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A.J. Henley - Learn Data Analysis with Python: Lessons in Coding

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Book:
Learn Data Analysis with Python: Lessons in Coding
Author:
AJ Henley / Dave Wolf
Publisher:
Apress
Genre:
Books / Home and family
Year:
2018
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Get started using Python in data analysis with this compact practical guide. This book includes three exercises and a case study on getting data in and out of Python code in the right format. Learn Data Analysis with Python also helps you discover meaning in the data using analysis and shows you how to visualize it.
Each lesson is, as much as possible, self-contained to allow you to dip in and out of the examples as your needs dictate. If you are already using Python for data analysis, you will find a number of things that you wish you knew how to do in Python. You can then take these techniques and apply them directly to your own projects.
If you arent using Python for data analysis, this book takes you through the basics at the beginning to give you a solid foundation in the topic. As you work your way through the book you will have a better of idea of how to use Python for data analysis when you are finished.
What You Will Learn

Get data into and out of Python code
Prepare the data and its format
Find the meaning of the data
Visualize the data using iPython

Who This Book Is For
Those who want to learn data analysis using Python. Some experience with Python is recommended but not required, as is some prior experience with data analysis or data science.

A.J. Henley: author's other books

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A.J. Henley and Dave Wolf 2018

A.J. Henley and Dave Wolf Learn Data Analysis with Python

1. How to Use This Book

A. J. Henley 1 and Dave Wolf 2

(1)

Washington, D.C., District of Columbia, USA

(2)

Sterling Business Advantage, LLC, Adamstown, Maryland, USA

If you are already using Python for data analysis, just browse this books table of contents. You will probably find a bunch of things that you wish you knew how to do in Python. If so, feel free to turn directly to that chapter and get to work. Each lesson is, as much as possible, self-contained.

Be warned! This book is more a workbook than a textbook.

If you arent using Python for data analysis, begin at the beginning. If you work your way through the whole workbook, you should have a better of idea of how to use Python for data analysis when you are done.

If you know nothing at all about data analysis, this workbook might not be the place to start. However, give it a try and see how it works for you.

Installing Jupyter Notebook

The fastest way to install and use Python is to do what you already know how to do, and you know how to use your browser. Why not use Jupyter Notebook?

What Is Jupyter Notebook?

Jupyter Notebook is an interactive Python shell that runs in your browser. When installed through Anaconda, it is easy to quickly set up a Python development environment. Since its easy to set up and easy to run, it will be easy to learn Python.

Jupyter Notebook turns your browser into a Python development environment. The only thing you have to install is Anaconda. In essence, it allows you to enter a few lines of Python code, press CTRL+Enter, and execute the code. You enter the code in cells and then run the currently selected cell. There are also options to run all the cells in your notebook. This is useful if you are developing a larger program.

What Is Anaconda?

Anaconda is the easiest way to ensure that you dont spend all day installing Jupyter. Simply download the Anaconda package and run the installer. The Anaconda software package contains everything you need to create a Python development environment. Anaconda comes in two versionsone for Python 2.7 and one for Python 3.x. For the purposes of this guide, install the one for Python 2.7.

Anaconda is an open source data-science platform. It contains over 100 packages for use with Python, R, and Scala. You can download and install Anaconda quickly with minimal effort. Once installed, you can update the packages or Python version or create environments for different projects.

Getting Started

Download and install Anaconda at https://www.anaconda.com/download .
Once youve installed Anaconda, youre ready to create your first notebook. Run the Jupyter Notebook application that was installed as part of Anaconda.
Your browser will open to the following address: http://localhost:8888 . If youre running Internet Explorer, close it. Use Firefox or Chrome for best results. From there, browse to http://localhost:8888 .
Start a new notebook. On the right-hand side of the browser, click the drop-down button that says "New" and select Python or Python 2 .
This will open a new iPython notebook in another browser tab. You can have many notebooks open in many tabs.
Jupyter Notebook contains cells. You can type Python code in each cell. To get started (for Python 2.7), type print "Hello, World!" in the first cell and hit CTRL+Enter. If youre using Python 3.5, then the command is print("Hello, World!") .

Getting the Datasets for the Workbooks Exercises

Download the dataset files from http://www.ajhenley.com/dwnld .
Upload the file datasets.zip to Anaconda in the same folder as your notebook.
Run the Python code in Listing to unzip the datasets.

path_to_zip_file = "datasets.zip"

directory_to_extract_to = ""

import zipfile

zip_ref = zipfile.ZipFile(path_to_zip_file, 'r')

zip_ref.extractall(directory_to_extract_to)

zip_ref.close()

Listing 1-1

Unzipping dataset.zip

A.J. Henley and Dave Wolf 2018

A.J. Henley and Dave Wolf Learn Data Analysis with Python

2. Getting Data Into and Out of Python

A. J. Henley 1 and Dave Wolf 2

(1)

Washington, D.C., District of Columbia, USA

(2)

Sterling Business Advantage, LLC, Adamstown, Maryland, USA

The first stage of data analysis is getting the data. Moving your data from where you have it stored into your analytical tools and back out again can be a difficult task if you don't know what you are doing. Python and its libraries try to make it as easy as possible.

With just a few lines of code, you will be able to import and export data in the following formats:

CSV
Excel
SQL

Loading Data from CSV Files

Normally, data will come to us as files or database links. See Listing to learn how to load data from a CSV file.

import pandas as pd

Location = "datasets/smallgradesh.csv"

df = pd.read_csv(Location, header=None)

Listing 2-1

Loading Data from CSV File

Now, let's take a look at what our data looks like (Listing ):

df.head()

Listing 2-2

Display First Five Lines of Data

As you can see, our dataframe lacks column headers. Or, rather, there are headers, but they weren't loaded as headers; they were loaded as row one of your data. To load data that includes headers, you can use the code shown in Listing .

import pandas as pd

Location = "datasets/gradedata.csv"

df = pd.read_csv(Location)

Listing 2-3

Loading Data from CSV File with Headers

Then, as before, we take a look at what the data looks like by running the code shown in Listing .

df.head()

Listing 2-4

Display First Five Lines of Data

If you have a dataset that doesn't include headers, you can add them afterward. To add them, we can use one of the options shown in Listing .

import pandas as pd

Location = "datasets/smallgrades.csv"

# To add headers as we load the data...

df = pd.read_csv(Location, names=['Names','Grades'])

# To add headers to a dataframe

df.columns = ['Names','Grades']

Listing 2-5

Loading Data from CSV File and Adding Headers

Your Turn

Can you make a dataframe from a file you have uploaded and imported on your own? Let's find out. Go to the following website, which contains U.S. Census data ( http://census.ire.org/data/bulkdata.html ), and download the CSV datafile for a state. Now, try to import that data into Python.

Saving Data to CSV

Maybe you want to save your progress when analyzing data. Maybe you are just using Python to massage some data for later analysis in another tool. Or maybe you have some other reason to export your dataframe to a CSV file. The code shown in Listing is an example of how to do this.

import pandas as pd

names = ['Bob','Jessica','Mary','John','Mel']

grades = [76,95,77,78,99]

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