pandas dataframe index match

Question

I'm wondering if there is a more efficient way to do an "index & match" type function that is popular in excel. For example - given two pandas DataFrames, update the df_1 with information found in df_2:

import pandas as pd

df_1 = pd.DataFrame({'num_a':[1, 2, 3, 4, 5],
                     'num_b':[2, 4, 1, 2, 3]})    
df_2 = pd.DataFrame({'num':[1, 2, 3, 4, 5],
                     'name':['a', 'b', 'c', 'd', 'e']})

I'm working with data sets that have ~80,000 rows in both df_1 and df_2 and my goal is to create two new columns in df_1, "name_a" and "name_b".

Below is the most efficient method that I could come up with. There has to be a better way!

name_a = []
name_b = []
for i in range(len(df_1)):

    name_a.append(df_2.name.iloc[df_2[
                  df_2.num == df_1.num_a.iloc[i]].index[0]])
    name_b.append(df_2.name.iloc[df_2[
                  df_2.num == df_1.num_b.iloc[i]].index[0]])

df_1['name_a'] = name_a
df_1['name_b'] = name_b

Resulting in:

>>> df_1.head()
   num_a  num_b name_a name_b
0      1      2      a      b
1      2      4      b      d
2      3      1      c      a
3      4      2      d      b
4      5      3      e      c

pandas dataframe index match

3 Answers