Reshaping Pandas DataFrame with pivot_table in Python

Python Pandas Numpy: Exercise-14 with Solution

Reshape a Pandas DataFrame using the pivot_table function.

Sample Solution:

Python Code:

import pandas as pd

# Create a sample DataFrame
data = {'Date': ['2012-01-01', '2012-01-01', '2012-01-02', '2012-01-02'],
        'Category': ['A', 'B', 'A', 'B'],
        'Values': [10, 20, 30, 40]}

df = pd.DataFrame(data)

# Use pivot_table to reshape the DataFrame
pivot_df = pd.pivot_table(df, values='Values', index='Date', columns='Category', aggfunc='sum')

# Display the reshaped DataFrame
print(pivot_df)

Output:

Category     A   B
Date              
2012-01-01  10  20
2012-01-02  30  40

Explanation:

In the exerciser above,

• We create a sample DataFrame (df) with columns 'Date', 'Category', and 'Values'.
• The pd.pivot_table function is used to reshape the DataFrame. We specify the values to aggregate ('Values'), the index ('Date'), the columns ('Category'), and the aggregation function ('sum').
• The result is a new DataFrame (pivot_df) with 'Date' as the index, 'Category' as columns, and the sum of 'Values' for each combination of date and category.

Flowchart:

Python Code Editor:

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