Reshaping Pandas DataFrame with pivot_table in Python
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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