Pandas Dataframe Total Row

I have a dataframe, something like: foo bar qux 0 a 1 3.14 1 b 3 2.72 2 c 2 1.62 3 d 9 1.41 4 e 3 0.58 and I would like to add

Solution 1:

Append a totals row with

df.append(df.sum(numeric_only=True), ignore_index=True)

The conversion is necessary only if you have a column of strings or objects.

It's a bit of a fragile solution so I'd recommend sticking to operations on the dataframe, though. eg.

baz = 2*df['qux'].sum() + 3*df['bar'].sum()

Solution 2:

df.loc["Total"] = df.sum()

works for me and I find it easier to remember. Am I missing something? Probably wasn't possible in earlier versions.

I'd actually like to add the total row only temporarily though. Adding it permanently is good for display but makes it a hassle in further calculations.

Just found

df.append(df.sum().rename('Total'))

This prints what I want in a Jupyter notebook and appears to leave the df itself untouched.

Solution 3:

New Method

To get both row and column total:

import numpy as np
import pandas as pd


df = pd.DataFrame({'a': [10,20],'b':[100,200],'c': ['a','b']})

df.loc['Column_Total']= df.sum(numeric_only=True, axis=0)
df.loc[:,'Row_Total'] = df.sum(numeric_only=True, axis=1)

print(df)


                 a      b    c  Row_Total
0             10.0  100.0    a      110.0
1             20.0  200.0    b      220.0
Column_Total  30.0  300.0  NaN      330.0

Solution 4:

Use DataFrame.pivot_table with margins=True:

import pandas as pd
data = [('a',1,3.14),('b',3,2.72),('c',2,1.62),('d',9,1.41),('e',3,.58)]
df = pd.DataFrame(data, columns=('foo', 'bar', 'qux'))

Original df:

  foo  bar   qux
0   a    1  3.14
1   b    3  2.72
2   c    2  1.62
3   d    9  1.41
4   e    3  0.58

Since pivot_table requires some sort of grouping (without the index argument, it'll raise a ValueError: No group keys passed!), and your original index is vacuous, we'll use the foo column:

df.pivot_table(index='foo',
               margins=True,
               margins_name='total',  # defaults to 'All'
               aggfunc=sum)

Voilà!

       bar   qux
foo             
a        1  3.14
b        3  2.72
c        2  1.62
d        9  1.41
e        3  0.58
total   18  9.47

Solution 5:

Alternative way (verified on Pandas 0.18.1):

import numpy as np
total = df.apply(np.sum)
total['foo'] = 'tot'
df.append(pd.DataFrame(total.values, index=total.keys()).T, ignore_index=True)

Result:

   foo   bar   qux
0    a     1  3.14
1    b     3  2.72
2    c     2  1.62
3    d     9  1.41
4    e     3  0.58
5  tot    18  9.47