Pandas - MultiIndex

### Create a multi Index dataframe

Create a hierarichal index
Check each variable for understanding
create a multi index dataframe using randn, hierarichal index and columns
select the data outer index
select the data using outer and inner index
select the columns
select the data from the particular column in specific outer index
Check index.names
Assign index.names
Get the partiuclar value from the dataframe
cross section - xs  -- (indexname, levelname)

import pandas as pd

outside = ['G1','G1','G1','G2','G2','G2']
inside = [1,2,3,1,2,3]
hier_index = list(zip(outside,inside))

print(hier_index)

[('G1', 1), ('G1', 2), ('G1', 3), ('G2', 1), ('G2', 2), ('G2', 3)]

hier_index = pd.MultiIndex.from_tuples(hier_index)

from numpy.random import randn

df = pd.DataFrame(randn(6,2), hier_index, columns=['A','B'] )

print(df)

             A         B
G1 1  0.872359 -1.185877
   2  0.120721 -0.398765
   3  0.204190  2.519279
G2 1 -1.943766  0.402523
   2  0.893575 -0.451102
   3  1.519624  0.602376

print(df.loc['G1'])

          A         B
1  0.872359 -1.185877
2  0.120721 -0.398765
3  0.204190  2.519279

df.loc['G1'].loc[1]

A    0.872359
B   -1.185877
Name: 1, dtype: float64

df['A']

G1  1    0.872359
    2    0.120721
    3    0.204190
G2  1   -1.943766
    2    0.893575
    3    1.519624
Name: A, dtype: float64

df.loc['G1'].loc[1]['A']

0.8723594201586252

df.loc['G1']['A']

1    0.872359
2    0.120721
3    0.204190
Name: A, dtype: float64

df

		A	B
G1	1	0.872359	-1.185877
	2	0.120721	-0.398765
	3	0.204190	2.519279
G2	1	-1.943766	0.402523
	2	0.893575	-0.451102
	3	1.519624	0.602376

df.index.names

FrozenList([None, None])

df.index.names = ['level','num']

df.index.names

FrozenList(['level', 'num'])

df

		A	B
level	num
G1	1	0.872359	-1.185877
	2	0.120721	-0.398765
	3	0.204190	2.519279
G2	1	-1.943766	0.402523
	2	0.893575	-0.451102
	3	1.519624	0.602376

df.xs(1, level='num')

	A	B
level
G1	0.872359	-1.185877
G2	-1.943766	0.402523