目錄(95章)
倒序
- 封面
- 版權頁
- Credits
- About the Author
- About the Reviewer
- www.PacktPub.com
- Support files eBooks discount offers and more
- Preface
- What this book covers
- What you need for this book
- Who this book is for
- Sections
- Conventions
- Reader feedback
- Customer support
- 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 更新時間：2021-07-30 09:52:07
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