Pandas — Hierarchical Index & Cross-section

Open your Colab notebook and here are the follow-up exercises! — #PySeries#Episode 10

print(‘Hello, Advanced Pandas: Hierarchical Index & Cross-section!’)

Initializing a multi-level DataFrame:

import numpy as np
import pandas as pd
from numpy.random import randn
np.random.seed(101)

Pandas — MultiIndex & Advanced Index

As a convenience, we can pass a list of arrays directly into a special method below to construct a MultiIndex automatically:

outside=[‘G1’,’G1',’G1',’G2',’G2',’G2']
inside=[1,2,3,1,2,3]
hier_index=list(zip(outside, inside))
hier_index=pd.MultiIndex.from_tuples(hier_index)

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

df

The reason that the MultiIndex matters is that it can allow you to do grouping, selection, and reshaping operations such as:

Calling Data:

# If we want everything that is under ‘G1’, type df.loc():
# We will get a sub-set ‘G1’ of the DataFrame; check it out:

df.loc[‘G1’]

We can continue to indexing off this, going deeper…

df.loc[‘G1’].loc[1]

And deeper, and deeper…

The basic idea we can from the outside index continue calling inside deeper!

df.loc['G1'].loc[1][0]

2.706849839399938

Naming Columns:

# Indexes do not have any names

df.index.names

FrozenList([None, None])

But you can pass a list of names, for instance:

df.index.names = [‘Groups’, ‘Nums’]
# Now when we call it we have the outside label 
# as ‘Groups’ and inside ‘Nums’

df

Location and grabbing — Cross-Section:

Location and grab the group ‘G2’, nuns 2, ‘B’ column like this:

df.loc[‘G2’].loc[2][‘B’]
##Cross Section — Multi Level Index
# Let’s say we want to grab everything under ‘Nums’ = 1 
# with both groups;
# What differentiates it from the loc method is that
# we can skip or go inside the multi-level index
# This is to say: grab a cross-section where the 
# level is equal 1 and level is equal ‘Nums’

df.xs(1, level=’Nums’)

print(“Thank you for Reading this Post! See you in the next PySeries Episode o/”)

Colab File link:)

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