MultiIndex Hierarchy¶

Hierarchical Indexing¶

1. Definition¶

MultiIndex enables multiple index levels:

import pandas as pd

arrays = [
    ['A', 'A', 'B', 'B'],
    [1, 2, 1, 2]
]
index = pd.MultiIndex.from_arrays(arrays, names=['letter', 'number'])
s = pd.Series([10, 20, 30, 40], index=index)

2. From Tuples¶

tuples = [('A', 1), ('A', 2), ('B', 1), ('B', 2)]
index = pd.MultiIndex.from_tuples(tuples)

3. From Product¶

index = pd.MultiIndex.from_product([['A', 'B'], [1, 2]])

Accessing Data¶

1. Level Selection¶

s['A']  # All entries with level 0 = 'A'
s['A', 1]  # Specific entry

2. Cross-section¶

df = s.unstack()  # Pivot to DataFrame
# letter  A   B
# number       
# 1      10  30
# 2      20  40

3. Slicing¶

s.loc[('A', 1):('B', 1)]  # Range

DataFrame with MultiIndex¶

1. Rows and Columns¶

df = pd.DataFrame(
    [[1, 2], [3, 4], [5, 6], [7, 8]],
    index=pd.MultiIndex.from_tuples([('A', 'x'), ('A', 'y'), ('B', 'x'), ('B', 'y')]),
    columns=['col1', 'col2']
)

2. Selecting¶

df.loc['A']  # All rows where first level = 'A'
df.loc[('A', 'x')]  # Specific row

3. Stacking¶

stacked = df.stack()  # Add column as index level
unstacked = stacked.unstack()  # Remove level