我有一个数据框,如下所示:
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
我可以简单地计算列成对相关:
pt = pt.rolling(3).corr()
产生:
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
...
如何类似地计算数据帧的成对差异?我猜这相当于使用滚动窗口1。
编辑:正如评论中指出的,上面的示例实际上并不是我没有注意到的按列相关。
最佳答案
如果要9列:
# 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)
输出:
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
关于python - Pandas 成对算术,类似于rolling()。corr(),我们在Stack Overflow上找到一个类似的问题:https://stackoverflow.com/questions/59106942/