Скачать или смотреть How to Add and Count Non-Zero Values of Rows Based on Current Date in a Pandas DataFrame

Learn how to efficiently add and count non-zero values in your Pandas DataFrame based on the current date, including a step-by-step guide and example code.
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Introduction

Working with data often requires us to extract specific insights according to certain criteria. When dealing with time-series data in Python using Pandas, one common task is to compute and summarize values based on a dynamic date condition. This post will illustrate how to add and count non-zero values in your DataFrame rows, specifically up to a designated current date.

The Problem

Consider the following scenario: you have a DataFrame that tracks account_id, contract_id, and various transaction amounts recorded on specific dates. For analysis purposes, you want to calculate:

Total: The sum of non-zero values from the first column up to the column of a specified current date.

Active Months: The count of non-zero values from the same range.

For instance, if our current date is May 1, 2021, we will consider the amounts mentioned from December 1, 2020, up to the specified date.

Sample DataFrame

Here’s a brief look at our initial DataFrame before processing:

account_idcontract_id2020-12-012021-01-012021-02-012021-03-012021-04-012021-05-012021-06-011A200.00.00.00.00.00.00.01B300.0300.00.00.00.00.00.01C0.00.00.0400.0400.0400.0400.02K100.0100.0100.00.00.00.00.02F0.00.050.050.050.050.050.0You want to manipulate this DataFrame such that you generate two new columns based on the criteria above: a total of relevant amounts and active_months which counts the number of months with non-zero values.

Step-by-Step Solution

Step 1: Filter the Relevant Columns

First, we need to identify which columns are relevant for our calculations, which means selecting all date columns up to the current date (May 1, 2021, in this example). We can achieve this via boolean indexing on the columns.

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

Step 2: Calculate Total and Active Months

Now, we can calculate the total and active_months for our filtered DataFrame:

Total: This is computed by summing the filtered rows across the columns.

Active Months: This counts how many of the values are non-zero in the filtered rows.

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

Step 3: Viewing the Output

After executing the above commands, your DataFrame will look like this:

account_idcontract_idtotalactive_months2020-12-012021-01-012021-02-012021-03-012021-04-012021-05-012021-06-011A2001200.00.00.00.00.00.00.01B6002300.0300.00.00.00.00.00.01C120030.00.00.0400.0400.0400.0400.02K3003100.0100.0100.00.00.00.00.02F20040.00.050.050.050.050.050.0Conclusion

By following these steps, you've successfully calculated the total and active months based on non-zero values in a Pandas DataFrame using a dynamic current date. This technique can be applied to various types of time-series data, be it financial records, sales data, or any other kind of numerical tracking across time.

By leveraging Pandas, you can make data analysis more efficient and insightful. Happy coding!