Generating Lagged Pandas Columns

Summary

The webpage describes a Python method for generating lagged columns in Pandas DataFrame for time series data analysis.

Abstract

The webpage titled "Generating Lagged Pandas Columns" focuses on a Python technique used for data preprocessing in time series data analysis. It discusses the challenge of predicting a variable using a trailing window of its previous values, requiring the addition of lagged columns to the data. The author presents a straightforward loop with a list comprehension to create lagged features, demonstrated on Tesla returns data from Yahoo Finance. The process involves using the DataFrame.shift method to shift the values, pd.concat method to join the dataframes, and a list comprehension to concatenate the names of unlagged columns with the lag period. This method is considered efficient and error-reducing.

Opinions

The author believes that adding lagged columns to time series data can be messy, especially when dealing with multiple lag periods.
The author suggests that the presented method, involving a loop and a list comprehension, is more efficient and less error-prone than manually adding lagged columns.
The author emphasizes the importance of having a concise way to generate lagged features, as it is a common requirement in time series analysis.
The author uses Tesla returns data from Yahoo Finance to demonstrate the method, implying its applicability to real-world financial data analysis.
The author promotes an AI service at the end of the article, suggesting its relevance and usefulness for readers interested in this topic.

Python — Data Preprocessing

In time series data, sometimes we wish to predict some variable given only a trailing window of its previous values. In order to use models that expect the input with predictors as columns with rows aligned with the outcome (such as scikit-learn estimator API), this requires adding lagged columns to our data.

This can get messy if we do it column by column, and even messier if we have multiple lag periods we want to calculate features for!

The solution to this is a fairly straightforward loop, with a list comprehension for naming lagged features according to their lag period. I’ve chosen to demonstrate this on some Tesla returns from Yahoo finance. First, let’s import the data.

Now, here’s the lagged features loop! Explanations will follow…

and so on… up to Adj Close_lag10

As always, here are the ingredients:

DataFrame.shift: The main workhorse of the loop, this pandas dataframe method produces a new dataframe with the same index but with values shifted the number of rows specified.

pd.concat: This pandas method allows us to join the dataframes of lagged features produced by shift to each other one at a time.

[x + "_lag" + str(window) for x in df.columns]: This list comprehension concatenates together the name of the unlagged column with the string "_lag" and the period by which the loop is currently shifting features by. You can use whatever string you’d like to identify lagged columns.

I’ve found that the above snippet has saved me from having to copy and paste code a bunch of times. Consequently, I’ve made fewer mistakes due to forgetting to change column names or other things in pasted code. As generating lagged features is something that often comes up in analyzing time series, it’s helpful to have a concise way to do it.