I have a dataframe as follows:
fsym EOS BTC BNB
time
2018-11-30 00:00:00+00:00 -0.051903 -0.069088 -0.058162
2018-12-01 00:00:00+00:00 0.026936 0.044739 0.040303
2018-12-02 00:00:00+00:00 -0.034843 -0.012935 -0.005900
2018-12-03 00:00:00+00:00 -0.108108 -0.070375 -0.028180
2018-12-04 00:00:00+00:00 -0.048583 0.019509 0.131986
I can calculate a column pairwise correlation simply with:
pt = pt.rolling(3).corr()
which yields:
sym EOS BTC BNB
time fsym
2018-11-30 00:00:00+00:00 EOS NaN NaN NaN
BTC NaN NaN NaN
BNB NaN NaN NaN
2018-12-01 00:00:00+00:00 EOS NaN NaN NaN
BTC NaN NaN NaN
BNB NaN NaN NaN
2018-12-02 00:00:00+00:00 EOS 1.000000 0.952709 0.938688
BTC 0.952709 1.000000 0.999066
BNB 0.938688 0.999066 1.000000
2018-12-03 00:00:00+00:00 EOS 1.000000 0.998738 0.969385
BTC 0.998738 1.000000 0.980492
BNB 0.969385 0.980492 1.000000
...
How can I similarly calculate the pairwise differences for the dataframe? It would be the equivalent of using a rolling window of 1 I guess.
EDIT: As pointed out in the comments, the above example isn't actually a columnwise correlation which I hadn't noticed.
If you want the 9 columns:
# test data
df = pd.DataFrame(np.arange(12).reshape(-1,3), columns=list('abc'))
s = df.values
new_cols = pd.MultiIndex.from_product([df.columns, df.columns])
pd.DataFrame((s[:,None,:] - s[:, :, None]).reshape(len(df), -1),
index=df.index,
columns=new_cols)
Output:
a b c
a b c a b c a b c
0 0 1 2 -1 0 1 -2 -1 0
1 0 1 2 -1 0 1 -2 -1 0
2 0 1 2 -1 0 1 -2 -1 0
3 0 1 2 -1 0 1 -2 -1 0