Class 12 - Libraries/class12_libraries.ipynb

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   "source": [
    "# We'll start at 2:10 PM \n",
    "\n",
    "Waiting for others to join the call :)\n",
    "\n",
    "https://www.youtube.com/watch?v=tbneQDc2H3I&ab_channel=KarolGVEVO\n",
    "\n",
    "https://www.youtube.com/watch?v=sDKnKzYyx5c&ab_channel=KarolGVEVO"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "843e46f7",
   "metadata": {
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   },
   "source": [
    "<center><img src='img/cdy.png' style='width:500px; float: left; margin: 0px 30px 15px 0px'></center>\n",
    "\n",
    "# Libraries\n",
    "## Class 12 - Data Science Curriculum \n",
    "\n",
    "<br>\n",
    "\n",
    "#### Women Building Change scholarship program 2023 🇧🇮\n",
    "March 27, 2023\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "48500da2",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Recap last class\n",
    "\n",
    "- Functions\n",
    "\n",
    "```python\n",
    "\n",
    "def function_name(param1, param2):\n",
    "    # do something\n",
    "    return value\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "82476939",
   "metadata": {
    "slideshow": {
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    }
   },
   "source": [
    "# 🚀 Today's agenda\n",
    "\n",
    "- Libraries"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d7c97442",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Google Colab: https://colab.research.google.com/\n",
    "\n",
    "<br>\n",
    "<center><img src='img/superhero.gif' style='height:350px; float: center; margin: 0px 30px 15px 0px'></center>\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "44aa29f4",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# 🏋🏿‍♀️ Coding Warmup "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "03da43f8",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "def bonj_len(name):\n",
    "    return f\"Bonjour {name.upper()}, your name has {len(name)} letters\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "926ef4ce",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Bonjour VIVIANA, your name has 7 letters'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bonj_len(\"Viviana\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "08b592c0",
   "metadata": {
    "slideshow": {
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   },
   "outputs": [],
   "source": [
    "students = ['Jessica', 'Clarisse', 'Ornella', \"Milena\", \"Greta\", \"Allegresse\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "87b09265",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
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   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Bonjour JESSICA, your name has 7 letters',\n",
       " 'Bonjour CLARISSE, your name has 8 letters',\n",
       " 'Bonjour ORNELLA, your name has 7 letters',\n",
       " 'Bonjour MILENA, your name has 6 letters',\n",
       " 'Bonjour GRETA, your name has 5 letters',\n",
       " 'Bonjour ALLEGRESSE, your name has 10 letters']"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[bonj_len(student) for student in students]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b3200614",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Libraries\n",
    "\n",
    "- A library is basically a bunch of code someone else wrote, so by importing it, we don’t have to rewrite it\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b1b2ddf4",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "- Today we will learn some of the most popular ones and how to install them"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "500ab680",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Some libraries we are going to learn today\n",
    "\n",
    "- Python Standard Library\n",
    "- NumPy\n",
    "- Pandas\n",
    "- Matplotlib\n",
    "- Wikipedia"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b84d396f",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Python Standard Library\n",
    "\n",
    "- These libraries come with Python already, you don't need to install them"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c22ba367",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Random"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "497b4cd7",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "import random"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "83887625",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
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   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "random.randint(0,10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "a3052ba4",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
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   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Greta', 'Jessica']"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "students = ['Jessica', 'Clarisse', 'Ornella', 'Milena', 'Greta', 'Allegresse']\n",
    "random.choices(students, k=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "c5ec5aa9",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
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   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Milena', 'Jessica', 'Clarisse', 'Ornella', 'Allegresse', 'Greta']"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "random.shuffle(students)\n",
    "students"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b405fbe7",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Os\n",
    "\n",
    "Interacting with the operating system "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c890ed77",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
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   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/Users/vivianamarquez/Desktop/teaching/burundi/DataScience_Burundi2023/Class 12 - Libraries'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import os\n",
    "\n",
    "# get the path of the current working directory\n",
    "path = os.getcwd()\n",
    "path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3beb0431",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['functions.png',\n",
       " 'rain2.png',\n",
       " '.DS_Store',\n",
       " 'kitchen.jpeg',\n",
       " 'superhero.gif',\n",
       " 'clock.gif',\n",
       " 'bye.gif',\n",
       " 'birthdays.png',\n",
       " 'tab.png',\n",
       " 'dic.png',\n",
       " 'list.png',\n",
       " 'food.webp',\n",
       " 'bool_operators.png',\n",
       " 'crab.png',\n",
       " 'range.png',\n",
       " 'cdy.png',\n",
       " 'remove_pop.png',\n",
       " 'rain.png',\n",
       " 'date.webp']"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "os.listdir('/Users/vivianamarquez/Desktop/teaching/burundi/DataScience_Burundi2023/Class 12 - Libraries/img')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "70e0147b",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['superhero.gif', 'clock.gif', 'bye.gif']"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "entries = os.listdir('img')\n",
    "\n",
    "# get only .png files\n",
    "png_files = [entry for entry in entries if entry.endswith('.gif')]\n",
    "png_files"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "38e8be87",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "a694a41c",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.007710933685302734\n"
     ]
    }
   ],
   "source": [
    "import time\n",
    "\n",
    "# compute execution time of a block of code\n",
    "start_time = time.time()\n",
    "\n",
    "total = 0\n",
    "for i in range(50000):\n",
    "    total = total + i\n",
    "\n",
    "# elapsed time\n",
    "elapsed_time = time.time() - start_time\n",
    "print(elapsed_time)\n",
    "# 0.011977910995483398"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "97c8536a",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Milena\n",
      "Jessica\n",
      "Clarisse\n",
      "Ornella\n",
      "Allegresse\n",
      "Greta\n"
     ]
    }
   ],
   "source": [
    "for student in students:\n",
    "    print(student)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "03951a7d",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Milena\n",
      "Jessica\n",
      "Clarisse\n",
      "Ornella\n",
      "Allegresse\n",
      "Greta\n"
     ]
    }
   ],
   "source": [
    "for student in students:\n",
    "    print(student)\n",
    "    time.sleep(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "213be782",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Datetime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b240d1c0",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "14"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pytz\n",
    "import datetime\n",
    "\n",
    "# create a datetime object - George Orwell birth date\n",
    "birth = datetime.datetime(1903, 6, 14)\n",
    "\n",
    "# access attribute - dot notation\n",
    "birth.day"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "c390974b",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "43750 days, 13:00:43.636612\n"
     ]
    }
   ],
   "source": [
    "print(datetime.datetime.today() - birth)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "f3636155",
   "metadata": {
    "scrolled": true,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.datetime(2023, 3, 26, 20, 0, 43, 783337, tzinfo=<DstTzInfo 'Africa/Bujumbura' CAT+2:00:00 STD>)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "now = datetime.datetime.now(tz=pytz.timezone(\"Africa/Bujumbura\")) # https://en.wikipedia.org/wiki/List_of_tz_database_time_zones\n",
    "now"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "83c71da6",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "<br>\n",
    "<center><img src='img/date.webp' style='height:350px; float: center; margin: 0px 30px 15px 0px'></center>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "46acee53",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Jun 14 03'"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "birth.strftime('%b %d %y')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "647581b8",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Collections"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "9382be88",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Counter({1: 1, 3: 2, 5: 4, 6: 3, 8: 1, 4: 2, 2: 1})"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from collections import Counter\n",
    "\n",
    "A = [1,3,5,6,8,6,5,4,5,6,5,4,3,2]\n",
    "Counter(A)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "008fd15c",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# NumPy\n",
    "\n",
    "- Numerical Python\n",
    "- Numeric computing library"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "f384c0ad",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "2456790e",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3, 4])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array([1, 2, 3, 4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "9705c233",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.75 s ± 64.6 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
     ]
    }
   ],
   "source": [
    "def mean(numbers):\n",
    "    return sum(numbers)/len(numbers)\n",
    "                            \n",
    "my_numbers = range(0,100_000_000)\n",
    "                            \n",
    "%timeit mean(my_numbers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "20040b42",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "108 ms ± 1.45 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
     ]
    }
   ],
   "source": [
    "my_numbers = np.arange(0,100_000_000)\n",
    "%timeit np.mean(my_numbers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "c50407ea",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0., 0., 0., 0., 0.])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.zeros(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "a19eb367",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1., 1., 1., 1., 1.])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.ones(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "799dd8d4",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Pandas\n",
    "\n",
    "- A fast, powerful, flexible and easy to use data analysis and manipulation tool\n",
    "\n",
    "# Sklearn\n",
    "\n",
    "- Machine learning library\n",
    "\n",
    "# Matplotlib\n",
    "\n",
    "- Visualization library"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "0bd476e5",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn import datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "a5f3e01c",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "digits = datasets.load_digits()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "2ad16720",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "X = pd.DataFrame(digits.data, columns=digits.feature_names)\n",
    "y = pd.DataFrame(digits.target, columns=['numbers'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "acc13f7b",
   "metadata": {
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\n",
      "text/plain": [
       "<Figure size 480x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.gray()\n",
    "plt.matshow(digits.images[5])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "c4c9e3d0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pixel_0_0</th>\n",
       "      <th>pixel_0_1</th>\n",
       "      <th>pixel_0_2</th>\n",
       "      <th>pixel_0_3</th>\n",
       "      <th>pixel_0_4</th>\n",
       "      <th>pixel_0_5</th>\n",
       "      <th>pixel_0_6</th>\n",
       "      <th>pixel_0_7</th>\n",
       "      <th>pixel_1_0</th>\n",
       "      <th>pixel_1_1</th>\n",
       "      <th>...</th>\n",
       "      <th>pixel_6_6</th>\n",
       "      <th>pixel_6_7</th>\n",
       "      <th>pixel_7_0</th>\n",
       "      <th>pixel_7_1</th>\n",
       "      <th>pixel_7_2</th>\n",
       "      <th>pixel_7_3</th>\n",
       "      <th>pixel_7_4</th>\n",
       "      <th>pixel_7_5</th>\n",
       "      <th>pixel_7_6</th>\n",
       "      <th>pixel_7_7</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>...</td>\n",
       "      <td>9.0</td>\n",
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       "183         2.0        0.0  \n",
       "1419        0.0        0.0  \n",
       "1462        4.0        0.0  \n",
       "595         0.0        0.0  \n",
       "1602        0.0        0.0  \n",
       "\n",
       "[5 rows x 64 columns]"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "1c5fa806",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1797, 64)"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "43f03bda",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pixel_0_0    float64\n",
       "pixel_0_1    float64\n",
       "pixel_0_2    float64\n",
       "pixel_0_3    float64\n",
       "pixel_0_4    float64\n",
       "              ...   \n",
       "pixel_7_3    float64\n",
       "pixel_7_4    float64\n",
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       "pixel_7_6    float64\n",
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       "Length: 64, dtype: object"
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     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "4c338072",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['pixel_0_0', 'pixel_0_1', 'pixel_0_2', 'pixel_0_3', 'pixel_0_4',\n",
       "       'pixel_0_5', 'pixel_0_6', 'pixel_0_7', 'pixel_1_0', 'pixel_1_1',\n",
       "       'pixel_1_2', 'pixel_1_3', 'pixel_1_4', 'pixel_1_5', 'pixel_1_6',\n",
       "       'pixel_1_7', 'pixel_2_0', 'pixel_2_1', 'pixel_2_2', 'pixel_2_3',\n",
       "       'pixel_2_4', 'pixel_2_5', 'pixel_2_6', 'pixel_2_7', 'pixel_3_0',\n",
       "       'pixel_3_1', 'pixel_3_2', 'pixel_3_3', 'pixel_3_4', 'pixel_3_5',\n",
       "       'pixel_3_6', 'pixel_3_7', 'pixel_4_0', 'pixel_4_1', 'pixel_4_2',\n",
       "       'pixel_4_3', 'pixel_4_4', 'pixel_4_5', 'pixel_4_6', 'pixel_4_7',\n",
       "       'pixel_5_0', 'pixel_5_1', 'pixel_5_2', 'pixel_5_3', 'pixel_5_4',\n",
       "       'pixel_5_5', 'pixel_5_6', 'pixel_5_7', 'pixel_6_0', 'pixel_6_1',\n",
       "       'pixel_6_2', 'pixel_6_3', 'pixel_6_4', 'pixel_6_5', 'pixel_6_6',\n",
       "       'pixel_6_7', 'pixel_7_0', 'pixel_7_1', 'pixel_7_2', 'pixel_7_3',\n",
       "       'pixel_7_4', 'pixel_7_5', 'pixel_7_6', 'pixel_7_7'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "b9fe1513",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       0.0\n",
       "1       0.0\n",
       "2       0.0\n",
       "3       0.0\n",
       "4       0.0\n",
       "       ... \n",
       "1792    0.0\n",
       "1793    0.0\n",
       "1794    0.0\n",
       "1795    0.0\n",
       "1796    0.0\n",
       "Name: pixel_0_0, Length: 1797, dtype: float64"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['pixel_0_0']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "d25eee1f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pixel_0_0</th>\n",
       "      <th>pixel_1_0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <td>...</td>\n",
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       "      <td>0.0</td>\n",
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       "      <th>1793</th>\n",
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       "      <th>1794</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1795</th>\n",
       "      <td>0.0</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>1797 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      pixel_0_0  pixel_1_0\n",
       "0           0.0        0.0\n",
       "1           0.0        0.0\n",
       "2           0.0        0.0\n",
       "3           0.0        0.0\n",
       "4           0.0        0.0\n",
       "...         ...        ...\n",
       "1792        0.0        0.0\n",
       "1793        0.0        0.0\n",
       "1794        0.0        0.0\n",
       "1795        0.0        0.0\n",
       "1796        0.0        0.0\n",
       "\n",
       "[1797 rows x 2 columns]"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['pixel_0_0', 'pixel_1_0']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "438a725d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       10.0\n",
       "1        0.0\n",
       "2        0.0\n",
       "3       14.0\n",
       "4        0.0\n",
       "        ... \n",
       "1792     8.0\n",
       "1793    12.0\n",
       "1794     2.0\n",
       "1795     4.0\n",
       "1796    20.0\n",
       "Name: pixel_0_2, Length: 1797, dtype: float64"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['pixel_0_2'].apply(lambda row: row*2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "2d33df6b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "15.0"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[3, 3]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3f9b8aad",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Wikipedia \n",
    "\n",
    "- Wikipedia library, as its name suggests, facilitates accessing and fetching information from Wikipedia. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "e6c711be",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "ename": "ModuleNotFoundError",
     "evalue": "No module named 'wikipedia'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
      "\u001b[0;32m/var/folders/n7/q93jwpcs6jndz6qqvj4mhtcm0000gn/T/ipykernel_80574/2840010940.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mwikipedia\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'wikipedia'"
     ]
    }
   ],
   "source": [
    "import wikipedia"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "f94bf753",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting wikipedia\n",
      "  Using cached wikipedia-1.4.0.tar.gz (27 kB)\n",
      "  Preparing metadata (setup.py) ... \u001b[?25ldone\n",
      "\u001b[?25hRequirement already satisfied: beautifulsoup4 in /Users/vivianamarquez/opt/anaconda3/lib/python3.9/site-packages (from wikipedia) (4.11.1)\n",
      "Requirement already satisfied: requests<3.0.0,>=2.0.0 in /Users/vivianamarquez/opt/anaconda3/lib/python3.9/site-packages (from wikipedia) (2.28.1)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in /Users/vivianamarquez/opt/anaconda3/lib/python3.9/site-packages (from requests<3.0.0,>=2.0.0->wikipedia) (2022.9.24)\n",
      "Requirement already satisfied: charset-normalizer<3,>=2 in /Users/vivianamarquez/opt/anaconda3/lib/python3.9/site-packages (from requests<3.0.0,>=2.0.0->wikipedia) (2.0.4)\n",
      "Requirement already satisfied: idna<4,>=2.5 in /Users/vivianamarquez/opt/anaconda3/lib/python3.9/site-packages (from requests<3.0.0,>=2.0.0->wikipedia) (3.3)\n",
      "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /Users/vivianamarquez/opt/anaconda3/lib/python3.9/site-packages (from requests<3.0.0,>=2.0.0->wikipedia) (1.26.11)\n",
      "Requirement already satisfied: soupsieve>1.2 in /Users/vivianamarquez/opt/anaconda3/lib/python3.9/site-packages (from beautifulsoup4->wikipedia) (2.3.1)\n",
      "Building wheels for collected packages: wikipedia\n",
      "  Building wheel for wikipedia (setup.py) ... \u001b[?25ldone\n",
      "\u001b[?25h  Created wheel for wikipedia: filename=wikipedia-1.4.0-py3-none-any.whl size=11695 sha256=861698131dd14de109186cba4391ef0f1543a06d0704e740d623b9300e2970cc\n",
      "  Stored in directory: /Users/vivianamarquez/Library/Caches/pip/wheels/c2/46/f4/caa1bee71096d7b0cdca2f2a2af45cacf35c5760bee8f00948\n",
      "Successfully built wikipedia\n",
      "Installing collected packages: wikipedia\n",
      "Successfully installed wikipedia-1.4.0\n"
     ]
    }
   ],
   "source": [
    "! pip install wikipedia"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "10bf5c57",
   "metadata": {},
   "outputs": [],
   "source": [
    "import wikipedia as wiki"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "00dfb7d8",
   "metadata": {},
   "outputs": [],
   "source": [
    "wiki.set_lang('fr')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "98c2c707",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Burundi',\n",
       " 'Histoire du Burundi',\n",
       " 'Équipe du Burundi de football',\n",
       " 'Culture du Burundi',\n",
       " 'Université du Burundi',\n",
       " 'Génocides au Burundi',\n",
       " \"Afrique de l'Est\",\n",
       " 'Religion au Burundi',\n",
       " '2003',\n",
       " 'Langues au Burundi']"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wiki.search('Burundi')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "763f1588",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"Cet article présente les faits saillants de l'histoire du Burundi, un pays d'Afrique de l'est situé dans la région des Grands Lacs et entouré aujourd'hui par la République démocratique du Congo à l'ouest, le Rwanda au nord et la Tanzanie à l'est et au sud. Sa capitale politique est Gitega, sa capitale économique est Bujumbura.\\nLe Royaume du Burundi, fondé probablement au XVIIe siècle, est devenu nominalement une composante de l'Afrique orientale allemande en 1890. Après la Première Guerre mondiale, il passe sous la domination de l'Empire colonial belge, au sein de la province du Ruanda-Urundi, elle-même intégrée au Congo belge en 1925. Le Burundi devient indépendant le 1er juillet 1962, sous le régime de la monarchie constitutionnelle.\\nLe pays connaît ensuite une instabilité politique durable, marquée par des coups d'état, des massacres inter-communautaires, puis une guerre civile à partir de 1993. Celle-ci s'est conclue en 2000 par les accords d'Arusha, ouvrant une période de paix précaire. Depuis 2010, les tensions sont à nouveau particulièrement importantes.\""
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wiki.summary('Histoire du Burundi')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "040ee2db",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'https://upload.wikimedia.org/wikipedia/commons/8/86/Africa_%28orthographic_projection%29.svg'"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wiki.page('Histoire du Burundi').images[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "48abbe5b",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# ⏪ Today's recap\n",
    "\n",
    "- Libraries\n",
    "    - Python Standard Library\n",
    "    - NumPy\n",
    "    - Pandas\n",
    "    - Matplotlib\n",
    "    - Wikipedia"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ba820ff4",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "# Practice lab by Cherie\n",
    "\n",
    "https://colab.research.google.com/drive/11vUEuXeAMKhehExFaWThsfDtbz38fJES?usp=sharing"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c3c47662",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "<center><img src='img/bye.gif' style='height:250px;'></center> \n",
    "\n",
    "# Next class: Python practice 🎓\n",
    "# See you next Friday!"
   ]
  }
 ],
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  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
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   "codemirror_mode": {
    "name": "ipython",
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