Name: Python Business Intelligence Cookbook
Author: Robert Dempsey
ISBN: 9781785289668

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Python Business Intelligence Cookbook

Robert Dempsey

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Credits
About the Author
About the Reviewer
www.PacktPub.com
Support files, eBooks, discount offers, and more
Preface
What this book covers
What you need for this book
Who this book is for
Sections
Conventions
Reader feedback
Customer support
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

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Python Business Intelligence Cookbook