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I understand how to calculate a rolling sum, std or average. Example:
df['MA10'] = df['Asset1'].rolling(10).mean()
But I don't understand the syntax to calculate the rolling correlation between two dataframes columns: df['Asset1'] and df['Asset2']
The documentation doesn't provide any example regarding the correlation.
https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.rolling.html
Any insights?
Thanks!
934
Rolling correlations are simply applying a correlation between two time series (say sales of product x and product y) as a rolling window calculation. One major benefit of a rolling correlation is that we can visualize the change in correlation over time. The sample data (above) is charted (below).
Initialize two variables, col1 and col2, and assign them the columns that you want to find the correlation of. Find the correlation between col1 and col2 by using df[col1]. corr(df[col2]) and save the correlation value in a variable, corr. Print the correlation value, corr.
You can plot correlation between two columns of pandas dataframe using sns. regplot(x=df['column_1'], y=df['column_2']) snippet. What is this? You can see the correlation of the two columns of the dataframe as a scatterplot.
Window Rolling Mean (Moving Average)The moving average calculation creates an updated average value for each row based on the window we specify. The calculation is also called a “rolling mean” because it's calculating an average of values within a specified range for each row as you go along the DataFrame.
It's in there, even if hidden a bit:
df['Asset1'].rolling(10).corr(df['Asset2'])
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