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

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