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Learning pandas
Learning pandas

Learning pandas: High performance data manipulation and analysis using Python , Second Edition

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Learning pandas

Up and Running with pandas

In this chapter, we will cover how to install pandas and start using its basic functionality. The content of the book is provided as IPython and Jupyter notebooks, and hence we will also take a quick look at using both of those tools.

This book will utilize the Anaconda scientific Python distribution from Continuum. Anaconda is a popular Python distribution with both free and paid components. Anaconda provides cross-platform support, including Windows, Mac, and Linux. The base distribution of Anaconda installs pandas, IPython and Jupyter Notebook, thereby making it almost trivial to get started.

In this chapter will cover the following topics:

  • Installation of Anaconda, pandas, and IPython/Jupyter Notebook
  • Using IPython and Jupyter Notebook
  • Jupyter and its notebooks
  • Setting up your pandas environments
  • A quick introduction to the pandas Series and DataFrame...

Installation of Anaconda

This book will utilize Anaconda Python version 3, specifically 3.6.1. At the time of writing, pandas is at version 0.20.2. The Anaconda installer by default will install Python, IPython, Jupyter Notebook, and pandas.

Anaconda Python can be downloaded from the Continuum Analytics website at http://continuum.io/downloads. The web server will identify your browser's operating system and present you with an appropriate software download file for that platform.

When opening this URL in your browser you will see a page similar to the following:

Click on the link for the installer for your platform. This will present you with a download page similar to the following:

Download the 3.x installer. The current version of Anaconda and which will be used in this book is 4.3.1, with Python 3.6.1:

This changes frequently and by the time you read this it has probably...

IPython and Jupyter Notebook

So far we have executed Python from the command line or Terminal. This is the default Read-Eval-Print-Loop (REPL) that comes with Python. This can be used to run all the examples in this book, but the book will use IPython for statements in the text and the code package Jupyter Notebook. Let's take a brief look at both.

IPython

IPython is an alternate shell for interactively working with Python. It provides several enhancements to the default REPL provided by Python.

If you want to learn about IPython in more detail, check out the documentation at https://ipython.org/ipython-doc/3/interactive/tutorial.html

To start IPython, simply execute the ipython command from the command line/Terminal...

Introducing the pandas Series and DataFrame

Let's jump into using some pandas with a brief introduction to pandas two main data structures, the Series and the DataFrame. We will examine the following:

  • Importing pandas into your application
  • Creating and manipulating a pandas Series
  • Creating and manipulating a pandas DataFrame
  • Loading data from a file into a DataFrame

Importing pandas

Every notebook we will use starts by importing pandas and several other useful Python libraries first. It will also set up several options to control how pandas renders output in a Jupyter Notebook. This code consists of the following:

The first statement imports NumPy and refers to items in the library as np.. We won't go into much...

Visualization

We will dive into visualization in quite some depth in Chapter 14, Visualization, but prior to then we will occasionally perform a quick visualization of data in pandas. Creating a visualization of data is quite simple with pandas. All that needs to be done is to call the .plot() method. The following demonstrates by plotting the Close value of the stock data:

Summary

In this chapter, we installed the Anaconda Scientific version of Python. This also installs pandas and Jupyter Notebook, setting you right up with an environment for performing data manipulation and analysis, along with creating notebooks to visualize, present, and share your analyses.

We also took an introductory look at the pandas Series and DataFrame objects, demonstrating some of the fundamental capabilities. This exposition showed you how to perform a few basic operations that you can use to get up and running with pandas prior to diving in and learning all the details.

In the next several chapters, we will dive deep into the operations of the Series and DataFrame, with the next chapter focusing specifically on the Series.

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Key benefits

  • • Get comfortable using pandas and Python as an effective data exploration and analysis tool
  • • Explore pandas through a framework of data analysis, with an explanation of how pandas is well suited for the various stages in a data analysis process
  • • A comprehensive guide to pandas with many of clear and practical examples to help you get up and using pandas

Description

You will learn how to use pandas to perform data analysis in Python. You will start with an overview of data analysis and iteratively progress from modeling data, to accessing data from remote sources, performing numeric and statistical analysis, through indexing and performing aggregate analysis, and finally to visualizing statistical data and applying pandas to finance. With the knowledge you gain from this book, you will quickly learn pandas and how it can empower you in the exciting world of data manipulation, analysis and science.

Who is this book for?

This book is ideal for data scientists, data analysts, Python programmers who want to plunge into data analysis using pandas, and anyone with a curiosity about analyzing data. Some knowledge of statistics and programming will be helpful to get the most out of this book but not strictly required. Prior exposure to pandas is also not required.

What you will learn

  • • Understand how data analysts and scientists think about of the processes of gathering and understanding data
  • • Learn how pandas can be used to support the end-to-end process of data analysis
  • • Use pandas Series and DataFrame objects to represent single and multivariate data
  • • Slicing and dicing data with pandas, as well as combining, grouping, and aggregating data from multiple sources
  • • How to access data from external sources such as files, databases, and web services
  • • Represent and manipulate time-series data and the many of the intricacies involved with this type of data
  • • How to visualize statistical information
  • • How to use pandas to solve several common data representation and analysis problems within finance

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Publication date : Jun 30, 2017
Length: 446 pages
Edition : 2nd
Language : English
ISBN-13 : 9781787120310
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Publication date : Jun 30, 2017
Length: 446 pages
Edition : 2nd
Language : English
ISBN-13 : 9781787120310
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Table of Contents

15 Chapters
pandas and Data Analysis Chevron down icon Chevron up icon
Up and Running with pandas Chevron down icon Chevron up icon
Representing Univariate Data with the Series Chevron down icon Chevron up icon
Representing Tabular and Multivariate Data with the DataFrame Chevron down icon Chevron up icon
Manipulating DataFrame Structure Chevron down icon Chevron up icon
Indexing Data Chevron down icon Chevron up icon
Categorical Data Chevron down icon Chevron up icon
Numerical and Statistical Methods Chevron down icon Chevron up icon
Accessing Data Chevron down icon Chevron up icon
Tidying Up Your Data Chevron down icon Chevron up icon
Combining, Relating, and Reshaping Data Chevron down icon Chevron up icon
Data Aggregation Chevron down icon Chevron up icon
Time-Series Modelling Chevron down icon Chevron up icon
Visualization Chevron down icon Chevron up icon
Historical Stock Price Analysis Chevron down icon Chevron up icon

Customer reviews

Rating distribution
Full star icon Full star icon Full star icon Half star icon Empty star icon 3.5
(2 Ratings)
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Terry Letsche Sep 06, 2019
Full star icon Full star icon Full star icon Full star icon Full star icon 5
This book was truly brand-new looking, and it arrived promptly.
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Stephen Brown Aug 01, 2017
Full star icon Full star icon Empty star icon Empty star icon Empty star icon 2
I have the McKinney book ("Python for Data Analysis", first edition) and was getting this hoping that it filled in some of the gaps in that one, especially with some more advanced grouping/aggregation.What I liked:* You can get the kindle version super cheap if you buy the hard copy* A little more up-to-date in a few places than the McKinney book (although the second edition of that one is coming out in Fall 2017)What I didn't like:* Lots of typos and poor editing*** Just as one example, in both the kindle and printed version, most of the figures in Chapter 6 are off by one from where they should be, so if you see Text1: Figure 1, Text 2: Figure 2, then Text1 explicitly refers to the contents of Figure 1, but you have to skip down to the next figure (Figure 2) to see the actual correct figure. There are lots of other examples, but I just gave up even making note of them after a while. How embarrassing!!!* Tons of whitespace and huge fonts that are obviously used as filler (apparently this is typical of Packt books; I tend to stick with O'Reilly if possible, so I didn't know that going in)* Much more expensive than McKinney while not really covering any topics better, and a few not as wellI tried to write a balanced review, but I was honestly more disappointed in this book than almost any other technical book I've purchased in the past 20 years, especially given how expensive it is. Do yourself a favor and just stick with the McKinney book, especially when the second edition comes out in a few months.
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