python pandas window functions

pandas is a powerful data manipulation library in python, and it provides various tools for working with time-series data. window functions are an essential part of pandas when it comes to analyzing and manipulating time-series data. in this tutorial, we will explore the basics of window functions in pandas with code examples.
before we begin, make sure you have pandas installed. you can install it using the following command:
let's start by importing pandas and creating a sample dataframe for our tutorial:
window functions operate on a set of rows related to the current row. they are useful for tasks like computing rolling averages, cumulative sums, and more.
the rolling function is a popular window function in pandas. it allows you to perform calculations on a specific window of data.
this will add a new column 'rolling_mean' to our dataframe, containing the 3-day rolling mean of the 'value' column.
the expanding function is another window function that considers all previous rows.
this will add a new column 'expanding_sum' to our dataframe, containing the expanding sum of the 'value' column.
you can also define custom functions and apply them using the apply function.
this will add a new column 'custom_function_result' to our dataframe, containing the result of applying the custom window function.
in this tutorial, we covered the basics of window functions in pandas, focusing on the rolling and expanding functions. we also explored how to use custom window functions for more specific analysis. window functions are powerful tools for time-series data manipulation, providing flexibility and ease of use in various analytical tasks.
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