Browse Library
Sign In Start Free Trial
Close icon Search icon
Account
Browse Library Advanced Search Sign In Start Free Trial

Add to playlist

Create a Playlist

Modal Close icon
You need to login to use this feature.
  • Book Overview & Buying Python Business Intelligence Cookbook
  • Table Of Contents Toc
  • Feedback & Rating feedback
Python Business Intelligence Cookbook

Python Business Intelligence Cookbook

By : Dempsey
3.6 (9)
Buy this Book
close
close
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)
close
close
close
Preface
Icon
Preface
Icon
What this book covers
Icon
What you need for this book
Icon
Who this book is for
Icon
Sections
Icon
Conventions
Icon
Reader feedback
Icon
Customer support
Lock Free Chapter
1
1. Getting Set Up to Gain Business Intelligence
Icon
1. Getting Set Up to Gain Business Intelligence
Icon
Introduction
Icon
Installing Anaconda
Icon
Learn about the Python libraries we will be using
Icon
Installing, configuring, and running MongoDB
Icon
Installing Rodeo
Icon
Starting Rodeo
Icon
Installing Robomongo
Icon
Using Robomongo to query MongoDB
Icon
Downloading the UK Road Safety Data dataset
2
2. Making Your Data All It Can Be
Icon
2. Making Your Data All It Can Be
Icon
Importing a CSV file into MongoDB
Icon
Importing an Excel file into MongoDB
Icon
Importing a JSON file into MongoDB
Icon
Importing a plain text file into MongoDB
Icon
Retrieving a single record using PyMongo
Icon
Retrieving multiple records using PyMongo
Icon
Inserting a single record using PyMongo
Icon
Inserting multiple records using PyMongo
Icon
Updating a single record using PyMongo
Icon
Updating multiple records using PyMongo
Icon
Deleting a single record using pymongo
Icon
Deleting multiple records using PyMongo
Icon
Importing a CSV file into a Pandas DataFrame
Icon
Renaming column headers in Pandas
Icon
Filling in missing values in Pandas
Icon
Removing punctuation in Pandas
Icon
Removing whitespace in Pandas
Icon
Removing any string from within a string in Pandas
Icon
Merging two datasets in Pandas
Icon
Titlecasing anything
Icon
Uppercasing a column in Pandas
Icon
Updating values in place in Pandas
Icon
Standardizing a Social Security number in Pandas
Icon
Standardizing dates in Pandas
Icon
Converting categories to numbers in Pandas for a speed boost
3
3. Learning What Your Data Truly Holds
Icon
3. Learning What Your Data Truly Holds
Icon
Creating a Pandas DataFrame from a MongoDB query
Icon
Creating a Pandas DataFrame from a CSV file
Icon
Creating a Pandas DataFrame from an Excel file
Icon
Creating a Pandas DataFrame from a JSON file
Icon
Creating a data quality report
Icon
Generating summary statistics for the entire dataset
Icon
Generating summary statistics for object type columns
Icon
Getting the mode of the entire dataset
Icon
Generating summary statistics for a single column
Icon
Getting a count of unique values for a single column
Icon
Getting the minimum and maximum values of a single column
Icon
Generating quantiles for a single column
Icon
Getting the mean, median, mode, and range for a single column
Icon
Generating a frequency table for a single column by date
Icon
Generating a frequency table of two variables
Icon
Creating a histogram for a column
Icon
Plotting the data as a probability distribution
Icon
Plotting a cumulative distribution function
Icon
Showing the histogram as a stepped line
Icon
Plotting two sets of values in a probability distribution
Icon
Creating a customized box plot with whiskers
Icon
Creating a basic bar chart for a single column over time
4
4. Performing Data Analysis for Non Data Analysts
chevron up
Icon
4. Performing Data Analysis for Non Data Analysts
Icon
Performing a distribution analysis
Icon
Performing categorical variable analysis
Icon
Performing a linear regression
Icon
Performing a time-series analysis
Icon
Performing outlier detection
Icon
Creating a predictive model using logistic regression
Icon
Creating a predictive model using a random forest
Icon
Creating a predictive model using Support Vector Machines
Icon
Saving a predictive model for production use
5
5. Building a Business Intelligence Dashboard Quickly
Icon
5. Building a Business Intelligence Dashboard Quickly
Icon
Creating reports in Excel directly from a Pandas DataFrame
Icon
Creating customizable Excel reports using XlsxWriter
Icon
Building a shareable dashboard using IPython Notebook and matplotlib
Icon
Exporting an IPython Notebook Dashboard to HTML
Icon
Exporting an IPython Notebook Dashboard to PDF
Icon
Exporting an IPython Notebook Dashboard to an HTML slideshow
Icon
Building your First Flask application in 10 minutes or less
Icon
Creating and saving your plots for your Flask BI dashboard
Icon
Building a business intelligence dashboard in Flask
6
Index
Icon
Index

Performing categorical variable analysis

Categorical variable analysis helps us understand the categorical types of data. Categorical types are non-numeric. In this recipe, we're using days of the week. Technically, it's a category as opposed to purely numeric data. The creators of the dataset have already converted the category—the name of the day of the week—to a number. If they had not done this, we could use Pandas to do it for us, and then perform our analysis.

In this recipe, we are going to plot the distribution of casualties by the day of the week.

How to do it…

  1. First, import the Python libraries that you need:

    import pandas as pd
import numpy as np
import matplotlib as plt
import matplotlib.pyplot as plt
%matplotlib inline

  2. Next, define a variable for the accidents data file, import the data, and view the top five rows:

    accidents_data_file = '/Users/robertdempsey/Dropbox/private/Python Business Intelligence Cookbook/Data/Stats19-Data1979-2004/Accidents7904.csv'
accidents = pd.read_csv...
Visually different images
CONTINUE READING
83
Tech Concepts
36
Programming languages
73
Tech Tools
Icon Unlimited access to the largest independent learning library in tech of over 8,000 expert-authored tech books and videos.
Icon Innovative learning tools, including AI book assistants, code context explainers, and text-to-speech.
Icon 50+ new titles added per month and exclusive early access to books as they are being written.
Python Business Intelligence Cookbook
End of Section 4
Next Section
notes
bookmark Notes and Bookmarks search Search in title playlist Add to playlist font-size Font size

Change the font size

margin-width Margin width

Change margin width

day-mode Day/Sepia/Night Modes

Change background colour

Close icon Search
Country selected
Close icon Your notes and bookmarks

Confirmation

Modal Close icon
claim successful
Order Page Read Now

Buy this book with your credits?

Modal Close icon
Are you sure you want to buy this book with one of your credits?
Close
YES, BUY

Submit Your Feedback

Modal Close icon
Modal Close icon
Modal Close icon