What is a Series?

A Series is a one-dimensional labeled array. Think of it as a single column from a DataFrame or an enhanced version of a Python list with labels (index).

Creating Series

Create Series from various sources:

import pandas as pd

# From list
s = pd.Series([1, 2, 3, 4, 5])

# With custom index
s = pd.Series([1, 2, 3, 4, 5], index=['a', 'b', 'c', 'd', 'e'])

# From dictionary
data = {'a': 1, 'b': 2, 'c': 3}
s = pd.Series(data)

# From NumPy array
import numpy as np
arr = np.array([1, 2, 3])
s = pd.Series(arr)

Accessing Elements

Access by label or position:

s = pd.Series([10, 20, 30], index=['a', 'b', 'c'])

# By label
print(s['a'])        # 10
print(s[['a', 'c']]) # a: 10, c: 30

# By position
print(s[0])          # 10
print(s.iloc[0])     # 10 (explicit position)

# Slicing
print(s['a':'c'])    # a: 10, b: 20, c: 30

Series Operations

Perform operations on Series:

s1 = pd.Series([1, 2, 3, 4])
s2 = pd.Series([5, 6, 7, 8])

# Arithmetic
print(s1 + s2)       # [6, 8, 10, 12]
print(s1 * 2)        # [2, 4, 6, 8]

# Comparison
print(s1 > 2)        # [False, False, True, True]

# Boolean indexing
print(s1[s1 > 2])    # [3, 4]

Series Methods

Common Series methods:

s = pd.Series([1, 2, 3, 4, 5, None])

# Statistics
print(s.sum())       # Sum
print(s.mean())      # Mean
print(s.std())       # Standard deviation
print(s.count())     # Count non-null values

# Data info
print(s.describe())  # Summary statistics
print(s.value_counts())  # Count unique values

Handling Missing Data

Work with NaN values:

s = pd.Series([1, 2, None, 4, 5])

# Check for missing
print(s.isnull())    # Boolean Series
print(s.notnull())   # Inverse

# Drop missing
s_clean = s.dropna()

# Fill missing
s_filled = s.fillna(0)

Series vs DataFrame

  • Series: One-dimensional (single column)
  • DataFrame: Two-dimensional (multiple columns)
  • DataFrame columns: Are Series objects
  • Use Series: For single-variable data
  • Use DataFrame: For multi-variable data

Next Steps

Learn about DataFrames which are built from Series, or explore filtering operations.