydney', 5) ,
('Riti', 31, 'Delhi' , 7) ,
('Aadi', 16, np.NaN, 11) ,
('Mohit', 31,'Delhi' , 7) ,
('Veena', np.NaN, 'Delhi' , 4) ,
('Shaunak', 35, 'Mumbai', 5 ),
('Shaun', 35, 'Colombo', 11)
]
# Object of Dataframe class created
empObj = pd.DataFrame(emp, columns=['Name', 'Age', 'City', 'Experience'], index=['a', 'b', 'c', 'd', 'e', 'f', 'g'])
# Counting the unique values in column 'Age' also including NaN
uValues = empObj['Age'].nunique(dropna=False)
print('Number of unique values in 'Age' column including NaN:)
print(uValues)
Output :
Number of unique values in 'Age' column including NaN:
5

Counting unique values in each column of the dataframe :

To count the number of Unique values in each columns

CODE:-

#Program :

import numpy as np
import pandas as pd
# Data list
emp = [('jack', 34, 'Sydney', 5) ,
('Riti', 31, 'Delhi' , 7) ,
('Aadi', 16, np.NaN, 11) ,
('Mohit', 31,'Delhi' , 7) ,
('Veena', np.NaN, 'Delhi' , 4) ,
('Shaunak', 35, 'Mumbai', 5 ),
('Shaun', 35, 'Colombo', 11)
]
# Object of Dataframe class created
empObj = pd.DataFrame(emp, columns=['Name', 'Age', 'City', 'Experience'], index=['a', 'b', 'c', 'd', 'e', 'f', 'g'])
# Counting the unique values in each column
uValues = empObj.nunique()
print('In each column the number of unique values are')
print(uValues)
Output :
In each column the number of unique values are
Name          7
Age           4
City          4
Experience    4
dtype: int64

To include the NaN, just pass dropna into the function.

Get Unique values in multiple columns :

To get unique values in multiple columns, we have to pass all the contents of columns as a series object into the .unique( ) function

CODE:-

#program :

import numpy as np
import pandas as pd
# Data list
emp = [('jack', 34, 'Sydney', 5) ,
('Riti', 31, 'Delhi' , 7) ,
('Aadi', 16, np.NaN, 11) ,
('Mohit', 31,'Delhi' , 7) ,
('Veena', np.NaN, 'Delhi' , 4) ,
('Shaunak', 35, 'Mumbai', 5 ),
('Shaun', 35, 'Colombo', 11)
]
# Object of Dataframe class created
empObj = pd.DataFrame(emp, columns=['Name', 'Age', 'City', 'Experience'], index=['a', 'b', 'c', 'd', 'e', 'f', 'g'])
# Obtain the Unique values in multiple columns i.e. Name & Age
uValues = (empObj['Name'].append(empObj['Age'])).unique()
print('The unique values in column "Name" & "Age" :')
print(uValues)
Output :
The unique values in column "Name" & "Age" :
['jack' 'Riti' 'Aadi' 'Mohit' 'Veena' 'Shaunak' 'Shaun' 34.0 31.0 16.0 nan
35.0]

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