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Python for data analysis: data wrangling with pandas, numpy & jupyter / Wes McKinney

By: McKinney, WesMaterial type: Text

TextPublication details: Mumbai: Shroff publishers & distributors pvt.ltd. (O'Reilly) 2022Edition: 3rd EdDescription: xvi, 561 pISBN: 9789355421906Subject(s): Computer science: PythonLOC classification: QA76.73.P98

Contents:

1. Preliminaries; 2. Python language basics, IPython, and Jupyter notebooks; 3. Built-in data structures, functions, and files; 4. NumPy basics: arrays and vectorized computation; 5. Getting started with pandas; 6. Data loading, storage, and file formats; 7. Data cleaning and preparation; 8. Data wrangling: join, combine, and reshape; 9. Plotting and visualization; 10. Data aggregation and group operations; 11. Time series; 12. Advanced pandas; 13. Introduction to modeling libraies in Python; 14. Data analysis examples; 15. Advanced NumPy; 16. More on the IPython system

Summary: "Get complete instructions for manipulating, processing, cleaning, and crunching datasets in Python. You'll learn the latest versions of pandas, NumPy, IPython, and Jupyter in the process".

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Item type	Current library	Collection	Shelving location	Call number	Copy number	Status	Date due	Barcode	Item holds
Book	ICTS	Technology	Rack No 3	QA76.73.P98 (Browse shelf (Opens below))	1	Available		02599

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1. Preliminaries;
2. Python language basics, IPython, and Jupyter notebooks;
3. Built-in data structures, functions, and files;
4. NumPy basics: arrays and vectorized computation;
5. Getting started with pandas;
6. Data loading, storage, and file formats;
7. Data cleaning and preparation;
8. Data wrangling: join, combine, and reshape;
9. Plotting and visualization;
10. Data aggregation and group operations;
11. Time series;
12. Advanced pandas;
13. Introduction to modeling libraies in Python;
14. Data analysis examples;
15. Advanced NumPy;
16. More on the IPython system

"Get complete instructions for manipulating, processing, cleaning, and crunching datasets in Python. You'll learn the latest versions of pandas, NumPy, IPython, and Jupyter in the process".

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