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

Description

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 BookWant to minimize risk and optimize profits of your business? Learn to create efficient analytical reports with ease using this highly practical, easy-to-follow guideLearn to apply Python for business intelligence tasks―preparing, exploring, analyzing, visualizing and reporting―in order to make more informed business decisions using data at handLearn to explore and analyze business data, and build business intelligence dashboards with the help of various insightful recipesWho 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 LearnInstall Anaconda, MongoDB, and everything you need to get started with your data analysisPrepare data for analysis by querying cleaning and standardizing dataExplore your data by creating a Pandas data frame from MongoDBGain powerful insights, both statistical and predictive, to make informed business decisionsVisualize your data by building dashboards and generating reportsCreate a complete data processing and business intelligence systemIn 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. Cover Copyright Credits About the Author About the Reviewer www.PacktPub.com Table of Contents Preface Chapter 1: Getting Set Up to Gain Business Intelligence Introduction Installing Anaconda Learn about the Python libraries we will be using Installing, configuring, and running MongoDB Installing Rodeo Starting Rodeo Installing Robomongo Using Robomongo to query MongoDB Downloading the UK Road Safety Data dataset Chapter 2: Making Your Data All It Can Be Importing a CSV file into MongoDB Importing an Excel file into MongoDB Importing a JSON file into MongoDB Importing a plain text file into MongoDB Retrieving a single record using PyMongo Retrieving multiple records using PyMongo Inserting a single record using PyMongo Inserting multiple records using PyMongo Updating a single record using PyMongo Updating multiple records using PyMongo Deleting a single record using pymongo Deleting multiple records using PyMongo Importing a CSV file into a Pandas DataFrame Renaming column headers in Pandas Filling in missing values in Pandas Removing punctuation in Pandas Removing whitespace in Pandas Removing any string from within a string in Pandas Merging two datasets in Pandas Titlecasing anything Uppercasing a column in Pandas Updating values in place in Pandas Standardizing a Social Security number in Pandas Standardizing dates in Pandas Converting categories to numbers in Pandas for a speed boost Chapter 3: Learning What Your Data Truly Holds Creating a Pandas DataFrame from a MongoDB query Creating a Pandas DataFrame from a CSV file Creating a Pandas DataFrame from an Excel file Creating a Pandas DataFrame from a JSON file Creating a data quality report Generating summary statistics for the entire dataset Generating summary statistics for object type columns Getting the mode of the entire dataset Generating summary statistics for a single column Getting a count of unique values for a single column Getting the minimum and maximum values of a single column Generating quantiles for a single column Getting the mean, median, mode, and range for a single column Generating a frequency table for a single column by date Generating a frequency table of two variables Creating a histogram for a column Plotting the data as a probability distribution Plotting a cumulative distribution function Showing the histogram as a stepped line Plotting two sets of values in a probability distribution Creating a customized box plot with whiskers Creating a basic bar chart for a single column over time Chapter 4: Performing Data Analysis for Non-Data Analysts Performing a distribution analysis Performing categorical variable analysis Performing a linear regression Performing a time-series analysis Performing outlier detection Creating a predictive model using logistic regression Creating a predictive model using a random forest Creating a predictive model using Support Vector Machines Saving a predictive model for production use Chapter 5: Building a Business Intelligence Dashboard Quickly Creating reports in Excel directly from a Pandas DataFrame Creating customizable Excel reports using XlsxWriter Building a shareable Dashboard using iPython Notebook and matplotlib Exporting an iPython Notebook Dashboard to HTML Exporting an iPython Notebook Dashboard to PDF Exporting an iPython Notebook Dashboard to an HTML slideshow Building your First Flask application in 10 minutes or less Creating and saving your plots for your Flask BI Dashboard Building a business intelligence dashboard in Flask Index