Python Data Science Handbook

The “Python Data Science Handbook” is a comprehensive guide that delves into the practical aspects of data science using Python. It covers essential topics such as data manipulation, visualization, machine learning, and more. The handbook provides detailed explanations and examples to help readers understand and apply Python tools and libraries effectively in the field of data science. With a focus on practicality and real-world applications, this resource serves as a valuable reference for both beginners and experienced data scientists looking to enhance their Python skills and excel in the realm of data science.

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Geographic Data with Basemap

One common type of visualization in data science is that of geographic data. Matplotlib's main tool for this type of visualization is the Basemap toolkit, which is one of several Matplotlib toolkits w...

Working with Time Series

Python Data Science Handbook

Pandas was developed in the context of financial modeling, so as you might expect, it contains a fairly extensive set of tools for working with dates, times, and time-indexed data. Date and time data ...

Data Manipulation with Pandas

Python Data Science Handbook

In the previous chapter, we dove into detail on NumPy and its ndarray object, which provides efficient storage and manipulation of dense typed arrays in Python. Here we'll build on this knowledge by l...

Python Data Science Handbook

This website contains the full text of the Python Data Science Handbook by Jake VanderPlas; the content is available on GitHub in the form of Jupyter notebooks. The text is released under the CC-BY-NC...

Preface

Python Data Science Handbook

What Is Data Science? This is a book about doing data science with Python, which immediately begs the question: what is data science ? It's a surprisingly hard definition to nail down, especially give...

IPython: Beyond Normal Python

Python Data Science Handbook

There are many options for development environments for Python, and I'm often asked which one I use in my own work. My answer sometimes surprises people: my preferred environment is IPython plus a tex...

Help and Documentation in IPython

Python Data Science Handbook

If you read no other section in this chapter, read this one: I find the tools discussed here to be the most transformative contributions of IPython to my daily workflow. When a technologically-minded ...

Structured Data: NumPy's Structured Arrays

Python Data Science Handbook

While often our data can be well represented by a homogeneous array of values, sometimes this is not the case. This section demonstrates the use of NumPy's structured arrays and record arrays , which ...

Hierarchical Indexing

Python Data Science Handbook

Up to this point we've been focused primarily on one-dimensional and two-dimensional data, stored in Pandas Series and DataFrame objects, respectively. Often it is useful to go beyond this and store h...

Handling Missing Data

Python Data Science Handbook

The difference between data found in many tutorials and data in the real world is that real-world data is rarely clean and homogeneous. In particular, many interesting datasets will have some amount o...

Introducing Pandas Objects

Python Data Science Handbook

At the very basic level, Pandas objects can be thought of as enhanced versions of NumPy structured arrays in which the rows and columns are identified with labels rather than simple integer indices. A...

Data Indexing and Selection

Python Data Science Handbook

In Chapter 2 , we looked in detail at methods and tools to access, set, and modify values in NumPy arrays. These included indexing (e.g., arr[2, 1] ), slicing (e.g., arr[:, 1:5] ), masking (e.g., arr[...