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  • Book Overview & Buying Python Business Intelligence Cookbook
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Python Business Intelligence Cookbook

Python Business Intelligence Cookbook

By : Dempsey
3.6 (9)
Buy this Book
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Python Business Intelligence Cookbook

Python Business Intelligence Cookbook

3.6 (9)
By: Dempsey
Buy this Book

Overview of this book

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.
Table of Contents (7 chapters)
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Preface
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Preface
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What this book covers
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What you need for this book
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Who this book is for
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Sections
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Conventions
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Reader feedback
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Customer support
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1
1. Getting Set Up to Gain Business Intelligence
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1. Getting Set Up to Gain Business Intelligence
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Introduction
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Installing Anaconda
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Learn about the Python libraries we will be using
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Installing, configuring, and running MongoDB
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Installing Rodeo
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Starting Rodeo
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Installing Robomongo
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Using Robomongo to query MongoDB
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Downloading the UK Road Safety Data dataset
2
2. Making Your Data All It Can Be
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2. Making Your Data All It Can Be
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Importing a CSV file into MongoDB
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Importing an Excel file into MongoDB
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Importing a JSON file into MongoDB
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Importing a plain text file into MongoDB
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Retrieving a single record using PyMongo
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Retrieving multiple records using PyMongo
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Inserting a single record using PyMongo
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Inserting multiple records using PyMongo
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Updating a single record using PyMongo
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Updating multiple records using PyMongo
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Deleting a single record using pymongo
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Deleting multiple records using PyMongo
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Importing a CSV file into a Pandas DataFrame
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Renaming column headers in Pandas
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Filling in missing values in Pandas
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Removing punctuation in Pandas
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Removing whitespace in Pandas
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Removing any string from within a string in Pandas
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Merging two datasets in Pandas
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Titlecasing anything
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Uppercasing a column in Pandas
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Updating values in place in Pandas
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Standardizing a Social Security number in Pandas
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Standardizing dates in Pandas
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Converting categories to numbers in Pandas for a speed boost
3
3. Learning What Your Data Truly Holds
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3. Learning What Your Data Truly Holds
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Creating a Pandas DataFrame from a MongoDB query
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Creating a Pandas DataFrame from a CSV file
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Creating a Pandas DataFrame from an Excel file
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Creating a Pandas DataFrame from a JSON file
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Creating a data quality report
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Generating summary statistics for the entire dataset
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Generating summary statistics for object type columns
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Getting the mode of the entire dataset
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Generating summary statistics for a single column
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Getting a count of unique values for a single column
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Getting the minimum and maximum values of a single column
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Generating quantiles for a single column
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Getting the mean, median, mode, and range for a single column
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Generating a frequency table for a single column by date
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Generating a frequency table of two variables
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Creating a histogram for a column
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Plotting the data as a probability distribution
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Plotting a cumulative distribution function
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Showing the histogram as a stepped line
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Plotting two sets of values in a probability distribution
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Creating a customized box plot with whiskers
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Creating a basic bar chart for a single column over time
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4. Performing Data Analysis for Non Data Analysts
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4. Performing Data Analysis for Non Data Analysts
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Performing a distribution analysis
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Performing categorical variable analysis
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Performing a linear regression
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Performing a time-series analysis
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Performing outlier detection
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Creating a predictive model using logistic regression
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Creating a predictive model using a random forest
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Creating a predictive model using Support Vector Machines
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Saving a predictive model for production use
5
5. Building a Business Intelligence Dashboard Quickly
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5. Building a Business Intelligence Dashboard Quickly
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Creating reports in Excel directly from a Pandas DataFrame
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Creating customizable Excel reports using XlsxWriter
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Building a shareable dashboard using IPython Notebook and matplotlib
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Exporting an IPython Notebook Dashboard to HTML
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Exporting an IPython Notebook Dashboard to PDF
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Exporting an IPython Notebook Dashboard to an HTML slideshow
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Building your First Flask application in 10 minutes or less
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Creating and saving your plots for your Flask BI dashboard
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Building a business intelligence dashboard in Flask
6
Index
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Index

Creating a Pandas DataFrame from an Excel file

While many people will tell you to get data out of Excel as quickly as you can, Pandas provides a function to import data directly from Excel files. This saves you the time of converting the file.

How to do it…

  1. To create a Pandas DataFrame from an Excel file, first import the Python libraries that you need:

    import pandas as pd

  2. Next, define a variable for the accidents data file and enter the full path to the data file:

    customer_data_file = 'customer_data.xlsx'

  3. After that, create a DataFrame from the Excel file using the read_excel method provided by Pandas, as follows:

    customers = pd.read_excel(customer_data_file,
sheetname=0,
header=0,
index_col=False,
keep_default_na=True
)

  4. Finally, use the head() command on the DataFrame to see the top five rows of data:

    customers.head()

How it works…

After importing Pandas and creating a variable from the path to our Excel file, we use the read_excel() function to create a DataFrame from the spreadsheet. The first...

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