- Python Business Intelligence Cookbook
- Table of Contents
- Python Business Intelligence Cookbook
- Credits
- About the Author
- About the Reviewer
- www.PacktPub.com
- Preface
- 1. Getting Set Up to Gain Business Intelligence
- 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
- 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
- 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
- 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
Robomongo allows you to run any query against a MongoDB that would use the MongoDB command-line utility. This is a great way to test the queries that you'll write and to view the results.
To use this recipe, you need to have a working installation of MongoDB and have Robomongo installed.
You can use Robomongo to run any query against MongoDB that you would run at the command line. Use the following command to retrieve a single record:
db.getCollection('accidents').findOne()
You can view the results in multiple formats:
- Tree mode
- Table mode
- Text mode
By default, Robomongo will show you the results in tree mode as shown in the following screenshot:
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