Creating pivot tables in pandas is like rearranging your data’s furniture to better suit the room’s layout. Let’s rearrange.
Pivot Tables: A Quick Setup
Pivot tables in pandas allow you to summarize and analyze your dataset in a spreadsheet-like format, making it easier to see comparisons, patterns, and trends:
import pandas as pd
# Sample DataFrame
data = {
'Date': ['2024-01-01', '2024-01-01', '2024-01-02', '2024-01-02'],
'Category': ['Fruit', 'Vegetable', 'Fruit', 'Vegetable'],
'Product': ['Apple', 'Carrot', 'Banana', 'Broccoli'],
'Amount': [12, 15, 19, 20]
}
df = pd.DataFrame(data)
# Creating a pivot table
pivot = df.pivot_table(values='Amount', index='Category', columns='Date', aggfunc='sum')
print(pivot)
This code example transforms the DataFrame into a pivot table that sums up the amounts by category and date, offering a clear view of daily sales by product type.
Pivoting your data in pandas can transform rows of data into a clear summary report, shining a spotlight on important aspects of your information landscape.