Index Objects¶

Index objects provide axis labels for pandas data structures. Understanding indexes is fundamental to effective pandas usage.

Index Purpose¶

Index serves as an immutable container for axis labels.

1. Label Container¶

import pandas as pd

idx = pd.Index(['a', 'b', 'c', 'd'])
s = pd.Series([10, 20, 30, 40], index=idx)
print(s)

a    10
b    20
c    30
d    40
dtype: int64

2. Immutability¶

Indexes cannot be modified in place:

idx = pd.Index(['a', 'b', 'c'])
# idx[0] = 'x'  # TypeError: Index does not support mutable operations

3. Index Types¶

pd.Index         # Generic index
pd.RangeIndex    # Memory-efficient integer range
pd.DatetimeIndex # Datetime labels
pd.MultiIndex    # Hierarchical index
pd.CategoricalIndex  # Categorical data

Index Operations¶

Index supports set-like operations for data alignment.

1. Set Union¶

idx1 = pd.Index(['a', 'b', 'c'])
idx2 = pd.Index(['b', 'c', 'd'])

print(idx1.union(idx2))
# Index(['a', 'b', 'c', 'd'], dtype='object')

2. Set Intersection¶

print(idx1.intersection(idx2))
# Index(['b', 'c'], dtype='object')

3. Set Difference¶

print(idx1.difference(idx2))
# Index(['a'], dtype='object')

Automatic Alignment¶

pandas automatically aligns data based on index labels during operations.

1. Series Alignment¶

s1 = pd.Series([1, 2, 3], index=['a', 'b', 'c'])
s2 = pd.Series([10, 20], index=['b', 'c'])
result = s1 + s2
print(result)

a     NaN
b    12.0
c    23.0
dtype: float64

2. DataFrame Alignment¶

df1 = pd.DataFrame({'A': [1, 2]}, index=['x', 'y'])
df2 = pd.DataFrame({'A': [10, 20]}, index=['y', 'z'])
print(df1 + df2)

3. Fill Value¶

result = s1.add(s2, fill_value=0)

Reindexing¶

Change the index of a Series or DataFrame with reindex.

1. Basic Reindexing¶

s = pd.Series([1, 2, 3], index=['a', 'b', 'c'])
s_new = s.reindex(['a', 'b', 'c', 'd'])
print(s_new)

a    1.0
b    2.0
c    3.0
d    NaN
dtype: float64

2. Fill Missing Values¶

s_new = s.reindex(['a', 'b', 'c', 'd'], fill_value=0)

3. Forward Fill¶

s_new = s.reindex(['a', 'b', 'c', 'd'], method='ffill')

RangeIndex¶

Default integer index optimized for memory efficiency.

1. Automatic Creation¶

s = pd.Series([10, 20, 30])
print(s.index)
# RangeIndex(start=0, stop=3, step=1)

2. Reset Index¶

df = pd.DataFrame({'A': [1, 2, 3]}, index=['x', 'y', 'z'])
df_reset = df.reset_index(drop=True)
print(df_reset.index)
# RangeIndex(start=0, stop=3, step=1)

3. Memory Efficiency¶

RangeIndex stores only start, stop, and step, not individual values.