Python statistics github

GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together. If nothing happens, download GitHub Desktop and try again. If nothing happens, download Xcode and try again.

If nothing happens, download the GitHub extension for Visual Studio and try again. Inspired by Allen Downey's books Think Stats and Think Bayesthis is an attempt to learn Statistics using an application-centric programming approach. Showcase real-life examples and what statistics to use in each of those examples. Almost every book teaches a concept and shows an example.

Ultimately, every topic gets treated separately and no holistic view is presented. Here, we would take examples and see how to make sense out of it.

We would be using Marijuana prices in various states of the USA, along with demographic data of the USA based on the latest census data. There will be separate ipython notebooks - grouped by topic similarities. Users could choose to install Anaconda, if they want. If using Anaconda or Enthought, please ensure that all libraries listed in the requirements.

Note to Windows Users : Neither of us use Windows. From past workshop experiences, Windows users have faced issues installing the way explained below. It is advisable to install Anaconda and ensure that all the libraries listed in the requirements.

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Skip to content. Dismiss Join GitHub today GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together. Sign up. Introduction to Statistics using Python. Jupyter Notebook Python. Jupyter Notebook Branch: master. Find file.

python statistics github

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Introduction to Statistics Inspired by Allen Downey's books Think Stats and Think Bayesthis is an attempt to learn Statistics using an application-centric programming approach. Objective Showcase real-life examples and what statistics to use in each of those examples. Find variance of price in selected states. Find variance of selected states by week of month Define distribution. Plot histograms Determining outliers Plots, quantiles, box plots, percentiles in weed price data Continuous distributions exponential distribution, normal distribution Introduction to Probability Hypothesis testing.

Check if weed price across states are similar or not. Check for different qualities of weed Resampling Simple regression model: Predict weed price for the next month. User should know how to write functions; read in a text file csv, txt, fwf and parse them; conditional and looping constructs; using standard libraries like os, sys; lists, list comprehension, dictionaries It is good to know basics of the following: Numpy Scipy Pandas Matplotlib Seaborn IPython and IPython notebook - Everything here would be an IPython notebook Software Requirements Python 2.

You signed in with another tab or window. Reload to refresh your session.Watch the video overview for a first hand-look at the powerful data integration capabilities included in the CData Python Connectors.

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Real Python. The replication commands include many features that allow for intelligent incremental updates to cached data. The GitHub Connector includes a library of 50 plus functions that can manipulate column values into the desired result. These customizations are supported at runtime using human-readable schema files that are easy to edit.

Connecting to and working with your data in Python follows a basic pattern, regardless of data source:. Once you import the extension, you can work with all of your enterprise data using the python modules and toolkits that you already know and love, quickly building apps that help you drive business. The data-centric interfaces of the GitHub Python Connector make it easy to integrate with popular tools like pandas and SQLAlchemy to visualize data in real-time.

Your end-users can interact with the data presented by the GitHub Connector as easily as interacting with a database table. View All Products. View All Drivers.

Support Resources. Order Online Contact Us. About Us. Testimonials Press Contact Us Resellers. Features Powerful metadata querying enables SQL-like access to non-database sources Push down query optimization pushes SQL operations down to the server whenever possible, increasing performance Client-side query execution engine, supports SQL operations that are not available server-side Connect to live GitHub data, for real-time data access Full support for data aggregation and complex JOINs in SQL queries Secure connectivity through modern cryptography, including TLS 1.

CData Python Connectors in Action! Connecting to and working with your data in Python follows a basic pattern, regardless of data source: Configure the connection properties to GitHub Query GitHub to retrieve or update data Connect your GitHub data with Python data tools. Connecting to GitHub in Python To connect to your data from Python, import the extension and create a connection: import cdata.

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To find out more about the cookies we use, see our Privacy Policy. Accept Decline.This Python tutorial for beginners provides complete overview of Python. Explorer Python features, Python syntax, python applications, python projects. Go from zero to hero with this python tutorial. But what is Python? Python is a general-purpose programming language that is interpreted, object-oriented and dynamically-typed.

The implementation we widely use is CPython written in C. Python has powerful frameworks and libraries. Top 7 reasons why you must learn python. Python is definitely easy to learn, that is why python is taught to university students- to create interest in programming. You can gain expertise in python with this free python tutorial.

Python is:. Unique features which make Python most popular programming language on the planet. To learn about variables, operators, and other topics, refer to the links above. Here, we will talk about the syntax of Python code. You can choose one or more of the following. Here, you can write, edit, test and debug code. It has build automation, code linting, testing, and debugging. This speeds up your work. It is lightweight and simple.

Pricing: Freemium Sublime Text 3 is a popular code editor and also supports other languages. It is fast, customizable, and has a large community.

It has packages available for debugging, auto-completion, and code linting,etc. Pricing: Free Atom is an editor by GitHub and is open-source.

It is customizable and has packages like autocomplete-python, linter-flake8, and python-debugger. It has a very simple UI for beginners, but also has many useful features like syntax error highlighting, debugging, code completion, and step-through expression evaluation. Pricing: Freemium PyCharm is not for beginners.Welcome to this tutorial about data analysis with Python and the Pandas library.

This tutorial looks at pandas and the plotting package matplotlib in some more depth.

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It simplifies the loading of data from external sources such as text files and databases, as well as providing ways of analysing and manipulating data once it is loaded into your computer. The features provided in pandas automate and simplify a lot of the common tasks that would take many lines of code to write in the basic Python langauge. Pandas is a hugely popular, and still growing, Python library used across a range of disciplines from environmental and climate science, through to social science, linguistics, biology, as well as a number of applications in industry such as data analytics, financial trading, and many others.

In the Introduction to Python tutorial we had a look at how Python had grown rapidly in terms of users over the last decade or so, based on traffic to the StackOverflow question and answer site. A similar graph has been produced showing the growth of Pandas compared to some other Python software libraries!

Based on StackOverflow question views per month. These graphs of course should be taken with a pinch of salt, as there is no agreed way of absolutely determing programming langauge and library popularity, but they are interesting to think about nonetheless.

Pandas is best suited for structured, labelled data, in other words, tabular data, that has headings associated with each column of data. You can read more about the Pandas package at the Pandas project website. Here we briefly discuss the different ways you can folow this tutorial.

But do not use Windows Notepad! Personally this is how I like to work with Python as it frees you from the distractions of an IDE like Spyder, and reduces the number of problems that can arise from the Spyder program being set-up incorrectly.

Finally there is IPython, which lets you type in Python commands line-by-line, similar to Matlab and and RStudio, or an R console session.

python statistics github

It can also be installed on your laptop relatively easily. It is included in the Anconda Python distibution which can be downloaded here. Be sure to download the Python 3 version! The basics of Spyder were covered in the Introduction to Python tutorial. You can follow this tutorial by writing scripts saved as. Although it looks simple, this way can quite tricky to set up with Windows, it is probably easiest on Linux or Mac.

It lets you type in Python commands line-by-line, and then immediately execute them. Note for interactive IPython users: If you are following this tutorial with IPython, you do not need to use print functions to get IPython to display variables or other Python objects. IPython will automatically print out variable simply when you type in the variable name and press enter.

So for example:. IPython users: When you see a print function used in this tutorial, e. All the examples in this tutorial assume you have installed the Python library pandaseither through installing a scientific Python distribution such as Anaconda, or by installing it using a package-manager, such as conda or pip.

To use any of the features of Pandas, you will need to have an import statement at the top of your script like so:. By convention, the pandas module is almost always imported this way as pd. Every time we use a pandas feature thereafter, we can shorten what we type by just typing pdsuch as pd. If you are running Python interactively, such as in IPython, you will need to type in the same import statement at the start of each interactive session.

Reminder for IPython users : You do not need the print function wrapped around the variable here. Just type pd.

Remember this every time you see a print function for the remainder of this tutorial.Python materials for the statistics course of the CogMaster. Authors: S.

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Charron, G. R is a language dedicated to statistics. Python is a general purpose language with statistics module. R has more statistical analysis features than Python, and specialized syntaxes.

However, when it comes to building complex analysis pipelines that mix statistics with e. The scipy lecture notes have a chapter on statistics in Python that is kept up to date and is a good complement to these notes for statistic topics not specific to experimental psyschology.

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To install Python, we recommend that you download Anaconda Python. Tip Why Python for statistics in experimental psychology? See also Scipy lecture notes The scipy lecture notes have a chapter on statistics in Python that is kept up to date and is a good complement to these notes for statistic topics not specific to experimental psyschology. Note To install Python, we recommend that you download Anaconda Python. Basic statistics 1. Interacting with data 1. Work environment: IPython 1.

Basic array manipulation: numpy 1. Basic plotting: pylab 1. The box plot 1. More plots 1. Mixed-type data: pandas 1. Inputing data 1. Manipulating data 1.

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Plotting data 1. Hypothesis testing: two-group comparisons 1. Paired tests 1.

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A simple linear regression 1. Multiple Regression 1. FMRI signals 2. Getting ready: installing the software 2. Installation 2.If you find this content useful, please consider supporting the work by buying the book! Effective data-driven science and computation requires understanding how data is stored and manipulated.

This section outlines and contrasts how arrays of data are handled in the Python language itself, and how NumPy improves on this. Understanding this difference is fundamental to understanding much of the material throughout the rest of the book. Users of Python are often drawn-in by its ease of use, one piece of which is dynamic typing. While a statically-typed language like C or Java requires each variable to be explicitly declared, a dynamically-typed language like Python skips this specification.

For example, in C you might specify a particular operation as follows:. Notice the main difference: in C, the data types of each variable are explicitly declared, while in Python the types are dynamically inferred. This means, for example, that we can assign any kind of data to any variable:.

Here we've switched the contents of x from an integer to a string. The same thing in C would lead depending on compiler settings to a compilation error or other unintented consequences:. This sort of flexibility is one piece that makes Python and other dynamically-typed languages convenient and easy to use. Understanding how this works is an important piece of learning to analyze data efficiently and effectively with Python.

But what this type-flexibility also points to is the fact that Python variables are more than just their value; they also contain extra information about the type of the value. We'll explore this more in the sections that follow. The standard Python implementation is written in C. This means that every Python object is simply a cleverly-disguised C structure, which contains not only its value, but other information as well.

It's actually a pointer to a compound C structure, which contains several values. Looking through the Python 3. This means that there is some overhead in storing an integer in Python as compared to an integer in a compiled language like C, as illustrated in the following figure:.

Notice the difference here: a C integer is essentially a label for a position in memory whose bytes encode an integer value.

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A Python integer is a pointer to a position in memory containing all the Python object information, including the bytes that contain the integer value. This extra information in the Python integer structure is what allows Python to be coded so freely and dynamically. All this additional information in Python types comes at a cost, however, which becomes especially apparent in structures that combine many of these objects. Let's consider now what happens when we use a Python data structure that holds many Python objects.

The standard mutable multi-element container in Python is the list. We can create a list of integers as follows:. But this flexibility comes at a cost: to allow these flexible types, each item in the list must contain its own type info, reference count, and other information—that is, each item is a complete Python object. In the special case that all variables are of the same type, much of this information is redundant: it can be much more efficient to store data in a fixed-type array.

The difference between a dynamic-type list and a fixed-type NumPy-style array is illustrated in the following figure:.

At the implementation level, the array essentially contains a single pointer to one contiguous block of data. The Python list, on the other hand, contains a pointer to a block of pointers, each of which in turn points to a full Python object like the Python integer we saw earlier.GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together.

If nothing happens, download GitHub Desktop and try again.

python statistics github

If nothing happens, download Xcode and try again. If nothing happens, download the GitHub extension for Visual Studio and try again.

In fact, as this thoughtful essay makes clear, in many cases it is irresponsible to publish amateur visualizations, which at best will dilute those that experts with domain expertise are publishing. We won't be making any predictions or doing any statistical modelling, although we may look critically at some other models. However, I have always found that the most important and beneficial prerequisite is a will to learn new things so if you have this quality, you'll definitely get something out of this code-along session.

Also, if you'd like to watch and not code along, you'll also have a great time and these notebooks will be downloadable afterwards also.

If you are going to code along and use the Anaconda distribution of Python 3 see belowI ask that you install it before the session. Note: We may be making some live submissions to Kaggle so, if you want to do that, get yourself an account before the session. The first option is to click on either the Binder or Colab badge above. These will spin up the necessary computational environment for you so you can write and execute Python code from the comfort of your browser.

They are free services. Due to this, the resources are not guaranteed, though they usually work well. If you want as close to a guarantee as possible, follow the instructions below to set up your computational environment locally that is, on your own computer. To get set up for this live coding session, clone this repository.

You can do so by executing the following in your terminal:. Alternatively, you can download the zip file of the repository at the top of the main page of the repository. If you prefer not to use git or don't have experience with it, this a good option.

If you do not already have the Anaconda distribution of Python 3, go get it n. Navigate to the relevant directory covidEDA-tutorial and install required packages in a new conda environment:.

The code in this repository is released under the MIT license. Read more at the Open Source Initiative. All text remains the Intellectual Property of DataCamp. If you wish to reuse, adapt or remix, get in touch with me at hugo at datacamp com to request permission. Skip to content. Dismiss Join GitHub today GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together.

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