python - how do I transfer values from one df to another

user_13412850 0
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These are the two dfs

Rating = ['A', 'AAA', 'AA', 'BBB', 'BB', 'B']
val = [4560.0, 64.0, 456.0, 34.0, 534.0, 54.0]
df = pd.DataFrame(dict(zip(Rating,val)),index=[0]).T
df.columns = ['values']
df1 = pd.DataFrame(['AA','AA','AA','AA','A','A'],columns=['Rating'])
df1.set_index('Rating',inplace=True)
df1 = df1.merge(df,left_index=True,right_index=True)

I used merge which work, but problem is it doesnt preserve the sequence of my original df1 dataFrame

df1 = df1.merge(df,left_index=True,right_index=True)

How can I run merge and preserve the sequence as well?

edited################

I get this output enter image description here

I want the sequencing to be what was in the original df i.e. ['AA','AA','AA','AA','A','A']

pandas merge python
2 Answers
df = pd.DataFrame({
    'Rating' : ['A', 'AAA', 'AA', 'BBB', 'BB', 'B'],
    'val' : [4560.0, 64.0, 456.0, 34.0, 534.0, 54.0]
})
df
###
  Rating     val
0      A  4560.0
1    AAA    64.0
2     AA   456.0
3    BBB    34.0
4     BB   534.0
5      B    54.0

Keeping df1 as yours, but don't set_index() additionally.

df1 = pd.DataFrame(['AA','AA','AA','AA','A','A'],columns=['Rating'])
df1
###
  Rating
0     AA
1     AA
2     AA
3     AA
4      A
5      A

Doing the merge()

df1 = df1.merge(df,left_on='Rating', right_on='Rating')
df1
###
  Rating     val
0     AA   456.0
1     AA   456.0
2     AA   456.0
3     AA   456.0
4      A  4560.0
5      A  4560.0

Then set_index()

df1.set_index('Rating', inplace=True)
df1
###
           val
Rating        
AA       456.0
AA       456.0
AA       456.0
AA       456.0
A       4560.0
A       4560.0

With different df1

df1 = pd.DataFrame(['AA', 'A', 'A', 'A', 'AA', 'AA'], columns=['Rating'])
df1
###
  Rating
0     AA
1      A
2      A
3      A
4     AA
5     AA

Doing the merge()

df1 = df1.merge(df,left_on='Rating', right_on='Rating', how='left')
df1
###
  Rating     val
0     AA   456.0
1      A  4560.0
2      A  4560.0
3      A  4560.0
4     AA   456.0
5     AA   456.0
user_145278860

In this particular case, you can use sort_index() after the merging:

df1 = df1.merge(df,left_index=True,right_index=True).sort_index(ascending=False)

Returns:

    values
AA   456.0
AA   456.0
AA   456.0
AA   456.0
A   4560.0
A   4560.0
user_118970070
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