Скачать или смотреть How to Add New Rows to a Pandas DataFrame for Calculated Other Values Based on Time Grouping

Learn how to create new rows in a Pandas DataFrame that calculate the `Other` electricity usage per hour by using group sums.
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Creating New Rows in a Pandas DataFrame: The "Other" Usage Calculation

Pandas is a powerful tool for data manipulation in Python, particularly when dealing with time series data. One common challenge is adding new rows to a DataFrame based on calculations involving existing rows. In this post, we'll tackle how to add a new row for every hour labeled "Other," representing the difference between total usage and the sum of other devices' consumption.

The Problem

Imagine having a DataFrame that logs the electricity usage of various appliances in your home, categorized by hour. The challenge arises when you want to add an "Other" category to represent the remaining electricity usage that isn't attributed to existing devices.

Input DataFrame Example

Here's a sample of the data you may have:

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

Your goal is to calculate what is left from the total usage kWh after summing the usage of the other devices and insert this as an Other row.

Our Approach: Calculation Steps

Here's a clear breakdown of how to achieve this:

Group by Hour: Start by grouping the DataFrame by the hour (here defined in your DateTime column).

Sum the kWh for Each Group: For every hour, calculate the total electricity usage and also sum the usage of all devices except for "Total Usage."

Calculate the Remaining Usage: Subtract the sum of all devices (excluding "Total Usage") from the total usage to calculate "Other."

Insert New Rows: Finally, create and insert rows labeled "Other" into the DataFrame for each hour.

Sample Implementation

Here's how you would write this out in Python using Pandas:

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

Output

The result will look like this:

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

Conclusion

Now you have a comprehensive overview of how to append calculated rows to your DataFrame based on your existing data. This method not only helps to find insights like the ‘Other’ category for your electricity usage but also enhances your data's usability for further analysis!

Happy coding!