检查具有不同值系统的两个数据框列的相似性

Question

I have two columns of two different dataframes. The following chunks are the first 5 rows, but each column is much longer:

A = pd.DataFrame(['30-34', '20-24', '20-24', '15-19', '00-04'])

and

B = pd.DataFrame(['6','4', '4', '3', '0'])

I want to check whether both columns coincide, considering that

0 represents 00-04; 
1 represents 05-09; 
2 represents 10-14;
3 represents 15-19;
4 represents 20-24;
5 represents 25-29;
and 6 represents 30-34.

The desired output would be the number of non-matching elements. In the sample given, the desired output is "0", because the first 5 values of both columns are matching. I would give an approach that I tried but I have absolutely no idea.

Answer 1

IIUC, you have ranges, 5 by 5, and you want to match them to their integer division.

(B.astype(int).values == A[0].str.split('-', expand=True).astype(int)//5).all(axis=1)

output:

0    True
1    True
2    True
3    True
4    True

Check if the columns coincide:

(B.astype(int).values ==
 A[0].str.split('-', expand=True).astype(int)//5
).all(axis=1).all()

output: True

Intermediate steps:

# split on "-"
>>> A[0].str.split('-', expand=True)
    0   1
0  30  34
1  20  24
2  20  24
3  15  19
4  00  04

# get integer division
>>> A[0].str.split('-', expand=True).astype(int)//5
   0  1
0  6  6
1  4  4
2  4  4
3  3  3
4  0  0

# check if equals B
>>> B.astype(int).values == A[0].str.split('-', expand=True).astype(int)//5
      0     1
0  True  True
1  True  True
2  True  True
3  True  True
4  True  True

Answer 2

You can map the strings to integers with map , then compare with the other dataframe and count the number of non-matching items:

import pandas as pd
import io

data ='''0      30-34
1      20-24
2      20-24
3      15-19
4      00-04'''

data1 = '''0      6
1      4
2      4
3      3
4      0'''

df = pd.read_csv(io.StringIO(data), sep='\s+', names=['idx', 'string'])
df1 = pd.read_csv(io.StringIO(data1), sep='\s+', names=['idx', 'value'])

df['value'] = df['string'].map({'00-04': 0, '05-09':1, '10-14':2, '15-19':3, '20-24':4, '25-30':5, '30-34':6})

sum(df['value'] != df1['value'])