Скачать или смотреть How to Groupby, Sum, and Calculate Standard Deviation Divided by Mean in Python Using Pandas

Learn how to efficiently group data by month, compute sums, and calculate the standard deviation divided by mean for each item in Python with Pandas.
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How to Groupby, Sum, and Calculate Standard Deviation Divided by Mean in Python Using Pandas

Data analysis often involves working with time-series data, especially when it comes to financial information that is categorized by months. Many analysts face the challenge of grouping their data effectively, calculating mean values, and understanding variance in their datasets. In this guide, we explore a common problem involving a DataFrame in Python: how to group by month, sum values, and calculate the standard deviation divided by the mean.

Problem Overview

Imagine a dataset containing various ITEM_IDs with corresponding values recorded over different months. Your goal is to:

Group the dataset by ITEM_ID and YearMonth.

Sum the VALUE for each ITEM_ID per month.

Account for any missing months by inserting 0 as their value.

Calculate the standard deviation of the monthly summed VALUE, divided by the mean for each ITEM_ID.

Let’s take a closer look at how we can achieve this step-by-step using Python and Pandas.

Step 1: Preparing the DataFrame

First, we need to create a sample DataFrame. Below is an example dataset that contains sales data for different items over several months:

[[See Video to Reveal this Text or Code Snippet]]

Step 2: Grouping and Summing the Values

To get the monthly sums for each ITEM_ID, we can use the groupby method. Here’s how we can do that:

[[See Video to Reveal this Text or Code Snippet]]

This will yield a DataFrame with summed values for each item by month.

Step 3: Inserting Missing Months with Zero Values

Next, we need to ensure that each ITEM_ID has an entry for all months in our desired date range (from 2019-07 to 2020-06). For any missing month, we'll insert a row with a value of 0.

To simplify this, we can define the number of months and use functions to calculate the mean and standard deviation as follows:

[[See Video to Reveal this Text or Code Snippet]]

Step 4: Final Calculation

Now, we just need to compute the standard deviation divided by the mean for each ITEM_ID:

[[See Video to Reveal this Text or Code Snippet]]

Conclusion

By following the steps outlined above, you can effectively group your data by month, compute summed values, and calculate essential statistics such as standard deviation and mean. This approach is valuable for financial analyses and understanding trends over time in your data.

Summary

Group by ITEM_ID and YearMonth.

Sum the VALUE for each item.

Insert zeroes for missing months.

Calculate the mean and standard deviation.

By mastering these techniques in Python using Pandas, you'll enhance your ability to analyze and interpret financial data more efficiently. Happy coding!