How can I merge together several pandas dataframes on a certain column without 'pandas.merge'?

Question

I often find myself with several pandas dataframes in the following form:

import pandas as pd
df1 = pd.read_table(\'filename1.dat\')
df2 = pd.read_table(\'filen         


        

Answer 1

Assuming that the three dataframes have the same index, you could just add columns to get the desired dataframes and not worry about merging, like so,

import pandas as pd

#create the dataframe
colA = ['name1', 'name2', 'name3', 'name4']
first = [ 342, 822, 121, 3434]
second = [ 8,1,1,2]
third = [ 910,301,132, 299]
df1 = pd.DataFrame({'colA': colA, 'first': first})
df2 = pd.DataFrame({'colA': colA, 'second': second})
df3 = pd.DataFrame({'colA': colA, 'third': third})


df_merged = df1.copy()
df_merged['second']= df2.second
df_merged['third']= df3.third
print (df_merged.head())

    colA  first  second  third
0  name1    342       8    910
1  name2    822       1    301
2  name3    121       1    132
3  name4   3434       2    299

Answer 2

You can set columnA as the index and concat (reset index at the end):

dfs = [df1, df2, df3]

pd.concat([df.set_index('columnA') for df in dfs], axis=1).reset_index()
Out: 
  columnA  first_values  second_values  third_values
0   name1           342              8           910
1   name2           822              1           301
2   name3           121              1           132
3   name4          3434              2           299