georgehc/recitation_1 - Basic Python Review-Solution.ipynb Secret

## recitation_1 - Basic Python Review-Solution.ipynb
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 95-865 Recitation 1 - Basic Python Review\n",
    "\n",
    "*Contact TA: Xinyu Yao (xinyuyao@andrew.cmu.edu)*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 0. Setup the environment\n",
    "\n",
    "Download and install [Anaconda](https://www.anaconda.com/download/), [spaCy](https://spacy.io/) and run a Jupyter notebook.\n",
    "   * The tutorial slides included in HW1.zip (95-865 Anaconda, Jupyter, and spaCy setup tutorial.pdf) will guide you through this process (tested on Linux and Mac OSX).\n",
    "   * The following [video](https://www.youtube.com/watch?v=xKg2anRkg-M&index=1&list=PLcHK-U_3XaQvdjiU1d9r3dn_1xLyablE4) guides you through installing Anaconda and running Jupyer on Windows 10.\n",
    "      * Note: **DO** check the \"Add Anaconda to PATH\" option during the installation!\n",
    "      * Installation on Windows 10 requires Visual Studio 2017.\n",
    "   * The following [video](https://www.youtube.com/watch?v=hY_0YUKVNMU&index=3&list=PLcHK-U_3XaQvdjiU1d9r3dn_1xLyablE4) guides you through installing spaCy on Windows 10.\n",
    "      * Note: Run `conda config --add channels conda-forge` prior to installing the package.\n",
    "   \n",
    "If you already have a reliable way to install Python packages and run Jupyter notebooks, feel free to continue using that instead."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. Python basics\n",
    "\n",
    "The subproblems below use the following two lists:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = [13, 1, 55, 34, 2, 3, 89, 5, 8, 21]\n",
    "b = [17,55, 34, 2, 3, 8, 9, 1, 4, 5, 6, 7, 20, 21, 36]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### List intersection\n",
    "\n",
    "Find the elements that are common between the lists and store it in a new list `c`. Print list `c`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1, 34, 3, 2, 5, 8, 21, 55]\n"
     ]
    }
   ],
   "source": [
    "c = list(set(a).intersection(set(b)))\n",
    "\n",
    "print(c)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### List filtering\n",
    "\n",
    "Using a [list comprehension](https://www.pythonforbeginners.com/basics/list-comprehensions-in-python), construct a new list `d` containing only the **even** elements of `c`. Print list `d`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[34, 2, 8]\n"
     ]
    }
   ],
   "source": [
    "d = [item for item in c if item%2 == 0]\n",
    "\n",
    "print(d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[34, 2, 8]\n"
     ]
    }
   ],
   "source": [
    "d = []\n",
    "for item in c:\n",
    "    if item % 2 == 0:\n",
    "        d.append(item)\n",
    "print(d)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fancy list operations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Reverse the list `d` and print it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[34, 2, 8]\n"
     ]
    }
   ],
   "source": [
    "f = list(d)\n",
    "f.reverse()\n",
    "\n",
    "print(d)\n",
    "# print(f)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sort the list `d` in increasing order and print it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2, 8, 34]\n"
     ]
    }
   ],
   "source": [
    "d.sort()\n",
    "\n",
    "print(d)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sort the list `d` in decreasing order and print it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[34, 8, 2]\n"
     ]
    }
   ],
   "source": [
    "d.sort(reverse = True)\n",
    "\n",
    "print(d)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### User-defined Functions\n",
    "\n",
    "Write a function that takes as input a list and returns the maximum element of the list, **without** using a call to the `max()` function built into Python."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "19\n"
     ]
    }
   ],
   "source": [
    "def myMax(ls):\n",
    "    ls.sort(reverse = True)\n",
    "    return ls[0]\n",
    "\n",
    "print(myMax([1,2,19]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Using Loops to Evaluate Expressions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use a for loop to compute $\\sum_{n=1}^{100} 2^{-n}$.  Print your result."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.0\n"
     ]
    }
   ],
   "source": [
    "s = 0\n",
    "\n",
    "for n in range(1,101):\n",
    "    s = s + 2**(-n)\n",
    "\n",
    "print(s)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. Scientific computing with numpy\n",
    "\n",
    "Install the `numpy` Python package before starting this section."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### Numpy arrays"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Construct a 4x4 \"identity matrix\" `I`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1. 0. 0. 0.]\n",
      " [0. 1. 0. 0.]\n",
      " [0. 0. 1. 0.]\n",
      " [0. 0. 0. 1.]]\n"
     ]
    }
   ],
   "source": [
    "I = np.identity(4)\n",
    "\n",
    "print(I)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Construct a 4x4 \"zero matrix\" `O` "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0. 0. 0. 0.]\n",
      " [0. 0. 0. 0.]\n",
      " [0. 0. 0. 0.]\n",
      " [0. 0. 0. 0.]]\n"
     ]
    }
   ],
   "source": [
    "O = np.zeros((4,4))\n",
    "\n",
    "print(O)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Construct a 4x4 \"all-ones\" matrix `X` "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1. 1. 1. 1.]\n",
      " [1. 1. 1. 1.]\n",
      " [1. 1. 1. 1.]\n",
      " [1. 1. 1. 1.]]\n"
     ]
    }
   ],
   "source": [
    "X = np.ones((4,4))\n",
    "\n",
    "print(X)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Construct a 4x4 \"all-3s\" matrix `Y` "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[3. 3. 3. 3.]\n",
      " [3. 3. 3. 3.]\n",
      " [3. 3. 3. 3.]\n",
      " [3. 3. 3. 3.]]\n"
     ]
    }
   ],
   "source": [
    "Y = np.ones((4,4))*3\n",
    "\n",
    "print(Y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Numpy functions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Consider the matrix `Z` defined below and use numpy functions to answer the following questions:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 1  2  4]\n",
      " [ 2  3  5]\n",
      " [ 2  7 10]]\n"
     ]
    }
   ],
   "source": [
    "Z = np.array([[1,2,4],[2,3,5],[2,7,10]])\n",
    "\n",
    "print(Z)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Print the maximum element of `Z`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Z.max()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Print the maximum of each row of `Z`. Note that for this question and other questions below, your code should work even if Z is a different 2D array of numbers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 4,  5, 10])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Z.max(axis = 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Print the maximum of each column of `Z`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 2,  7, 10])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Z.max(axis = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 1  2  4]\n",
      " [ 2  3  5]\n",
      " [ 2  7 10]]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([ 2,  7, 10])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Z = np.array([[1,2,4],[2,3,5],[2,7,10]])\n",
    "print(Z)\n",
    "max(Z, key=lambda x:x[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Print a \"row-normalized\" version of `Z` so that each row sums to 1 (the row-normalized version of `Z` has the same number of rows and columns as `Z`). \n",
    "\n",
    "For example, if a row is `[1,2,4]`, then the version of this row that sums to 1 is `[1/7,2/7,4/7]` (although when printing this out, you would get a decimal representation like `[0.14285714,0.28571429,0.57142857]`)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 1  2  4]\n",
      " [ 2  3  5]\n",
      " [ 2  7 10]]\n",
      "Before reshaping: [ 7 10 19]\n",
      "After reshaping:\n",
      " [[ 7]\n",
      " [10]\n",
      " [19]]\n",
      "(3, 1)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[0.14285714, 0.28571429, 0.57142857],\n",
       "       [0.2       , 0.3       , 0.5       ],\n",
       "       [0.10526316, 0.36842105, 0.52631579]])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(Z)\n",
    "\n",
    "row_sum = Z.sum(axis=1)\n",
    "print(\"Before reshaping:\", row_sum)\n",
    "row_sum = row_sum.reshape(3,1)\n",
    "print(\"After reshaping:\\n\", row_sum)\n",
    "print(row_sum.shape)\n",
    "Z / row_sum"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Convert `Z` into a matrix with a single row (a row vector) and print it. There are multiple two ways to do this, any of them are fine."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1,  2,  4,  2,  3,  5,  2,  7, 10])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Z.flatten()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1,  2,  4,  2,  3,  5,  2,  7, 10])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Z.reshape(-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1,  2,  4,  2,  3,  5,  2,  7, 10])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Z.reshape(9)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Return the first 3 elements from `arr`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr = np.arange(0,11)\n",
    "arr[:3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Return the last 3 elements from `arr`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 8,  9, 10])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr[-3:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Return the elements that are larger than 5 but smaller than 10 in `arr`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[False False False False False False  True  True  True  True  True]\n",
      "[ True  True  True  True  True  True  True  True  True  True False]\n",
      "[False False False False False False  True  True  True  True False]\n"
     ]
    }
   ],
   "source": [
    "print(arr > 5)\n",
    "print(arr < 10)\n",
    "print((arr > 5) & (arr < 10))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Get the first 2 rows and first 2 columns of `mat` and name it `mat_slice`. Change `mat_slice[0][0]` to 1000. See what happened to the original matrix `mat`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original matrix\n",
      "[[11 12 13]\n",
      " [21 22 23]\n",
      " [31 32 33]]\n",
      "\n",
      "Sliced matrix\n",
      "[[11 12]\n",
      " [21 22]]\n",
      "\n",
      "Change the sliced matrix\n",
      "[[1000   12]\n",
      " [  21   22]]\n",
      "\n",
      "The original matrix now\n",
      "[[1000   12   13]\n",
      " [  21   22   23]\n",
      " [  31   32   33]]\n"
     ]
    }
   ],
   "source": [
    "mat = np.array([[11,12,13],[21,22,23],[31,32,33]])\n",
    "print(\"Original matrix\")\n",
    "print(mat)\n",
    "mat_slice = mat[:2,:2]\n",
    "print (\"\\nSliced matrix\")\n",
    "print(mat_slice)\n",
    "print (\"\\nChange the sliced matrix\")\n",
    "mat_slice[0][0] = 1000\n",
    "print (mat_slice)\n",
    "print(\"\\nThe original matrix now\")\n",
    "print(mat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr_2 = mat[0,1]\n",
    "arr_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr_2 = mat[0][1]\n",
    "arr_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original matrix\n",
      "[[11 12 13]\n",
      " [21 22 23]\n",
      " [31 32 33]]\n",
      "\n",
      "Sliced matrix\n",
      "[[11 12]\n",
      " [21 22]]\n",
      "\n",
      "Change the sliced matrix\n",
      "[[1000   12]\n",
      " [  21   22]]\n",
      "\n",
      "The original matrix now\n",
      "[[11 12 13]\n",
      " [21 22 23]\n",
      " [31 32 33]]\n"
     ]
    }
   ],
   "source": [
    "mat = np.array([[11,12,13],[21,22,23],[31,32,33]])\n",
    "print(\"Original matrix\")\n",
    "print(mat)\n",
    "mat_slice = np.array(mat[:2,:2])\n",
    "print (\"\\nSliced matrix\")\n",
    "print(mat_slice)\n",
    "print (\"\\nChange the sliced matrix\")\n",
    "mat_slice[0,0] = 1000\n",
    "print (mat_slice)\n",
    "print(\"\\nThe original matrix now\")\n",
    "print(mat)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. Plotting with matplotlib\n",
    "\n",
    "Install the `matplotlib` Python package before starting this section."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "%config InlineBackend.figure_format = 'retina'  # if you use a Mac with Retina display"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Line lot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.arange(10)\n",
    "y = x\n",
    "z = 2*x\n",
    "m = x**2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 248,
       "width": 368
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x,y, '-', label = 'y = x')\n",
    "plt.plot(x,z, '--', label = 'y = 2*x')\n",
    "plt.plot(x,m, ':', label = 'y = x^2')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Scatter plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.random.normal(size=1000)\n",
    "y = np.random.normal(size=1000)\n",
    "# y = np.sin(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 277,
       "width": 384
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(x,y, color = 'm')\n",
    "plt.xlabel('x-axis')\n",
    "plt.ylabel('y-axis')\n",
    "plt.title('My plot')\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Histograms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 248,
       "width": 375
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(y, bins = 20, color = 'pink',edgecolor = 'black')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Bar charts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 248,
       "width": 368
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "y = [3, 10, 7, 5, 3, 4.5, 6, 8.1]\n",
    "N = len(y)\n",
    "x = range(1, N+1)\n",
    "plt.bar(x, y, width=0.5, color=\"orange\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 248,
       "width": 372
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "z = {\"a\":1,\"b\":2,\"c\":3,\"d\":4}\n",
    "plt.bar(range(len(z)), z.values())\n",
    "plt.xticks(range(len(z)), list(z.keys()))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Just for fun: [XKCD](https://xkcd.com/)-style sketch plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 263,
       "width": 379
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "with plt.xkcd():\n",
    "    # Based on \"Stove Ownership\" from XKCD by Randall Monroe\n",
    "    # http://xkcd.com/418/\n",
    "\n",
    "    fig = plt.figure()\n",
    "    ax = fig.add_axes((0.1, 0.2, 0.8, 0.7))\n",
    "    ax.spines['right'].set_color('none')\n",
    "    ax.spines['top'].set_color('none')\n",
    "    plt.xticks([])\n",
    "    plt.yticks([])\n",
    "    ax.set_ylim([-30, 10])\n",
    "\n",
    "    data = np.ones(100)\n",
    "    data[70:] -= np.arange(30)\n",
    "\n",
    "    plt.annotate(\n",
    "        'THE DAY I REALIZED\\nI COULD COOK BACON\\nWHENEVER I WANTED',\n",
    "        xy=(70, 1), arrowprops=dict(arrowstyle='->'), xytext=(15, -10))\n",
    "\n",
    "    plt.plot(data)\n",
    "\n",
    "    plt.xlabel('time')\n",
    "    plt.ylabel('my overall health')\n",
    "    fig.text(\n",
    "        0.5, 0.05,\n",
    "        '\"Stove Ownership\" from xkcd by Randall Monroe',\n",
    "        ha='center')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}