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

how to pandas fast nested for loop for "non numeric" columns? because this for loop is way to slow:

for i in range(len(df1[column_A]):
    for j in range(len(df2[column_A]):
        if df1[column_A][i] == df2[column_A][j]:
            df1[column_B][i] = df2[column_B][j]
        else:
            pass

so any other way to do it by pandas itself or other libraries?

UPDATE:

and main goal is:

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

i also add question in official pandas github: https://github.com/pandas-dev/pandas/issues/59824

Solution

The nested loop you provided result in O(n^2) complexity, making it slow for larger datasets . Looping over the same range for both i and j, which is unnecessary. Instead you can use pd.merge

import pandas as pd

# Merge file1 and file2 on column A
merged_df = pd.merge(file1, file2, on='column_A') # assuming it is a pandas dataframe 

# Update file1_column_B with matched values from file2
file1_column_B = merged_df['column_B_y']

pd.merge() function merges two DataFrames ( file1 and file2 ) based on a common column ( column_A ).Also by default, pd.merge() performs an inner merge, which means only rows with matching values in column_A are included in the resulting DataFrame.

Time complexity of pd.merge is O(n + m) in best case scenario (concluding it) where n is the number of rows in the left DataFrame (file1) and m is the number of rows in the right DataFrame (file2). However, in the worst-case scenario (e.g., when there are many duplicate values in the merge column), the time complexity can be O(n × m).

You can also use numPy argsort + searchsorted

import numpy as np


sorted = np.argsort(file2['column_A'])

match = np.searchsorted(file2['column_A'][sorted], file1['column_A'])

file1_column_B = file2['column_B'][sorted[match]]

Time Complexity of above sort: argsort: O(n log n) searchsorted: O(m log n) Total: O(n log n + m log n)