| Description |
1 online resource (1 volume) : illustrations |
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text file |
| Series |
Quick answers to common problems |
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Quick answers to common problems.
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| Note |
Includes index. |
| Summary |
Annotation Leverage the computational power of Python with more than 60 recipes that arm you with the required skills to make informed business decisionsAbout This Book Want to minimize risk and optimize profits of your business? Learn to create efficient analytical reports with ease using this highly practical, easy-to-follow guide Learn to apply Python for business intelligence taskspreparing, exploring, analyzing, visualizing and reportingin order to make more informed business decisions using data at hand Learn to explore and analyze business data, and build business intelligence dashboards with the help of various insightful recipesWho This Book Is ForThis book is intended for data analysts, managers, and executives with a basic knowledge of Python, who now want to use Python for their BI tasks. If you have a good knowledge and understanding of BI applications and have a working system in place, this book will enhance your toolbox. What You Will Learn Install Anaconda, MongoDB, and everything you need to get started with your data analysis Prepare data for analysis by querying cleaning and standardizing data Explore your data by creating a Pandas data frame from MongoDB Gain powerful insights, both statistical and predictive, to make informed business decisions Visualize your data by building dashboards and generating reports Create a complete data processing and business intelligence systemIn DetailThe 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 BIvisualizing data by building stunning dashboards using Matplotlib, PyTables, and iPython Notebook. Style and approachThis is a step-by-step guide to help you prepare, explore, analyze and report data, written in a conversational tone to make it easy to grasp. Whether you're new to BI or are looking for a better way to work, you'll find the knowledge and skills here to get your job done efficiently. |
| Contents |
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. |
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Importing a plain text file into MongoDBRetrieving 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. |
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Removing any string from within a string in PandasMerging 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. |
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Generating summary statistics for the entire datasetGenerating 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. |
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Plotting the data as a probability distributionPlotting 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. |
| Subject |
Python (Computer program language)
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Python (Langage de programmation) |
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Python (Computer program language) |
| Other Form: |
Print version: Dempsey, Robert. Python Business Intelligence Cookbook. Birmingham : Packt Publishing, ©1900 |
| ISBN |
9781785289668 (electronic bk.) |
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1785289667 (electronic bk.) |
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178528746X |
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9781785287466 |
| Standard No. |
9781785287466 |
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