49 multi index data frames

In [14]:

import pandas as pd
import numpy as np
from numpy.random import randn
np.random.seed(3)
m1=randn(6,2)

In [6]:

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

In [7]:

hier_index

Out[7]:

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

In [9]:

hier_index1 = pd.MultiIndex.from_tuples(hier_index)

In [19]:

hier_index1

Out[19]:

MultiIndex(levels=[['G1', 'G2'], [1, 2, 3]],
           labels=[[0, 0, 0, 1, 1, 1], [0, 1, 2, 0, 1, 2]])

In [15]:

df = pd.DataFrame(m1,index=hier_index1,columns=['A','B'])

In [20]:

df

Out[20]:

		A	B
G1	1	1.788628	0.436510
	2	0.096497	-1.863493
	3	-0.277388	-0.354759
G2	1	-0.082741	-0.627001
	2	-0.043818	-0.477218
	3	-1.313865	0.884622

In [21]:

df.loc['G1']

Out[21]:

	A	B
1	1.788628	0.436510
2	0.096497	-1.863493
3	-0.277388	-0.354759

In [18]:

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

Out[18]:

A    1.788628
B    0.436510
Name: 1, dtype: float64

In [23]:

df.index.names=["gr","nm"]

In [24]:

df

Out[24]:

		A	B
gr	nm
G1	1	1.788628	0.436510
	2	0.096497	-1.863493
	3	-0.277388	-0.354759
G2	1	-0.082741	-0.627001
	2	-0.043818	-0.477218
	3	-1.313865	0.884622

In [27]:

df.xs(["G1",1])

Out[27]:

A    1.788628
B    0.436510
Name: (G1, 1), dtype: float64

In [30]:

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

Out[30]:

	A	B
gr
G1	1.788628	0.436510
G2	-0.082741	-0.627001