Creating a dataframe with multi-index columns from a dictionary - python-3.x

Question

Suppose I have the following:
a data frame with multi-index columns
a dictionary that's been created from the data frame with multi-index columns in order to efficiently manipulate it.
df = pd.DataFrame([
[100,90,80,70,36,45],
[101,78,65,88,55,78],
[92,77,42,79,43,32],
[103,98,76,54,45,65]],
index = pd.date_range(start='2022-01-01', periods=4)
)
df.columns = pd.MultiIndex.from_tuples(
(("mkf", "Open"),
("mkf", "Close"),
("tdf", "Open"),
("tdf","Close"),
("ghi","Open"),
("ghi", "Close"))
)
df
mkf tdf ghi
Open Close Open Close Open Close
2022-01-01 100 90 80 70 36 45
2022-01-02 101 78 65 88 55 78
2022-01-03 92 77 42 79 43 32
2022-01-04 103 98 76 54 45 65
df_dict = {c:df[c].assign(r=np.log(df[(c, 'Close')]).diff()) for c in df.columns.levels[0]}
df_dict
{'ghi': Open Close r
2022-01-01 36 45 NaN
2022-01-02 55 78 0.550046
2022-01-03 43 32 -0.890973
2022-01-04 45 65 0.708651,
'mkf': Open Close r
2022-01-01 100 90 NaN
2022-01-02 101 78 -0.143101
2022-01-03 92 77 -0.012903
2022-01-04 103 98 0.241162,
'tdf': Open Close r
2022-01-01 80 70 NaN
2022-01-02 65 88 0.228842
2022-01-03 42 79 -0.107889
2022-01-04 76 54 -0.380464}
What is the best way to transform the dictionary back to a data frame in its original form (i.e. with multi-index columns)?
mkf tdf ghi
Open Close r Open Close r Open Close r
2022-01-01 100 90 NaN 80 70 NaN 36 45 NaN
2022-01-02 101 78 0.55 65 88 -0.14 55 78 0.23
2022-01-03 92 77 -0.89 42 79 -0.12 43 32 -0.10
2022-01-04 103 98 0.71 76 54 0.24 45 65 -0.38

Answer 1

pd.concat([df_dict[c] for c in df_dict.keys()], axis = 1,
keys = df_dict.keys())
ghi mkf tdf
Open Close r Open Close r Open Close r
2022-01-01 36 45 NaN 100 90 NaN 80 70 NaN
2022-01-02 55 78 0.550046 101 78 -0.143101 65 88 0.228842
2022-01-03 43 32 -0.890973 92 77 -0.012903 42 79 -0.107889
2022-01-04 45 65 0.708651 103 98 0.241162 76 54 -0.380464