How iterate pandas dataframe if condition is met then add new rows to df

Question

I want to iterate each row of df2['name]' if the coin name is also found in df1 I want to create a new row in df2 with the tag names of df1. Alternatively create a new Df3 which ever the easiest.

I have tried many solution such as iterating but I had no luck

for row in df2['name']:
  
   for row2 in df1['name']:
   
      if row == row2:
        ...cannot complete this step...

My main issue it is that I don't know what is the best methodology to approach this. Iterations or not? I am sure that pandas has a better method but I cannot work it out.

I have a pandas dataframe that looks like that

df1

	name	tags
1	bitcoin	mineable
2	ethereum	pow

and a second one like this

df2

	name	value
1	bitcoin	0
2	ethereum	0.242424

Df3 the one I want to create

	name	value	tags
1	bitcoin	0	mineable
2	ethereum	0.242424	pow

Hello, instead of iterating, you can use ```pandas.merge``` to join dataframes based on defined key which is ```name``` in your case. https://pandas.pydata.org/docs/getting_started/comparison/comparison_with_sql.html#inner-join — s3nh, Jun 28 '21 at 07:23

score 1 · Answer 1 · answered Jun 28 '21 at 07:22

There is no need to iterate

import pandas as pd
from io import StringIO

df1 = pd.read_csv(StringIO("""name  tags
bitcoin     mineable
ethereum    pow"""), sep="\s+")


df2 = pd.read_csv(StringIO("""name  value
bitcoin     0
ethereum    0.242424"""), sep="\s+")

>>> df1.merge(df2, on="name")
       name      tags     value
0   bitcoin  mineable  0.000000
1  ethereum       pow  0.242424

How iterate pandas dataframe if condition is met then add new rows to df

1 Answers1