{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Jupyter notebooks\n",
    "Important shortcuts:\n",
    "\n",
    "- run and move to next cell: shift + return\n",
    "- run and stay on same cell: alt + return\n",
    "- insert cell below: ctrl + m, then b  (or then a for \"above\")\n",
    "\n",
    "Two modes:\n",
    "- insert mode\n",
    "- edit mode"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Getting Help\n",
    "- Shortcuts: press h in edit mode\n",
    "- press tab inside method calls (press tab again to see more):\n",
    "![tab help](images/tab-help.png)\n",
    "- use \"?\" and run cell"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "np.bincount()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "np.bincount?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exercise:\n",
    "Use the help to find out what the options to ``np.unique`` are.\n",
    "Use np.unique to convert the array ``['one', 'two', 'three', 'one', 'two', 'three']`` into the array ``[0, 2, 1, 0, 2, 1]``."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ar = ['one', 'two', 'three', 'one', 'two', 'three']\n",
    "# your solution here"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting with matplotlib\n",
    "Need to use either \n",
    "```\n",
    "% matplotlib inline\n",
    "```\n",
    "or\n",
    "```\n",
    "% matplotlib notebook\n",
    "```\n",
    "Only one in each notebook!\n",
    "using ``inline`` will just sent ``png`` images to browser, using ``notebook`` will provide\n",
    "interactivity and allow updating old figures.\n",
    "With ``notebook`` you need to make sure to create a new figure before plotting, otherwise the last one will be updated!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib notebook\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "X = np.random.normal(size=(12, 2))\n",
    "plt.scatter(X[:, 0], X[:, 1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "plt.plot(X[:, 0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# create a new figure\n",
    "plt.figure()\n",
    "plt.plot(X[:, 0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exercise\n",
    "Create a new figure and plot a sin wave. You can use ``np.linspace`` to create equally spaced numbers in a given range."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Best practices for data analysis in Jupyter\n",
    "- use standard imports\n",
    "- don't ``import *``\n",
    "- be mindful of the state in the notebook!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "x = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "x = x + 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "print(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Avoid cells you can't run again:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data = {'a': [1, 2, 3], 'b': [999, 1, 2]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "column_a = data.pop('a')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "print(column_a)\n",
    "print(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Not mutating variables helps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "x = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "x2 = x + 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "print(x2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exercise\n",
    "Rewrite the code for the ``data`` dict above so that you don't mutate ``data``, but that the ``print`` stays the same."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# solution Here"
   ]
  }
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