Python for data analysis

Technology

First published: 2012
Print length: 466 pagesO'Reilly Media, Incorporated, 2012

Description

Wes McKinney introduces Python as a practical environment for cleaning, transforming, analyzing, and presenting structured data. The book centers on pandas, the open-source library McKinney created, while also explaining how it works with NumPy, Jupyter, and Python’s broader scientific-computing ecosystem. Code-driven chapters move through arrays, tables, missing values, file formats, reshaping, joins, grouping, aggregation, time series, and basic visualization. Rather than teaching programming in isolation, the examples organize techniques around the untidy datasets and repeated operations common in analytical work. The book serves readers who need to turn raw information into reliable results, showing how Python can replace fragile manual procedures with inspectable, reusable workflows suited to large or data-intensive tasks.

Reading profile

About this profile

Collected’s assessment, not reader ratings. Complexity runs from 1 (least demanding) to 5 (most demanding).

Writing: How much attention the sentences and language require.
Ideas: How much interpretation or abstract reasoning the book asks of you.
Structure: How much you need to keep track of speakers, timelines and connections.
Background knowledge: Prior knowledge needed to follow the book, beyond what it explains.
Emotional intensity: Emotional pressure, from gentle (1) to intense (5). Separate from difficulty.

Writing style

Descriptive · Direct · Technical

Pace

Measured

Tone

Cerebral · Detached · Earnest

Structure

Reference

Focus

Ideas · Information

Emotional intensity

1 / 5

Reading complexity

1–5 · higher means more demanding

Writing: 3 / 5
Ideas: 4 / 5
Structure: 3 / 5
Background knowledge: 3 / 5

Themes

Knowledge and truthTechnology and societyWork

About Wes McKinney

Wes McKinney is a software developer whose books grow out of his work building open-source tools for analytical computing. He created pandas after encountering the persistent friction of cleaning and processing data in quantitative-finance research, and Python for Data Analysis turns that practical experience into direct, code-driven instruction. His wider career has extended from hands-on analysis to the underlying infrastructure of data systems: he co-created Apache Arrow, which provides shared foundations for in-memory data, and created Ibis, a Python interface for working across database and SQL systems. What distinguishes his work is this movement between toolmaking and explanation, with recurring attention to making structured-data tasks more productive and coherent.

Book details

For the print edition listed above.

Publisher: O'Reilly Media, Incorporated
Language: English
ISBN: 9781449323622

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