- 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
Sometimes, you need to delete a single record, while at other times you need to delete many. This recipe shows you how to delete multiple records matching a filter.
As before, determine the records that you want to delete, and create a filter for them.
from pymongo import MongoClient
client = MongoClient('localhost', 27017)
db = client.pythonbicookbook
customers = db.customers
result = customers.delete_many({
'contact_requested': False })
print(result.deleted_count)-
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