I am trying to calcualte and then visualize the rolling correlation between multiple columns in a 180 (3 in this example) days window.
My data is formatted like that (in the orginal file there are 12 columns plus the timestamp and thousands of rows):
import numpy as np
import pandas as pd
df = pd.DataFrame({"Timestamp" : ['1993-11-01' ,'1993-11-02', '1993-11-03', '1993-11-04','1993-11-15'], "Austria" : [6.18 ,6.18, 6.17, 6.17, 6.40],"Belgium" : [7.05, 7.05, 7.2, 7.5, 7.6],"France" : [7.69, 7.61, 7.67, 7.91, 8.61]},index = [1, 2, 3,4,5])
Timestamp Austria Belgium France
1 1993-11-01 6.18 7.05 7.69
2 1993-11-02 6.18 7.05 7.61
3 1993-11-03 6.17 7.20 7.67
4 1993-11-04 6.17 7.50 7.91
5 1993-11-15 6.40 7.60 8.61
I cant just use this formula, because I get a formatting error if I do because of the Timestamp column:
df.rolling(2).corr(df)
ValueError: could not convert string to float: '1993-11-01'
When I drop the Timestamp column I get a result of 1.0 for every cell, thats also not right and additionally I lose the Timestamp which I will need for the visualization graph in the end.
df_drop = df.drop(columns=['Timestamp'])
df_drop.rolling(2).corr(df_drop)
Austria Belgium France
1 NaN NaN NaN
2 NaN NaN 1.0
3 1.0 1.0 1.0
4 -inf1.0 1.0
5 1.0 1.0 1.0
Any experiences how to do the rolling correlation with multiple columns and a data index?
Building on the answer of Shreyans Jain I propose the following. It should work with an arbitrary number of columns:
import itertools as it
# omit timestamp-col
cols = list(df.columns)[1:]
# -> ['Austria', 'Belgium', 'France']
col_pairs = list(it.combinations(cols, 2))
# -> [('Austria', 'Belgium'), ('Austria', 'France'), ('Belgium', 'France')]
res = pd.DataFrame()
for pair in col_pairs:
# select the first three letters of each name of the pair
corr_name = f"{pair[0][:3]}_{pair[1][:3]}_corr"
res[corr_name] = df[list(pair)].\
rolling(min_periods=1, window=3).\
corr().iloc[0::2, -1].reset_index(drop=True)
print(str(res))
Aus_Bel_corr Aus_Fra_corr Bel_Fra_corr
0 NaN NaN NaN
1 NaN NaN NaN
2 -1.000000 -0.277350 0.277350
3 -0.755929 -0.654654 0.989743
4 0.693375 0.969346 0.849167
The NaN-Values at the beginning result from the windowing.
Update: I uploaded a notebook with detailed explanations for what happens inside the loop.
https://github.com/cknoll/demo-material/blob/main/pandas/pandas_rolling_correlation_iloc.ipynb