Pandas fillna Column

  1. DataFrame.fillna() Method
  2. Fill Entire DataFrame With Specified Value Using the DataFrame.fillna() Method
  3. Fill NaN Values of the Specified Column With a Specified Value

This tutorial explains how we can fill NaN values with specified values using the DataFrame.fillna() method.

We will use the below DataFrame in this article.

import numpy as np
import pandas as pd

roll_no = [501, 502, 503, 504, 505]

student_df = pd.DataFrame({
    "Roll No": [501, 502, np.nan, 504, 505, 506],
    'Name': ["Jennifer", "Travis", "Bob", "Emma", "Luna", "Anish"],
    'Income(in $)': [200, 400, np.nan, 30, np.nan, np.nan],
    'Age': [17, 18, np.nan, 16, 18, np.nan]
})

print(student_df)

Output:

   Roll No      Name  Income(in $)   Age
0    501.0  Jennifer         200.0  17.0
1    502.0    Travis         400.0  18.0
2      NaN       Bob           NaN   NaN
3    504.0      Emma          30.0  16.0
4    505.0      Luna           NaN  18.0
5    506.0     Anish           NaN   NaN

DataFrame.fillna() Method

Syntax

DataFrame.fillna(value=None, 
                 method=None, 
                 axis=None, 
                 inplace=False, 
                 limit=None, 
                 downcast=None)

The DataFrame.fillna() method enables us to fill the NaN values in the DataFrame with the specified value or method.

Fill Entire DataFrame With Specified Value Using the DataFrame.fillna() Method

import numpy as np
import pandas as pd

roll_no = [501, 502, 503, 504, 505]

student_df = pd.DataFrame({
    "Roll No": [501, 502, np.nan, 504, 505, 506],
    'Name': ["Jennifer", "Travis", "Bob", "Emma", "Luna", "Anish"],
    'Income(in $)': [200, 400, np.nan, 30, np.nan, np.nan],
    'Age': [17, 18, np.nan, 16, 18, np.nan]
})
filled_df = student_df.fillna(0)

print("DataFrame with NaN values")
print(student_df, "\n")

print("After applying fillna() to the DataFrame:")
print(filled_df, "\n")

Output:

DataFrame with NaN values
   Roll No      Name  Income(in $)   Age
0    501.0  Jennifer         200.0  17.0
1    502.0    Travis         400.0  18.0
2      NaN       Bob           NaN   NaN
3    504.0      Emma          30.0  16.0
4    505.0      Luna           NaN  18.0
5    506.0     Anish           NaN   NaN 

After applying fillna() to the DataFrame:
   Roll No      Name  Income(in $)   Age
0    501.0  Jennifer         200.0  17.0
1    502.0    Travis         400.0  18.0
2      0.0       Bob           0.0   0.0
3    504.0      Emma          30.0  16.0
4    505.0      Luna           0.0  18.0
5    506.0     Anish           0.0   0.0 

It replaces all the NaN values in the DataFrame student_df by 0 which is passed as an argument to the DataFrame.fillna() method.

import numpy as np
import pandas as pd

roll_no = [501, 502, 503, 504, 505]

student_df = pd.DataFrame({
    "Roll No": [501, 502, np.nan, 504, 505, 506],
    'Name': ["Jennifer", "Travis", "Bob", "Emma", "Luna", "Anish"],
    'Income(in $)': [200, 400, np.nan, 30, np.nan, np.nan],
    'Age': [17, 18, np.nan, 16, 18, np.nan]
})
filled_df = student_df.fillna(method='ffill')

print("DataFrame with NaN values")
print(student_df, "\n")

print("After applying fillna() to the DataFrame:")
print(filled_df, "\n")

Output:

DataFrame with NaN values
   Roll No      Name  Income(in $)   Age
0    501.0  Jennifer         200.0  17.0
1    502.0    Travis         400.0  18.0
2      NaN       Bob           NaN   NaN
3    504.0      Emma          30.0  16.0
4    505.0      Luna           NaN  18.0
5    506.0     Anish           NaN   NaN 

After applying fillna() to the DataFrame:
   Roll No      Name  Income(in $)   Age
0    501.0  Jennifer         200.0  17.0
1    502.0    Travis         400.0  18.0
2    502.0       Bob         400.0  18.0
3    504.0      Emma          30.0  16.0
4    505.0      Luna          30.0  18.0
5    506.0     Anish          30.0  18.0 

It fills all the NaN values in the student_df by the value that comes before the NaN value in the same column as of NaN value.

Fill NaN Values of the Specified Column With a Specified Value

To fill particular values with specified values, we pass a dictionary to the fillna() method with column name as a key and value to be used for NaN values of that column as a value.

import numpy as np
import pandas as pd

roll_no = [501, 502, 503, 504, 505]

student_df = pd.DataFrame({
    "Roll No": [501, 502, np.nan, 504, 505, 506],
    'Name': ["Jennifer", "Travis", "Bob", "Emma", "Luna", "Anish"],
    'Income(in $)': [200, 400, np.nan, 300, np.nan, np.nan],
    'Age': [17, 18, np.nan, 16, 18, np.nan]
})
filled_df = student_df.fillna({'Age': 17, 'Income(in $)': 300})

print("DataFrame with NaN values")
print(student_df, "\n")

print("After applying fillna() to the DataFrame:")
print(filled_df, "\n")

Output:

DataFrame with NaN values
   Roll No      Name  Income(in $)   Age
0    501.0  Jennifer         200.0  17.0
1    502.0    Travis         400.0  18.0
2      NaN       Bob           NaN   NaN
3    504.0      Emma         300.0  16.0
4    505.0      Luna           NaN  18.0
5    506.0     Anish           NaN   NaN 

After applying fillna() to the DataFrame:
   Roll No      Name  Income(in $)   Age
0    501.0  Jennifer         200.0  17.0
1    502.0    Travis         400.0  18.0
2      NaN       Bob         300.0  17.0
3    504.0      Emma         300.0  16.0
4    505.0      Luna         300.0  18.0
5    506.0     Anish         300.0  17.0 

It fills all the NaN values in the Age column with value 17 and all the NaN values in the Income(in $) column with 300. The NaN values in the Roll No column are left as they are.

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