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Description:

pdwindowdecorators 0.0.1

pd-window-decorators
Easily apply windowing to functions that mutate Pandas DataFrames. Useful for data science projects where you want to apply a function for a DataFrame using smaller chunks of the DataFrame automatically. A moving window can be easily applied to the function using Python decorators.
Sliding Window
To apply a sliding window, import df_sliding_window from pd_window_decorators and apply it to your function. The decorator takes a timedelta object as a required argument to define the slice size. By default, the decorator will look for a Pandas DataFrame named df in the function arguments. The DataFrame must also have a time column. The column name is ds by default. The decorator also expects the function to return a DataFrame.
Example
Using the decorator with default arguments:
@df_sliding_window(window_size=timedelta(days=2))
def sum_all(df):
df.loc[:, 'sum'] = df['y'].sum()
return df

Using the decorator with custom arguments:
@df_sliding_window(window_size=timedelta(days=2), df_name='my_df', time_column='my_time')
def sum_all(my_df):
df.loc[:, 'sum'] = df['y'].sum()
return df

Note that in the second example, the DataFrame is named my_df and the df_name argument is set to my_df, so they match.
Arguments

Argument
Type
Optional
Description

window_size
timedelta
True
The size of the window to apply to the function.

df_name
str
False
The name of the DataFrame variable to pass to the function as an argument. Defaults to df.

time_column
str
False
The name of the column in the DataFrame that contains the time information. Defaults to ds.