In this tutorial, we want to replace null values in a Pandas DataFrame. In order to do this, we use the the fillna() method of Pandas.

Import Libraries

First, we import the following python modules:

import numpy as np
import pandas as pd

Create Pandas DataFrame

Next, we create a Pandas DataFrame with some example data from a dictionary:

mydict = {
    "language": ["Python", np.nan, "Python", "Java"],
    "framework": ["FastAPI", np.nan, "Django", np.nan],
    "users": [np.nan, 7000, 20000, np.nan],
df = pd.DataFrame(mydict)

Replace Missing Values with Constant Values

Now, we would like to replace all null values of the DataFrame with constant values.

The null values of the columns "language" and "framework" should be replaced with the value "unknown". The null values of the column "users" should be replaced with the value 0.

To do this, we use fillna() method of Pandas and pass a dictionary with the new values as argument:

new_values = {
    "language": "unknown",
    "framework": "unknown",
    "users": 0
df_cleaned = df.fillna(value=new_values)

Replace Missing Values with Aggregated Values

Next, we would like to replace null values of the DataFrame with aggregated values.

The null values of the column "users"  should be replaced with the mean of the column values.

To do this, we use the mean() method of Pandas for calculating the mean of the column and the fillna() method of Pandas for replacing the null values with the mean:

users_mean = df['users'].mean()
df['users'] = df['users'].fillna(value=users_mean)


Congratulations! Now you are one step closer to become an AI Expert. You have seen that it is very easy to replace null values in a Pandas DataFrame. We can simply use the fillna() method of Pandas. Try it yourself!


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