Скачать или смотреть How to Create a New DataFrame in Pandas with Conditional Row Selection and Calculated Columns

Learn how to filter rows in a Pandas DataFrame based on column values and add computed columns for advanced data analysis.
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How to Create a New DataFrame in Pandas with Conditional Row Selection and Calculated Columns

In data analysis, we often face the challenge of working with large datasets and wanting to extract meaningful information from them. One common scenario is creating a new DataFrame that consists of selected rows from an existing DataFrame, based on certain conditions or criteria. This guide will guide you step by step through the process of creating a new Pandas DataFrame that filters rows based on values from a specific column and adds computed columns that enhance your analysis.

Understanding the Problem

Suppose you have a DataFrame — let's call it div — with several columns, including one labeled annum. You want to create a new DataFrame that contains only the rows where the annum column has non-null values. Additionally, you would like to calculate a new column growth that shows the growth rate of each row’s annum value compared to the next row. This kind of operation is essential in financial analyses, where growth metrics are used to make informed decisions.

Step-by-Step Solution

Let's break down the steps necessary to achieve this.

1. Filter Rows with Non-null Values

First, you will need to create a new DataFrame that contains only the rows from div where the annum values are not null. This can be done using the notnull() function provided by Pandas.

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

2. Calculating the Growth Rate

Next, you want to compute the growth column. The growth for each row can be calculated by dividing the annum value of the current row by the annum value of the next row and then subtracting 1 from the quotient. To achieve this, you can utilize the shift() function to shift the rows up.

Here’s how you can implement this:

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

3. Complete Example Code

Putting it all together, here is a complete example of how to create the new DataFrame as described:

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

4. Result

The resulting DataFrame (df2) will contain:

The filtered rows (only non-null annum values).

The new growth column that computes the growth rate based on adjacent values.

Summary

By following the steps outlined above, you can effectively create a new Pandas DataFrame that meets your specific requirements. The filtering of rows based on column values and the addition of a calculated growth rate column are invaluable techniques that enhance your data analysis capabilities. Whether you are working in finance, analytics, or any field that uses data, mastering Pandas will significantly improve your workflow.

If you have further questions or need more specific guidance, feel free to ask!