根据另一个数据帧对一个数据帧中的数据进行排序的最佳方法是什么？

Question

I have 2 datasets and I am trying to sort data from one dataset based on the values of the second dataset and create a new column in the first dataset. If the values in dataset 1 and 2 are matching then I want to populate the new column as true else false. What is the best way of doing this in Python? May code (given below) is not working.

Data:

df1
    Index   ID  type 1  type2
0   1   A.34    6.3 7.1
1   2   A.35    5.8 7.3
2   3   A.36    6.8 5.2
3   4   A.37    7.8 6.4
4   5   A.38    6.9 8.8


df2
    Index   ID  Type 2
0   1   A.55    6.7
1   2   A.35    3.6
2   3   A.69    5.8
3   4   A.34    9.2
4   5   A.38    7.7

# Required Output
df3
Index   ID  type 1  type2   Status
0   1   A.34    6.3 7.1 bad
1   2   A.35    5.8 7.3 good
2   3   A.36    4.1 2.6 bad
3   4   A.37    7.8 6.4 bad
4   5   A.38    6.9 8.8 good


# The code I wrote is giving me ‘bad’ for all the rows: 

Boolean = []
for x in df1.ID:
    if x == x in df2.ID:
        Boolean.append('good')
    else:
        Boolean.append('bad')
print (Boolean)

# Output obtained with code
Output: 
['bad', 'bad', 'bad', 'bad', 'bad']

Thank you.

Answer 1

I think this is what you are looking for:

import pandas as pd

data1 = {
    'Index': [1, 2, 3, 4, 5],
    'ID': ['A.34', 'A.35', 'A.36', 'A.37', 'A.38'],
    'type 1': [6.3, 5.8, 6.8, 7.8, 6.9],
    'type2': [7.1, 7.3, 5.2, 6.4, 8.8]}

data2 = {
    'Index': [1, 2, 3, 4, 5],
    'ID': ['A.55', 'A.35', 'A.69', 'A.34', 'A.38'],
    'Type 2': [6.7, 3.6, 5.8, 9.2, 7.7]}

df1 = pd.DataFrame(data=data1)
df2 = pd.DataFrame(data=data2)

merge_cols = ['Index', 'ID']

df = pd.merge(df1, df2[merge_cols], how='left', left_on=merge_cols, right_on=merge_cols, indicator=True)

d = {'left_only':'bad', 'both':'good'}
df['_merge'] = df['_merge'].map(d)
df.rename(columns={'_merge': 'Status'}, inplace=True)
df

The output df looks like this:

    Index   ID  type 1  type2   Status
0   1   A.34    6.3 7.1 bad
1   2   A.35    5.8 7.3 good
2   3   A.36    6.8 5.2 bad
3   4   A.37    7.8 6.4 bad
4   5   A.38    6.9 8.8 good

EDIT: edited to merge on both columns Index and ID