Python for data science for dummies
Unleash the power of Python for your data analysis projects with For Dummies! Python is the preferred programming language for data scientists and combines the best features of Matlab, Mathematica, and R into libraries specific to data analysis and visualization. Python for Data Science For Dummie...
| Other Authors: | |
|---|---|
| Format: | eBook |
| Language: | Inglés |
| Published: |
Hoboken, N.J.:
Wiley
c2015.
Hoboken, New Jersey : 2015. |
| Edition: | 1st edition |
| Series: | --For dummies.
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| Subjects: | |
| See on Biblioteca Universitat Ramon Llull: | https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009629913906719 |
Table of Contents:
- Title Page; Copyright Page; Table of Contents; Introduction; About This Book; Foolish Assumptions; Icons Used in This Book; Beyond the Book; Where to Go from Here; Part I Getting Started with Python for Data Science; Chapter 1 Discovering the Match between Data Science and Python; Defining the Sexiest Job of the 21st Century; Considering the emergence of data science; Outlining the core competencies of a data scientist; Linking data science and big data; Understanding the role of programming; Creating the Data Science Pipeline; Preparing the data; Performing exploratory data analysis
- Learning from dataVisualizing; Obtaining insights and data products; Understanding Python's Role in Data Science; Considering the shifting profile of data scientists; Working with a multipurpose, simple, and efficient language; Learning to Use Python Fast; Loading data; Training a model; Viewing a result; Chapter 2 Introducing Python's Capabilities and Wonders; Why Python?; Grasping Python's core philosophy; Discovering present and future development goals; Working with Python; Getting a taste of the language; Understanding the need for indentation; Working at the command line or in the IDE
- Getting pythonxyGetting WinPython; Installing Anaconda on Windows; Installing Anaconda on Linux; Installing Anaconda on Mac OS X; Downloading the Datasets and Example Code; Using IPython Notebook; Defining the code repository; Understanding the datasets used in this book; Chapter 4 Reviewing Basic Python; Working with Numbers and Logic; Performing variable assignments; Doing arithmetic; Comparing data using Boolean expressions; Creating and Using Strings; Interacting with Dates; Creating and Using Functions; Creating reusable functions; Calling functions in a variety of ways
- Using Conditional and Loop StatementsMaking decisions using the if statement; Choosing between multiple options using nested decisions; Performing repetitive tasks using for; Using the while statement; Storing Data Using Sets, Lists, and Tuples; Performing operations on sets; Working with lists; Creating and using Tuples; Defining Useful Iterators; Indexing Data Using Dictionaries; Part II Getting Your Hands Dirty with Data; Chapter 5 Working with Real Data; Uploading, Streaming, and Sampling Data; Uploading small amounts of data into memory; Streaming large amounts of data into memory
- Sampling data
