Python Business Intelligence Cookbook
eBook - ePub

Python Business Intelligence Cookbook

Robert Dempsey

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  1. 202 pages
  2. English
  3. ePUB (adapté aux mobiles)
  4. Disponible sur iOS et Android
eBook - ePub

Python Business Intelligence Cookbook

Robert Dempsey

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À propos de ce livre

Leverage the computational power of Python with more than 60 recipes that arm you with the required skills to make informed business decisions

About This Book

  • Want to minimize risk and optimize profits of your business? Learn to create efficient analytical reports with ease using this highly practical, easy-to-follow guide
  • Learn to apply Python for business intelligence tasks—preparing, exploring, analyzing, visualizing and reporting—in order to make more informed business decisions using data at hand
  • Learn to explore and analyze business data, and build business intelligence dashboards with the help of various insightful recipes

Who This Book Is For

This book is intended for data analysts, managers, and executives with a basic knowledge of Python, who now want to use Python for their BI tasks. If you have a good knowledge and understanding of BI applications and have a "working" system in place, this book will enhance your toolbox.

What You Will Learn

  • Install Anaconda, MongoDB, and everything you need to get started with your data analysis
  • Prepare data for analysis by querying cleaning and standardizing data
  • Explore your data by creating a Pandas data frame from MongoDB
  • Gain powerful insights, both statistical and predictive, to make informed business decisions
  • Visualize your data by building dashboards and generating reports
  • Create a complete data processing and business intelligence system

In Detail

The amount of data produced by businesses and devices is going nowhere but up. In this scenario, the major advantage of Python is that it's a general-purpose language and gives you a lot of flexibility in data structures. Python is an excellent tool for more specialized analysis tasks, and is powered with related libraries to process data streams, to visualize datasets, and to carry out scientific calculations. Using Python for business intelligence (BI) can help you solve tricky problems in one go.

Rather than spending day after day scouring Internet forums for "how-to" information, here you'll find more than 60 recipes that take you through the entire process of creating actionable intelligence from your raw data, no matter what shape or form it's in. Within the first 30 minutes of opening this book, you'll learn how to use the latest in Python and NoSQL databases to glean insights from data just waiting to be exploited.

We'll begin with a quick-fire introduction to Python for BI and show you what problems Python solves. From there, we move on to working with a predefined data set to extract data as per business requirements, using the Pandas library and MongoDB as our storage engine.

Next, we will analyze data and perform transformations for BI with Python. Through this, you will gather insightful data that will help you make informed decisions for your business. The final part of the book will show you the most important task of BI—visualizing data by building stunning dashboards using Matplotlib, PyTables, and iPython Notebook.

Style and approach

This is a step-by-step guide to help you prepare, explore, analyze and report data, written in a conversational tone to make it easy to grasp. Whether you're new to BI or are looking for a better way to work, you'll find the knowledge and skills here to get your job done efficiently.

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Informations

Année
2015
ISBN
9781785287466

Python Business Intelligence Cookbook


Table of Contents

Python Business Intelligence Cookbook
Credits
About the Author
About the Reviewer
www.PacktPub.com
Support files, eBooks, discount offers, and more
Why subscribe?
Free access for Packt account holders
Preface
What this book covers
What you need for this book
Who this book is for
Sections
Getting ready
How to do it

How it works

There's more

See also
Conventions
Reader feedback
Customer support
Downloading the example code
Errata
Piracy
Questions
1. Getting Set Up to Gain Business Intelligence
Introduction
Installing Anaconda
Getting ready
How to do it

Mac OS X 10.10.4
Windows 8.1
Linux Ubuntu server 14.04.2 LTS
How it works

Learn about the Python libraries we will be using
Installing, configuring, and running MongoDB
Getting ready
How to do it

Mac OS X
Windows
Linux
How it works

Installing Rodeo
Getting ready
How to do it

How it works

Starting Rodeo
Getting ready
How to do it

Installing Robomongo
Getting ready
How to do it

Mac OS X
Windows
Using Robomongo to query MongoDB
Getting ready
How to do it

Downloading the UK Road Safety Data dataset
How to do it

How it works

Why we are using this dataset
2. Making Your Data All It Can Be
Importing a CSV file into MongoDB
Getting ready
How to do it

How it works

There's more

Importing an Excel file into MongoDB
Getting ready
How to do it

How it works

Importing a JSON file into MongoDB
Getting ready
How to do it

Importing a plain text file into MongoDB
How to do it

How it works

Retrieving a single record using PyMongo
Getting ready
How to do it

How it works

Retrieving multiple records using PyMongo
Getting ready
How to do it

How it works

Inserting a single record using PyMongo
Getting ready
How to do it

How it works

Inserting multiple records using PyMongo
Getting ready
How to do it

How it works

Updating a single record using PyMongo
Getting ready
How to do it

How it works

Updating multiple records using PyMongo
Getting ready
How to do it

How it works

Deleting a single record using pymongo
Getting ready
How to do it

How it works

Deleting multiple records using PyMongo
Getting ready
How to do it

How it works

Importing a CSV file into a Pandas DataFrame
Getting ready
How to do it

How it works

There's more

Renaming column headers in Pandas
Getting ready
How to do it

How it works

Filling in missing values in Pandas
Getting ready
How to do it

How it works

Removing punctuation in Pandas
Getting ready
How to do it

How it works

Removing whitespace in Pandas
Getting ready
How to do it

How it works

Removing any string from within a string in Pandas
Getting ready
How to do it

How it works

Merging two datasets in Pandas
Getting ready
How to do it

How it works

Titlecasing anything
Getting ready
How to do it

How it works

Uppercasing a column in Pandas
Getting ready
How to do it

How it works

Updating values in place in Pandas
Getting ready
How to do it

How it works

Standardizing a Social Security number in Pandas
Getting ready
How to do it

How it works

Standardizing dates in Pandas
Getting ready
How to do it

How it works

Converting categories to numbers in Pandas for a speed boost
Getting ready
How to do it

How it works

3. Learning What Your Data Truly Holds
Creating a Pandas DataFrame from a MongoDB query
Getting ready
How to do it

How it works

Creating a Pandas DataFrame from a CSV file
How to do it

How it works

Creating a Pandas DataFrame from an Excel file
How to do it

How it works

Creating a Pandas DataFrame from a JSON file
How to do it

How it works

Creating a data quality report
Getting ready
How to do it

How it works

Generating summary statistics for the entire dataset
How to do it

How it works

Generating summary statistics for object type columns
How to do it

How it works

Getting the mode of the entire dataset
How to do it

How it works

Generating summary statistics for a single column
How to do it

How it works

Getting a count of unique values for a single column
How to do it

How it works

Additional Arguments
Getting the minimum and maximum values of a single column
How to do it

How it works

Generating quantiles for a single column
How to do it

How it works

Getting the mean, median, mode, and range for a single column
How to do it

How it works

Generating a frequency table for a single column by date
Getting ready
How to do it

How it works

Generating a frequency table of two variables
Getting ready
How to do it

How...

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