<p>另一种解决方案:</p>
<pre><code>val_list = [['ALPHA01', [[1573582200000, 96.96746826171875], [1573582260000, 97.45691935221355], [1573582320000, 97.4244893391927], [1573582380000, 97.32177734375], [1573582440000, None], [1573582500000, None]]], ['BETA01', [[1573582200000, 89.6187235514323], [1573582260000, 90.69141642252605], [1573582320000, 90.83389282226562], [1573582380000, 90.83180745442708], [1573582440000, 90.72371419270833], [1573582500000, None]]], ['ALPHA02', [[1573582200000, 96.67467244466145], [1573582260000, 97.1467997233073], [1573582320000, 97.23036702473958], [1573582380000, 97.26894124348958], [1573582440000, None], [1573582500000, None]]], ['BETA02', [[1573582200000, 90.92616780598958], [1573582260000, 91.39727783203125], [1573582320000, 91.28725179036458], [1573582380000, 91.39530436197917], [1573582440000, 91.26514689127605], [1573582500000, None]]]]
index = [x[0] for x in val_list[0][1]]
val_dict = dict(val_list)
df = pd.DataFrame(val_dict, index=index)
for col in df.columns:
df[col] = [elem[1] for elem in df[col]]
df
ALPHA01 BETA01 ALPHA02 BETA02
1573582200000 96.967468 89.618724 96.674672 90.926168
1573582260000 97.456919 90.691416 97.146800 91.397278
1573582320000 97.424489 90.833893 97.230367 91.287252
1573582380000 97.321777 90.831807 97.268941 91.395304
1573582440000 NaN 90.723714 NaN 91.265147
1573582500000 NaN NaN NaN NaN
</code></pre>