u003cpu003eGet 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.u003c/pu003e u003cpu003eWritten 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.u003c/pu003e u003culu003e u003cliu003eUse the IPython shell and Jupyter notebook for exploratory computingu003c/liu003e u003cliu003eLearn basic and advanced features in NumPy (Numerical Python)u003c/liu003e u003cliu003eGet started with data analysis tools in the pandas libraryu003c/liu003e u003cliu003eUse flexible tools to load, clean, transform, merge, and reshape datau003c/liu003e u003cliu003eCreate informative visualizations with matplotlibu003c/liu003e u003cliu003eApply the pandas groupby facility to slice, dice, and summarize datasetsu003c/liu003e u003cliu003eAnalyze and manipulate regular and irregular time series datau003c/liu003e u003cliu003eLearn how to solve real-world data analysis problems with thorough, detailed examplesu003c/liu003e u003c/ulu003e
