如何使用条件将一个数据框列的值与另一个数据框列的值匹配？

Question

I have two dataframes:

Row No.    Subject    
1      Apple
2      Banana
3      Orange
4      Lemon
5      Strawberry


row_number Subjects Special?
1    Banana      Yes
2    Lemon       No
3    Apple       No
4    Orange      No
5    Strawberry  Yes
6    Cranberry   Yes
7    Watermelon  No

I want to change the Row No. of the first dataframe to match the second. It should be like this:

Row No.    Subject   
3      Apple
1      Banana
4      Orange
2      Lemon
5      Strawberry

I have tried this code:

for index, row in df1.iterrows():
    if df1['Subject'] == df2['Subjects']:
        df1['Row No.'] = df2['row_number']

But I get the error:

ValueError: Can only compare identically-labeled Series objects

Does that mean the dataframes have to have the same amount of rows and columns? Do they have to be labelled the same too? Is there a way to bypass this limitation?

Edit: I have found a promising alternative formula:

for x in df1['Subject']:
    if x in df2['Subjects'].values:
        df2.loc[df2['Subjects'] == x]['row_number'] = df1.loc[df1['Subject'] == x]['Row No.']

But it appears it doesn't modify the first dataframe like I want it to. Any tips why? Furthermore, I get this warning:

A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

Answer 1

I would avoid using for loops especially when pandas has such great methods to handle these types of problems already.

Using pd.Series.replace

Here is a vectorized way of doing this -

1. d is the dictionary that maps the fruit to the number in second dataframe
2. You can use df.Subject.replace(d) to now simply replace the keys in the dict d to their values.
3. Overwrite the Row No. column with this now.

d = dict(zip(df2['Subjects'], df2['row_number']))
df1['Row No.'] = df1.Subject.replace(d)
print(df1)

      Subject  Row No.
0       Apple        3
1      Banana        1
2      Orange        4
3       Lemon        2
4  Strawberry        5

---

Using pd.merge

Let's try simply merging the 2 dataframe and replace the column completely.

ddf = pd.merge(df1['Subject'], 
               df2[['row_number','Subjects']], 
               left_on='Subject', 
               right_on='Subjects', 
               how='left').drop('Subjects',1)

ddf.columns = df1.columns[::-1]
print(ddf)

      Subject  Row No.
0       Apple       3
1      Banana       1
2      Orange       4
3       Lemon       2
4  Strawberry       5

Answer 2

Assuming the first is df1 and the second is df2, this should do what you want it to:

import pandas as pd

d1 = {'Row No.': [1, 2, 3, 4, 5], 'Subject': ['Apple', 'Banana', 'Orange', 
     'Lemon', 'Strawberry']}
df1 = pd.DataFrame(data=d1)

d2 = {'row_number': [1, 2, 3, 4, 5, 6, 7], 'Subjects': ['Banana', 'Lemon', 'Apple', 
'Orange', 'Strawberry', 'Cranberry', 'Watermelon'], 'Special?': ['Yes', 'No', 
     'No', 'No', 
     'Yes', 'Yes', 'No']}
df2 = pd.DataFrame(data=d2)

for x in df1['Subject']:
    if x in df2['Subjects'].values:
        df1.loc[df1['Subject'] == x, 'Row No.'] = (df2.loc[df2['Subjects'] == x]['row_number']).item()

#print(df1)
#print(df2)

In your edited answer it looks like you had the dataframes swapped and you were missing the item() to get the actual row_number value and not the Series object.