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
Leverage the computational power of Python with more than 60 recipes that arm you with the required skills to make informed business decisions About This Book Want to minimize risk and optimize profits of your business? Learn to create efficient analytical reports with ease using this highly practic...
| Otros Autores: | |
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| Formato: | Libro electrónico |
| Idioma: | Inglés |
| Publicado: |
Birmingham, England ; Mumbai, [India] :
Packt Publishing
2015.
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| Edición: | 1st edition |
| Colección: | Quick answers to common problems
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| Materias: | |
| Ver en Biblioteca Universitat Ramon Llull: | https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009629918806719 |
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
- Creating a predictive model using a random forest
