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Fillna in multiple columns in place in Python Pandas
  • Last Updated : 17 Dec, 2020

In this article, we are going to write python script to fill multiple columns in place in Python using pandas library. A data frame is a 2D data structure that can be stored in CSV, Excel, .dB, SQL formats. We will be using Pandas Library of python to fill the missing values in Data Frame.

Let’s understand this with implementation:

First creating a Dataset with pandas

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# Importing Required Libraries
import pandas as pd
import numpy as np
  
# Creating a sample dataframe with NaN values
dataframe = pd.DataFrame({'Count': [1, np.nan,
                                    np.nan, 4,
                                    2, np.nan,
                                    np.nan, 5, 6],
                            
    'Name': ['Geeks','for', 'Geeks','a','portal','for',
             'computer', 'Science','Geeks'],
                          'Category':list('ppqqrrsss')})
  
# Printing The dataframe
display(dataframe)

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Output:



Example 1: Filling missing columns values with fixed values:

We can use fillna() function to impute the missing values of a data frame to every column defined by a dictionary of values.The limitation of this method is that we can only use constant values to be filled.

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# Importing Required Libraries
import pandas as pd
import numpy as np
  
# Creating a sample dataframe with NaN values
dataframe = pd.DataFrame({'Count': [1, np.nan, np.nan, 4, 2,
                                    np.nan,np.nan, 5, 6], 
                            
    'Name': ['Geeks','for', 'Geeks','a','portal','for',
             'computer', 'Science','Geeks'],
                          'Category':list('ppqqrrsss')})
  
# Creating a constant value for coloumn Count
constant_values = {'Count': 10}
dataframe = dataframe.fillna(value = constant_values)
  
# Printing the dataframe
display(dataframe)

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Output:

Example 2: Filling missing columns values with mean():

In this method, the values are defined by a method called mean() which finds out the mean of existing values of the given column and then imputes the mean values in each of the missing (NaN) values.



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# Importing Required Libraries
import pandas as pd
import numpy as np
  
# Creating a sample dataframe with NaN values
dataframe = pd.DataFrame({'Count': [1, np.nan, np.nan, 4, 2,
                                    np.nan,np.nan, 5, 6], 
                            
    'Name': ['Geeks','for', 'Geeks','a','portal','for',
             'computer', 'Science','Geeks'],
                          'Category':list('ppqqrrsss')})
  
  
# Filling Count column with mean of Count column
dataframe.fillna(dataframe['Count'].mean(), inplace = True)
  
# Printing the Dataframe
display(dataframe)

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Output: 

Example 3: Filling missing column values with mode().

The mode is the value that appears most often in a set of data values. If X is a discrete random variable, the mode is the value x at which the probability mass function takes its maximum value. In other words, it is the value that is most likely to be sampled.

Python3

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# Importing Required Libraries
import pandas as pd
import numpy as np
  
# Creating a sample dataframe with NaN values
dataframe = pd.DataFrame({'Count': [1, np.nan, np.nan,
                                    1, 2, np.nan,np.nan,
                                    5, 1], 
                            
    'Name': ['Geeks','for', 'Geeks','a','portal','for',
             'computer', 'Science','Geeks'],
                          'Category':list('ppqqrrsss')})
  
# Using Mode() function to impute the values using fillna
dataframe.fillna(dataframe['Count'].mode()[0], inplace = True)
  
# Printing the Dataframe
display(dataframe)

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Output:

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