Pandas Indexing Deep Dive

Introduction

Advanced indexing techniques in Pandas for complex data selection.

MultiIndex

import pandas as pd

# Create multi-level index
arrays = [
    ["A", "A", "B", "B"],
    [1, 2, 1, 2]
]
index = pd.MultiIndex.from_arrays(arrays, names=["Group", "Number"])
df = pd.DataFrame({"Value": [10, 20, 30, 40]}, index=index)

# Indexing multiindex
df.loc["A"]
df.loc[("B", 1)]
df.xs("A", level="Group")

Query Method

df = pd.DataFrame({
    "A": [1, 2, 3, 4],
    "B": [10, 20, 30, 40],
    "C": ["x", "y", "z", "w"]
})

# SQL-like syntax
df.query("A > 2 and B < 40")
df.query("C == 'x' | C == 'y'")

Boolean Indexing

# Complex conditions
mask = (df["A"] > 1) & (df["B"] < 30)
df[mask]

# isin
df[df["C"].isin(["x", "y"])]

# between
df[df["A"].between(2, 3)]

Practice Problems

1. Create and index MultiIndex DataFrame
2. Use query with variables
3. Slice MultiIndex levels
4. Combine multiple conditions
5. Use loc vs iloc effectively