QST: how to pandas fast nested for loop for "non numeric" columns?

[ganbaaelmer]

Research

• I have searched the [pandas] tag on StackOverflow for similar questions.

• I have asked my usage related question on StackOverflow.

Link to question on StackOverflow

https://stackoverflow.com/questions/78992395/how-to-pandas-fast-nested-for-loop-for-non-numeric-columns

Question about pandas

"""
input:

df1:
name rpm power
0 John 1500 high+
1 Mary 1400 high-
2 Sally 300 low-
3 Doe 700 medium-
4 July 1000 medium+

df2:
name age
0 Peter 77
1 Sally 44
2 Micky 22
3 Sally 34
4 July 50
5 Bob 20

required output is:

but i want it df2:
name age rpm power
0 Peter 77 0 NA
1 Sally 44 300 low-
2 Micky 22 0 NA
3 Sally 34 300 low-
4 July 50 1000 medium+
5 Bob 20 0 NA
"""

[ganbaaelmer]

found slow solution:

`
import pandas as pd
import numpy as np

df1 = pd.DataFrame({
'name': ['John', 'Mary', 'Sally', 'Doe', 'July'],
'rpm': [1500, 1400, 300, 700, 1000],
'power': ['high+', 'high-', 'low-', 'medium-', 'medium+']
})

df2 = pd.DataFrame({
'name': ['Peter', 'Sally', 'Micky', 'Sally', 'July', 'Bob'],
'age': [77, 44, 22, 34, 50, 20]
})

print("\ndf1:\n", df1)
print("\ndf2:\n", df2)

merged = pd.merge(df2, df1, on='name', how='left')

merged['rpm'].fillna(0, inplace=True)
merged['power'].fillna('NA', inplace=True)

print("\nmerged:\n", merged)
`

[ganbaaelmer]

found slow solution but if you have more faster solution then let me know. please comment on this post