如果行名存在于熊猫中另一个数据框的列名中，则选择数据框的行值

Question

If I have df1

df1 = pd.DataFrame({'Col_Name': {0: 'A', 1: 'b', 2: 'c'}, 'X': {0: 12, 1: 23, 2: 223}, 'Z': {0: 42, 1: 33, 2: 28 }})

and df2

df2 = pd.DataFrame({'Col': {0: 'Y', 1: 'X', 2: 'Z'}, 'Low1': {0: 0, 1: 0, 2: 0}, 'High1': {0: 10, 1: 10, 2: 630}, 'Low2': {0: 10, 1: 10, 2: 630}, 'High2': {0: 50, 1: 50, 2: 3000}, 'Low3': {0: 50, 1: 50, 2: 3000}, 'High3': {0: 100, 1: 100, 2: 8500}, 'Low4': {0: 100, 1: 100, 2: 8500}, 'High4': {0: 'np.inf', 1: 'np.inf', 2: 'np.inf'}})

Select the row values of df2 if row is present in column name of df1.

Expected Output: df3

df3 = pd.DataFrame({'Col': {0: 'X', 1: 'Z'}, 'Low1': {0: 0, 1: 0}, 'High1': {0: 10, 1: 630}, 'Low2': {0: 10, 1: 630}, 'High2': {0: 50, 1: 3000}, 'Low3': {0: 50, 1: 3000}, 'High3': {0: 100, 1: 8500}, 'Low4': {0: 100, 1: 8500}, 'High4': {0: 'np.inf', 1: 'np.inf'}})

How to do it?

Answer 1

You can pass a boolean list to select the rows of df2 that you want. This list can be created by looking at each value in the Col column and asking if the value is in the columns of df1

df3 = df2[[col in df1.columns for col in df2['Col']]]

Answer 2

you can drop the non-relevant col and use the other columns...

df3 = df2[df2['Col'].isin(list(df1.drop('Col_Name',axis=1).columns))]