Replace NaN Values with Zeros in Pandas DataFrame

NaN stands for Not A Number and is one of the common ways to represent the missing value in the data. It is a special floating-point value and cannot be converted to any other type than float. NaN value is one of the major problems in Data Analysis. It is very essential to deal with NaN in order to get the desired results.

Methods to replace NaN values with zeros in Pandas DataFrame:

• fillna()
  The fillna() function is used to fill NA/NaN values using the specified method.
• replace()
  The dataframe.replace() function in Pandas can be defined as a simple method used to replace a string, regex, list, dictionary etc. in a DataFrame.

Steps to replace NaN values:

• For one column using pandas:

  df['DataFrame Column'] = df['DataFrame Column'].fillna(0)
  

• For one column using numpy:

  df['DataFrame Column'] = df['DataFrame Column'].replace(np.nan, 0)
  

• For the whole DataFrame using pandas:

  df.fillna(0)
  

• For the whole DataFrame using numpy:

  df.replace(np.nan, 0)
  

Method 1: Using fillna() function for a single column

Example:

# importing libraries
import pandas as pd
import numpy as np

nums = {'Set_of_Numbers': [2, 3, 5, 7, 11, 13,
                           np.nan, 19, 23, np.nan]}

# Create the dataframe
df = pd.DataFrame(nums, columns =['Set_of_Numbers'])

# Apply the function
df['Set_of_Numbers'] = df['Set_of_Numbers'].fillna(0)

# print the DataFrame
df

Output:

Method 2: Using replace() function for a single column

Example:

# importing libraries
import pandas as pd
import numpy as np

nums = {'Car Model Number': [223, np.nan, 237, 195, np.nan,
                             575, 110, 313, np.nan, 190, 143,
                             np.nan],
       'Engine Number': [4511, np.nan, 7570, 1565, 1450, 3786,
                         2995, 5345, 7777, 2323, 2785, 1120]}

# Create the dataframe
df = pd.DataFrame(nums, columns =['Car Model Number'])

# Apply the function
df['Car Model Number'] = df['Car Model Number'].replace(np.nan, 0)

# print the DataFrame
df

Output:

Method 3: Using fillna() function for the whole dataframe

Example:

# importing libraries
import pandas as pd
import numpy as np

nums = {'Number_set_1': [0, 1, 1, 2, 3, 5, np.nan,
                         13, 21, np.nan],
       'Number_set_2': [3, 7, np.nan, 23, 31, 41,
                        np.nan, 59, 67, np.nan],
       'Number_set_3': [2, 3, 5, np.nan, 11, 13, 17,
                        19, 23, np.nan]}

# Create the dataframe
df = pd.DataFrame(nums)

# Apply the function
df = df.fillna(0)

# print the DataFrame
df

Output:

Method 4: Using replace() function for the whole dataframe

Example:

# importing libraries
import pandas as pd
import numpy as np

nums = {'Student Name': [ 'Shrek', 'Shivansh', 'Ishdeep',
                         'Siddharth', 'Nakul', 'Prakhar',
                         'Yash', 'Srikar', 'Kaustubh',
                         'Aditya',  'Manav', 'Dubey'],
        'Roll No.': [ 18229, 18232, np.nan, 18247, 18136,
                     np.nan, 18283, 18310, 18102, 18012,
                     18121, 18168],
        'Subject ID': [204, np.nan, 201, 105, np.nan, 204,
                       101, 101, np.nan, 165, 715, np.nan],
       'Grade Point': [9, np.nan, 7, np.nan, 8, 7, 9, 10,
                       np.nan, 9, 6, 8]}

# Create the dataframe
df = pd.DataFrame(nums)

# Apply the function
df = df.replace(np.nan, 0)

# print the DataFrame
df

Output:

Attention geek! Strengthen your foundations with the Python Programming Foundation Course and learn the basics.

To begin with, your interview preparations Enhance your Data Structures concepts with the Python DS Course.