about_py.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# About Python"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Contents\n",
    "\n",
    "- [About Python](#About-Python)  \n",
    "  - [Overview](#Overview)  \n",
    "  - [What’s Python?](#What’s-Python?)  \n",
    "  - [Scientific Programming](#Scientific-Programming)  \n",
    "  - [Learn More](#Learn-More)  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Overview\n",
    "\n",
    "In this lecture we will\n",
    "\n",
    "- Outline what Python is  \n",
    "- Showcase some of its abilities  \n",
    "- Compare it to some other languages  \n",
    "\n",
    "\n",
    "At this stage it’s **not** our intention that you try to replicate all you see\n",
    "\n",
    "We will work through what follows at a slow pace later in the lecture series\n",
    "\n",
    "Our only objective for this lecture is to give you some feel of what Python is, and what it can do"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## What’s Python?\n",
    "\n",
    "[Python](https://www.python.org) is a general purpose programming language conceived in 1989 by Dutch programmer [Guido van Rossum](https://en.wikipedia.org/wiki/Guido_van_Rossum)\n",
    "\n",
    "Python is free and open source, with development coordinated through the [Python Software Foundation](https://www.python.org/psf/)\n",
    "\n",
    "Python has experienced rapid adoption in the last decade, and is now one of the most popular programming languages"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Common Uses\n",
    "\n",
    "Python is a general purpose language used in almost all application domains\n",
    "\n",
    "- communications  \n",
    "- web development  \n",
    "- CGI and graphical user interfaces  \n",
    "- games  \n",
    "- multimedia, data processing, security, etc., etc., etc.  \n",
    "\n",
    "\n",
    "Used extensively by Internet service and high tech companies such as\n",
    "\n",
    "- [Google](https://www.google.com/)  \n",
    "- [Dropbox](https://www.dropbox.com/)  \n",
    "- [Reddit](https://www.reddit.com/)  \n",
    "- [YouTube](https://www.youtube.com/)  \n",
    "- [Walt Disney Animation](https://pydanny-event-notes.readthedocs.org/en/latest/socalpiggies/20110526-wda.html), etc., etc.  \n",
    "\n",
    "\n",
    "Often used to [teach computer science and programming](http://cacm.acm.org/blogs/blog-cacm/176450-python-is-now-the-most-popular-introductory-teaching-language-at-top-us-universities/fulltext)\n",
    "\n",
    "For reasons we will discuss, Python is particularly popular within the scientific community\n",
    "\n",
    "- academia, NASA, CERN, Wall St., etc., etc.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Relative Popularity\n",
    "\n",
    "The following chart, produced using Stack Overflow Trends, shows one measure of the relative popularity of Python\n",
    "\n",
    "<img src=\"https://s3-ap-southeast-2.amazonaws.com/lectures.quantecon.org/py/_static/figures/python_vs_rest.png\" style=\"width:90%;height:90%\">\n",
    "\n",
    "  \n",
    "The figure indicates not only that Python is widely used but also that adoption of Python has accelerated significantly since 2012\n",
    "\n",
    "We suspect this is driven at least in part by uptake in the scientific\n",
    "domain, particularly in rapidly growing fields like data science\n",
    "\n",
    "For example, the popularity of [pandas](http://pandas.pydata.org/), a library for data analysis with Python has exploded, as seen here\n",
    "\n",
    "(The corresponding time path for MATLAB is shown for comparison)\n",
    "\n",
    "<img src=\"https://s3-ap-southeast-2.amazonaws.com/lectures.quantecon.org/py/_static/figures/pandas_vs_matlab.png\" style=\"width:90%;height:90%\">\n",
    "\n",
    "  \n",
    "Note that pandas takes off in 2012, which is the same year that we seek\n",
    "Python’s popularity begin to spike in the first figure\n",
    "\n",
    "Overall, it’s clear that\n",
    "\n",
    "- Python is [one of the most popular programming languages worldwide](http://spectrum.ieee.org/computing/software/the-2017-top-programming-languages)  \n",
    "- Python is a major tool for scientific computing, accounting for a rapidly rising share of scientific work around the globe  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Features\n",
    "\n",
    "Python is a [high level language](https://en.wikipedia.org/wiki/High-level_programming_language) suitable for rapid development\n",
    "\n",
    "It has a relatively small core language supported by many libraries\n",
    "\n",
    "Other features:\n",
    "\n",
    "- A multiparadigm language, in that multiple programming styles are supported (procedural, object-oriented, functional, etc.)  \n",
    "- Interpreted rather than compiled  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Syntax and Design\n",
    "\n",
    "\n",
    "<a id='index-2'></a>\n",
    "One nice feature of Python is its elegant syntax — we’ll see many examples later on\n",
    "\n",
    "Elegant code might sound superfluous but in fact it’s highly beneficial because it makes the syntax easy to read and easy to remember\n",
    "\n",
    "Remembering how to read from files, sort dictionaries and other such routine tasks means that you don’t need to break your flow in order to hunt down correct syntax\n",
    "\n",
    "Closely related to elegant syntax is elegant design\n",
    "\n",
    "Features like iterators, generators, decorators, list comprehensions, etc. make Python highly expressive, allowing you to get more done with less code\n",
    "\n",
    "[Namespaces](https://en.wikipedia.org/wiki/Namespace) improve productivity by cutting down on bugs and syntax errors"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Scientific Programming\n",
    "\n",
    "\n",
    "<a id='index-3'></a>\n",
    "Python has become one of the core languages of scientific computing\n",
    "\n",
    "It’s either the dominant player or a major player in\n",
    "\n",
    "- [Machine learning and data science](http://scikit-learn.org/stable/)  \n",
    "- [Astronomy](http://www.astropy.org/)  \n",
    "- [Artificial intelligence](https://wiki.python.org/moin/PythonForArtificialIntelligence)  \n",
    "- [Chemistry](http://chemlab.github.io/chemlab/)  \n",
    "- [Computational biology](http://biopython.org/wiki/Main_Page)  \n",
    "- [Meteorology](https://pypi.org/project/meteorology/)  \n",
    "- etc., etc.  \n",
    "\n",
    "\n",
    "Its popularity in economics is also beginning to rise\n",
    "\n",
    "This section briefly showcases some examples of Python for scientific programming\n",
    "\n",
    "- All of these topics will be covered in detail later on  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Numerical programming\n",
    "\n",
    "\n",
    "<a id='index-4'></a>\n",
    "Fundamental matrix and array processing capabilities are provided by the excellent [NumPy](http://www.numpy.org/) library\n",
    "\n",
    "NumPy provides the basic array data type plus some simple processing operations\n",
    "\n",
    "For example, let’s build some arrays"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "hide-output": false
   },
   "outputs": [],
   "source": [
    "import numpy as np                     # Load the library\n",
    "\n",
    "a = np.linspace(-np.pi, np.pi, 100)    # Create even grid from -π to π\n",
    "b = np.cos(a)                          # Apply cosine to each element of a\n",
    "c = np.sin(a)                          # Apply sin to each element of a"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let’s take the inner product:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "hide-output": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.706168622523819e-16"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b @ c"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The number you see here might vary slightly but it’s essentially zero\n",
    "\n",
    "(For older versions of Python and NumPy you need to use the [np.dot](http://docs.scipy.org/doc/numpy/reference/generated/numpy.dot.html) function)\n",
    "\n",
    "The [SciPy](http://www.scipy.org) library is built on top of NumPy and provides additional functionality\n",
    "\n",
    "\n",
    "<a id='tuple-unpacking-example'></a>\n",
    "For example, let’s calculate $ \\int_{-2}^2 \\phi(z) dz $ where $ \\phi $ is the standard normal density"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "hide-output": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9544997361036417"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from scipy.stats import norm\n",
    "from scipy.integrate import quad\n",
    "\n",
    "ϕ = norm()\n",
    "value, error = quad(ϕ.pdf, -2, 2)  # Integrate using Gaussian quadrature\n",
    "value"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "SciPy includes many of the standard routines used in\n",
    "\n",
    "- [linear algebra](http://docs.scipy.org/doc/scipy/reference/linalg.html)  \n",
    "- [integration](http://docs.scipy.org/doc/scipy/reference/integrate.html)  \n",
    "- [interpolation](http://docs.scipy.org/doc/scipy/reference/interpolate.html)  \n",
    "- [optimization](http://docs.scipy.org/doc/scipy/reference/optimize.html)  \n",
    "- [distributions and random number generation](http://docs.scipy.org/doc/scipy/reference/stats.html)  \n",
    "- [signal processing](http://docs.scipy.org/doc/scipy/reference/signal.html)  \n",
    "- [etc., etc.](http://docs.scipy.org/doc/scipy/reference/index.html)  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Graphics\n",
    "\n",
    "\n",
    "<a id='index-5'></a>\n",
    "The most popular and comprehensive Python library for creating figures and graphs is [Matplotlib](http://matplotlib.org/)\n",
    "\n",
    "- Plots, histograms, contour images, 3D, bar charts, etc., etc.  \n",
    "- Output in many formats (PDF, PNG, EPS, etc.)  \n",
    "- LaTeX integration  \n",
    "\n",
    "\n",
    "Example 2D plot with embedded LaTeX annotations\n",
    "\n",
    "<img src=\"https://s3-ap-southeast-2.amazonaws.com/lectures.quantecon.org/py/_static/figures/qs.png\" style=\"width:70%;height:70%\">\n",
    "\n",
    "  \n",
    "Example contour plot\n",
    "\n",
    "<img src=\"https://s3-ap-southeast-2.amazonaws.com/lectures.quantecon.org/py/_static/figures/bn_density1.png\" style=\"width:70%;height:70%\">\n",
    "\n",
    "  \n",
    "Example 3D plot\n",
    "\n",
    "<img src=\"https://s3-ap-southeast-2.amazonaws.com/lectures.quantecon.org/py/_static/figures/career_vf.png\" style=\"width:70%;height:70%\">\n",
    "\n",
    "  \n",
    "More examples can be found in the [Matplotlib thumbnail gallery](http://matplotlib.org/gallery.html)\n",
    "\n",
    "Other graphics libraries include\n",
    "\n",
    "- [Plotly](https://plot.ly/python/)  \n",
    "- [Bokeh](http://bokeh.pydata.org/en/latest/)  \n",
    "- [VPython](http://www.vpython.org/) — 3D graphics and animations  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Symbolic Algebra\n",
    "\n",
    "It’s useful to be able to manipulate symbolic expressions, as in Mathematica or Maple\n",
    "\n",
    "\n",
    "<a id='index-6'></a>\n",
    "The [SymPy](http://www.sympy.org/) library provides this functionality from within the Python shell"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "hide-output": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3*x + y"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sympy import Symbol\n",
    "\n",
    "x, y = Symbol('x'), Symbol('y')  # Treat 'x' and 'y' as algebraic symbols\n",
    "x + x + x + y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can manipulate expressions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "hide-output": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "x**2 + 2*x*y + y**2"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "expression = (x + y)**2\n",
    "expression.expand()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "solve polynomials"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "hide-output": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[-1/2 - sqrt(7)*I/2, -1/2 + sqrt(7)*I/2]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sympy import solve\n",
    "\n",
    "solve(x**2 + x + 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "and calculate limits, derivatives and integrals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "hide-output": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "oo"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sympy import limit, sin, diff\n",
    "\n",
    "limit(1 / x, x, 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "hide-output": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "limit(sin(x) / x, x, 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "hide-output": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "cos(x)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diff(sin(x), x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The beauty of importing this functionality into Python is that we are working within a fully fledged programming language\n",
    "\n",
    "Can easily create tables of derivatives, generate LaTeX output, add it to figures, etc., etc."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Statistics\n",
    "\n",
    "Python’s data manipulation and statistics libraries have improved rapidly over\n",
    "the last few years"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Pandas\n",
    "\n",
    "\n",
    "<a id='index-7'></a>\n",
    "One of the most popular libraries for working with data is [pandas](http://pandas.pydata.org/)\n",
    "\n",
    "Pandas is fast, efficient, flexible and well designed\n",
    "\n",
    "Here’s a simple example, using some fake data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "hide-output": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "               price    weight\n",
      "2010-12-28  0.471435 -1.190976\n",
      "2010-12-29  1.432707 -0.312652\n",
      "2010-12-30 -0.720589  0.887163\n",
      "2010-12-31  0.859588 -0.636524\n",
      "2011-01-01  0.015696 -2.242685\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "np.random.seed(1234)\n",
    "\n",
    "data = np.random.randn(5, 2)  # 5x2 matrix of N(0, 1) random draws\n",
    "dates = pd.date_range('28/12/2010', periods=5)\n",
    "\n",
    "df = pd.DataFrame(data, columns=('price', 'weight'), index=dates)\n",
    "print(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "hide-output": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "price     0.411768\n",
       "weight   -0.699135\n",
       "dtype: float64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Other Useful Statistics Libraries\n",
    "\n",
    "\n",
    "<a id='index-8'></a>\n",
    "- [statsmodels](http://statsmodels.sourceforge.net/) — various statistical routines  \n",
    "\n",
    "\n",
    "\n",
    "<a id='index-9'></a>\n",
    "- [scikit-learn](http://scikit-learn.org/) — machine learning in Python (sponsored by Google, among others)  \n",
    "\n",
    "\n",
    "\n",
    "<a id='index-10'></a>\n",
    "- [pyMC](http://pymc-devs.github.io/pymc/) — for Bayesian data analysis  \n",
    "\n",
    "\n",
    "\n",
    "<a id='index-11'></a>\n",
    "- [pystan](https://pystan.readthedocs.org/en/latest/) Bayesian analysis based on [stan](http://mc-stan.org/)  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Networks and Graphs\n",
    "\n",
    "Python has many libraries for studying graphs\n",
    "\n",
    "\n",
    "<a id='index-12'></a>\n",
    "One well-known example is [NetworkX](http://networkx.github.io/)\n",
    "\n",
    "- Standard graph algorithms for analyzing network structure, etc.  \n",
    "- Plotting routines  \n",
    "- etc., etc.  \n",
    "\n",
    "\n",
    "Here’s some example code that generates and plots a random graph, with node color determined by shortest path length from a central node"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "hide-output": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import networkx as nx\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "np.random.seed(1234)\n",
    "\n",
    "# Generate random graph\n",
    "p = dict((i,(np.random.uniform(0, 1),np.random.uniform(0, 1))) for i in range(200))\n",
    "G = nx.random_geometric_graph(200, 0.12, pos=p)\n",
    "pos = nx.get_node_attributes(G, 'pos')\n",
    "\n",
    "# find node nearest the center point (0.5, 0.5)\n",
    "dists = [(x - 0.5)**2 + (y - 0.5)**2 for x, y in list(pos.values())]\n",
    "ncenter = np.argmin(dists)\n",
    "\n",
    "# Plot graph, coloring by path length from central node\n",
    "p = nx.single_source_shortest_path_length(G, ncenter)\n",
    "plt.figure()\n",
    "nx.draw_networkx_edges(G, pos, alpha=0.4)\n",
    "nx.draw_networkx_nodes(G,\n",
    "                       pos,\n",
    "                       nodelist=list(p.keys()),\n",
    "                       node_size=120, alpha=0.5,\n",
    "                       node_color=list(p.values()),\n",
    "                       cmap=plt.cm.jet_r)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cloud Computing\n",
    "\n",
    "\n",
    "<a id='index-13'></a>\n",
    "Running your Python code on massive servers in the cloud is becoming easier and easier\n",
    "\n",
    "\n",
    "<a id='index-14'></a>\n",
    "A nice example is [Anaconda Enterprise](https://www.anaconda.com/enterprise/)\n",
    "\n",
    "See also\n",
    "\n",
    "\n",
    "<a id='index-15'></a>\n",
    "- [Amazon Elastic Compute Cloud](http://aws.amazon.com/ec2/)  \n",
    "\n",
    "\n",
    "\n",
    "<a id='index-16'></a>\n",
    "- The [Google App Engine](https://cloud.google.com/appengine/) (Python, Java, PHP or Go)  \n",
    "\n",
    "\n",
    "\n",
    "<a id='index-17'></a>\n",
    "- [Pythonanywhere](https://www.pythonanywhere.com/)  \n",
    "\n",
    "\n",
    "\n",
    "<a id='index-18'></a>\n",
    "- [Sagemath Cloud](https://cloud.sagemath.com/)  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Parallel Processing\n",
    "\n",
    "\n",
    "<a id='index-19'></a>\n",
    "Apart from the cloud computing options listed above, you might like to consider\n",
    "\n",
    "\n",
    "<a id='index-20'></a>\n",
    "- [Parallel computing through IPython clusters](http://ipython.org/ipython-doc/stable/parallel/parallel_demos.html)  \n",
    "\n",
    "\n",
    "\n",
    "<a id='index-21'></a>\n",
    "- The [Starcluster](http://star.mit.edu/cluster/) interface to Amazon’s EC2  \n",
    "\n",
    "\n",
    "\n",
    "<a id='index-23'></a>\n",
    "- GPU programming through [PyCuda](https://wiki.tiker.net/PyCuda), [PyOpenCL](https://mathema.tician.de/software/pyopencl/), [Theano](http://deeplearning.net/software/theano/) or similar  \n",
    "\n",
    "\n",
    "\n",
    "<a id='intfc'></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Other Developments\n",
    "\n",
    "There are many other interesting developments with scientific programming in Python\n",
    "\n",
    "Some representative examples include\n",
    "\n",
    "\n",
    "<a id='index-24'></a>\n",
    "- [Jupyter](http://jupyter.org/) — Python in your browser with code cells, embedded images, etc.  \n",
    "\n",
    "\n",
    "\n",
    "<a id='index-25'></a>\n",
    "- [Numba](http://numba.pydata.org/) — Make Python run at the same speed as native machine code!  \n",
    "\n",
    "\n",
    "\n",
    "<a id='index-26'></a>\n",
    "- [Blaze](http://blaze.pydata.org/) — a generalization of NumPy  \n",
    "\n",
    "\n",
    "\n",
    "<a id='index-27'></a>\n",
    "- [PyTables](http://www.pytables.org) — manage large data sets  \n",
    "\n",
    "\n",
    "\n",
    "<a id='index-28'></a>\n",
    "- [CVXPY](https://github.com/cvxgrp/cvxpy) — convex optimization in Python  "
   ]
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    "## Learn More\n",
    "\n",
    "- Browse some Python projects on [GitHub](https://github.com/trending?l=python)  \n",
    "- Have a look at [some of the Jupyter notebooks](http://nbviewer.jupyter.org/) people have shared on various scientific topics  \n",
    "\n",
    "\n",
    "\n",
    "<a id='index-29'></a>\n",
    "- Visit the [Python Package Index](https://pypi.org/)  \n",
    "- View some of the question people are asking about Python on [Stackoverflow](http://stackoverflow.com/questions/tagged/python)  \n",
    "- Keep up to date on what’s happening in the Python community with the [Python subreddit](https://www.reddit.com:443/r/Python/)  "
   ]
  }
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