Pandas Practice Questions Tutorial

Fill Missing Values in a Pandas DataFrame with a Specific Value

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A Fill Missing Values in a Pandas DataFrame with a Specific Value

Short Description of the Program:
This program replaces all missing values (NaN) in a DataFrame with a specific value such as 0 for numeric columns or 'Unknown' for text columns.

Explanation:

fillna(value): Used to fill NaN values with a given value.
inplace=True: Applies the changes directly to the original DataFrame.
Can be applied column-wise for different types of data.

Program:

import pandas as pd

import numpy as np

data = {

    'Name': ['Alice', 'Bob', 'Charlie', 'Diana', 'Eva', 'Frank'],

    'Age': [25, np.nan, 22, 28, 26, 35],

    'Gender': ['Female', 'Male', 'Male', 'Female', 'Female', 'Male'],

    'City': ['New York', 'Los Angeles', np.nan, 'Houston', 'Phoenix', 'Seattle']

}
df = pd.DataFrame(data)

df['Age'].fillna(0, inplace=True)

df['City'].fillna('Unknown', inplace=True)

print(df)

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Pandas Practice Questions Tutorial

Pandas Practice Questions Tutorial

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Fill Missing Values in a Pandas DataFrame with a Specific Value

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