Summary  
This chapter demonstrates how to resample time-indexed data into weekly intervals using a data manipulation library, apply aggregation functions, reset and rename the index for clarity, and export the results.  

General domain of usage  
Weekly performance reporting

Generating weekly performance reports is a common requirement for tracking business progress and identifying trends over time. When you have time-stamped data—such as daily sales, website visits, or operational metrics—it's helpful to group this data by week to create summaries that highlight key numbers and changes. Grouping by week allows you to spot patterns and outliers more easily than reviewing daily records. In Python, you can use the `pandas` library to efficiently group data by week and calculate totals or averages for important metrics.

import pandas as pd

# Sample time-stamped data
data = {
    "date": pd.date_range("2024-01-01", periods=30, freq="D"),
    "sales": [100 + i*5 for i in range(30)]
}
df = pd.DataFrame(data)
df.set_index("date", inplace=True)

# Resample by week, summing sales for each week
weekly_sales = df.resample("W").sum()
print(weekly_sales)

The code above demonstrates how to use the `resample` method in `pandas` to group daily data into weekly intervals. By resampling with the `"W"` frequency, you aggregate all rows that fall within the same week and can apply summary functions such as `sum()` or `mean()`. This approach is particularly useful for creating weekly performance summaries that are easy to interpret.

When formatting your weekly summaries, aim for clarity and readability. Present key metrics—such as total sales, number of transactions, or average values—in a table format, with each row representing a week. Use clear column names and ensure the week start or end dates are visible, so anyone reviewing the report can quickly understand the time period and the summarized data.

import pandas as pd

# Continue from previous example
weekly_summary = df.resample("W").agg({"sales": "sum"})
weekly_summary.reset_index(inplace=True)
weekly_summary.rename(columns={"date": "week_ending", "sales": "total_sales"}, inplace=True)

# Export to CSV for sharing or further analysis
weekly_summary.to_csv("weekly_performance_summary.csv", index=False)
print(weekly_summary)

To automate recurring weekly reports, consider scheduling your script to run at the end of each week using a task scheduler such as cron (on Linux or Mac) or Task Scheduler (on Windows). Make sure your data source is updated before the script runs, and configure the script to save the summary with a clear filename, possibly including the week number or date range. Automating the process reduces manual effort, ensures consistency, and helps you stay up-to-date with your performance tracking.

What pandas method is used to resample time series data by week?

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Automating Weekly Performance Reports