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Home > Data > Data Analysis > Learning Pandas
Learning Pandas
Learning Pandas

Learning Pandas: Get to grips with pandas - a versatile and high-performance Python library for data manipulation, analysis, and discovery

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Profile Icon Michael Heydt
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Profile Icon Michael Heydt
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Table of content icon View table of contents Preview book icon Preview Book

Learning Pandas

Chapter 2. Installing pandas

In this chapter, we will cover how to install pandas using the Anaconda Scientific Python distribution from Continuum Analytics. Anaconda is a popular Python distribution with both free and paid components, and it has cross-platform support—including Windows, Mac, and Linux. The base distribution installs pandas as part of the default installation, so it makes getting started with pandas simple.

The chapter will examine installing both pandas and Python through Anaconda, as this book assumes that you are new to both pandas and Python. This can include readers who are coming from an R environment to learn data manipulation skills using pandas. Those who already have more advanced Python skills can feel free to move onto later chapters or use alternative Python distributions and package managers, as well as virtualized development environments for multiple Python distributions.

In general, the remaining chapters of this book will focus on data manipulation...

Getting Anaconda

We will focus on installing Anaconda Python and ensuring that pandas is up to date within that distribution. You are not limited to using pandas with Anaconda, as pandas is supported by most Python distributions—although the specific installation tasks on each distribution may differ from those covered in this chapter. If you use another Python distribution, feel free to use your package manager of choice or pip from PyPI.

Note

I would say most Python distributions because—being a Mac user—I've found it very difficult (if not impossible) to install pandas into the default Python provided in OS X by Apple.

At the time of writing, pandas is at Version 0.15.1. The current version of Anaconda is 2.1.9 that contains Python 2.7.8, but comes with pandas 0.14.1 by default. Therefore, we will update to v0.15.1 using the conda package manager provided by Anaconda.

Anaconda Python can be downloaded from the Continuum Analytics website at http://continuum.io...

Installing Anaconda

The installation of Anaconda is straightforward, but varies slightly by platform. We will cover the installation of Anaconda on Linux, Mac, and Windows platforms. After this installation, pandas will likely need to be updated, which is an identical process across platforms using the conda package manager.

Installing Anaconda on Linux

The download will place a shell script/installer on your system (the following shell script/installer assumes to be downloaded to the ~/Download folder). The name of the file will differ depending upon the Anaconda version and the architecture of Linux selected. This example is using Ubuntu 13.10, AMD64 platform and Anaconda Version 2.1.0. The file downloaded in this scenario is Anaconda-2.1.0-Linux-x86_64.sh.

Once downloaded, make the script executable and run it with the following command:

mh@ubuntu:~/Downloads$ chmod +x Anaconda-2.1.0-Linux-x86_64.sh
mh@ubuntu:~/Downloads$ ./Anaconda-2.1.0-Linux-x86_64.sh

The script will execute and you will...

Ensuring pandas is up to date

Now that Anaconda is installed, we can check the version of pandas that is installed either from within the Python interpreter or from the command line. The means to perform both of these is the same on each platform, and this will be demonstrated from an OS X terminal.

From within the Anaconda Python interpreter, you can check the version of pandas on the system by importing pandas and then examining the version with the following two Python statements:

>>>import pandas as pd
>>>print (pd.__version__)

The preceding commands will then report the version of pandas. The following screenshot shows that v0.14.1 is the currently installed version:

Ensuring pandas is up to date

This has reported that pandas version is 0.14.1, which is not the most recent, so we may want to update.

You can also check the pandas version using the conda package manager from the command line as follows (which also reports that version is 0.14.1):

Michaels-MacBook-Pro:~ michaelheydt$ conda list pandas...

Running a small pandas sample in IPython

Now that Python and pandas is installed, let's write our first pandas application. We will write it in the IPython interpreter. IPython is an alternative shell for executing Python applications, and it conveniently provides numeric sequence numbers for thin input and output for example purposes. This is convenient for matching specific code examples in the book and will be used in all examples.

Note

IPython or IPython Notebooks will be the tools for all remaining examples in the book.

IPython is started using the ipython command from the shell or command line:

Michaels-MacBook-Pro:~ michaelheydt$ ipython
Python 2.7.8 |Anaconda 2.1.0 (x86_64)| (default, Aug 21 2014, 15:21:46) 
Type "copyright", "credits" or "license" for more information.

IPython 2.2.0 -- An enhanced Interactive Python.
Anaconda is brought to you by Continuum Analytics.
Please check out: http://continuum.io/thanks and https://binstar.org
?         ...

Starting the IPython Notebook server

IPython Notebooks are a web server-based interactive environment that combine Python code execution, text, mathematics, plots, and rich media into a single document, along with automatic persistence of code and an easy means of deploying code to the Web. You can find more details on the IPython Notebook site at http://ipython.org/notebook.html.

IPython Notebooks are an exceptional way to learn both Python and pandas. This book will neither assume the use of IPython Notebooks, nor teach their usage beyond the brief examples given in this section. However, the code provided with the book are IPython Notebook files, so demonstrating how to run the server provided by Anaconda is worth a few paragraphs of explanation.

The IPython Notebook server can be started with the following shell command (the same on all platforms):

ipython notebook

You will get a small amount of output on the console:

elheydt/.ipython/profile_default'
2014-12-06 21:36:11.547 [NotebookApp...

Getting Anaconda

We will focus on installing Anaconda Python and ensuring that pandas is up to date within that distribution. You are not limited to using pandas with Anaconda, as pandas is supported by most Python distributions—although the specific installation tasks on each distribution may differ from those covered in this chapter. If you use another Python distribution, feel free to use your package manager of choice or pip from PyPI.

Note

I would say most Python distributions because—being a Mac user—I've found it very difficult (if not impossible) to install pandas into the default Python provided in OS X by Apple.

At the time of writing, pandas is at Version 0.15.1. The current version of Anaconda is 2.1.9 that contains Python 2.7.8, but comes with pandas 0.14.1 by default. Therefore, we will update to v0.15.1 using the conda package manager provided by Anaconda.

Anaconda Python can be downloaded from the Continuum Analytics website at http://continuum.io/downloads. The web server will...

Installing Anaconda

The installation of Anaconda is straightforward, but varies slightly by platform. We will cover the installation of Anaconda on Linux, Mac, and Windows platforms. After this installation, pandas will likely need to be updated, which is an identical process across platforms using the conda package manager.

Installing Anaconda on Linux

The download will place a shell script/installer on your system (the following shell script/installer assumes to be downloaded to the ~/Download folder). The name of the file will differ depending upon the Anaconda version and the architecture of Linux selected. This example is using Ubuntu 13.10, AMD64 platform and Anaconda Version 2.1.0. The file downloaded in this scenario is Anaconda-2.1.0-Linux-x86_64.sh.

Once downloaded, make the script executable and run it with the following command:

mh@ubuntu:~/Downloads$ chmod +x Anaconda-2.1.0-Linux-x86_64.sh
mh@ubuntu:~/Downloads$ ./Anaconda-2.1.0-Linux-x86_64.sh

The script will execute and you will...

Ensuring pandas is up to date

Now that Anaconda is installed, we can check the version of pandas that is installed either from within the Python interpreter or from the command line. The means to perform both of these is the same on each platform, and this will be demonstrated from an OS X terminal.

From within the Anaconda Python interpreter, you can check the version of pandas on the system by importing pandas and then examining the version with the following two Python statements:

>>>import pandas as pd
>>>print (pd.__version__)

The preceding commands will then report the version of pandas. The following screenshot shows that v0.14.1 is the currently installed version:

This has reported that pandas version is 0.14.1, which is not the most recent, so we may want to update.

You can also check the pandas version using the conda package manager from the command line as follows (which also reports that version is 0.14.1):

Michaels-MacBook-Pro:~ michaelheydt$ conda list pandas...
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Description

If you are a Python programmer who wants to get started with performing data analysis using pandas and Python, this is the book for you. Some experience with statistical analysis would be helpful but is not mandatory.

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Concepts :
Data Analysis
Tools :
Pandas

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Table of Contents

13 Chapters
1. A Tour of pandas Chevron down icon Chevron up icon
1. A Tour of pandas
pandas and why it is important
pandas and IPython Notebooks
Referencing pandas in the application
Primary pandas objects
Loading data from files and the Web
Simplicity of visualization of pandas data
Summary
2. Installing pandas Chevron down icon Chevron up icon
2. Installing pandas
Getting Anaconda
Installing Anaconda
Ensuring pandas is up to date
Running a small pandas sample in IPython
Starting the IPython Notebook server
Installing and running IPython Notebooks
Using Wakari for pandas
Summary
3. NumPy for pandas Chevron down icon Chevron up icon
3. NumPy for pandas
Installing and importing NumPy
Benefits and characteristics of NumPy arrays
Creating NumPy arrays and performing basic array operations
Selecting array elements
Logical operations on arrays
Slicing arrays
Reshaping arrays
Combining arrays
Splitting arrays
Useful numerical methods of NumPy arrays
Summary
4. The pandas Series Object Chevron down icon Chevron up icon
4. The pandas Series Object
The Series object
Importing pandas
Creating Series
Size, shape, uniqueness, and counts of values
Peeking at data with heads, tails, and take
Looking up values in Series
Arithmetic operations
The special case of Not-A-Number (NaN)
Boolean selection
Reindexing a Series
Slicing a Series
Summary
5. The pandas DataFrame Object Chevron down icon Chevron up icon
5. The pandas DataFrame Object
Creating DataFrame from scratch
Example data
Selecting columns of a DataFrame
Selecting rows and values of a DataFrame using the index
Selecting rows of a DataFrame by Boolean selection
Modifying the structure and content of DataFrame
Arithmetic on a DataFrame
Resetting and reindexing
Hierarchical indexing
Summarized data and descriptive statistics
Summary
6. Accessing Data Chevron down icon Chevron up icon
6. Accessing Data
Setting up the IPython notebook
Reading and writing JSON files
Accessing data on the web and in the cloud
Reading and writing from/to SQL databases
Reading data from remote data services
Summary
7. Tidying Up Your Data Chevron down icon Chevron up icon
7. Tidying Up Your Data
What is tidying your data?
Setting up the IPython notebook
Working with missing data
Handling duplicate data
Transforming Data
Summary
8. Combining and Reshaping Data Chevron down icon Chevron up icon
8. Combining and Reshaping Data
Setting up the IPython notebook
Concatenating data
Merging and joining data
Stacking and unstacking
Performance benefits of stacked data
Summary
9. Grouping and Aggregating Data Chevron down icon Chevron up icon
9. Grouping and Aggregating Data
Setting up the IPython notebook
The split, apply, and combine (SAC) pattern
Split
Apply
Discretization and Binning
Summary
10. Time-series Data Chevron down icon Chevron up icon
10. Time-series Data
Setting up the IPython notebook
Representation of dates, time, and intervals
Introducing time-series data
Calculating new dates using offsets
Handling holidays using calendars
Normalizing timestamps using time zones
Manipulating time-series data
Summary
11. Visualization Chevron down icon Chevron up icon
11. Visualization
Setting up the IPython notebook
Plotting basics with pandas
Common plots used in statistical analyses
Multiple plots in a single chart
Summary
12. Applications to Finance Chevron down icon Chevron up icon
12. Applications to Finance
Setting up the IPython notebook
Obtaining and organizing stock data from Yahoo!
Plotting time-series prices
Performing a moving-average calculation
Volatility calculation
Determining risk relative to expected returns
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Index Chevron down icon Chevron up icon
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Natester Jun 06, 2015
Full star icon Full star icon Full star icon Full star icon Full star icon 5
I've been working with the pandas library for a while but had been looking for a text to help navigate the rich feature set of the pandas library. I purchased this book as soon as it became available and I'm quite satisfied with the content.I skipped the first few chapters, but if you are new to Python and using Python packages, do be sure to go through the content.The next couple of chapters discuss the inner workings of pandas DataFrame and Series. Worth going through as it provides a foundation for the remainder of the book's examples.Around chapter 6 is where the application examples dig in and they are quite useful. I've referred to many of these examples. They include reading and writing data with different data sources, slicing and dicing data and running stats on your data.Examples towards the end of the book get progressively sophisticated with shaping data. I didn't read everything in those chapters, but towards the end of the book are some chapters on data visualization and working with time series data. Definitely a "must" if you are looking to make use of pandas in your data analysis work.I keep this ebook in my reference collection and refer to it when in need to figure out how to solve a data issue where pandas might be a good fit. A helpful book in the Python + data space.
Amazon Verified review Amazon
Loris Jun 20, 2015
Full star icon Full star icon Full star icon Full star icon Full star icon 5
In my job I make use of many scientific libraries and Pandas is one of those. I have been looking for a good Pandas reference book for a while and I got this book as soon as it was published. I am not exaggerating when I say that it is one of the best python-related book I ever read. It is not only well written but it is also very well organized and structured. The book begins by providing detailed instructions on how to install Pandas on Linux, MacOS X, and Windows. In the firsts chapters it introduces NumPy and both Pandas Series and DataFrames. These firsts chapters are really important, especially for beginners, as they explain basic concepts that will be used continuously through the book. The author also indicates in which situations Pandas behave differently from NumPy, something I ignored before reading the book.I found very useful the description of the different ways to access rows and columns in DataFrames (loc, iloc, ix, etc.). The author clearly explains which is the best method to use in different scenarios and gives important tips regarding the performances of the different methods. Personally the chapters I found more useful were those about “Tidying Up your Data”, “Combining and Reshaping Data”, and “Grouping and Aggregating Data”. These are not easy concepts and the author did a very good job explaining them and providing a lot of clear examples. I believe these chapters are where you realize how Pandas can greatly simplify data analysis. The chapter about visualization is particularly useful to those who do not have experience with matplotlib and want to learn how to do quick plots with pandas.To conclude, the book is an excellent guide to Pandas not only if you are a beginner but also if you already have some experience with the library. Beside being well written, it covers all the mayor features of Pandas and each topic is complemented with a lot of code.
Amazon Verified review Amazon
Trevor May 20, 2015
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Warning! This is not a book for learning statistical methods of data analysis. Do not buy if that is what you are looking for. If you are interested in learning the tools for data analysis in python, then this book is for you.This book is great for anyone who wants to understand how to use the pandas library. The book is larger than most Packtpub books. The size is primarily due to the number of topics covered and the rich interactive set of examples to illustrate each topic.All the code in the book can be downloaded in the form of ipython notebooks. Which is by far the best learning median for python. This greatly enhances not only your ability to follow along with the examples, but to explore each topic yourself by altering the code to reinforce what you have learned.Also, the books really does the best job I've seen at building each piece of the puzzle one step at a time. The author assumes basic knowledge of python, and some familiarity with statistical definitions. Otherwise, nothing is referenced without first being explained, and everything is introduced in a logical way.If you want to understand how to use pandas from the ground up, then this book is for you.
Amazon Verified review Amazon
Harmon L. Jul 26, 2016
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Excellent
Amazon Verified review Amazon
Lidija Novak Jan 05, 2019
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Its a beginners book and a bit outdated, still what counts is its content. Straight forward, sharp and great set up of content and examples. What I hate is meaningless nonsense, this author is great of keeping text short but informative.Have not yet finished reading the book but the first chapters have answered all my previous questions I had on Pandas after reading another book. Great buy! After this book I will buy Pandas for Finance written by same author - Michael Heydt!
Amazon Verified review Amazon

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About the author

Profile icon Michael Heydt
Michael Heydt
Michael Heydt is an independent consultant, programmer, educator, and trainer. He has a passion for learning and sharing his knowledge of new technologies. Michael has worked in multiple industry verticals, including media, finance, energy, and healthcare. Over the last decade, he worked extensively with web, cloud, and mobile technologies and managed user experiences, interface design, and data visualization for major consulting firms and their clients. Michael's current company, Seamless Thingies , focuses on IoT development and connecting everything with everything. Michael is the author of numerous articles, papers, and books, such as D3.js By Example, Instant Lucene. NET, Learning Pandas, and Mastering Pandas for Finance, all by Packt. Michael is also a frequent speaker at .NET user groups and various mobile, cloud, and IoT conferences and delivers webinars on advanced technologies.
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