Book Image

Python Business Intelligence Cookbook

Book Image

Python Business Intelligence Cookbook

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 (12 chapters)
Python Business Intelligence Cookbook
Credits
About the Author
About the Reviewer
www.PacktPub.com
Preface
Index

Generating a frequency table for a single column by date


A frequency table is another way of summarizing data; it shows the number of times a value occurs. In this recipe, we will create a frequency table of casualties by date.

Getting ready

To use this recipe, you need to have MongoDB running, and to have the accidents data imported.

How to do it…

  1. To generate a frequency table for a single column by date, begin by importing the Python libraries that we need:

    import pandas as pd
    import numpy as np
    from pymongo import MongoClient
  2. Next, connect to MongoDB, and run a query specifying the five fields to be retrieved from the MongoDB data:

    client = MongoClient('localhost', 27017)
    db = client.pythonbicookbook
    collection = db.accidents
    fields = {'Date':1,
              'Police_Force':1,
              'Accident_Severity':1,
              'Number_of_Vehicles':1,
              'Number_of_Casualties':1}
    data = collection.find({}, fields)
  3. Next, create a DataFrame from the results of the query:

    accidents = pd.DataFrame(list...