Python For Data Analysis Data Wrangling With Pandas Numpy & Jupyter
Material type:
- 9789355421906
- 005.133 (PYT) MCK
Item type | Current library | Collection | Call number | Copy number | Status | Date due | Barcode | Item holds | |
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Alliance School of Law | Computer Science and Information Technology | 005.133 (PYT) MCK (Browse shelf(Opens below)) | Checked out | 21/11/2023 | E12337 | |||
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ACED | Computer Science and Information Technology | 005.133 (PYT) MCK (Browse shelf(Opens below)) | 1 | Not for loan | E11448 | |||
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ACED | Computer Science and Information Technology | 005.133 (PYT) MCK (Browse shelf(Opens below)) | 2 | Available | E11449 | |||
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ACED | Computer Science and Information Technology | 005.133 (PYT) MCK (Browse shelf(Opens below)) | 3 | Checked out | 02/04/2025 | E11450 | ||
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Alliance School of Liberal Arts and Humanities | 005.133 (PYT) MCK (Browse shelf(Opens below)) | 4 | Available | LA01405 |
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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