Pandas - Fill missing values in a DataFrame using a custom function
Pandas: Custom Function Exercise-16 with Solution
Write a Pandas program to apply a custom function to fill missing values in a DataFrame.
This exercise shows how to apply a custom function to fill missing values in a DataFrame based on column-specific logic.
Sample Solution :
Code :
import pandas as pd
import numpy as np
# Create a sample DataFrame with missing values
df = pd.DataFrame({
'A': [1, 2, np.nan],
'B': [np.nan, 5, 6],
'C': [7, 8, np.nan]
})
# Define a custom function to fill missing values based on column logic
def fill_missing(x):
if pd.isna(x):
return 0 # Fill NaN with 0
return x
# Apply the function element-wise using applymap()
df_filled = df.applymap(fill_missing)
# Output the result
print(df_filled)
Output:
A B C 0 1.0 0.0 7.0 1 2.0 5.0 8.0 2 0.0 6.0 0.0
Explanation:
- Created a DataFrame with missing values (NaN).
- Defined a function fill_missing() that replaces missing values with 0.
- Applied the function element-wise to the DataFrame using applymap().
- Returned a DataFrame where all missing values have been filled with 0.
Python-Pandas Code Editor:
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