Python/Pandas: filter and organize the rows and columns of a dataframe based on another dataframe

Question

I have two dataframes like df1, df2 below. I would like to:

• filter df1 : that is, remove rows and columns, so that it has the same index elements and columns as df2. The elements within the table for the columns and rows kept should be not be modified.
• In addition, I would like to organize the rows and columns of this 'filtered' dataframe so that it has rows and columns in the same order as df2.

Dataframe df1 is:

index	x_3	x_1	x_2
10	110	126	112
11	131	140	143
12	130	128	116
13	118	150	125
14	102	117	110
15	103	105	148
16	116	114	114
17	120	132	110

..and a second dataframe ( df2 ) like:

index	x_1	x_2	x_3
10	1	1	5
11	4	1	2
14	2	2	4
15	1	2	1
16	2	4	1

The final result would be df3, that is:

index	x_1	x_2	x_3
10	126	112	110
11	140	143	131
14	117	110	102
15	105	148	103
16	114	114	116

Any insights?

Answer 1

You can use .reindex_like to conform the index and columns of df1 according to index and columns of df2 :

df3 = df1.reindex_like(df2)

---

>>> df3

       x_1  x_2  x_3
index               
10     126  112  110
11     140  143  131
14     117  110  102
15     105  148  103
16     114  114  116

Answer 2

df1.loc[df2.index, df2.columns]

Shubham's answer is quite pythonic. Using loc is also a simple way to go from first principles.