Window functions in Pandas provide a powerful way to perform operations on a series of data, allowing you to compute statistics and other aggregations over a window of data points. These functions are particularly useful in time series analysis and other situations where you need to consider a range of data points around each observation.
Pandas supports four main types of windowing operations −
We will now learn how each of these can be applied on DataFrame objects.
A rolling window operation involves moving a fixed-size window across the data, performing an aggregation function (like sum or mean) within that window. It is very flexible and supports various time-based operations.
To perform this operation we can use the rolling() function. This function can be applied on a series of data. Specify the window=n argument and apply the appropriate statistical function on top of it. The rolling() function returns the pandas.typing.api.Rolling object.
Example
Following is the example of applying the rolling window operation on DataFrame using the rolling() function.
Output:
Note: Since the window size is 3, for first two elements there are nulls and from third the value will be the average of the n, n-1 and n-2 elements. Thus we can also apply various functions as mentioned above.
A weighted window applies a non-rectangular window function, often used in signal processing. The win_type parameter is used to specify the window type, which corresponds to a window function from the scipy.signal library.
Example
This example demonstrates applying the weighted window operation on the Pandas Series object.
Output:
An expanding window calculates the aggregation statistic over all the data points available up to the current point, allowing for cumulative calculations.
The expanding() function can be applied on a series of data. Specify the min_periods=n argument and apply the appropriate statistical function on top of it. This function returns a pandas.typing.api.Expanding object.
Example
Here is an example of applying the expanding window operation on the DataFame object.
Output:
This type of windowing operation applies exponential weighting to previous data points, which means that older data points are given progressively less importance.
The ewm() function is applied on a series of data. Specify any of the com, span, halflife argument and apply the appropriate statistical function on top of it. It assigns the weights exponentially. This function returns pandas.typing.api.ExponentialMovingWindow object.
Output:
Window functions are majorly used in finding the trends within the data graphically by smoothing the curve. If there is lot of variation in the everyday data and a lot of data points are available, then taking the samples and plotting is one method and applying the window computations and plotting the graph on the results is another method. By these methods, we can smooth the curve or the trend.