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Pandas Series

The one-dimensional Pandas data structure — a labelled array for a single column

A Pandas Series is a one-dimensional labelled array. Think of it as a single column from a spreadsheet — it has values and an index (row labels). When you access one column from a DataFrame (df["Sales"]), you get a Series. Understanding Series is important because most Pandas column operations work at the Series level — filtering, applying functions, computing statistics, handling nulls.

Example

Creating and working with Series
import pandas as pd

# Create a Series
sales = pd.Series([45000, 18000, 62000, 9000, 51000],
                  name="Sales",
                  index=["Jan", "Feb", "Mar", "Apr", "May"])

print(sales)
# Jan    45000
# Feb    18000
# Mar    62000
# Apr     9000
# May    51000
# Name: Sales, dtype: int64

# Access
sales["Jan"]       # 45000 — by label
sales.iloc[0]      # 45000 — by position
sales[sales > 20000]  # filter: Jan, Mar, May values

# Statistics
sales.sum()        # 185000
sales.mean()       # 37000.0
sales.describe()   # count, mean, std, min, 25%, 50%, 75%, max

# Useful methods
sales.value_counts()   # frequency of each unique value
sales.unique()         # array of unique values
sales.nunique()        # count of unique values
sales.isnull().sum()   # count of null/NaN values

Key Points

  • ✓A Series has values and an index — index can be numbers or labels
  • ✓df["column"] returns a Series; df[["col1","col2"]] returns a DataFrame
  • ✓.iloc[n] accesses by position; .loc["label"] accesses by label
  • ✓.describe() gives a full statistical summary in one call
  • ✓.value_counts() shows frequency of each unique value — great for categorical columns

Practice Question

How do you access a single column "Sales" from a DataFrame df as a Series?

Related Topics

Pandas DataFramesThe core Pandas data structure — a 2D table with rows and columnsNumPy ArraysFast numerical computation with NumPy arrays — the engine behind PandasData Cleaning with PandasRename columns, fix data types, remove duplicates, and standardise messy real-world data