Creating lags and leads of a column

Python
#1

Lags and leads is used in a DataFrame to analyze time-series data and identify patterns or trends over time. By creating lags and leads of a column, we can compare the values of a column at different time points and identify any changes or trends over time. In Pandas, we can create lags and leads of a column in a DataFrame using various methods. Here are some of the common methods:

1. Using "shift()" function:

The shift() function allows you to move the values in a column up or down by a specified number of rows. A positive number of rows creates a lagged version of the column, while a negative number of rows creates a lead version of the column. The shift() function can be used to create time series features, among other things.

  • Import the necessary libraries.
  • Load the DataFrame you want to create lags and leads of.
  • Use the shift() function to create lags and leads of a column.
  • The resulting DataFrame will have two additional columns: one with the lagged values of the specified column and one with the lead values of the specified column.
Example:

2. Using "diff()" function:

The diff() function calculates the difference between each value in a column and the previous value in the same column. A positive difference indicates that the current value is higher than the previous value, while a negative difference indicates that the current value is lower than the previous value. A positive argument to diff() creates a lagged version of the column, while a negative argument creates a lead version of the column. The diff() function can be used to create time series features, among other things.

  • Import the necessary libraries.
  • Load the DataFrame you want to create lags and leads of.
  • Use the diff() function to create lags and leads of a column.
  • The resulting DataFrame will have two additional columns: one with the lagged values of the specified column and one with the lead values of the specified column.
Example:

3. Using "shift()" and "concat()" functions:

The shift() function allows you to move the values in a column up or down by a specified number of rows. A positive number of rows creates a lagged version of the column, while a negative number of rows creates a lead version of the column. The concat() function is used to concatenate two or more DataFrames along a specified axis. This method can be useful if you need to create multiple lagged or leaded versions of a column.

  • Import the necessary libraries.
  • Load the DataFrame you want to create lags and leads of.
  • Use the shift() function to create lags and leads of a column.
  • Concatenate the original DataFrame with the lagged and leaded versions using the concat() function.
  • The resulting DataFrame will have two additional columns: one with the lagged values of the specified column and one with the lead values of the specified column.
Example: