Pandas

Chapter 04 overview

Think Data Science & Management

Chapter Roadmap

1. Pandas Series
2. Handling datasets
3. DataFrames
4. Missing data
5. Data operations

Chapter Goals

• Explain how Series and DataFrames organize labeled business data
• Load CSV, Excel, HTML, and SQL data sources
• Inspect rows, columns, indexes, data types, shape, and summaries
• Select, filter, transform, sort, aggregate, and combine table values
• Detect, interpret, drop, and fill missing values

Why Pandas

Pandas gives Python a practical table workflow.

• Rows represent records
• Columns represent variables
• Labels make code easier to read
• Methods support repeatable analysis

Series

A Series is a one-dimensional labeled object.

Use a Series when one column or one measured quantity needs labels, data types, and pandas operations.

DataFrames

A DataFrame is a two-dimensional table.

DataFrames are the main pandas object for management data because they can hold customer records, sales transactions, survey responses, and operational metrics.

Loading Data

Pandas can read common business data sources.

• CSV files
• Excel files
• HTML tables
• SQL query results

Inspecting Tables

Before analysis, inspect the data.

• head() and tail()
• shape
• info()
• describe()
• column names and data types

Missing Data

Missing values need interpretation before treatment.

• NaN often marks missing numerical values
• pd.NA supports nullable pandas dtypes
• isna() detects missing values
• dropna() removes incomplete rows or columns
• fillna() replaces missing values

Data Operations

Common analysis workflows combine smaller table operations.

• Select and filter rows
• Sort records
• Group and aggregate
• Concatenate compatible tables
• Merge or join related tables

Chapter 4 Vocabulary

Series, DataFrame, Index, dtype, CSV file, Excel file, read_csv(), head(), info(), describe(), loc, iloc, Boolean indexing, missing data, NaN, pd.NA, isna(), dropna(), fillna(), GroupBy, aggregation, merge().

What To Carry Forward

Pandas turns raw table files into inspectable, cleanable, and analyzable data objects.

That workflow supports the visualization, statistics, and modeling chapters that follow.

Carry Forward

• Use the section practice exercises to check runnable code skills.