Скачать или смотреть How to Find the Minimum Value in a DataFrame Column Based on Multiple Conditions with Pandas

Learn how to efficiently use Pandas to find the minimum value in a column based on category levels from two other columns, and highlight specific rows in a DataFrame.
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Solving the Problem: Minimum Values Based on Conditions

Are you struggling with how to find the minimum value in a specific column of your DataFrame, but only when certain conditions in other columns are met? If you're using Pandas in Python, you’re not alone. Many users want to manipulate their DataFrames based on various filtering criteria, particularly when working with datasets that include categorical variables.

In this guide, we will explore how to use Pandas to achieve this and create new columns based on your findings. We will walk through an example that clearly illustrates the solution to the given problem.

The Challenge

Consider you have the following DataFrame named df, which has three columns: A, B, and C:

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

From this DataFrame, you want to create a new DataFrame df2, which will include:

X: Unique values from column A,

Y: Corresponding "YES" values from column B, and

Z: The minimum value from column C, but only for those rows where B is "YES".

Additionally, you want to create another DataFrame df3 to indicate which values in column C correspond to the minimum value identified, using a new column D.

Breaking Down the Solution

Step 1: Filter and Group Data

First, you need to filter the DataFrame to only keep the rows where column B equals "YES". Then, you can group by column A to find the minimum value in column C:

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

Explanation:

df.query('B == "YES"'): Filters the DataFrame to include only rows where B is "YES".

.groupby('A', as_index=False): Groups the filtered DataFrame by column A.

.min(): Gets the minimum values of column C within the grouped data.

.set_axis(...): Renames the resulting columns to X, Y, and Z accordingly.

Step 2: Highlight Minimum Values in the Original DataFrame

To identify the minimum values in column C for B = "YES" and flag those rows in a new column D in the original DataFrame df, we can do the following:

Identify the minimum values per category group.

Create a new column D initialized to 0.

Update column D to 1 where the original C value matches the identified minimum.

Here is how you can do it with code:

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

Summary of Resulting DataFrames

After executing the above steps, you will have:

df2:

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

df3:

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

Conclusion

Using Pandas to manipulate DataFrames based on conditions is straightforward when you know the right functions to use. This solution allows you to efficiently find the minimum values based on specific conditions and emphasize them as needed in your data analyses. Leveraging tools like groupby, query, and DataFrame manipulations will streamline your data processing tasks tremendously.

Feel free to copy the code snippets and experiment with your own data!