问题
I want to add values of 4 Dataframes with the same indexes in Pandas. If there are two dataframes, df1 and df2, we may write:
df1.add(df2)
and for 3 dataframes:
df3.add(df2.add(df1))
I wonder if there is a more general way to do so in Python.
回答1:
Option 1
Use sum
sum([df1, df2, df3, df4])
Option 2
Use reduce
from functools import reduce
reduce(pd.DataFrame.add, [df1, df2, df3, df4])
Option 3
Use pd.concat and pd.DataFrame.sum with level=1
This only works if there is a single level to the dataframe indices. We've have to get a little more cute to make it work. I recommend the other options.
pd.concat(dict(enumerate([df1, df2, df3, df4]))).sum(level=1)
Setup
df = pd.DataFrame([[1, -1], [complex(0, 1), complex(0, -1)]])
df1, df2, df3, df4 = [df] * 4
Demo
sum([df1, df2, df3, df4])
0 1
0 (4+0j) (-4+0j)
1 4j -4j
from functools import reduce
reduce(pd.DataFrame.add, [df1, df2, df3, df4])
0 1
0 (4+0j) (-4+0j)
1 4j -4j
pd.concat(dict(enumerate([df1, df2, df3, df4]))).sum(level=1)
0 1
0 (4+0j) (-4+0j)
1 4j -4j
Timing
small data
%timeit sum([df1, df2, df3, df4])
%timeit reduce(pd.DataFrame.add, [df1, df2, df3, df4])
%timeit pd.concat(dict(enumerate([df1, df2, df3, df4]))).sum(level=1)
1000 loops, best of 3: 591 µs per loop
1000 loops, best of 3: 456 µs per loop
100 loops, best of 3: 3.61 ms per loop
larger data
df = pd.DataFrame([[1, -1], [complex(0, 1), complex(0, -1)]])
df = pd.concat([df] * 1000, ignore_index=True)
df = pd.concat([df] * 100, axis=1, ignore_index=True)
df1, df2, df3, df4 = [df] * 4
%timeit sum([df1, df2, df3, df4])
%timeit reduce(pd.DataFrame.add, [df1, df2, df3, df4])
%timeit pd.concat(dict(enumerate([df1, df2, df3, df4]))).sum(level=1)
100 loops, best of 3: 3.94 ms per loop
100 loops, best of 3: 2.9 ms per loop
1 loop, best of 3: 1min per loop
来源：https://stackoverflow.com/questions/44973981/summing-up-more-than-two-dataframes-with-the-same-indexes-in-pandas