In pandas, how can I get a DataFrame as the output while I sum the DataFrame

Question

While I sum a DataFrame, it returns a Series:

In [1]: import pandas as pd
In [2]: df = pd.DataFrame([[1, 2, 3], [2, 3, 3]], columns=['a', 'b', 'c'])

In [3]: df
Out[3]: 
      a  b  c
   0  1  2  3
   1  2  3  3

   In [4]: s = df.sum()

   In [5]: type(s)
   Out[5]: pandas.core.series.Series

I know I can construct a new DataFrame by this Series. But, is there any more "pandasic" way?

Answer 1

I'm going to go ahead and say... "No", I don't think there is a direct way to do it, the pandastic way (and pythonic too) is to be explicit:

pd.DataFrame(df.sum(), columns=['sum'])

or, more elegantly, using a dictionary (be aware that this copies the summed array):

pd.DataFrame({'sum': df.sum()})

As @root notes it's faster to use:

pd.DataFrame(np.sum(df.values, axis=0), columns=['sum'])

(As the zen of python states: "practicality beats purity", so if you care about this time, use this).

However, perhaps the most pandastic way is to just use the Series! :)

.

Some %timeits for your tiny example:

In [11]: %timeit pd.DataFrame(df.sum(), columns=['sum'])
1000 loops, best of 3: 356 us per loop

In [12]: %timeit pd.DataFrame({'sum': df.sum()})
1000 loops, best of 3: 462 us per loop

In [13]: %timeit  pd.DataFrame(np.sum(df.values, axis=0), columns=['sum'])
1000 loops, best of 3: 205 us per loop

and for a slightly larger one:

In [21]: df = pd.DataFrame(np.random.randn(100000, 3), columns=list('abc'))

In [22]: %timeit pd.DataFrame(df.sum(), columns=['sum'])
100 loops, best of 3: 7.99 ms per loop

In [23]: %timeit pd.DataFrame({'sum': df.sum()})
100 loops, best of 3: 8.3 ms per loop

In [24]: %timeit  pd.DataFrame(np.sum(df.values, axis=0), columns=['sum'])
100 loops, best of 3: 2.47 ms per loop

Answer 2

Often it is necessary not only to convert the sum of the columns into a dataframe, but also to transpose the resulting dataframe. There is also a method for this:

df.sum().to_frame().transpose()