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 a 'total' row to the end of dataframe:

     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  total  18   9.47

I've tried to use the sum command but I end up with a Series, which although I can convert back to a Dataframe, doesn't maintain the data types:

tot_row = pd.DataFrame(df.sum()).T
tot_row['foo'] = 'tot'
tot_row.dtypes:
     foo    object
     bar    object
     qux    object

I would like to maintain the data types from the original data frame as I need to apply other operations to the total row, something like:

baz = 2*tot_row['qux'] + 3*tot_row['bar']

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()

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.

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

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

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

Building on JMZ answer

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

if you want to continue using your current index you can name the sum series using .rename() as follows:

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

This will add a row at the bottom of the table.

Building on answer from Matthias Kauer.

To add row total:

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

To add column total,

df.loc[:,"Column_Total"] = df.sum(axis=1)

This is the way that I do it, by transposing and using the assign method in combination with a lambda function. It makes it simple for me.

df.T.assign(GrandTotal = lambda x: x.sum(axis=1)).T

Following helped for me to add a column total and row total to a dataframe.

Assume dft1 is your original dataframe... now add a column total and row total with the following steps.

from io import StringIO
import pandas as pd

#create dataframe string
dfstr = StringIO(u"""
a;b;c
1;1;1
2;2;2
3;3;3
4;4;4
5;5;5
""")

#create dataframe dft1 from string
dft1 = pd.read_csv(dfstr, sep=";")

## add a column total to dft1
dft1['Total'] = dft1.sum(axis=1)

## add a row total to dft1 with the following steps

sum_row = dft1.sum(axis=0) #get sum_row first
dft1_sum=pd.DataFrame(data=sum_row).T #change it to a dataframe

dft1_sum=dft1_sum.reindex(columns=dft1.columns) #line up the col index to dft1
dft1_sum.index = ['row_total'] #change row index to row_total

dft1.append(dft1_sum) # append the row to dft1

Actually all proposed solutions render the original DataFrame unusable for any further analysis and can invalidate following computations, which will be easy to overlook and could lead to false results.

This is because you add a row to the data, which Pandas cannot differentiate from an additional row of data.

Example:

import pandas as pd
data = [1, 5, 6, 8, 9]
df = pd.DataFrame(data)
df
df.describe()

yields

	0
0	1
1	5
2	6
3	8
4	9

	0
count	5
mean	5.8
std	3.11448
min	1
25%	5
50%	6
75%	8
max	9

After

df.loc['Totals']= df.sum(numeric_only=True, axis=0)

the dataframe looks like this

	0
0	1
1	5
2	6
3	8
4	9
Totals	29

This looks nice, but the new row is treated as if it was an additional data item, so df.describe will produce false results:

	0
count	6
mean	9.66667
std	9.87252
min	1
25%	5.25
50%	7
75%	8.75
max	29

So: Watch out! and apply this only after doing all other analyses of the data or work on a copy of the DataFrame!