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Welcome to Data Analysis in Python! — Data Analysis in Python 0.1 documentation

Welcome to Data Analysis in Python! Python is an increasingly popular tool for data analysis. In recent years, a number of libraries have reached maturity, allowing R and Stata users to take advantage of the beauty, flexibility, and performance of Python without sacrificing the functionality these older programs have accumulated over the years. Library for working with tabular data (analogous to. In R, or everything you ever did in Stata), and a guide to installing libraries to expand Python. The topic p...

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Welcome to Data Analysis in Python! Python is an increasingly popular tool for data analysis. In recent years, a number of libraries have reached maturity, allowing R and Stata users to take advantage of the beauty, flexibility, and performance of Python without sacrificing the functionality these older programs have accumulated over the years. Library for working with tabular data (analogous to. In R, or everything you ever did in Stata), and a guide to installing libraries to expand Python. The topic p...

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Welcome to Data Analysis in Python! — Data Analysis in Python 0.1 documentation | data-analysis-in-python.org Reviews

https://data-analysis-in-python.org

Welcome to Data Analysis in Python! Python is an increasingly popular tool for data analysis. In recent years, a number of libraries have reached maturity, allowing R and Stata users to take advantage of the beauty, flexibility, and performance of Python without sacrificing the functionality these older programs have accumulated over the years. Library for working with tabular data (analogous to. In R, or everything you ever did in Stata), and a guide to installing libraries to expand Python. The topic p...

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data-analysis-in-python.org

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Text Analysis — Data Analysis in Python 0.1 documentation

http://www.data-analysis-in-python.org/t_text_analysis.html

Python is a phenomenally good tool for text analysis, and there are a few good tools out there you can use. Natural Language Tool Kit (NLTK). The most used library in social science is probably the “Natural Language Tool Kit”, normally referred to as “NLTK”. The library has lots of tools, and is very user friendly. By Steven Bird, Ewan Klein, and Edward Loper (talk about generous! However, if you want a paper copy, you can also find it on Amazon here. Is a nice looking new library that seems to have a.

2

ST: iPython — Data Analysis in Python 0.1 documentation

http://www.data-analysis-in-python.org/st_ipython.html

Check back for updates. What is an iPython Notebook? Data Analysis in Python. Note to R Users. Note to Stata Users. 1 Setting Up Python. Big Data / Parallelization. ST: Git and Github. 2015, Nick Eubank. Powered by Sphinx 1.4.1. Alabaster 0.7.7.

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2. Basic Python — Data Analysis in Python 0.1 documentation

http://www.data-analysis-in-python.org/2_basic_python.html

This site provides a number of links to recommended introduction to Python tutorials (no reason to re-invent the wheel by trying to write a new one! These different tutorials each have their own strengths and weaknesses, and readers are encouraged to shop around a little. Before that list, however, some guidance:. Data types: integers, floats, strings, booleans, lists, dictionaries, and sets (tuples are kinda optional). Understanding mutable versus immutable data types. Methods for manipulating strings.

4

Making Python faster — Data Analysis in Python 0.1 documentation

http://www.data-analysis-in-python.org/t_super_fast.html

But, if you do indeed need to make code you’ve written faster, here are some words of guidance, written as a kind of “checklist” for approaching the problem from easiest to hardest solutions. 1 Find Your Bottlenecks! The easiest way to profile your Python code is using the iPython. Command ( documented here. If you haven’t used iPython. Commands before, you can learn more about them here. 2 Check your memory management. 3 Use other people’s (compiled) functions. As a general rule, code you write in Pytho...

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Note to Stata Users — Data Analysis in Python 0.1 documentation

http://www.data-analysis-in-python.org/python_for_stata.html

Note to Stata Users. There’s good news and bad news for you Stata users (of whom I am definitely one – I spent 6 years working exclusively in Stata). The goods news is that you have no bad habits developed from R, and you’re about to enter a world of phenomenal flexibility and power. Everything you know from Stata is inside a single DataFrame object in Python. Think of it as “zooming out”. If you’re someone who is used to working with macros (using the. Commands) – either directly or in for-loops &...

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Welcome to Data Analysis in Python! — Data Analysis in Python 0.1 documentation

Welcome to Data Analysis in Python! Python is an increasingly popular tool for data analysis. In recent years, a number of libraries have reached maturity, allowing R and Stata users to take advantage of the beauty, flexibility, and performance of Python without sacrificing the functionality these older programs have accumulated over the years. Library for working with tabular data (analogous to. In R, or everything you ever did in Stata), and a guide to installing libraries to expand Python. The topic p...

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