Скачать или смотреть How to Find Maximum and Minimum Values in Pandas DataFrames Based on Conditions

Discover how to efficiently calculate maximum and minimum values across different columns in a Pandas DataFrame while considering linked column relationships.
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Max & Min Calculation in Pandas DataFrames: A Step-by-Step Guide

When working with data in Python, specifically in a Pandas DataFrame, you might come across a situation where you need to find specific maximum and minimum values across various columns based on relationships between them. In this guide, we will explore how to identify and manipulate these values efficiently.

The Problem at Hand

Imagine you have a DataFrame structured like this:

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In this DataFrame:

Columns with names starting with A link to those starting with X.

Similarly, columns starting with B correlate with those starting with Y.

Your goal is to derive two new columns: Tstrt and Tend, which hold the largest start value and its corresponding end value from the relevant columns. Additionally, another set of columns: Ustrt and Uend, must be derived based on the linked columns.

The Proposed Solution

To achieve this, we will employ the power of Pandas to filter and perform lookups easily. Below is a step-by-step breakdown of the solution:

Step 1: Filter the DataFrame

Begin by isolating the start and end columns using regex:

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Step 2: Identify Maximum Values

Next, leverage the idxmax function to find the index of the maximum value in each row:

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Step 3: Map Relationships

Define how the columns correlate:

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Step 4: Lookup Values for Tstrt and Tend

Get the values of the maximum start and corresponding end:

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Step 5: Fill Linked Columns Ustrt and Uend

Finally, fill your new columns based on the mapping:

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Final Output

After executing the above code, your DataFrame will yield:

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Conclusion

By employing the above techniques, you can efficiently manipulate DataFrames in Pandas to identify and relate data based on column dependencies. This approach not only provides a clear pathway to achieving desired outcomes but also enhances your data processing skills in Python.

Using Pandas with proper functions can significantly ease these tasks and allow for more complex manipulations as your data needs grow. Happy data analyzing!