how to sum up columns from different dataframes into a single dataframe in pandas-CodePudding

Sample data

import pandas as pd

df1 = pd.DataFrame() 
df1["Col1"] = [0,2,4,6,2] 
df1["Col2"] = [5,1,3,4,0]
df1["Col3"] = [8,0,5,1,7]
df1["Col4"] = [1,4,6,0,8]
#df1_new = df1.iloc[:, 1:3]

df2 = pd.DataFrame() 
df2["Col1"] = [8,2,4,6,2,3,5] 
df2["Col2"] = [3,7,3,4,0,6,8]
df2["Col3"] = [5,0,5,1,7,9,1]
df2["Col4"] = [0,4,6,0,8,6,0]
#df2_new = df1.iloc[:, 1:3]

dataframes = [df1, df2]

for df in dataframes:
    df_new=df.iloc[:, 1:3]
    print(df_new.sum(axis=0))

result from above looks like this:

Col2    13  
Col3    21  
dtype: int64
Col2    31  
Col3    28  
dtype: int64

But how can I sum up both dataframes and put it into a single one?

Result should look like this:

CodePudding user response：

You can either sum the dataframes separately and then add the results, or sum the concatenated dataframes:

df1.iloc[:,1:3].sum()   df2.iloc[:,1:3].sum()

pd.concat([df1,df2]).iloc[:,1:3].sum()

In both cases the result is

Col2    44
Col3    49
dtype: int64

CodePudding user response：

Get the columnwise sums of both dataframes, take the middle two columns of each, and add them together. Then, transpose the result to turn the rows into columns:

(df1.iloc[:, 1:3].sum()   df2.iloc[:, 1:3].sum())).T

This outputs:

   Col2  Col3
0    44    49

CodePudding user response：

Here is one way:

print((df1[["Col2", "Col3"]]   df2[["Col2", "Col3"]]).sum().to_frame().T)

Or, if you need to use iloc for the columns, here is another way:

print((df1.iloc[:, 1:3]   df2.iloc[:, 1:3]).sum().to_frame().T)

Output (same for both):

   Col2  Col3
0  30.0  39.0