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Python business intelligence cookbook : leverage the computational power of Python with more than 60 recipes that arm you with the required skills to make informed business decisions /
Annotation
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Birmingham, UK :
Packt Publishing,
2015.
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| Colección: | Quick answers to common problems.
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| Temas: | |
| Acceso en línea: | Texto completo Texto completo |
Tabla de Contenidos:
- 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.
- 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.
- 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.
- 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.
- 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.