Get total of Pandas column

Question

I have a Pandas data frame, as shown below, with multiple columns and would like to get the total of column, MyColumn.

print df

           X           MyColumn      Y              Z   
0          A           84         13.0           69.0   
1          B           76         77.0          127.0   
2          C           28         69.0           16.0   
3          D           28         28.0           31.0   
4          E           19         20.0           85.0   
5          F           84        193.0           70.0

My attempt:

I have attempted to get the sum of the column using groupby and .sum():

Total = df.groupby['MyColumn'].sum()

print Total

This causes the following error:

TypeError: 'instancemethod' object has no attribute '__getitem__'

Expected Output

I'd have expected the output to be as follows:

Or alternatively, I would like df to be edited with a new row entitled TOTAL containing the total:

           X           MyColumn      Y              Z   
0          A           84         13.0           69.0   
1          B           76         77.0          127.0   
2          C           28         69.0           16.0   
3          D           28         28.0           31.0   
4          E           19         20.0           85.0   
5          F           84        193.0           70.0   
TOTAL                  319

For an illustration of why pandas is not pythonic, look no further than the confusion over how to simply sum a column. — user1416227, May 20 '17 at 18:23

score 380 · Accepted Answer · edited Aug 15 '22 at 16:39

You should use sum:

Total = df['MyColumn'].sum()
print(Total)
319

Then you use loc with Series, in that case the index should be set as the same as the specific column you need to sum:

df.loc['Total'] = pd.Series(df['MyColumn'].sum(), index=['MyColumn'])
print(df)
         X  MyColumn      Y      Z
0        A      84.0   13.0   69.0
1        B      76.0   77.0  127.0
2        C      28.0   69.0   16.0
3        D      28.0   28.0   31.0
4        E      19.0   20.0   85.0
5        F      84.0  193.0   70.0
Total  NaN     319.0    NaN    NaN

because if you pass scalar, the values of all rows will be filled:

df.loc['Total'] = df['MyColumn'].sum()
print(df)
         X  MyColumn      Y      Z
0        A        84   13.0   69.0
1        B        76   77.0  127.0
2        C        28   69.0   16.0
3        D        28   28.0   31.0
4        E        19   20.0   85.0
5        F        84  193.0   70.0
Total  319       319  319.0  319.0

Two other solutions are with at, and ix see the applications below:

df.at['Total', 'MyColumn'] = df['MyColumn'].sum()
print(df)
         X  MyColumn      Y      Z
0        A      84.0   13.0   69.0
1        B      76.0   77.0  127.0
2        C      28.0   69.0   16.0
3        D      28.0   28.0   31.0
4        E      19.0   20.0   85.0
5        F      84.0  193.0   70.0
Total  NaN     319.0    NaN    NaN

df.ix['Total', 'MyColumn'] = df['MyColumn'].sum()
print(df)
         X  MyColumn      Y      Z
0        A      84.0   13.0   69.0
1        B      76.0   77.0  127.0
2        C      28.0   69.0   16.0
3        D      28.0   28.0   31.0
4        E      19.0   20.0   85.0
5        F      84.0  193.0   70.0
Total  NaN     319.0    NaN    NaN

Note: Since Pandas v0.20, ix has been deprecated. Use loc or iloc instead.

That's great :) Thanks for the explanation, may I ask what `.loc` does in the above example? — Enigmatic, Dec 22 '16 at 15:36
`loc` is for [setting with enlargement](http://pandas.pydata.org/pandas-docs/stable/indexing.html#setting-with-enlargement). — jezrael, Dec 22 '16 at 15:38
Hmmm, docs says `The .loc/.ix/[] operations can perform enlargement when setting a non-existant key for that axis.`, so `loc` or `ix` or `[]`. in next [section](http://pandas.pydata.org/pandas-docs/stable/indexing.html#fast-scalar-value-getting-and-setting) is writes `at may enlarge the object in-place as above if the indexer is missing.` So all methods are good, but `at` is fastest I think. — jezrael, Dec 22 '16 at 15:55
This is great. Is it possible to put "Total" in column X by modifying *df.loc['Total'] = df['MyColumn'].sum()* — SModi, May 17 '21 at 15:33

Psidom · Answer 2 · 2018-06-04T14:30:21.813

Another option you can go with here:

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

#         X  MyColumn      Y       Z
#0        A     84.0    13.0    69.0
#1        B     76.0    77.0   127.0
#2        C     28.0    69.0    16.0
#3        D     28.0    28.0    31.0
#4        E     19.0    20.0    85.0
#5        F     84.0   193.0    70.0
#Total  NaN    319.0     NaN     NaN

You can also use append() method:

df.append(pd.DataFrame(df.MyColumn.sum(), index = ["Total"], columns=["MyColumn"]))

Update:

In case you need to append sum for all numeric columns, you can do one of the followings:

Use append to do this in a functional manner (doesn't change the original data frame):

# select numeric columns and calculate the sums
sums = df.select_dtypes(pd.np.number).sum().rename('total')

# append sums to the data frame
df.append(sums)
#         X  MyColumn      Y      Z
#0        A      84.0   13.0   69.0
#1        B      76.0   77.0  127.0
#2        C      28.0   69.0   16.0
#3        D      28.0   28.0   31.0
#4        E      19.0   20.0   85.0
#5        F      84.0  193.0   70.0
#total  NaN     319.0  400.0  398.0

Use loc to mutate data frame in place:

df.loc['total'] = df.select_dtypes(pd.np.number).sum()
df
#         X  MyColumn      Y      Z
#0        A      84.0   13.0   69.0
#1        B      76.0   77.0  127.0
#2        C      28.0   69.0   16.0
#3        D      28.0   28.0   31.0
#4        E      19.0   20.0   85.0
#5        F      84.0  193.0   70.0
#total  NaN     638.0  800.0  796.0

1.st exampe: `FutureWarning: The pandas.np module is deprecated and will be removed from pandas in a future version. Import numpy directly instead` — jaromrax, Sep 22 '20 at 12:43
this solution works well for me thanks. How do I select certain columns to compute totals ? — snow, Oct 12 '22 at 05:06

score 11 · Answer 3 · edited Mar 22 '18 at 14:21

11

Similar to getting the length of a dataframe, len(df), the following worked for pandas and blaze:

Total = sum(df['MyColumn'])

or alternatively

Total = sum(df.MyColumn)
print Total

edited Mar 22 '18 at 14:21

Ronak Shah

377,200
20
156
213

answered Oct 12 '17 at 15:02

Jeff Crites

111
1
2

score 5 · Answer 4 · answered Jan 20 '20 at 06:51

5

There are two ways to sum of a column

dataset = pd.read_csv("data.csv")

1: sum(dataset.Column_name)

2: dataset['Column_Name'].sum()

If there is any issue in this the please correct me..

answered Jan 20 '20 at 06:51

Suraj Verma

463
6
8

score 2 · Answer 5 · edited Oct 08 '18 at 07:12

As other option, you can do something like below

Group   Valuation   amount
    0   BKB Tube    156
    1   BKB Tube    143
    2   BKB Tube    67
    3   BAC Tube    176
    4   BAC Tube    39
    5   JDK Tube    75
    6   JDK Tube    35
    7   JDK Tube    155
    8   ETH Tube    38
    9   ETH Tube    56

Below script, you can use for above data

import pandas as pd    
data = pd.read_csv("daata1.csv")
bytreatment = data.groupby('Group')
bytreatment['amount'].sum()

Get total of Pandas column

5 Answers5

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