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Python For Data Analysis Data Wrangling With Pandas Numpy & Jupyter

By: Material type: TextTextLanguage: English Publication details: Mumbai : Shroff Publishers & Distributors Pvt. Ltd., 2022Edition: 3Description: 561ISBN:
  • 9789355421906
Subject(s): DDC classification:
  • 005.133 (PYT) MCK
Summary: Get the definitive handbook for manipulating, processing, cleaning, and crunching datasets in Python. Updated for Python 3.10 and pandas 1.4, the third edition of this hands-on guide is packed with practical case studies that show you how to solve a broad set of data analysis problems effectively. You'll learn the latest versions of pandas, NumPy, and Jupyter in the process. Written by Wes McKinney, the creator of the Python pandas project, this book is a practical, modern introduction to data science tools in Python. It's ideal for analysts new to Python and for Python programmers new to data science and scientific computing. Data files and related material are available on GitHub. Use the Jupyter notebook and IPython shell for exploratory computing Learn basic and advanced features in NumPy Get started with data analysis tools in the pandas library Use flexible tools to load, clean, transform, merge, and reshape data Create informative visualizations with matplotlib Apply the pandas groupby facility to slice, dice, and summarize datasets Analyze and manipulate regular and irregular time series data
List(s) this item appears in: New Arrivals for the Month of March 2023 - Computer Science and Data Science | New Arrivals for the Month of September - 2023
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Item type Current library Collection Call number Copy number Status Date due Barcode Item holds
Book Book Alliance School of Law Computer Science and Information Technology 005.133 (PYT) MCK (Browse shelf(Opens below)) Checked out 21/11/2023 E12337
Reference Book Reference Book ACED Computer Science and Information Technology 005.133 (PYT) MCK (Browse shelf(Opens below)) 1 Not for loan E11448
Book Book ACED Computer Science and Information Technology 005.133 (PYT) MCK (Browse shelf(Opens below)) 2 Checked out 16/06/2025 E11449
Book Book ACED Computer Science and Information Technology 005.133 (PYT) MCK (Browse shelf(Opens below)) 3 Available E11450
Book Book Alliance Ascent College 005.133 (PYT) MCK (Browse shelf(Opens below)) Available AC02400
Book Book Alliance School of Advanced Computing Computer Science and Information Technology 005.133 (PYT) MCK (Browse shelf(Opens below)) Available E16363
Book Book Alliance School of Advanced Computing Computer Science and Information Technology 005.133 (PYT) MCK (Browse shelf(Opens below)) Available E16361
Book Book Alliance School of Advanced Computing Computer Science and Information Technology 005.133 (PYT) MCK (Browse shelf(Opens below)) Available E16364
Book Book Alliance School of Advanced Computing Computer Science and Information Technology 005.133 (PYT) MCK (Browse shelf(Opens below)) Available E16365
Book Book Alliance School of Advanced Computing Computer Science and Information Technology 005.133 (PYT) MCK (Browse shelf(Opens below)) Available E16362
Book Book Alliance School of Law BA/BBA.LLB 005.133 (PYT) MCK (Browse shelf(Opens below)) Available L11286
Book Book Alliance School of Liberal Arts and Humanities 005.133 (PYT) MCK (Browse shelf(Opens below)) 4 Available LA01405
Total holds: 0

Get the definitive handbook for manipulating, processing, cleaning, and crunching datasets in Python. Updated for Python 3.10 and pandas 1.4, the third edition of this hands-on guide is packed with practical case studies that show you how to solve a broad set of data analysis problems effectively. You'll learn the latest versions of pandas, NumPy, and Jupyter in the process.

Written by Wes McKinney, the creator of the Python pandas project, this book is a practical, modern introduction to data science tools in Python. It's ideal for analysts new to Python and for Python programmers new to data science and scientific computing. Data files and related material are available on GitHub.

Use the Jupyter notebook and IPython shell for exploratory computing
Learn basic and advanced features in NumPy
Get started with data analysis tools in the pandas library
Use flexible tools to load, clean, transform, merge, and reshape data
Create informative visualizations with matplotlib
Apply the pandas groupby facility to slice, dice, and summarize datasets
Analyze and manipulate regular and irregular time series data

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