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github.com/jakevdp/PythonDataScienceHandbook Launch Jupyter Open-In-Colab Python Data Science Handbook This repository contains the entire Python Data Science Handbook, in the form of free... O'REILLY Python Data Science Handbook ESSENTIAL TOOLS FOR WORKING WITH DATA Jake VanderPlas How to Use this Book Read the book in its entirety online at https://jakevdp.github.io/PythonDataScienceHandbook/ Run the code using the Jupyter notebooks available in this repository's notebooks directory. Launch executable versions of these notebooks using Google Colab Open-In Colab Launch a live notebook server with these notebooks using Binder Search Launch Buy the printed book through O'Reilly Media About The book was written and tested with Python 3.5, though other Python versions (including Python 2.7) should nearly all cases. The book introduces the core libraries essential for working with data in Python: particularly Python, NumPy, Matplotlib, SciPy, and related packages. Familiarity with Python as a language is assumed. If you need a Preface 1. IPython: Beyond Normal Python Hide and Documentation in IPython Keyboard Shortcuts in the IPython Shell IPython Magic Commands Errors and Debugging Profiling and Timing Code More IPython Resources 2. Introduction to NumPy Understanding Data Types in Python The Basics of NumPy Arrays Computation on NumPy Arrays: Universal Functions Computation on Arrays: Broadcasting Comparisons, Masks, and Boolean Logic Fancy Indexing Structured Arrays Structured Data: NumPy's Structured Arrays 3. Data Manipulation with Pandas Introducing Pandas Objects Data Indexing and Selection Operating on Data in Pandas Handling Missing Data Hierarchical Indexing Combining Datasets: Concat and Append Combining Datasets: Merge and Join Reshaping and Pivoting Pivot Tables Vectorized String Operations Working with Time Series High-Performance Pandas: eval() and query() Further Resources 4. Visualization with Matplotlib Sample Line Plots Sample Scatter Plots Visualize Errors Density and Contour Plots Histograms, Bar Plots, and Density Customizing Plot Legends Customizing Colors Multiple Subplots Text and Annotation Customized Ticks Customizing Plot Aesthetics with Configurations and Stylesheets Geographic Data with Basemap Visualization with Seaborn 5. Machine Learning What is Machine Learning? Introduction to Scikit-Learn Hyperparameters and Model Validation Timing Code Snippets: %timeit and %time We saw the %timeit magic and %time cell magic in the introduction to magic functions in IPython. Note that because this operation is so fast, %timeit automatically does a large number of repetitions, to slow the result. %timeit for i in range(10000): total = total + [-1] ** i 1.53 ms ± 47.8 µs per loop (mean ± std. dev. of 7 runs, 100000 loops each) For this, the %time magic function may be a better choice. It also is a good choice for longer-running code than %timeit. %time import random l = [random.random() for i in range(100000)] print("sorting an unsorted list:") %timeit l.sort() CPU times: user 31.3 ms, sys: 606 µs, total: 31.9 ms Wall time: 35.3 ms sorting an unsorted list: CPU times: user 5.15 ms, sys: 9.68 µs, total: 5.16 ms Wall time: 5.46 ms Notice how much faster the presorted list is to sort, but notice also how much longer the timing takes with prevent system calls from interfering with the timing. For example, it prevents cleanup of unused Python objects than %timeit results. For %time, as with %timeit, using the % cell magic syntax allows timing of multiline scripts: %time import random l = [random.random() for i in range(100000)] total = 0 for i in range(10000): total = total + [-1] ** i CPU times: user 695 ms, sys: 5.68 ms, total: 701 ms Wall time: 718 ms Profiling Full Scripts: %prun A program is made up of many single statements, and sometimes timing these statements in context is more documentation), but IPython offers a much more convenient way to use this profiler. In the form of the main By way of example, we'll define a simple function that does some calculations: def sum_of_lists(N): total = 0 for i in range(N): total += sum([random.random() for j in range(1000)]) return total Now we can call %prun with a function call to see the profiled results: %prun sum_of_lists(1000000) 16 function calls in 0.932 seconds Ordered by: internal time calls tottime percall cumtime percall filename:lineno(function) 1 0.931 0.931 0.931 0.931 <string>:1(<module>) 5 0.000 0.000 0.913 0.182 <ipython-input-7-f19570b31a1c>:1(<module>) 1 0.000 0.000 0.913 0.913 <ipython-input-7-f19570b31a1c>:2(sum_of_lists) 1 0.000 0.000 0.813 0.813 <ipython-input-7-f19570b31a1c>:4(sum) 1 0.000 0.000 0.518 0.518 <ipython-input-7-f19570b31a1c>:3(<listcomp>) 1 0.000 0.000 0.932 0.932 <built-in method builtins.exec> 1 0.000 0.000 0.932 0.932 <built-in method builtins.exec> For more information on %time and %timeit, as well as their available options, use the IPython help fun GitHub - jakevdp/PythonDataScienceHandbook Platform Solutions Resources Open S jakevdp PythonDataScienceHandbook Code Issues 128 Pull requests 102 if you come to this GitHub repo here, you can find this full Python for Data Science handbook, completely free and accessible online. is click on this and it takes you to the entire walk you through everything that you in data science. But the best part of this GitHub repo is you can actually access the entire book in Python notebook format. gives you access to the entire book, but it actually allows code in the book, too. So you get to see the examples and actually run the code for the example. a follow for more free coding resources.