Pandas Add Values of Column to Different Dataframe

Question

So I have a DataFrame, df1, that has 3 columns, A, B, and C as such:

                           A           B           C
Arizona                    0    2.800000    5.600000
California                 0   18.300000   36.600000
Colorado                   0    2.666667    5.333333
Connecticut                0    0.933333    1.866667
Delaware                   0    0.100000    0.200000
Florida                    0    0.833333    1.666667
Georgia                    0    0.000000    0.000000
Hawaii                     0    1.000000    2.000000
Illinois                   0    3.366667    6.733333
Indiana                    0    0.000000    0.000000
Iowa                       0    0.000000    0.000000

I then have another dataframe, df2, that has just one column, D:

                    D
Arizona            13
California         18
Colorado            5
Connecticut        15
Delaware            7
Florida             5
Georgia            13
Hawaii              3
Illinois           21
Indiana             2
Iowa                4

What I'd like to do is add the values of column D to all the columns in df1. By add I mean take the value of [Arizona, A] and add it to the value of [Arizona, D] not add column D as a new column. So far I tried using

df1 + df2 #returned all NaN
df1 + df2['D'] #Also returned all NaN
df1['A'] + df2['D'] #Returned a new dataframe with each as a separate column

I'm now not entirely sure where to go from here so I'd love some advice on how to solve this. It doesn't seem like it should be difficult and I'm probably missing something obvious. Any help would be appreciated.

MaxU - stand with Ukraine · Accepted Answer · 2016-08-15 18:56:19Z

2

you can use add() method:

In [22]: df1.add(df2.D, axis='index')
Out[22]:
                A          B          C
Arizona      13.0  15.800000  18.600000
California   18.0  36.300000  54.600000
Colorado      5.0   7.666667  10.333333
Connecticut  15.0  15.933333  16.866667
Delaware      7.0   7.100000   7.200000
Florida       5.0   5.833333   6.666667
Georgia      13.0  13.000000  13.000000
Hawaii        3.0   4.000000   5.000000
Illinois     21.0  24.366667  27.733333
Indiana       2.0   2.000000   2.000000
Iowa          4.0   4.000000   4.000000

answered Aug 15, 2016 at 18:56

MaxU - stand with Ukraine

212k37 gold badges402 silver badges436 bronze badges

Sign up to request clarification or add additional context in comments.

7 Comments

Nehal J Wani Over a year ago

Your answer was very quick. How did you generate the dataframe in ipython so quickly?

MaxU - stand with Ukraine Over a year ago

@NehalJWani, read_clipboard

JSolomonCulp Over a year ago

Fantastic! Hadn't thought to use axis = 'index'. Somewhat interesting side note, using df2.D works, but using df2[[0]] doesn't and returns a df filled with NaN. Will accept as answer in 6 minutes when it lets me

MaxU - stand with Ukraine Over a year ago

@JSolomonCulp, because df2[[0]] - returns dataframe, not a series. You can either add series with or a dataframe of the same shape (in both cases it will be aligned by indexes)... But you can do it this way: df.add(df2.ix[:, 0], axis='index')

MaxU - stand with Ukraine Over a year ago

@JSolomonCulp, sure, as i showed before: df2.ix[:, 0] - gives you 0-th column

|

h72696368 · Accepted Answer · 2016-08-15 19:04:44Z

0

are you trying to do something like this?

df1 = DataFrame({'A':{'a':1, 'b':2}, 'B':{'a':10, 'b':20}})
df2 = DataFrame({'C':{'a':2, 'b':2}})
df1['A+C'] = df1['A'] + df2['C']
df1['B+C'] = df1['B'] + df2['C']

print (df1)
   A   B  A+C  B+C
a  1  10    3   12
b  2  20    4   22

answered Aug 15, 2016 at 19:04

h72696368

11 bronze badge

Collectives™ on Stack Overflow

Pandas Add Values of Column to Different Dataframe

2 Answers 2

7 Comments

Comments

Your Answer

Hot Network Questions

Collectives™ on Stack Overflow

2 Answers 2

7 Comments

Comments

Your Answer

Sign up or log in

Post as a guest

Related