Скачать или смотреть How to Train a Linear Regression for Each Pandas DataFrame Row and Generate the Slope

Learn how to apply linear regression on each row of a Pandas DataFrame to calculate and extract the slope coefficients effectively.
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This video is based on the question https://stackoverflow.com/q/78175287/ asked by the user 'Giampaolo Levorato' ( https://stackoverflow.com/u/8964393/ ) and on the answer https://stackoverflow.com/a/78175332/ provided by the user 'e-motta' ( https://stackoverflow.com/u/16646078/ ) at 'Stack Overflow' website. Thanks to these great users and Stackexchange community for their contributions.

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How to Train a Linear Regression for Each Pandas DataFrame Row and Generate the Slope

When working with time series or sequential data, it can often be useful to analyze how relationships between variables evolve over time. A common method employed for this purpose is linear regression. In this post, we will discuss how to apply linear regression to each row of a Pandas DataFrame to compute the slope of the linear regression line based on specific columns of data.

The Problem

Suppose you have created a Pandas DataFrame with a structure similar to the one below:

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

The resulting DataFrame has columns where:

col1 is the target variable.

predFeature serves as a predictive feature.

The goal is to create an additional column named slope that holds the coefficients from the linear regression model trained on subsets of the DataFrame.

Example Row Calculations

For instance:

The slope for the 5th row would use:

Independent values: [1, 2, 3, 4]

Dependent values: [11, 22, 33, 24]

This results in a slope of approximately 0.102.

The Solution

Step 1: Import Necessary Libraries

To kick things off, make sure you have the libraries needed for linear regression imports. You will specifically require numpy, pandas, and sklearn:

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

Step 2: Define a Function to Calculate the Slope

Next, you will create a function that leverages the LinearRegression class from sklearn to compute the slope of the linear regression line for the given features and targets.

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

Step 3: Apply the Function on DataFrame Rows

With the function ready, apply it to compute the slope for each row based on arrayTarget and arrayPred:

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

Result Validation

After implementing the above, your DataFrame will automatically include the new slope column, accurately reflecting the computed values:

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

You can expect an output such as:

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

Conclusion

In summary, training a linear regression model for each row in a Pandas DataFrame is an effective method to examine how relationships change over time. By incorporating the slope of the regression line, you can gain deeper insights into your data dynamics. Remember, the approach demonstrated may not be optimal for extremely large DataFrames due to efficiency concerns, but it provides a straightforward way to accomplish your goal.

Happy coding!