Python for data analysis: data wrangling with pandas, numpy, and ipython
McKinney, Wes
Python for data analysis: data wrangling with pandas, numpy, and ipython - 2nd ed. - Navi Mumbai: Shroff Publishers & Distributors PVT LTD, 2019. - xvi, 522p.;21cms.
All Indian Reprints of O'Reilly titles are printed in Greyscale.
Get complete instructions for manipulating, processing, cleaning, and crunching datasets in Python. Updated for Python 3.6, the second 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, IPython, 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 IPython shell and Jupiter notebook for exploratory computing
Learn basic and advanced features in NumPy (Numerical Python)
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 group by facility to slice, dice, and summarize datasets
Analyze and manipulate regular and irregular time series data
Learn how to solve real-world data analysis problems with thorough, detailed examples.
9789352136414
Computer Science--Data Analysis
Computer Science--Programming Language
Informative visualizations with matplotlib
005.133 M15, 2
Python for data analysis: data wrangling with pandas, numpy, and ipython - 2nd ed. - Navi Mumbai: Shroff Publishers & Distributors PVT LTD, 2019. - xvi, 522p.;21cms.
All Indian Reprints of O'Reilly titles are printed in Greyscale.
Get complete instructions for manipulating, processing, cleaning, and crunching datasets in Python. Updated for Python 3.6, the second 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, IPython, 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 IPython shell and Jupiter notebook for exploratory computing
Learn basic and advanced features in NumPy (Numerical Python)
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 group by facility to slice, dice, and summarize datasets
Analyze and manipulate regular and irregular time series data
Learn how to solve real-world data analysis problems with thorough, detailed examples.
9789352136414
Computer Science--Data Analysis
Computer Science--Programming Language
Informative visualizations with matplotlib
005.133 M15, 2