Gå till index
Python Data Science Handbook
0% färdig
0/67 Steps

Introduktion

IPython: Beyond Normal Python8 Ämnen

Introduction to NumPy9 Ämnen

Understanding Data Types in Python

The Basics of NumPy Arrays

Computation on NumPy Arrays: Universal Functions

Aggregations: Min, Max, and Everything In Between

Computation on Arrays: Broadcasting

Comparisons, Masks, and Boolean Logic

Fancy Indexing

Sorting Arrays

Structured Data: NumPy's Structured Arrays

Understanding Data Types in Python

Data Manipulation with Pandas13 Ämnen

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

Aggregation and Grouping

Pivot Tables

Vectorized String Operations

Working with Time Series

HighPerformance Pandas: eval() and query()

Further Resources

Introducing Pandas Objects

Visualization with Matplotlib15 Ämnen

Simple Line Plots

Simple Scatter Plots

Visualizing Errors

Density and Contour Plots

Histograms, Binnings, and Density

Customizing Plot Legends

Customizing Colorbars

Multiple Subplots

Text and Annotation

Customizing Ticks

Customizing Matplotlib: Configurations and Stylesheets

ThreeDimensional Plotting in Matplotlib

Geographic Data with Basemap

Visualization with Seaborn

Further Resources

Simple Line Plots

Machine Learning15 Ämnen

What Is Machine Learning?

Introducing ScikitLearn

Hyperparameters and Model Validation

Feature Engineering

In Depth: Naive Bayes Classification

In Depth: Linear Regression

InDepth: Support Vector Machines

InDepth: Decision Trees and Random Forests

In Depth: Principal Component Analysis

InDepth: Manifold Learning

In Depth: kMeans Clustering

In Depth: Gaussian Mixture Models

InDepth: Kernel Density Estimation

Application: A Face Detection Pipeline

Further Machine Learning Resources

What Is Machine Learning?

Appendix: Figure Code
avsnitt 2, Ämne 8
Pågår
More IPython Resources
januari 17, 2021
avsnitt Progress
0% färdig
In this chapter, we’ve just scratched the surface of using IPython to enable data science tasks. Much more information is available both in print and on the Web, and here we’ll list some other resources that you may find helpful.
Web Resources
 The IPython website: The IPython website links to documentation, examples, tutorials, and a variety of other resources.
 The nbviewer website: This site shows static renderings of any IPython notebook available on the internet. The front page features some example notebooks that you can browse to see what other folks are using IPython for!
 A gallery of interesting Jupyter Notebooks: This evergrowing list of notebooks, powered by nbviewer, shows the depth and breadth of numerical analysis you can do with IPython. It includes everything from short examples and tutorials to fullblown courses and books composed in the notebook format!
 Video Tutorials: searching the Internet, you will find many videorecorded tutorials on IPython. I’d especially recommend seeking tutorials from the PyCon, SciPy, and PyData conferenes by Fernando Perez and Brian Granger, two of the primary creators and maintainers of IPython and Jupyter.
Books
 Python for Data Analysis: Wes McKinney’s book includes a chapter that covers using IPython as a data scientist. Although much of the material overlaps what we’ve discussed here, another perspective is always helpful.
 Learning IPython for Interactive Computing and Data Visualization: This short book by Cyrille Rossant offers a good introduction to using IPython for data analysis.
 IPython Interactive Computing and Visualization Cookbook: Also by Cyrille Rossant, this book is a longer and more advanced treatment of using IPython for data science. Despite its name, it’s not just about IPython–it also goes into some depth on a broad range of data science topics.
Finally, a reminder that you can find help on your own: IPython’s ?
based help functionality (discussed in Help and Documentation in IPython) can be very useful if you use it well and use it often. As you go through the examples here and elsewhere, this can be used to familiarize yourself with all the tools that IPython has to offer.