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   "cell_type": "markdown",
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   "metadata": {
    "id": "bA5ajAmk7XH6"
   },
   "source": [
    "# Statistical Thinking in Python (Part 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "30d27ee9",
   "metadata": {},
   "source": [
    "## Table of Contents  \n",
    "* [Chapter 1. Graphical EDA](#first-bullet)\n",
    "* [Chapter 2. Quantitative EDA](#second-bullet)\n",
    "* [Chapter 3. Thinking Probabilistically: Discrete Variables](#third-bullet)\n",
    "* [Chapter 4. Thinking Probabilistically: Continuous Variables](#fourth-bullet)\n",
    "* [Chapter 5. Final Thoughts](#fifth-bullet)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a23a750e-895d-4d6b-b854-db964c4a97d2",
   "metadata": {},
   "source": [
    "---\n",
    "## Chapter 1. Graphical EDA<a class=\"anchor\" id=\"first-bullet\"></a>\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "29d346b7",
   "metadata": {
    "tags": []
   },
   "source": [
    "Yogi Berra said, \"You can observe a lot by watching.\" The same is true with data. If you can appropriately display your data, you can already start to draw conclusions from it. I'll go even further. Exploring your data is a crucial step in your analysis.\n",
    "\n",
    "## Tuky's comment on exploratory data analysis\n",
    "When I say exploring your data, I mean organizing and plotting your data, and maybe computing a few numerical summaries about them. This idea is known as exploratory data analysis, or EDA, and was developed by one of the greatest statisticians of all time, John Tukey. He wrote a book entitled Exploratory Data Analysis in 1977 where he laid out the principles. In that book, he said,  \n",
    "> **\"Exploratory data analysis can never be the whole story, but nothing else can serve as the foundation stone.\"**\n",
    "\n",
    "I wholeheartedly agree with this, so we will begin our study of statistical thinking with EDA. Let's consider an example."
   ]
  },
  {
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   "execution_count": 48,
   "id": "2e25fdd8-4d84-45bc-80f0-949917e00a17",
   "metadata": {
    "executionTime": 66,
    "jupyter": {
     "outputs_hidden": false,
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    },
    "lastSuccessfullyExecutedCode": "# Importing the packages\nimport pandas as pd\nimport numpy as np\nimport seaborn as sns\nimport matplotlib.pyplot as plt\n\n# Importing the datasets\nall_states = pd.read_csv('datasets/2008_all_states.csv')\nswing_states = pd.read_csv('datasets/2008_swing_states.csv')"
   },
   "outputs": [],
   "source": [
    "# Importing the packages\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.image as mpimg\n",
    "\n",
    "# Importing the datasets\n",
    "all_states = pd.read_csv('datasets/2008_all_states.csv')\n",
    "swing_states = pd.read_csv('datasets/2008_swing_states.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "77716a42-2c6b-4bf5-ac2f-bcd4d893f751",
   "metadata": {},
   "source": [
    "## 2008 US swing state election results \n",
    "Here, we have a data set from [Data.gov](https://www.data.gov/) containing the election results of 2008 at the county level in each of the three major swing states of Pennsylvania, Ohio, and Florida. Those are the ones that largely decide elections in the US. "
   ]
  },
  {
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   "id": "29f55e66-4534-4473-8caf-a90acbc7bc5c",
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        "data": [
         {
          "county": "Erie County",
          "dem_share": 60.08,
          "index": 0,
          "state": "PA"
         },
         {
          "county": "Bradford County",
          "dem_share": 40.64,
          "index": 1,
          "state": "PA"
         },
         {
          "county": "Tioga County",
          "dem_share": 36.07,
          "index": 2,
          "state": "PA"
         },
         {
          "county": "McKean County",
          "dem_share": 41.21,
          "index": 3,
          "state": "PA"
         },
         {
          "county": "Potter County",
          "dem_share": 31.04,
          "index": 4,
          "state": "PA"
         },
         {
          "county": "Wayne County",
          "dem_share": 43.78,
          "index": 5,
          "state": "PA"
         },
         {
          "county": "Susquehanna County",
          "dem_share": 44.08,
          "index": 6,
          "state": "PA"
         },
         {
          "county": "Warren County",
          "dem_share": 46.85,
          "index": 7,
          "state": "PA"
         },
         {
          "county": "Ashtabula County",
          "dem_share": 56.94,
          "index": 8,
          "state": "OH"
         },
         {
          "county": "Lake County",
          "dem_share": 50.46,
          "index": 9,
          "state": "OH"
         },
         {
          "county": "Crawford County",
          "dem_share": 44.71,
          "index": 10,
          "state": "PA"
         },
         {
          "county": "Lucas County",
          "dem_share": 65.99,
          "index": 11,
          "state": "OH"
         },
         {
          "county": "Fulton County",
          "dem_share": 45.88,
          "index": 12,
          "state": "OH"
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         {
          "county": "Geauga County",
          "dem_share": 42.23,
          "index": 13,
          "state": "OH"
         },
         {
          "county": "Williams County",
          "dem_share": 45.26,
          "index": 14,
          "state": "OH"
         },
         {
          "county": "Wyoming County",
          "dem_share": 46.15,
          "index": 15,
          "state": "PA"
         },
         {
          "county": "Lackawanna County",
          "dem_share": 63.1,
          "index": 16,
          "state": "PA"
         },
         {
          "county": "Elk County",
          "dem_share": 52.2,
          "index": 17,
          "state": "PA"
         },
         {
          "county": "Forest County",
          "dem_share": 43.18,
          "index": 18,
          "state": "PA"
         },
         {
          "county": "Venango County",
          "dem_share": 40.24,
          "index": 19,
          "state": "PA"
         },
         {
          "county": "Erie County",
          "dem_share": 57.01,
          "index": 20,
          "state": "OH"
         },
         {
          "county": "Wood County",
          "dem_share": 53.61,
          "index": 21,
          "state": "OH"
         },
         {
          "county": "Cameron County",
          "dem_share": 39.92,
          "index": 22,
          "state": "PA"
         },
         {
          "county": "Pike County",
          "dem_share": 47.87,
          "index": 23,
          "state": "PA"
         },
         {
          "county": "Lycoming County",
          "dem_share": 37.77,
          "index": 24,
          "state": "PA"
         },
         {
          "county": "Sullivan County",
          "dem_share": 40.11,
          "index": 25,
          "state": "PA"
         },
         {
          "county": "Lorain County",
          "dem_share": 59.1,
          "index": 26,
          "state": "OH"
         },
         {
          "county": "Trumbull County",
          "dem_share": 61.48,
          "index": 27,
          "state": "OH"
         },
         {
          "county": "Mercer County",
          "dem_share": 49.85,
          "index": 28,
          "state": "PA"
         },
         {
          "county": "Henry County",
          "dem_share": 43.43,
          "index": 29,
          "state": "OH"
         },
         {
          "county": "Clinton County",
          "dem_share": 48.61,
          "index": 30,
          "state": "PA"
         },
         {
          "county": "Clarion County",
          "dem_share": 38.62,
          "index": 31,
          "state": "PA"
         },
         {
          "county": "Luzerne County",
          "dem_share": 54.25,
          "index": 32,
          "state": "PA"
         },
         {
          "county": "Defiance County",
          "dem_share": 44.69,
          "index": 33,
          "state": "OH"
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         {
          "county": "Jefferson County",
          "dem_share": 34.84,
          "index": 34,
          "state": "PA"
         },
         {
          "county": "Portage County",
          "dem_share": 54.59,
          "index": 35,
          "state": "OH"
         },
         {
          "county": "Columbia County",
          "dem_share": 47.75,
          "index": 36,
          "state": "PA"
         },
         {
          "county": "Huron County",
          "dem_share": 48.36,
          "index": 37,
          "state": "OH"
         },
         {
          "county": "Medina County",
          "dem_share": 45.89,
          "index": 38,
          "state": "OH"
         },
         {
          "county": "Seneca County",
          "dem_share": 48.62,
          "index": 39,
          "state": "OH"
         },
         {
          "county": "Clearfield County",
          "dem_share": 43.82,
          "index": 40,
          "state": "PA"
         },
         {
          "county": "Centre County",
          "dem_share": 55.97,
          "index": 41,
          "state": "PA"
         },
         {
          "county": "Monroe County",
          "dem_share": 58.23,
          "index": 42,
          "state": "PA"
         },
         {
          "county": "Paulding County",
          "dem_share": 43.92,
          "index": 43,
          "state": "OH"
         },
         {
          "county": "Northumberland County",
          "dem_share": 42.97,
          "index": 44,
          "state": "PA"
         },
         {
          "county": "Montour County",
          "dem_share": 42.38,
          "index": 45,
          "state": "PA"
         },
         {
          "county": "Butler County",
          "dem_share": 36.11,
          "index": 46,
          "state": "PA"
         },
         {
          "county": "Armstrong County",
          "dem_share": 37.53,
          "index": 47,
          "state": "PA"
         },
         {
          "county": "Hancock County",
          "dem_share": 38.23,
          "index": 48,
          "state": "OH"
         },
         {
          "county": "Putnam County",
          "dem_share": 28.79,
          "index": 49,
          "state": "OH"
         },
         {
          "county": "Union County",
          "dem_share": 42.65,
          "index": 50,
          "state": "PA"
         },
         {
          "county": "Mahoning County",
          "dem_share": 63.57,
          "index": 51,
          "state": "OH"
         },
         {
          "county": "Carbon County",
          "dem_share": 50.96,
          "index": 52,
          "state": "PA"
         },
         {
          "county": "Lawrence County",
          "dem_share": 47.43,
          "index": 53,
          "state": "PA"
         },
         {
          "county": "Ashland County",
          "dem_share": 38.07,
          "index": 54,
          "state": "OH"
         },
         {
          "county": "Crawford County",
          "dem_share": 40.18,
          "index": 55,
          "state": "OH"
         },
         {
          "county": "Richland County",
          "dem_share": 43.05,
          "index": 56,
          "state": "OH"
         },
         {
          "county": "Wyandot County",
          "dem_share": 41.56,
          "index": 57,
          "state": "OH"
         },
         {
          "county": "Wayne County",
          "dem_share": 42.49,
          "index": 58,
          "state": "OH"
         },
         {
          "county": "Van Wert County",
          "dem_share": 36.06,
          "index": 59,
          "state": "OH"
         },
         {
          "county": "Stark County",
          "dem_share": 52.76,
          "index": 60,
          "state": "OH"
         },
         {
          "county": "Northampton County",
          "dem_share": 56.24,
          "index": 61,
          "state": "PA"
         },
         {
          "county": "Schuylkill County",
          "dem_share": 45.6,
          "index": 62,
          "state": "PA"
         },
         {
          "county": "Columbiana County",
          "dem_share": 46.07,
          "index": 63,
          "state": "OH"
         },
         {
          "county": "Allen County",
          "dem_share": 39.43,
          "index": 64,
          "state": "OH"
         },
         {
          "county": "Indiana County",
          "dem_share": 46.39,
          "index": 65,
          "state": "PA"
         },
         {
          "county": "Snyder County",
          "dem_share": 35.22,
          "index": 66,
          "state": "PA"
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          "county": "Beaver County",
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          "state": "PA"
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          "county": "Mifflin County",
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          "state": "PA"
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         {
          "county": "Hardin County",
          "dem_share": 39.26,
          "index": 69,
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         {
          "county": "Lehigh County",
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          "county": "Huntingdon County",
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          "county": "Blair County",
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          "county": "Carroll County",
          "dem_share": 47.47,
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          "county": "Mercer County",
          "dem_share": 27.92,
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          "county": "Cambria County",
          "dem_share": 50.36,
          "index": 75,
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          "county": "Morrow County",
          "dem_share": 38.01,
          "index": 76,
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         },
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          "county": "Marion County",
          "dem_share": 45.45,
          "index": 77,
          "state": "OH"
         },
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          "county": "Juniata County",
          "dem_share": 32.12,
          "index": 78,
          "state": "PA"
         },
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          "county": "Auglaize County",
          "dem_share": 29.07,
          "index": 79,
          "state": "OH"
         },
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          "county": "Westmoreland County",
          "dem_share": 41.55,
          "index": 80,
          "state": "PA"
         },
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          "county": "Berks County",
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          "index": 81,
          "state": "PA"
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          "county": "Allegheny County",
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          "index": 82,
          "state": "PA"
         },
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          "county": "Holmes County",
          "dem_share": 28.94,
          "index": 83,
          "state": "OH"
         },
         {
          "county": "Tuscarawas County",
          "dem_share": 51.28,
          "index": 84,
          "state": "OH"
         },
         {
          "county": "Dauphin County",
          "dem_share": 54.58,
          "index": 85,
          "state": "PA"
         },
         {
          "county": "Perry County",
          "dem_share": 32.88,
          "index": 86,
          "state": "PA"
         },
         {
          "county": "Bucks County",
          "dem_share": 54.37,
          "index": 87,
          "state": "PA"
         },
         {
          "county": "Jefferson County",
          "dem_share": 50.1,
          "index": 88,
          "state": "OH"
         },
         {
          "county": "Knox County",
          "dem_share": 39.84,
          "index": 89,
          "state": "OH"
         },
         {
          "county": "Lebanon County",
          "dem_share": 40.45,
          "index": 90,
          "state": "PA"
         },
         {
          "county": "Logan County",
          "dem_share": 36.43,
          "index": 91,
          "state": "OH"
         },
         {
          "county": "Union County",
          "dem_share": 35.71,
          "index": 92,
          "state": "OH"
         },
         {
          "county": "Shelby County",
          "dem_share": 31.47,
          "index": 93,
          "state": "OH"
         },
         {
          "county": "Washington County",
          "dem_share": 47.61,
          "index": 94,
          "state": "PA"
         },
         {
          "county": "Coshocton County",
          "dem_share": 47.01,
          "index": 95,
          "state": "OH"
         },
         {
          "county": "Montgomery County",
          "dem_share": 60.49,
          "index": 96,
          "state": "PA"
         },
         {
          "county": "Delaware County",
          "dem_share": 40.1,
          "index": 97,
          "state": "OH"
         },
         {
          "county": "Harrison County",
          "dem_share": 48.76,
          "index": 98,
          "state": "OH"
         },
         {
          "county": "Darke County",
          "dem_share": 31.56,
          "index": 99,
          "state": "OH"
         },
         {
          "county": "Cumberland County",
          "dem_share": 43.11,
          "index": 100,
          "state": "PA"
         },
         {
          "county": "Bedford County",
          "dem_share": 27.32,
          "index": 101,
          "state": "PA"
         },
         {
          "county": "Lancaster County",
          "dem_share": 44.03,
          "index": 102,
          "state": "PA"
         },
         {
          "county": "Franklin County",
          "dem_share": 33.56,
          "index": 103,
          "state": "PA"
         },
         {
          "county": "Somerset County",
          "dem_share": 37.26,
          "index": 104,
          "state": "PA"
         },
         {
          "county": "Champaign County",
          "dem_share": 39.86,
          "index": 105,
          "state": "OH"
         },
         {
          "county": "Chester County",
          "dem_share": 54.64,
          "index": 106,
          "state": "PA"
         },
         {
          "county": "York County",
          "dem_share": 43.12,
          "index": 107,
          "state": "PA"
         },
         {
          "county": "Guernsey County",
          "dem_share": 45.31,
          "index": 108,
          "state": "OH"
         },
         {
          "county": "Miami County",
          "dem_share": 35.47,
          "index": 109,
          "state": "OH"
         },
         {
          "county": "Belmont County",
          "dem_share": 51.38,
          "index": 110,
          "state": "OH"
         },
         {
          "county": "Muskingum County",
          "dem_share": 46.33,
          "index": 111,
          "state": "OH"
         },
         {
          "county": "Fulton County",
          "dem_share": 25.34,
          "index": 112,
          "state": "PA"
         },
         {
          "county": "Fayette County",
          "dem_share": 49.79,
          "index": 113,
          "state": "PA"
         },
         {
          "county": "Philadelphia County",
          "dem_share": 83.56,
          "index": 114,
          "state": "PA"
         },
         {
          "county": "Adams County",
          "dem_share": 40.09,
          "index": 115,
          "state": "PA"
         },
         {
          "county": "Delaware County",
          "dem_share": 60.81,
          "index": 116,
          "state": "PA"
         },
         {
          "county": "Clark County",
          "dem_share": 48.73,
          "index": 117,
          "state": "OH"
         },
         {
          "county": "Greene County",
          "dem_share": 49.81,
          "index": 118,
          "state": "PA"
         },
         {
          "county": "Noble County",
          "dem_share": 41.77,
          "index": 119,
          "state": "OH"
         },
         {
          "county": "Fairfield County",
          "dem_share": 41.32,
          "index": 120,
          "state": "OH"
         },
         {
          "county": "Perry County",
          "dem_share": 48.46,
          "index": 121,
          "state": "OH"
         },
         {
          "county": "Montgomery County",
          "dem_share": 53.14,
          "index": 122,
          "state": "OH"
         },
         {
          "county": "Preble County",
          "dem_share": 34.01,
          "index": 123,
          "state": "OH"
         },
         {
          "county": "Monroe County",
          "dem_share": 54.74,
          "index": 124,
          "state": "OH"
         },
         {
          "county": "Greene County",
          "dem_share": 40.67,
          "index": 125,
          "state": "OH"
         },
         {
          "county": "Pickaway County",
          "dem_share": 38.96,
          "index": 126,
          "state": "OH"
         },
         {
          "county": "Morgan County",
          "dem_share": 46.29,
          "index": 127,
          "state": "OH"
         },
         {
          "county": "Fayette County",
          "dem_share": 38.25,
          "index": 128,
          "state": "OH"
         },
         {
          "county": "Washington County",
          "dem_share": 6.8,
          "index": 129,
          "state": "OH"
         },
         {
          "county": "Warren County",
          "dem_share": 31.75,
          "index": 130,
          "state": "OH"
         },
         {
          "county": "Ross County",
          "dem_share": 46.33,
          "index": 131,
          "state": "OH"
         },
         {
          "county": "Vinton County",
          "dem_share": 44.9,
          "index": 132,
          "state": "OH"
         },
         {
          "county": "Clermont County",
          "dem_share": 33.57,
          "index": 133,
          "state": "OH"
         },
         {
          "county": "Brown County",
          "dem_share": 38.1,
          "index": 134,
          "state": "OH"
         },
         {
          "county": "Jackson County",
          "dem_share": 39.67,
          "index": 135,
          "state": "OH"
         },
         {
          "county": "Meigs County",
          "dem_share": 40.47,
          "index": 136,
          "state": "OH"
         },
         {
          "county": "Pike County",
          "dem_share": 49.44,
          "index": 137,
          "state": "OH"
         },
         {
          "county": "Adams County",
          "dem_share": 37.62,
          "index": 138,
          "state": "OH"
         },
         {
          "county": "Gallia County",
          "dem_share": 36.71,
          "index": 139,
          "state": "OH"
         },
         {
          "county": "Scioto County",
          "dem_share": 46.73,
          "index": 140,
          "state": "OH"
         },
         {
          "county": "Lawrence County",
          "dem_share": 42.2,
          "index": 141,
          "state": "OH"
         },
         {
          "county": "Jackson County",
          "dem_share": 35.86,
          "index": 142,
          "state": "FL"
         },
         {
          "county": "Escambia County",
          "dem_share": 40.25,
          "index": 143,
          "state": "FL"
         },
         {
          "county": "Santa Rosa County",
          "dem_share": 25.81,
          "index": 144,
          "state": "FL"
         },
         {
          "county": "Okaloosa County",
          "dem_share": 27.33,
          "index": 145,
          "state": "FL"
         },
         {
          "county": "Holmes County",
          "dem_share": 17.06,
          "index": 146,
          "state": "FL"
         },
         {
          "county": "Walton County",
          "dem_share": 26.84,
          "index": 147,
          "state": "FL"
         },
         {
          "county": "Washington County",
          "dem_share": 25.93,
          "index": 148,
          "state": "FL"
         },
         {
          "county": "Nassau County",
          "dem_share": 27.93,
          "index": 149,
          "state": "FL"
         },
         {
          "county": "Gadsden County",
          "dem_share": 69.58,
          "index": 150,
          "state": "FL"
         },
         {
          "county": "Leon County",
          "dem_share": 62.23,
          "index": 151,
          "state": "FL"
         },
         {
          "county": "Jefferson County",
          "dem_share": 51.85,
          "index": 152,
          "state": "FL"
         },
         {
          "county": "Madison County",
          "dem_share": 48.44,
          "index": 153,
          "state": "FL"
         },
         {
          "county": "Hamilton County",
          "dem_share": 42.65,
          "index": 154,
          "state": "FL"
         },
         {
          "county": "Calhoun County",
          "dem_share": 29.53,
          "index": 155,
          "state": "FL"
         },
         {
          "county": "Liberty County",
          "dem_share": 27.67,
          "index": 156,
          "state": "FL"
         },
         {
          "county": "Columbia County",
          "dem_share": 32.94,
          "index": 157,
          "state": "FL"
         },
         {
          "county": "Duval County",
          "dem_share": 49.04,
          "index": 158,
          "state": "FL"
         },
         {
          "county": "Baker County",
          "dem_share": 21.15,
          "index": 159,
          "state": "FL"
         },
         {
          "county": "Bay County",
          "dem_share": 29.45,
          "index": 160,
          "state": "FL"
         },
         {
          "county": "Suwannee County",
          "dem_share": 28.17,
          "index": 161,
          "state": "FL"
         },
         {
          "county": "Taylor County",
          "dem_share": 30.27,
          "index": 162,
          "state": "FL"
         },
         {
          "county": "Wakulla County",
          "dem_share": 37.43,
          "index": 163,
          "state": "FL"
         },
         {
          "county": "Lafayette County",
          "dem_share": 19.33,
          "index": 164,
          "state": "FL"
         },
         {
          "county": "Saint Johns County",
          "dem_share": 34.08,
          "index": 165,
          "state": "FL"
         },
         {
          "county": "Gulf County",
          "dem_share": 30.15,
          "index": 166,
          "state": "FL"
         },
         {
          "county": "Clay County",
          "dem_share": 28.43,
          "index": 167,
          "state": "FL"
         },
         {
          "county": "Bradford County",
          "dem_share": 29.66,
          "index": 168,
          "state": "FL"
         },
         {
          "county": "Union County",
          "dem_share": 24.81,
          "index": 169,
          "state": "FL"
         },
         {
          "county": "Franklin County",
          "dem_share": 35.86,
          "index": 170,
          "state": "FL"
         },
         {
          "county": "Alachua County",
          "dem_share": 60.9,
          "index": 171,
          "state": "FL"
         },
         {
          "county": "Gilchrist County",
          "dem_share": 26.09,
          "index": 172,
          "state": "FL"
         },
         {
          "county": "Putnam County",
          "dem_share": 40.26,
          "index": 173,
          "state": "FL"
         },
         {
          "county": "Dixie County",
          "dem_share": 27.04,
          "index": 174,
          "state": "FL"
         },
         {
          "county": "Flagler County",
          "dem_share": 50.8,
          "index": 175,
          "state": "FL"
         },
         {
          "county": "Levy County",
          "dem_share": 36.35,
          "index": 176,
          "state": "FL"
         },
         {
          "county": "Marion County",
          "dem_share": 44.14,
          "index": 177,
          "state": "FL"
         },
         {
          "county": "Volusia County",
          "dem_share": 52.86,
          "index": 178,
          "state": "FL"
         },
         {
          "county": "Lake County",
          "dem_share": 43.19,
          "index": 179,
          "state": "FL"
         },
         {
          "county": "Citrus County",
          "dem_share": 41.85,
          "index": 180,
          "state": "FL"
         },
         {
          "county": "Sumter County",
          "dem_share": 36.39,
          "index": 181,
          "state": "FL"
         },
         {
          "county": "Seminole County",
          "dem_share": 48.6,
          "index": 182,
          "state": "FL"
         },
         {
          "county": "Brevard County",
          "dem_share": 44.74,
          "index": 183,
          "state": "FL"
         },
         {
          "county": "Orange County",
          "dem_share": 59.37,
          "index": 184,
          "state": "FL"
         },
         {
          "county": "Hernando County",
          "dem_share": 48.19,
          "index": 185,
          "state": "FL"
         },
         {
          "county": "Pasco County",
          "dem_share": 48.2,
          "index": 186,
          "state": "FL"
         },
         {
          "county": "Polk County",
          "dem_share": 46.91,
          "index": 187,
          "state": "FL"
         },
         {
          "county": "Osceola County",
          "dem_share": 59.93,
          "index": 188,
          "state": "FL"
         },
         {
          "county": "Pinellas County",
          "dem_share": 54.17,
          "index": 189,
          "state": "FL"
         },
         {
          "county": "Hillsborough County",
          "dem_share": 53.59,
          "index": 190,
          "state": "FL"
         },
         {
          "county": "Indian River County",
          "dem_share": 42.52,
          "index": 191,
          "state": "FL"
         },
         {
          "county": "Highlands County",
          "dem_share": 40.89,
          "index": 192,
          "state": "FL"
         },
         {
          "county": "Hardee County",
          "dem_share": 35.03,
          "index": 193,
          "state": "FL"
         },
         {
          "county": "Manatee County",
          "dem_share": 46.46,
          "index": 194,
          "state": "FL"
         },
         {
          "county": "Okeechobee County",
          "dem_share": 40.32,
          "index": 195,
          "state": "FL"
         },
         {
          "county": "Saint Lucie County",
          "dem_share": 56.11,
          "index": 196,
          "state": "FL"
         },
         {
          "county": "Sarasota County",
          "dem_share": 49.95,
          "index": 197,
          "state": "FL"
         },
         {
          "county": "DeSoto County",
          "dem_share": 43.76,
          "index": 198,
          "state": "FL"
         },
         {
          "county": "Martin County",
          "dem_share": 43.15,
          "index": 199,
          "state": "FL"
         },
         {
          "county": "Glades County",
          "dem_share": 41.61,
          "index": 200,
          "state": "FL"
         },
         {
          "county": "Charlotte County",
          "dem_share": 46.34,
          "index": 201,
          "state": "FL"
         },
         {
          "county": "Palm Beach County",
          "dem_share": 61.51,
          "index": 202,
          "state": "FL"
         },
         {
          "county": "Hendry County",
          "dem_share": 46.37,
          "index": 203,
          "state": "FL"
         },
         {
          "county": "Lee County",
          "dem_share": 44.78,
          "index": 204,
          "state": "FL"
         },
         {
          "county": "Collier County",
          "dem_share": 38.66,
          "index": 205,
          "state": "FL"
         },
         {
          "county": "Broward County",
          "dem_share": 67.45,
          "index": 206,
          "state": "FL"
         },
         {
          "county": "Miami-Dade County",
          "dem_share": 58.09,
          "index": 207,
          "state": "FL"
         },
         {
          "county": "Monroe County",
          "dem_share": 52.48,
          "index": 208,
          "state": "FL"
         },
         {
          "county": "Ottawa County",
          "dem_share": 53.16,
          "index": 209,
          "state": "OH"
         },
         {
          "county": "Sandusky County",
          "dem_share": 52.4,
          "index": 210,
          "state": "OH"
         },
         {
          "county": "Summit County",
          "dem_share": 58.36,
          "index": 211,
          "state": "OH"
         },
         {
          "county": "Athens County",
          "dem_share": 68.02,
          "index": 212,
          "state": "OH"
         },
         {
          "county": "Butler County",
          "dem_share": 38.53,
          "index": 213,
          "state": "OH"
         },
         {
          "county": "Clinton County",
          "dem_share": 34.58,
          "index": 214,
          "state": "OH"
         },
         {
          "county": "Cuyahoga County",
          "dem_share": 69.64,
          "index": 215,
          "state": "OH"
         },
         {
          "county": "Franklin County",
          "dem_share": 60.5,
          "index": 216,
          "state": "OH"
         },
         {
          "county": "Hamilton County",
          "dem_share": 53.53,
          "index": 217,
          "state": "OH"
         },
         {
          "county": "Highland County",
          "dem_share": 36.54,
          "index": 218,
          "state": "OH"
         },
         {
          "county": "Hocking County",
          "dem_share": 49.58,
          "index": 219,
          "state": "OH"
         },
         {
          "county": "Licking County",
          "dem_share": 41.97,
          "index": 220,
          "state": "OH"
         },
         {
          "county": "Madison County",
          "dem_share": 38.11,
          "index": 221,
          "state": "OH"
         }
        ],
        "schema": {
         "fields": [
          {
           "name": "index",
           "type": "integer"
          },
          {
           "name": "state",
           "type": "string"
          },
          {
           "name": "county",
           "type": "string"
          },
          {
           "name": "dem_share",
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         ],
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       },
       "total_rows": 222,
       "truncation_type": null
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      "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>state</th>\n",
       "      <th>county</th>\n",
       "      <th>dem_share</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>PA</td>\n",
       "      <td>Erie County</td>\n",
       "      <td>60.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>PA</td>\n",
       "      <td>Bradford County</td>\n",
       "      <td>40.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>PA</td>\n",
       "      <td>Tioga County</td>\n",
       "      <td>36.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>PA</td>\n",
       "      <td>McKean County</td>\n",
       "      <td>41.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>PA</td>\n",
       "      <td>Potter County</td>\n",
       "      <td>31.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>217</th>\n",
       "      <td>OH</td>\n",
       "      <td>Hamilton County</td>\n",
       "      <td>53.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>218</th>\n",
       "      <td>OH</td>\n",
       "      <td>Highland County</td>\n",
       "      <td>36.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>219</th>\n",
       "      <td>OH</td>\n",
       "      <td>Hocking County</td>\n",
       "      <td>49.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>220</th>\n",
       "      <td>OH</td>\n",
       "      <td>Licking County</td>\n",
       "      <td>41.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>221</th>\n",
       "      <td>OH</td>\n",
       "      <td>Madison County</td>\n",
       "      <td>38.11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>222 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    state           county  dem_share\n",
       "0      PA      Erie County      60.08\n",
       "1      PA  Bradford County      40.64\n",
       "2      PA     Tioga County      36.07\n",
       "3      PA    McKean County      41.21\n",
       "4      PA    Potter County      31.04\n",
       "..    ...              ...        ...\n",
       "217    OH  Hamilton County      53.53\n",
       "218    OH  Highland County      36.54\n",
       "219    OH   Hocking County      49.58\n",
       "220    OH   Licking County      41.97\n",
       "221    OH   Madison County      38.11\n",
       "\n",
       "[222 rows x 3 columns]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "swing_states[['state','county','dem_share']]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eb605f77-cb46-43d2-adef-367e63c4ffcf",
   "metadata": {},
   "source": [
    "## Plot the results with a histogram\n",
    "We take the Democratic share of the vote in the counties of all of the three swing states and plot them as a histogram. We can plot this as a histogram using the `matplotlib.pyplot` module's hist function. The height of each bar is the number of counties that had the given level of support for Obama. For example, the tallest bar is the number of counties that had between 40% and 50% of its votes cast for Obama. Right away, because there is more area in the histogram to the left of 50%, we can see that more counties voted for Obama's opponent, John McCain, than voted for Obama. Look at that. Just by making one plot, we could already draw a conclusion about the data, which would have been extraordinarily tedious by hand counting in the DataFrame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "0769cb96-dc6f-43bc-9ec3-c23b47359c44",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 500x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Set default Seaborn style\n",
    "sns.set()\n",
    "\n",
    "fig = plt.figure(figsize=(5, 4))\n",
    "\n",
    "plt.hist(swing_states['dem_share'], bins=20) # Alternatively, we can change the bin size as e.g. bin_edges = [0, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100]\n",
    "plt.xlabel('Percent of vote for Obama')\n",
    "plt.ylabel('Number of counties')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "350c3736-bc59-452a-8acc-4e90ebdbf981",
   "metadata": {},
   "source": [
    "## Drawback of histograms and Bee swamp plots\n",
    "\n",
    "The histogram of county-level election data was informative. We learned that more counties voted for McCain than for Obama. However, **a major drawback of using histograms** is that   \n",
    "1. **the same data set can look different depending on how the bins are chosen.** And choice of bins is in many ways arbitrary. This leads to **binning bias**; you might interpret your plot differently for two different choices of bin number.  \n",
    "2. An additional problem with histograms is that we are not plotting all of the data. That is, **we are sweeping the data into bins, and losing their actual values.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "d59727cb-d4f4-4a52-b8aa-6200c24f1112",
   "metadata": {
    "executionTime": 772,
    "lastSuccessfullyExecutedCode": "fig = plt.figure()\nfig.subplots_adjust(hspace=0.4, wspace=0.4)\nax = fig.add_subplot(2, 2, 1)\nplt.hist(swing_states['dem_share'], bins=5)\nplt.xlabel('Percent of vote for Obama')\nplt.ylabel('Number of counties')\n\nax = fig.add_subplot(2, 2, 2)\nplt.hist(swing_states['dem_share'], bins=10)\n\nax = fig.add_subplot(2, 2, 3)\nplt.hist(swing_states['dem_share'], bins=25)\nplt.xlabel('Percent of vote for Obama')\nplt.ylabel('Number of counties')\n\nax = fig.add_subplot(2, 2, 4)\nplt.hist(swing_states['dem_share'], bins=40)\nplt.xlabel('Percent of vote for Obama')\n\nplt.show()"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "fig.subplots_adjust(hspace=0.4, wspace=0.4)\n",
    "ax = fig.add_subplot(2, 2, 1)\n",
    "plt.hist(swing_states['dem_share'], bins=5)\n",
    "plt.xlabel('Percent of vote for Obama')\n",
    "plt.ylabel('Number of counties')\n",
    "\n",
    "ax = fig.add_subplot(2, 2, 2)\n",
    "plt.hist(swing_states['dem_share'], bins=10)\n",
    "\n",
    "ax = fig.add_subplot(2, 2, 3)\n",
    "plt.hist(swing_states['dem_share'], bins=25)\n",
    "plt.xlabel('Percent of vote for Obama')\n",
    "plt.ylabel('Number of counties')\n",
    "\n",
    "ax = fig.add_subplot(2, 2, 4)\n",
    "plt.hist(swing_states['dem_share'], bins=40)\n",
    "plt.xlabel('Percent of vote for Obama')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "68f37a71-87a1-4d09-b6c3-0760dc24e24d",
   "metadata": {},
   "source": [
    "## Remedy: Bee swarm plot  \n",
    "To remedy these problems we can make a bee swarm plot, also called a swarm plot. This is best shown by example. Here is a beeswarm plot of the vote totals in the three swing states. Each point in the plot represents the share of the vote Obama got in a single county. The position along the y-axis is the quantitative information. The data are spread in x to make them visible, but their precise location along the x-axis is unimportant. Notably, we no longer have any binning bias and all data are displayed. This plot may be conveniently generated using Seaborn. A requirement is that your data are in a well-organized Pandas DataFrame where each column is a feature and each row an observation. In this case, an observation is a county, and the features are state and the Democratic share of the vote.\n",
    "\n",
    "### Generating a bee swarm plot  \n",
    "A requirement is that your data are in a well-organized Pandas DataFrame where each column is a feature and each row an observation. In this case, an observation is a county, and the features are state and the Democratic share of the vote. To make the plot, you need to specify which column gives the values for the y-axis, in this case the share of the vote that went to the Democrat Barack Obama, and the values for the x-axis, in this case the state. And of course, you need to tell it which DataFrame contains the data.\n",
    "\n",
    "### 2008 US swing state election results\n",
    "From this plot, too, we can clearly see that Obama got less than 50% of the vote in the majority of counties in each of the three swing states. This time it is more detailed than a histogram, but without too much added visual complexity."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "b7e68632-2aaf-4ea9-b309-05d6df542f1c",
   "metadata": {
    "executionTime": 755,
    "lastSuccessfullyExecutedCode": "# Set default Seaborn style\nsns.set()\n\nsns.swarmplot(x='state', y='dem_share', data= swing_states, hue=\"state\")\nplt.xlabel('state')\nplt.ylabel('percent of vote for Obama')\nplt.legend([],[], frameon=False)\nplt.show()"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Set default Seaborn style\n",
    "sns.set()\n",
    "\n",
    "sns.swarmplot(x='state', y='dem_share', data= swing_states, hue=\"state\")\n",
    "plt.xlabel('state')\n",
    "plt.ylabel('percent of vote for Obama')\n",
    "plt.legend([],[], frameon=False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3f3fac42-f7cc-456b-9e73-bb842c2f781a",
   "metadata": {},
   "source": [
    "## Limited efficiency of swarm plots\n",
    "We saw in the above plot the clarity of bee swarm plots. However, there is a limit to their efficacy. For example, imagine we wanted to plot the county-level voting data for all states divided as east of the Mississippi River and all states west.  \n",
    "\n",
    "### 2008 US election results: East and West\n",
    "We make the swarm plot as before, but using a DataFrame that contains all states, with each classified as being east or west of the Mississippi. The bee swarm plot has a real problem. The edges have overlapping data points, which was necessary in order to fit all points onto the plot. We are now obfuscating data. So, using a bee swarm plot here is not the best option.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "17cab231-dcbd-4265-afa2-20eb68227690",
   "metadata": {
    "executionTime": 14574,
    "lastSuccessfullyExecutedCode": "sns.swarmplot(x='east_west', y='dem_share', data= all_states, hue='east_west')\nplt.ylabel('Percent of vote for Obama')\nplt.xlabel('')\nplt.legend([],[], frameon=False)\nplt.show()"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.swarmplot(x='east_west', y='dem_share', data= all_states, hue='east_west')\n",
    "plt.ylabel('Percent of vote for Obama')\n",
    "plt.xlabel('')\n",
    "plt.legend([],[], frameon=False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e7c6f1ec-14b1-449a-aeb9-1209d21a03e4",
   "metadata": {},
   "source": [
    "## Empirical cumulative distribution function (ECDF)\n",
    "As an alternative, we can compute an empirical cumulative distribution function, or ECDF. Again, this is best explained by example. Here is a picture of an ECDF of the percentage of swing state votes that went to Obama. \n",
    "\n",
    "* The **x-value** of an ECDF: **the quantity you are measuring**, in this case the percent of vote that sent to Obama. \n",
    "* The **y-value**: **the fraction of data points that have a value *\"smaller\"* than the corresponding x-value.**\n",
    "\n",
    "For example, 20% of counties in swing states had 36% or less of its people vote for Obama. Similarly, 75% of counties in swing states had 50% or less of its people vote for Obama.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "8cbe3023-9180-4900-b01e-1179331a7568",
   "metadata": {
    "executionTime": 46,
    "jupyter": {
     "outputs_hidden": false,
     "source_hidden": false
    },
    "lastSuccessfullyExecutedCode": "def ecdf(data):\n    \"\"\"Compute ECDF for a one-dimensional array of measurements.\"\"\"\n    # Number of data points: n\n    n = len(data)\n\n    # x-data for the ECDF: x\n    x = np.sort(data)\n\n    # y-data for the ECDF: y\n    y = np.arange(1, n+1) / n\n\n    return x, y"
   },
   "outputs": [],
   "source": [
    "def ecdf(data):\n",
    "    \"\"\"Compute ECDF for a one-dimensional array of measurements.\"\"\"\n",
    "    # Number of data points: n\n",
    "    n = len(data)\n",
    "\n",
    "    # x-data for the ECDF: x\n",
    "    x = np.sort(data)\n",
    "\n",
    "    # y-data for the ECDF: y\n",
    "    y = np.arange(1, n+1) / n\n",
    "\n",
    "    return x, y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "22cbec34-4fd1-4fa2-bba3-6c3cfb9e139f",
   "metadata": {
    "executionTime": 286,
    "lastSuccessfullyExecutedCode": "x, y = ecdf(swing_states['dem_share'])\n\nplt.plot(x, y, marker='.', linestyle='none')\nplt.xlabel('Percent of vote for Obama')\nplt.ylabel('ECDF')\nplt.margins(0.02) # Keeps data off plot edges\nplt.show()"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 500x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x, y = ecdf(swing_states['dem_share'])\n",
    "fig = plt.figure(figsize=(5, 4))\n",
    "plt.plot(x, y, marker='.', linestyle='none')\n",
    "plt.xlabel('Percent of vote for Obama')\n",
    "plt.ylabel('ECDF')\n",
    "plt.margins(0.02) # Keeps data off plot edges\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3150e7ae-c163-4315-bc75-97aa187fa5b2",
   "metadata": {},
   "source": [
    "## 2008 US swing state election ECDF\n",
    "Let's look at how to make one of these from our data. The x-axis is the sorted data. We need to generate it using the NumPy function sort, so we need to import Numpy, which we do using the alias np as is commonly done. The we can use `np.sort` to generate our x-data. The y-axis is evenly spaced data points with a maximum of one, which we can generate using the `np.arange` function and then dividing by the total number of data points. Once we specify the x and y values, we plot the points. By default, `plt.plot` plots lines connecting the data points. To plot our ECDF, we just want points. To achieve this we pass the string period and the string 'none' to the keywords arguments marker and linestyle, respectively. Finally, we use the `plt.margins` function to make sure none of the data points run over the side of the plot area. Choosing a value of point-02 gives a 2% buffer all around the plot.\n",
    "\n",
    "## Multiple ECDFs\n",
    "We can also easily plot multiple ECDFs on the same plot. For example, here are the ECDFs for the three swing states. We see that Ohio and Pennsylvania were similar, with Pennsylvania having slightly more Democratic counties. Florida, on the other hand, had a greater fraction of heavily Republican counties. *In my workflow, I almost always plot the ECDF first. It shows all the data and gives a complete picture of how the data are distributed.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "87e2579b-a1d3-408e-8135-70164652aaff",
   "metadata": {
    "executionTime": 407,
    "lastSuccessfullyExecutedCode": "# Compute ECDFs\nx_pa, y_pa = ecdf(swing_states[swing_states['state'] == 'PA']['dem_share'])\nx_oh, y_oh = ecdf(swing_states[swing_states['state'] == 'OH']['dem_share'])\nx_fl, y_fl = ecdf(swing_states[swing_states['state'] == 'FL']['dem_share'])\n\n# Plot all ECDFs on the same plot\n_ = plt.plot(x_pa, y_pa, marker='.', linestyle='none')\n_ = plt.plot(x_oh, y_oh, marker='.', linestyle='none')\n_ = plt.plot(x_fl, y_fl, marker='.', linestyle='none')\n\n# Annotate the plot\nplt.legend(('PA', 'OH', 'FL'), loc='lower right')\nplt.xlabel('Percent of vote for Obama')\nplt.ylabel('ECDF')\n\n# Display the plot\nplt.show()"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Compute ECDFs\n",
    "x_pa, y_pa = ecdf(swing_states[swing_states['state'] == 'PA']['dem_share'])\n",
    "x_oh, y_oh = ecdf(swing_states[swing_states['state'] == 'OH']['dem_share'])\n",
    "x_fl, y_fl = ecdf(swing_states[swing_states['state'] == 'FL']['dem_share'])\n",
    "\n",
    "# Plot all ECDFs on the same plot\n",
    "_ = plt.plot(x_pa, y_pa, marker='.', linestyle='none')\n",
    "_ = plt.plot(x_oh, y_oh, marker='.', linestyle='none')\n",
    "_ = plt.plot(x_fl, y_fl, marker='.', linestyle='none')\n",
    "\n",
    "# Annotate the plot\n",
    "plt.legend(('PA', 'OH', 'FL'), loc='lower right')\n",
    "plt.xlabel('Percent of vote for Obama')\n",
    "plt.ylabel('ECDF')\n",
    "\n",
    "# Display the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "24810148-2c87-4252-824d-29006ea0cbff",
   "metadata": {},
   "source": [
    "## Onward toward the whole story!\n",
    "You now have some great graphical EDA tools. You can quickly generate and investigate a histogram. You can immediately get a feel for your data by plotting all of them, with bee swarm plots or ECDFs. In almost every data set we encounter in this course, and in its sequel, and also in real life, we start with graphical EDA. Remember what Tukey said,\n",
    "\n",
    "> “Exploratory data analysis can never be the whole story, but nothing else can serve as the foundation stone.” — John Tukey\n",
    "\n",
    "### Coming up\n",
    "In the next chapter, we will build upon graphical EDA with quantitative EDA, which allows you to compute useful summary statistics. With our foundation stone in place, we will spend the last half of this course learning to **think probabilistically.** We will learn about **probability distributions for both discrete and continuous variables**, which provide the mathematical foundation for you to draw meaningful conclusions from your data. We will not get mired in mathematical details, but rather will unleash **the power of the NumPy random module to use hacker statistics** in order to simulate the probabilistic stories and distributions that we encounter. We will find that by writing a few lines of Python code, we can perform even putatively complicated statistical analyses. As we work through this course and its sequel, we will grow ever closer to being able to tell what Tukey calls **\"the whole story.\"**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8ae23d29-fb71-4105-baeb-b54841708e20",
   "metadata": {},
   "source": [
    "---\n",
    "## Chapter 2. Quantitative Exploratory Data Analysis<a class=\"anchor\" id=\"second-bullet\"></a>\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6313aff0-678a-4904-bb6c-398149c574c3",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Introduction to summary statistics: The sample mean and median  \n",
    "We have seen that histograms, bee swarm plots, and ECDFs provide effective summaries of data. But we often would like to summarize data even more succinctly, say in one or two numbers. These numerical summaries are not by any stretch a substitute for the graphical methods we have been employing, but they do take up a lot less real estate.\n",
    "\n",
    "Let's go back to the election data from the swing states again. **If we could summarize the percentage of the votes for Obama at the county level in Pennsylvania in one number, what would we choose?** **The first number that pops into my mind is the mean.** The mean for a given state is just the average percentage of votes over the counties. If we add the means as horizontal lines to the bee swarm plot, we see that they are a reasonable summary of the data.  \n",
    "\n",
    "## Mean vote percentage\n",
    "To compute the mean of a set of data, we use the `np.mean` function, here used to compute the mean county-level vote for Obama in Pennsylvania. To put it precisely, the mean, written here as x-bar,is the sum of all the data, divided by the number n of data points.  \n",
    "$$mean = \\bar{x} = {1 \\over n} \\sum_{i=1}^n x_{i}$$\n",
    "* **Major problem of the mean**: it is heavily influenced by outliers, or data points whose value is far greater or less than the most of the rest of the data\n",
    "\n",
    "Now, the mean is a useful statistic and easy to calculate, but **a major problem is that it is heavily influenced by outliers, or data points whose value is far greater or less than most of the rest of the data.**   \n",
    "\n",
    "## 2008 Utah election results\n",
    "Consider the county-level votes for Utah in the 2008 election. There are five counties that have high vote share for Obama, one of which has almost 60%. Even though the majority of the counties in Utah had less than 25% voting for Obama, these anomalous counties pull the mean higher up. So, when we compute the mean, we get about 28%. We might like a summary statistic that is immune to extreme data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "a85b1269-3daa-4696-b659-6ccab9387d28",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = all_states[['state','county','dem_share']]\n",
    "df = df[(df[\"state\"] == \"PA\") | (df[\"state\"] == \"OH\") | (df[\"state\"] == \"FL\") | (df[\"state\"] == \"UT\")]\n",
    "\n",
    "graph = sns.swarmplot(x='state', y='dem_share', data= df, hue=\"state\")\n",
    "\n",
    "# Calculate the mean by each state and add the mean lines on the plot\n",
    "graph.axhline(np.mean(df[df['state'] == 'PA']['dem_share']), xmin=0.040, xmax= 0.225)\n",
    "graph.axhline(np.mean(df[df['state'] == 'UT']['dem_share']), xmin=0.270, xmax=0.470, color='orange')\n",
    "graph.axhline(np.mean(df[df['state'] == 'OH']['dem_share']), xmin=0.520, xmax=0.735, color='green')\n",
    "graph.axhline(np.mean(df[df['state'] == 'FL']['dem_share']), xmin=0.795, xmax=0.96, color='red')\n",
    "plt.xlabel('State')\n",
    "plt.ylabel('Percent of vote for Obama')\n",
    "plt.legend([],[], frameon=False)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "20d9d0a5-0b4d-42a6-9910-8e6c56b6d1b8",
   "metadata": {},
   "source": [
    "## The median: immune to extreme data (outliers)\n",
    "The median provides exactly that. **The median is the middle value of a data set.** It is defined by how it is calculated: 1) sort the the data and 2) choose the datum in the middle. Because it is derived from the ranking of sorted data, and not on the values of the data, the median is immune to data that take on extreme values. Here it is displayed on the bee swarm plot. It is not tugged up by the counties with large fraction of votes for Obama. The median is computed by simply calling the `np.median` function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "61e59782-88ff-404b-b08b-8ef6533a91f2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "graph = sns.swarmplot(x='state', y='dem_share', data= df[df[\"state\"] == \"UT\"], color=\"orange\")\n",
    "\n",
    "# Calculate the mean by each state and add the mean lines on the plot\n",
    "graph.axhline(np.mean(df[df['state'] == 'UT']['dem_share']), xmin=0.35, xmax= 0.645, color='black')\n",
    "graph.axhline(np.median(df[df['state'] == 'UT']['dem_share']), xmin=0.35, xmax= 0.645, color='green')\n",
    "\n",
    "plt.text(0.16, 27,\"Mean\", horizontalalignment='left', size='medium', color='black', weight='semibold')\n",
    "plt.text(0.16, 22,\"Median\", horizontalalignment='left', size='medium', color='green', weight='semibold')\n",
    "\n",
    "plt.xlabel('State')\n",
    "plt.ylabel('Percent of vote for Obama')\n",
    "plt.legend([],[], frameon=False)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "25840ef5-ebe8-441a-a2b4-c5bb894050a6",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Percentiles, outliers, and box plots\n",
    "**The median is a special name for the 50th percentile**; that is 50% of the data are less than the median. Similarly, the 25th percentile is the value of the data point that is greater than 25% of the sorted data, and so on for any other percentile we want. Percentiles are useful summary statistics, and can be computed using `np.percentile`. We just pass a list of the percentiles we want (percentiles, not fractions), and it returns the data that match those percentiles.   \n",
    "<br>\n",
    "We can do this for all of the swing states. Let's compute the 25th, 50th, and 75th percentiles. We now have three summary statistics. Now the whole point of summary statistics was to keep things concise, but we're starting to get a lot of numbers here. Dealing with this issue is where quantitative EDA meets graphical EDA."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "0f952b6a-d927-4729-b9b1-892d82f530ca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Specify array of percentiles: percentiles\n",
    "percentiles = np.array([25, 50, 75])\n",
    "\n",
    "# Compute percentiles: ptiles_vers\n",
    "ptiles_vers = np.percentile(swing_states['dem_share'], percentiles)\n",
    "\n",
    "# Plot the ECDF\n",
    "x, y = ecdf(swing_states['dem_share'])\n",
    "plt.plot(x, y, marker='.', linestyle='none')\n",
    "plt.xlabel('Percent of vote for Obama')\n",
    "plt.ylabel('ECDF')\n",
    "plt.margins(0.02) # Keeps data off plot edges\n",
    "\n",
    "# Overlay percentiles as red diamonds.\n",
    "_ = plt.plot(ptiles_vers, percentiles/100, marker='D', color ='red', linestyle = 'none')\n",
    "\n",
    "plt.annotate(\"25th percentile = 37.3%\", (37.3+ 3, 0.25))\n",
    "plt.annotate(\"50th percentile = 41.8%\", (41.8+ 3, 0.5))\n",
    "plt.annotate(\"75th percentile = 49.9%\", (49.9+ 3, 0.75))\n",
    "\n",
    "# Show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a4dcfb12-19ec-4bc5-8371-69e50b15d0b7",
   "metadata": {},
   "source": [
    "## 2008 US election box plot\n",
    "**Box plots** were invented by John Tukey himself to display some of the salient features of a data set based on percentiles. Here, we see a box plot showing Obama's vote share from states east and west of the Mississippi River. \n",
    "* The center of box is the median, which we know is the 50th percentile of the data. \n",
    "* The edges of the boxes are the 25th and 75th percentile. \n",
    "* The total height of the box contains the middle 50% of the data, and is called the interquartile range, or IQR. \n",
    "* The whiskers extend a distance of 1.5 times the IQR, or to the extent of the data, whichever is more extreme. \n",
    "* Finally, any points outside of the whiskers are plotted as individual points, which we often demarcate as outliers. \n",
    "\n",
    "While there is no single definition for an outlier, being more than 2 IQRs away from the median is a common criterion. It is important to remember that an outlier is not necessarily an erroneous data point. You should not assume an outlier is erroneous unless you have some known reason to. Since there is zero evidence of any substantial voter fraud in the United States, these outliers are not erroneous. They are just data points with extreme values. When the number of data are very large and bee swarm plots are too cluttered, box plots are a great alternative. It makes sense, then, that constructing a box plot using `Seaborn` is exactly the same as making a bee swarm plot; we just use `sns .boxplot`. And of course we never forget to label the axes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "id": "466abf25-95a8-4ff6-8802-7d1b629103fd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 500x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a boxplot for the west and the east \n",
    "fig = plt.figure(figsize=(5, 4))\n",
    "_ = sns.boxplot(x='east_west', y='dem_share', data= all_states)\n",
    "_ = plt.xlabel('Region')\n",
    "_ = plt.ylabel('Percent of vote for Obama')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ffe2815d-a581-441a-b647-6314d4801499",
   "metadata": {},
   "source": [
    "## Variance and standard deviation\n",
    "Once again, let's look at the 2008 swing state data on the county level and think about other summary statistics we can calculate. In the bee swarm plot, I also show the means of each state with a horizontal line. In looking at this plot, **the mean seems to capture the magnitude of the data, but what about the *variability, or the spread, of the data?***  Florida seems to have more county-to-county variability than Pennsylvania or Ohio.\n",
    "\n",
    "## Computing the variance \n",
    "We can quantify this spread with the variance. **The variance is the average of the squared distance from the mean.** \n",
    "$$variance = {1 \\over n} \\sum_{i=1}^n (x_{i} - \\bar{x})^2$$\n",
    "\n",
    "That definition was a mouthful. Let's parse that more carefully with a graphical example, looking specifically at Florida. For each data point, we square the distance from the mean, and then take the average of all of these values. Calculation of the variance is implemented in the `np.var` function. Now, **because the calculation of the variance involves squared quantities, it does *not have the same units of what we have measured*, in this case the vote share for Obama.** Therefore, we are interested in the square root of the variance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "id": "569016a5-84e7-4189-8e19-422cc0f6bcfb",
   "metadata": {
    "executionTime": 381,
    "lastSuccessfullyExecutedCode": "# Compute the explicit variance and the numpy variance \nvariance = np.var(df[df['state'] == 'FL']['dem_share'])\n\n# Array of differences to mean: differences\ndf_fl = df[df['state'] == 'FL']['dem_share'] \ndifferences = df_fl - np.mean(df_fl)\n\n# Square the differences: diff_sq\ndiff_sq = differences**2\n\n# Compute the mean square difference: variance_explicit\nvariance_explicit = np.mean(diff_sq)\n\n# Print the result\nprint('Variance with np.var:', variance,'and Explicit variance:', variance_explicit)\n\ngraph = sns.swarmplot(x='state', y='dem_share', data= df[df[\"state\"] == \"FL\"])\n\n# Ad the mean line on the plot\ngraph.axhline(mean, xmin=0.35, xmax= 0.65, color='black')\ngraph.axhline(mean+11, xmin=0.35, xmax= 0.645, color='green')\n\nplt.text(0.16, mean-0.5, \"Mean\", horizontalalignment='left', size='medium', color='black', weight='semibold')\nplt.annotate(\"$X_i-$ Mean\", xy=(0.16, 48), xytext=(0.26, 48) , color='green', va=\"center\", ha=\"left\", arrowprops=dict(arrowstyle=\"<-[\", color=\"green\"))\n\nplt.xlabel('State')\nplt.ylabel('Percent of vote for Obama')\nplt.legend([],[], frameon=False)\n\nplt.show()"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Variance with np.var: 147.44\n",
      "Explicit variance: 147.44\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Compute the explicit variance and the numpy variance \n",
    "mean = np.mean(df[df['state'] == 'FL']['dem_share'])\n",
    "variance = np.var(df[df['state'] == 'FL']['dem_share'])\n",
    "\n",
    "# Array of differences to mean: differences\n",
    "df_fl = df[df['state'] == 'FL']['dem_share'] \n",
    "differences = df_fl - np.mean(df_fl)\n",
    "\n",
    "# Square the differences: diff_sq\n",
    "diff_sq = differences**2\n",
    "\n",
    "# Compute the mean square difference: variance_explicit\n",
    "variance_explicit = np.mean(diff_sq)\n",
    "\n",
    "# Print the result\n",
    "print(f'Variance with np.var: {variance:.2f}\\nExplicit variance: {variance_explicit:.2f}')\n",
    "\n",
    "graph = sns.swarmplot(x='state', y='dem_share', data= df[df[\"state\"] == \"FL\"])\n",
    "\n",
    "# Ad the mean line on the plot\n",
    "graph.axhline(mean, xmin=0.35, xmax= 0.65, color='black')\n",
    "graph.axhline(mean+11, xmin=0.35, xmax= 0.645, color='green')\n",
    "\n",
    "plt.text(0.16, mean-0.5, \"Mean\", horizontalalignment='left', size='medium', color='black', weight='semibold')\n",
    "plt.annotate(r\"$X_i- \\bar{X}$ \", xy=(0.16, 48), xytext=(0.26, 48) , color='green', va=\"center\", ha=\"left\", arrowprops=dict(arrowstyle=\"<-[\", color=\"green\"))\n",
    "\n",
    "plt.xlabel('State')\n",
    "plt.ylabel('Percent of vote for Obama')\n",
    "plt.legend([],[], frameon=False)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e899f2ac-2832-4571-9b09-d68c7513e1d1",
   "metadata": {},
   "source": [
    "## Computing the standard deviation\n",
    "The square root of the variance is called the **standard deviation.** \n",
    "$$\\text{standard deviation} = \\sqrt{variance} = \\sqrt{{1 \\over n} \\sum_{i=1}^n (x_{i} - \\bar{x})^2}$$\n",
    "This is calculated with the `np.std` function, and the results are the same as taking the square root of the variance. When we look at the swarm plot, **it is clear that the standard deviation is a reasonable metric for the typical spread of the data.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "0a8b3888-cf2f-494a-a10d-85fbce2e3974",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean: 41.32507462686567 Variance: 147.44278618846067 Standard deviation: 12.142602117687158\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Compute the standard deviation \n",
    "std = np.std(df[df['state'] == 'FL']['dem_share'])\n",
    "\n",
    "# Print the results\n",
    "print('Mean:', mean,'Variance:', variance, 'Standard deviation:', std)\n",
    "\n",
    "graph = sns.swarmplot(x='state', y='dem_share', data= df[df[\"state\"] == \"FL\"])\n",
    "\n",
    "# Ad the mean and the standard deviation lines on the plot\n",
    "graph.axhline(mean, xmin=0.35, xmax= 0.65, color='black')\n",
    "graph.axhline(mean+std, xmin=0.35, xmax= 0.645, color='purple')\n",
    "\n",
    "plt.text(0.16, mean-0.5, \"Mean\", horizontalalignment='left', size='medium', color='black', weight='semibold')\n",
    "plt.text(0.16, mean+std-0.5, \"Standard deviation\", horizontalalignment='left', size='medium', color='purple', weight='semibold')\n",
    "\n",
    "plt.xlabel('State')\n",
    "plt.ylabel('Percent of vote for Obama')\n",
    "plt.legend([],[], frameon=False)\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bec6d8c6-f89e-42f8-8cb2-05b8e60e981a",
   "metadata": {},
   "source": [
    "## Covariance and the Pearson correlation coefficient\n",
    "\n",
    "We have more data than just the vote share for Obama. We also know *the total number of votes in each county*. Let's look at **how these two quantities vary together.**  \n",
    "\n",
    "## 2008 US swing state election results\n",
    "We start by looking at a scatter plot of the county data for the three swing states, plotting **the percent vote for Obama versus the total number of votes in each county.** Immediately from the scatter plot, we see that *the twelve most populous counties all voted for Obama (blue box)*, and *that most of the counties with small populations voted for McCain (red box).*\n",
    "\n",
    "### Generating a scatter plot\n",
    "To generate a scatter plot, we plot the data as points by setting the marker and `linestyle` keyword arguments of `plt.plot`. So, we have exposed another graphical EDA technique: scatter plots! We would like to have a summary statistic to go along with the information we have just gleaned from the scatter plot. **We want a number that summarizes *how Obama's vote share varies with the total vote count.*** One such statistic is **the covariance.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "dd5446af-a9bc-4f5a-9f76-c8cb9dd7d137",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Adjust the range and the interval of x-axis and y-axis\n",
    "plt.xlim(0, 900, 100)\n",
    "plt.ylim(0, 90, 10)\n",
    "plt.yticks(range(0, 91, 10))\n",
    "\n",
    "# Add boxes to show favorable areas for Obama and for McCain\n",
    "plt.axvspan(0, 50, ymin=0.02, ymax=0.55, alpha=0.2, color='red')\n",
    "plt.axvspan(400, 900, ymin=0.56, ymax=0.98, alpha=0.1, color='blue')\n",
    "\n",
    "# Create a scatter plot\n",
    "_ = plt.plot(swing_states['total_votes']/1000, swing_states['dem_share'], marker='.', linestyle='none')\n",
    "_ = plt.xlabel('Total votes (thousands)')\n",
    "_ = plt.ylabel('Percent of vote for Obama')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "afb6122a-defe-49f8-b288-314e5803198e",
   "metadata": {},
   "source": [
    "## Covariance\n",
    "To understand where it comes from, let's annotate the scatter plot with the means of the two quantities we are interested in. Now let's look at **this data point, from Lucas County, Ohio**. This data point differs from the mean vote share for Obama, and the mean total votes. We can compute these differences for each data point. The covariance is the mean of the product of these differences. \n",
    "\n",
    "$$covariance = {1 \\over n} \\sum_{i=1}^n (x_{i} - \\bar{x})(y_{i} - \\bar{y})$$\n",
    "\n",
    "* **Positively correlated**: If $x$ and $y$ **both** tend to be above, or **both** below their respective means together, as they are in this data set, then the covariance is positive. This means that they are positively correlated: when $x$ is high, so is $y$; **when the county is *populous*, it has *more votes for Obama.***\n",
    "* **Negatively correlated**: Conversely, if $x$ is high while $y$ is low, the covariance is negative, and the data are negatively correlated, or anticorrelated, which is not the case for this data set.\n",
    "\n",
    "We can compute the covariance using built-in NumPy functions. However, if we want to have a more generally applicable measure of how two variables depend on each other, **we want it to be *dimensionless*, that is to *not have any units.***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "b51d92ae-45e4-4527-ad44-171f8058de6a",
   "metadata": {
    "executionTime": 547,
    "lastSuccessfullyExecutedCode": "# Adjust the range and the interval of x-axis and y-axis\nplt.xlim(0, 900, 100)\nplt.ylim(0, 90, 10)\nplt.yticks(range(0, 91, 10))\n\n# Create a scatter plot\nswing_states[colors] = [\"b\" if county == 'Lucas County' else \"r\" for county in swing_states['county']]\n\nplt.plot(swing_states['total_votes']/1000, swing_states['dem_share'], marker='.', linestyle='none', c=swing_states[colors])\nplt.xlabel('Total votes (thousands)')\nplt.ylabel('Percent of vote for Obama')\n\n# Add the lines to show the mean percent for Obama and the mean total votes \nplt.axvline(np.mean(swing_states['total_votes'])/1000, color='grey') # mean total votes\nplt.axhline(np.mean(swing_states['dem_share']), color='grey'); # mean percent for Obama\n\nplt.text((np.mean(swing_states['total_votes'])/1000)+10, 4, \"Mean total votes\", horizontalalignment='left', size='medium', color='black')\nplt.text(500, np.mean(swing_states['dem_share'])-3, \"Mean percent vote for Obama\", horizontalalignment='left', size='medium', color='black');\n"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "''"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Adjust the range and the interval of x-axis and y-axis\n",
    "plt.xlim(0, 900, 100)\n",
    "plt.ylim(0, 90, 10)\n",
    "plt.yticks(range(0, 91, 10))\n",
    "\n",
    "# Create a scatter plot\n",
    "colors = [\"g\" if county == 'Lucas County' else \"grey\" for county in swing_states['county']]\n",
    "plt.scatter(swing_states['total_votes']/1000, swing_states['dem_share'], marker='.', color=colors)\n",
    "plt.xlabel('Total votes (thousands)')\n",
    "plt.ylabel('Percent of vote for Obama')\n",
    "\n",
    "# Add the lines to show the mean percent for Obama and the mean total votes \n",
    "plt.axvline(np.mean(swing_states['total_votes'])/1000, color='grey') # mean total votes\n",
    "plt.axhline(np.mean(swing_states['dem_share']), color='grey'); # mean percent for Obama\n",
    "\n",
    "# Add arrows to show the distance between Lucas county and the means \n",
    "plt.annotate(\"\", xy=(90, 66), xytext=(218, 66) , color='green', va=\"center\", ha=\"left\", arrowprops=dict(arrowstyle=\"<->\", color=\"green\"))\n",
    "plt.annotate(\"\",xy=(218, 44), xytext=(218, 65), color='green', arrowprops=dict(arrowstyle=\"<->\", color=\"green\"), ha=\"center\", va=\"bottom\")\n",
    "\n",
    "plt.text((np.mean(swing_states['total_votes'])/1000)+10, 4, \"Mean total votes\", horizontalalignment='left', size='medium', color='black')\n",
    "plt.text(500, np.mean(swing_states['dem_share'])-3, \"Mean percent vote for Obama\", horizontalalignment='left', size='medium', color='black')\n",
    "plt.text(130, 70, \"Distance from mean total votes\", horizontalalignment='left', size='medium', color='green')\n",
    "plt.text(225, 55, \"Distance from mean total percent for Obama\", horizontalalignment='left', size='medium', color='green')\n",
    ";"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ee0ef2e8-74ee-46ed-a165-5ec5b3405aa6",
   "metadata": {},
   "source": [
    "## Pearson correlation coefficient\n",
    "We can **divide the covariance by the standard deviations of the x and y variables**. This is called the **Pearson correlation coefficient**, usually denoted by the Greek letter rho. It is a comparison of the **variability in the data due to codependence (the covariance)** to **the variability inherent to each variable independently (their standard deviations).** Conveniently, it is **dimensionless** and **ranges from -1 (for complete anticorrelation) to 1 (for complete correlation).**\n",
    "$$ \\rho = \\text{Pearson correlation} =  {covariance \\over \\text{(std of x)(std of y)}}$$\n",
    "$$= \\frac{\\text{Variability due to codependence}}{\\text{independent variability}}$$\n",
    "\n",
    "\n",
    "## Pearson correlation coefficient examples\n",
    "A value of zero means that there is no correlation at all between the data, as shown in the plot on the upper left. Data with intermediate values are shown on the other plots. As you can see, the Pearson correlation coefficient is a good metric for correlation between two variables. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "id": "97541cd5-000c-412a-90f5-1ac392f47785",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 700x600 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create example plots to show trends with different correlation coefficients \n",
    "\n",
    "# Set the seed for reproducibility\n",
    "np.random.seed(1234)\n",
    "\n",
    "# Generate x and y data\n",
    "n = 200\n",
    "x = np.random.randn(n)\n",
    "y0 = np.random.randn(n)\n",
    "y1 = 0.8*x + np.random.randn(n)\n",
    "y2 = -0.8*x + np.random.randn(n)\n",
    "y3 = 0.4*x + np.random.randn(n)\n",
    "\n",
    "# Calculate Pearson correlation coefficients\n",
    "r0 = np.corrcoef(x, y0)[0, 1]\n",
    "r1 = np.corrcoef(x, y1)[0, 1]\n",
    "r2 = np.corrcoef(x, y2)[0, 1]\n",
    "r3 = np.corrcoef(x, y3)[0, 1]\n",
    "\n",
    "# Create four subplots\n",
    "fig, axes = plt.subplots(nrows=2, ncols=2, figsize=(7, 6))\n",
    "ax = axes.flatten()\n",
    "\n",
    "# Plot the data and set the axis labels and titles\n",
    "def set_scatter_plot(ax, x, y, r):\n",
    "    ax.scatter(x, y)\n",
    "    ax.set_xlabel('x')\n",
    "    ax.set_ylabel('y')\n",
    "    ax.set_title(f'Correlation coefficient: {r:.2f}')\n",
    "\n",
    "# Plot the data using the set_scatter_plot function\n",
    "set_scatter_plot(ax[0], x, y0, r0)\n",
    "set_scatter_plot(ax[1], x, y1, r1)\n",
    "set_scatter_plot(ax[2], x, y2, r2)\n",
    "set_scatter_plot(ax[3], x, y3, r3)\n",
    " \n",
    "# Set the layout and show the plot\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0d4684b7-2ee4-406e-aff5-d24322089a25",
   "metadata": {},
   "source": [
    "---\n",
    "## Chapter 3. Thinking Probabilistically: Discrete Variables<a class=\"anchor\" id=\"third-bullet\"></a>\n",
    "---\n",
    "## 1. Probabilistic logic and statistical inference  \n",
    "Imagine you measured the petal lengths of 50 flowers of a certain species. Here is the ECDF of those measurements. From what you have just learned, you can compute the mean of those 50 measurements, and I'll annotate it on the ECDF with a vertical line. That is useful, but there are millions of these flowers on the planet. Can you tell me the mean petal length of all of the flowers of that species?\n",
    "\n",
    "If I measure another 50 flowers, I get a similar, but quantitatively different set of measurements. Can you tell me what value I would get for the mean petal length if I measured yet another 50 flowers? We just don't have the language to do that, without probability. **Probabilistic reasoning allows us to describe uncertainty.** Though you can't tell me exactly what the mean of the next 50 petal lengths you measure will be, you could say that it is more probable to be close to what you got in the first 50 measurements that it is to be much greater. We can go ahead and repeat the measurements over and over again.\n",
    "\n",
    "We see from the vertical lines that we expect the mean to be somewhere between 4-point-5 and 5 cm. **This is what probabilistic thinking is all about. Given a set of data, you describe probabilistically what you might expect if those data were acquired again and again and again. This is the heart of statistical inference. It is the process by which we go from measured data to probabilistic conclusions about what we might expect if we collected the same data again. Your data speak in the language of probability.**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "755548fb-bca7-4b6e-a4f3-89ae405548a1",
   "metadata": {
    "tags": []
   },
   "source": [
    "## 2. Random number generators and hacker statistics\n",
    "\n",
    "## Generating random numbers using the `np.random` module"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "id": "733bb6b1-773d-4903-8905-f2e731b7bf71",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 400x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Seed the random number generator\n",
    "np.random.seed(42)\n",
    "\n",
    "# Initialize random numbers: random_numbers\n",
    "random_numbers = np.empty(100000)\n",
    "\n",
    "# Generate random numbers by looping over range(100000)\n",
    "for i in range(100000):\n",
    "    random_numbers[i] = np.random.random()\n",
    "\n",
    "# Plot a histogram\n",
    "fig = plt.figure(figsize=(4, 3))\n",
    "_ = plt.hist(random_numbers)\n",
    "\n",
    "# Show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d321e460-0568-48d3-8ed0-0a95a6bc5b99",
   "metadata": {},
   "source": [
    "The histogram is *almost exactly flat across the top*, indicating that **there is equal chance that a randomly-generated number is in any of the bins of the histogram.**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6973be6e-1b59-47a9-8540-0c20f0585fa6",
   "metadata": {},
   "source": [
    "## The `np.random` module and Bernoulli trials\n",
    "\n",
    "You can think of a Bernoulli trial as a flip of a possibly biased coin. **Specifically, each coin flip has a probability of landing heads (success) and probability \n",
    "of landing tails (failure).** In this exercise, you will write a function to perform `n` Bernoulli trials, `perform_bernoulli_trials(n, p)`, which returns the number of successes out of `n` Bernoulli trials, each of which has probability `p` of success. To perform each Bernoulli trial, use the `np.random.random() function`, which returns a random number between zero and one."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "8814c809-83ce-4e35-a493-72813e7fe638",
   "metadata": {},
   "outputs": [],
   "source": [
    "def perform_bernoulli_trials(n, p):\n",
    "    \"\"\"Perform n Bernoulli trials with success probability p\n",
    "    and return number of successes.\"\"\"\n",
    "    # Initialize number of successes: n_success\n",
    "    n_success = 0\n",
    "\n",
    "    # Perform trials\n",
    "    for i in range(n):\n",
    "        # Choose random number between zero and one: random_number\n",
    "        random_number = np.random.random()\n",
    "\n",
    "        # If less than p, it's a success so add one to n_success\n",
    "        if random_number < p:\n",
    "            n_success += 1\n",
    "\n",
    "    return n_success"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9827a9d6-a866-44ad-9d9b-7000263d7697",
   "metadata": {},
   "source": [
    "## 🏛💰 Bank loans: How many defaults might we expect?\n",
    "\n",
    "Let's say a bank made 100 mortgage loans. It is possible that anywhere between 0 and 100 of the loans will be defaulted upon. **We would like to know the *probability of getting a given number of defaults*, given that the probability of a default is p = 0.05.** To investigate this, you will do a simulation. \n",
    "* You will perform 100 Bernoulli trials using the `perform_bernoulli_trials() function` you wrote in the previous exercise and record how many defaults we get. \n",
    "* Here, a success is a default. (Remember that the word \"success\" just means that the Bernoulli trial evaluates to True, i.e., did the loan recipient default?) \n",
    "* You will do this for another 100 Bernoulli trials. And again and again *until we have tried it 1000 times.*\n",
    "* Then, you will plot a histogram describing the probability of the number of defaults."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "id": "5245009d-1f41-458c-900f-7a19a3e4dfed",
   "metadata": {},
   "outputs": [
    {
     "data": {
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uJxERaYFGIZGZmYn3338f5ubmcHJyQkpKCgwNDTF8+HAEBATwIjcRUQuhUUjo6enB0NAQANCxY0fcv38fz549AwC4urri3r179dZAIiLSHo1C4u2338a///1vAEDnzp0hk8nw3//+F8DzR3YQEVHLoNGFaz8/P3z00UeQSqVYt24dvLy8sGTJEnh7e+PEiRNwdXWt73YSEZEWaHQmMXToUOzYsQP29vYAgFWrVqFTp044fPgwunTpgrCwsHptJBERaYdGIQEAnp6emDVrFgDA0tISu3fvxrVr17B//360a9euxuXIZDJER0fDw8MDzs7OmD17do2eIiuTyeDv749t27apvfbDDz9g1KhR6NWrF8aNGyf4MEIiIno1jUOiuLgYBw8eRHBwMGbNmoXFixfjyJEjKC8vr1U5sbGxOHToEFavXo3Dhw8r//i/qpzy8nL8/e9/x/nz59Veu3TpEhYvXozJkyfjm2++gbu7OwICApCRkVHrfSQiet1pFBIPHjyAj48PVq9ejd9++w1FRUVITU3F8uXLMXHiROTl5dWonPLycuzevRtBQUHw9PSEo6MjNm/ejKysLJw+fbrKbVJTU+Hr64vk5GSYmZmpvR4fH4+hQ4di2rRpsLe3x9KlS9GjRw98/vnnmuwqEdFrTaOQWL9+PUQiEY4dO4Z//etfOHz4MH766Sd88cUXyMvLQ3h4eI3KuX79OoqKiuDu7q5cZmZmhu7du6s8mvxFiYmJ8PDwwLFjx2BqaqrymkwmQ2pqqkp5ANC3b1/B8oiISJhGdzf98ssvWLt2LRwdHVWW9+7dGwsWLKjxfBKK22VfvoZhY2MjeCttcHCwYHlSqRTFxcWwtbWtcXm1oatbu0zV0RGr/NvSaGu/qqu3vvpdmz+35njMtPTjvSlqjD7XKCSMjY2hp6dX5WtWVlbQ0dGpUTklJSUAAH19fZXlBgYGKCgoqHW7SktLBcsrKyurdXkvEotFsLRspdG2ZmZGdaqbVNW0P5tzv7PtVBsN2ecahcQHH3yArVu3onfv3rCxsVEuf/r0KXbu3InJkyfXqBzFp7bLy8uV/weAsrIyGBnVfqcNDAyU5b1I0/JeJJPJIZUW12obHR0xzMyMIJWWoLJSVqf6myLF/jW26vqzvvpdW/sHVL+PTVFLP96borr0uZmZUY3OQGocEtOmTVP5/u7duxg2bBhcXFzQpk0bFBQUICUlBTKZDG+88UaNylQMM+Xk5KBDhw7K5Tk5ORrNk21hYQFjY2Pk5OSoLM/JyUHbtm1rXd7LKio0O/ArK2Uab0uvp+Z8zDTntjdXDdnnNQ6Jlx8L7uLiAgCoqKhQjvd3794dAJCdnV2jMh0dHWFiYoKkpCRlSEilUqSnp2PKlCk1bZqSSCSCi4sLLl++jIkTJyqXJyUl4Z133ql1edS0WJgaQCaTvxbDTURNRY1DYv/+/fVeub6+PqZMmYLIyEhYWVmhffv2iIiIgK2tLby9vVFZWYnc3FyYmpqqDEe9ip+fHwICAtC9e3cMHDgQR44cwe+//461a9fWe/upcZkY6UEsFiHyYAoyswsbvD4XRxtMG9W9weshaso0npkOADIyMnD58mUUFhbC0tISrq6u6NKlS63KCAoKQkVFBUJDQ1FaWgqJRIKEhATo6ekhMzMTXl5eCA8Ph6+vb43KGzBgANatW4fY2Fhs3rwZXbt2VXmECDV/mdmFyPiz9jc21JadjUmD10HU1GkUEnK5HJ9++im++uorlWEokUiE8ePHY926dTUuS0dHB4sXL8bixYvVXrOzs8ONGzcEt/3555+rXD5u3DiMGzeuxm0gIqKqaRQSu3btwpEjRxAUFISxY8fC2toaOTk5+Pbbb7F9+3Z069YNM2bMqOemEhFRY9MoJL7++mv4+/tj7ty5ymV2dnYIDAzEs2fP8OWXXzIkiIhaAI0+pvfw4UP069evytf69u2LzMzMOjWKiIiaBo1Con379oLXCq5fvw4rK6s6NYqIiJoGjULCx8cH27Ztww8//KC8cC2Xy/H9998jJiYGo0aNqtdGEhGRdmh0TWL27NlITk5GcHAwFi9eDEtLS+Tl5aGyshJubm6YP39+fbeTiIi0QKOQ0NPTw549e5CYmIgrV66goKAA5ubmkEgkGDRoUH23kYiItESjkBgzZgwWLlyIwYMHMxSIiFowje9uqutTVYmIqOnTKCTGjBmDvXv3qj1tlYiIWhaNhpvu3buH5ORkDBo0SPl47heJRCKcOXOmXhpIRETao1FItGvXDmPGjKnvthARUROjUUiEh4cDAC5cuIArV64gPz8frVu3hru7OyQSSb02kIiItEejkCgoKMCHH36I//73v9DR0YGFhQXy8/Oxfft2DBw4ENu2bVObZ5qIiJofjS5cr1u3Dnfv3sW2bduQlpaGCxcu4Ndff8XWrVtx7do1bN68ub7bSUREWqBRSJw9exaLFi3C0KFDIRKJnhckFsPb2xvBwcE4ceJEvTaSiIi0Q6OQkMvlaNOmTZWvtWvXDsXFxXVqFBERNQ0ahcT48eOxfft2FBUVqSyvqKjAgQMHMH78+HppHBERaZdGF66NjIxw7949eHl5wcvLC23btkVeXh4SExORlZUFc3NzhISEAHj+mYnaTGdKRERNh0Yhcfz4cZiYPJ8k/uLFiyqv2draIjU1Vfm94poFERE1PxqFxM8//1zf7SAioiZIo2sSRET0emBIEBGRIIYEEREJYkgQEZEghgQREQliSBARkSCGBBERCWJIEBGRIIYEEREJYkgQEZEghgQREQliSBARkSCGBBERCWJIEBGRIIYEEREJ0mg+CWq6xGIRxOLGmehJR4fvMYhaOoZECyIWi2BhYcw/3kRUbxgSLYhYLIKOjhiRB1OQmV3Y4PW5ONpg2qjuDV4PEWmP1kNCJpMhJiYGX331FQoLCyGRSBAWFoY333yzyvXz8vKwZs0anDt3DiKRCKNHj8aSJUtgZGSkXMfb2xv3799X2W78+PFYv359g+5LU5GZXYiMPwsavB47G5MGr4OItEvrIREbG4tDhw5h/fr1sLW1RUREBPz9/XHixAno6+urrR8UFISSkhLs3bsXUqkUy5cvR3FxMTZs2AAAKC4uxoMHD7Bz50706NFDuZ2hoWGj7RMRUUuh1cHr8vJy7N69G0FBQfD09ISjoyM2b96MrKwsnD59Wm39q1ev4vLly9iwYQN69OgBd3d3rFq1Ct9++y2ys7MBALdv34ZMJkOfPn1gbW2t/DI1NW3s3SMiava0GhLXr19HUVER3N3dlcvMzMzQvXt3XLlyRW395ORkWFtbw97eXrnMzc0NIpEIKSkpAIAbN26gTZs2MDc3b/gdICJq4bQ63JSVlQUAaNeuncpyGxsb5Wsvys7OVltXX18fFhYWePjwIYDnIWFsbIygoCCkpqbC0tISEyZMwLRp0yAW1y0TdXVrt73iLqPGutuIdzW1HM3xZ9nYxzs1Tp9rNSRKSkoAQO3ag4GBAQoK1C+8lpSUVHmdwsDAAGVlZQCAW7duQSqVYvjw4QgMDERKSgoiIiJQUFCA+fPna9xWsVgES8tWGm1rZmZU/UpEL2jOx0xzbntz1ZB9rtWQUFxMLi8vV7mwXFZWpnK30ovrl5eXqy0vKyuDsbExACA+Ph5lZWXKaxAODg54+vQptm/fjo8//ljjswmZTA6ptLhW2+joiGFmZgSptASVlTKN6tWkPmr+GuuYqU+NfbxT3frczMyoRmcgWg0JxdBRTk4OOnTooFyek5MDBwcHtfVtbW1x5swZlWXl5eXIz8+HjY0NgOdnJS+fbXTr1g3FxcUoKCiApaWlxu2tqNDswK+slGm8Lb2emvMx05zb3lw1ZJ9rdfDQ0dERJiYmSEpKUi6TSqVIT0+HRCJRW18ikSArK0vlMxCXL18GALi6ukIul2Po0KGIiYlR2S4tLQ3W1tZ1CggioteRVs8k9PX1MWXKFERGRsLKygrt27dHREQEbG1t4e3tjcrKSuTm5sLU1BSGhobo3bs3XFxcEBwcjBUrVqC4uBhhYWEYN24c2rZtCwAYNmwYEhIS0KVLFzg5OeHixYvYtWsXli9frs1dJSJqlrT+YbqgoCBUVFQgNDQUpaWlkEgkSEhIgJ6eHjIzM+Hl5YXw8HD4+vpCJBIhJiYGK1euxPTp02FgYIARI0YgJCREWd7ChQthYmKCTZs2ISsrC3Z2dli+fDkmTZqkxb0kImqetB4SOjo6WLx4MRYvXqz2mp2dHW7cuKGyrHXr1oiOjhYsT1dXF4GBgQgMDKz3thI1lsa8jVQmk0MmkzdafdS8aD0kiOj/WJgaQCaTN+pdapWVMuTnFzMoqEoMCaImxMRID2KxqNGe5GvX1hSLPnCFWCxiSFCVGBJETVBjPcmXqDr8/DwREQliSBARkSCGBBERCWJIEBGRIIYEEREJYkgQEZEghgQREQliSBARkSCGBBERCWJIEBGRIIYEEREJYkgQEZEghgQREQniU2AbQWNNINOYE9VQy1Ifx46ijOrK4iRHzQtDogGJRKJGn0CGqDYaYpKj6sriJEfNC0OiAYnFokadQMbF0QbTRnVv8Hqo5eAkR1QdhkQjaKwJZOxsTBq8DmqZOMkRCeEgNhERCWJIEBGRIIYEEREJYkgQEZEghgQREQliSBARkSCGBBERCWJIEBGRIIYEEREJYkgQEZEghgQREQliSBARkSCGBBERCWJIEBGRIIYEEREJYkgQEZEghgQREQliSBARkSCGBBERCdJ6SMhkMkRHR8PDwwPOzs6YPXs2Hjx4ILh+Xl4eFi5cCIlEAjc3N6xcuRIlJSUq6/zwww8YNWoUevXqhXHjxuHixYsNvRtERC2S1kMiNjYWhw4dwurVq3H48GHIZDL4+/ujvLy8yvWDgoJw//597N27F1u3bkViYiJWrFihfP3SpUtYvHgxJk+ejG+++Qbu7u4ICAhARkZGI+0REVHLoavNysvLy7F7924sWrQInp6eAIDNmzfDw8MDp0+fho+Pj8r6V69exeXLl/H999/D3t4eALBq1Sr4+/tjwYIFaNu2LeLj4zF06FBMmzYNALB06VJcvXoVn3/+OVatWtWo+0dEVdPR0fr70xahMfpRqyFx/fp1FBUVwd3dXbnMzMwM3bt3x5UrV9RCIjk5GdbW1sqAAAA3NzeIRCKkpKRgxIgRSE1NxbJly1S269u3L06fPt2wO0NE1bIwNYBMJoeZmVGj1Vkpk0NHLGqx9clkcohEDVefVkMiKysLANCuXTuV5TY2NsrXXpSdna22rr6+PiwsLPDw4UNIpVIUFxfD1ta2RuXVhlgsgpVVq1pto/i5rZjtjopKWZ3qrwkDfR3W14zr00ad2qhPLBbhaXE5KmXyBq9PT1cMY0O9FlufjlgEE2N9mJkZQl7L6sQ1DDKthoTigrO+vr7KcgMDAxQUFFS5/svrKtYvKytDaWmpYHllZWV1aqtIJIKOjmZpbWFqUKe6Wd/rVZ826mzs+kyM1X+PWZ/mxOKGG3bS6sCgoaEhAKhdpC4rK4ORkfrpqKGhYZUXtMvKymBsbAwDA4NalUdERK+m1ZBQDB3l5OSoLM/JyUHbtm3V1re1tVVbt7y8HPn5+bCxsYGFhQWMjY1rXB4REb2aVkPC0dERJiYmSEpKUi6TSqVIT0+HRCJRW18ikSArKwv3799XLrt8+TIAwNXVFSKRCC4uLsplCklJSXjnnXcaaC+IiFourV6T0NfXx5QpUxAZGQkrKyu0b98eERERsLW1hbe3NyorK5GbmwtTU1MYGhqid+/ecHFxQXBwMFasWIHi4mKEhYVh3LhxyjMFPz8/BAQEoHv37hg4cCCOHDmC33//HWvXrtXmrhIRNUsiuby218TrV2VlJTZt2oSjR4+itLQUEokEYWFhsLOzQ2ZmJry8vBAeHg5fX18AwJMnT7By5UqcP38eBgYGGDFiBEJCQpTXIwDg2LFjiI2NRVZWFrp27YrFixer3GZLREQ1o/WQICKiposfeyQiIkEMCSIiEsSQICIiQQwJIiISxJAgIiJBDAkiIhLEkCAiIkEMiQZQ2ylZqX5kZ2fDwcFB7evo0aPablqLtXPnTkydOlVl2e+//44pU6bA2dkZQ4YMwb59+7TUuparqn4PDQ1VO/aHDBlS57q0+liOlkoxJev69etha2uLiIgI+Pv748SJE1U+6pzqx/Xr12FgYIAzZ86oTMJiamqqxVa1XAcPHsSWLVtUnouWl5cHPz8/DBkyBCtXrsS1a9ewcuVKtGrVChMmTNBia1uOqvodAG7cuIE5c+ZgypQpymU6Ojp1ro8hUc9qOyUr1Z+bN2+iU6dOsLGx0XZTWrTs7Gx8+umnSEpKQqdOnVRe+/LLL6Gnp4dVq1ZBV1cX9vb2uH//PuLi4hgSdfSqfpfL5bh9+zYCAgJgbW1dr/VyuKmeVTclKzWcGzduqExtSw3jf//3f6Gnp4fjx4+jd+/eKq8lJyfDzc0Nurr/9/6zX79+uHfvHh4/ftzYTW1RXtXvf/zxB4qLi9GlS5d6r5dnEvWstlOyUv25efMmLC0t8cEHH+Du3bvo2LEj5s6di4EDB2q7aS3KkCFDBMe6s7Ky0K1bN5VlijO7hw8fok2bNg3evpbqVf1+8+ZNAMD+/ftx7tw5iMViDBw4EMHBwXUebuWZRD171ZSsdZ1ClYRVVFTgzp07KCgowMcff4y4uDg4OzsjICAAFy9e1HbzXhulpaVVHvsAePw3oJs3b0IsFsPGxgY7duzAsmXLcOHCBcybNw8yWd3mLueZRD17cUpWxf8BTqHa0HR1dZGUlAQdHR1lvzs5OeHWrVtISEjgo+IbSVVTDCvCwdjYWBtNei3MnTsX77//PiwtLQEA3bp1g7W1NSZNmoS0tDS14ana4JlEPavtlKxUf1q1aqUSzADw1ltvITs7W0stev1UNcWw4nse/w1HLBYrA0LhrbfeAoA6D3MzJOpZbadkpfpx69YtuLi4qPQ7APz222/o2rWrllr1+pFIJEhJSUFlZaVy2aVLl9C5c2e0bt1aiy1r2ZYsWYIZM2aoLEtLSwOAOh//DIl69uKUrD/99BOuX7+O4OBg5ZSs1DDs7e3RpUsXrFq1CsnJycjIyEB4eDiuXbuGuXPnart5r40JEybg6dOnWL58OW7fvo2jR49i7969+PDDD7XdtBZt+PDhuHjxImJiYvDHH38gMTERf//73+Hj41PnO/54TaIBBAUFoaKiAqGhocopWRMSEqCnp6ftprVYYrEYO3bsQFRUFD755BNIpVJ0794de/bsUbvbhhpO69atsWvXLqxduxbjx4+HtbU1lixZgvHjx2u7aS2al5cXtmzZgri4OMTHx8PU1BRjxozBJ598UueyOX0pEREJ4nATEREJYkgQEZEghgQREQliSBARkSCGBBERCWJIEBGRIIYEEREJYkgQaVlT+6hSU2sPaRdDgpSOHj0KBwcHZGZmarspavbu3Yv+/fujV69eiI2NrfF2y5Yt02ie34iICLi5ucHZ2RnHjh2r9fZCtm3bBgcHB+X3KSkpCAgIqLfy66o+2/Pdd99h8ODBcHJyQlhYWLXrp6eno0ePHlUef2lpaZg6dSr69OmDAQMGYNOmTWpPm338+DEWLlyIvn37wtXVFQsWLFB72ODLpk6dqjZXNKniYzmoyXv69Ck2bNgAT09PzJw5E3Z2dg1a382bN7Fr1y5MmjQJ7777boPM9qXw1VdfISMjo8HKr636bM+qVavQqVMnrF+/vtonwN68eRMBAQGoqKhQe+3Bgwfw8/ODs7MztmzZgoyMDGzevBn5+flYtWoVgOfzicyePRtPnz7FihUrUFFRgaioKMyaNQtHjx7lI3HqgCFBTV5BQQFkMhmGDh3aKE/Szc/PBwCMHj1abbJ5qrn8/Hz0798fffv2FVynvLwcBw4cQHR0tHJyopfFx8ejVatWiI2Nhb6+PgYNGgRDQ0OsXr0ac+bMwRtvvIFTp04hPT0d3333nfKpp2+//TZ8fHzwww8/YOzYsQ2yj68DDjc1IUOGDEF0dDQ2bNiA//f//h969eqFWbNm4d69e8p1qjo9TkpKgoODg/Ix2UePHkXPnj2RnJyMCRMmoGfPnhg+fDh+/vln3LlzB9OnT0fv3r0xbNgwfPfdd2rtSE1Nxbhx4+Dk5AQfHx98//33Kq+XlZVh48aNGDRoEJycnDBmzBi1dYYMGYJ169Zh+vTp6NWrF5YvXy643//5z3/w/vvvw9XVFX379sXChQvx8OFD5b4ohov+/ve/qwzVvKygoAAhISFwc3ODRCJBRERElbNynTlzBr6+vujZsyf69++PNWvWoLi4GMDz4SBF/06fPl1Zd2lpKaKiouDt7Q0nJye4uLjAz88Pv//+u7LcmvxsXrRs2TJ88803+PPPP+Hg4ICjR48CAE6ePImxY8eiV69e6NevHxYtWlTtnBg5OTkICQnBoEGD0KtXL7z33nv46aeflK9nZmaq1PFiGxT7KNSeqqSlpWHWrFno27cvXFxcMGfOHNy6dUtlnwHgs88+e+UQ5rlz5xATE4MPP/wQixYtqnKdCxcuYNCgQSoz3o0YMQIymQwXLlxQrtO5c2eVx2J37doV9vb2SExMFNyPl5WVleGzzz7DiBEj0LNnT3h7eyMuLk7lOKqsrERcXBx8fHzQq1cvODs7Y/Lkybh06ZJynW3btmHYsGE4e/YsxowZAycnJwwfPlxt6PLzzz9X1uXh4YEVK1bg6dOnNW5vY2BINDH79u3DnTt3EB4ejjVr1uC3337D0qVLa11ORUUFFi5ciMmTJ2P79u0wMjLCokWLMGfOHHh6emLHjh2wsbHB0qVL1SYlCQsLw8iRIxEbG4u33noLwcHBOHPmDIDnFzUDAwNx+PBh+Pn5Yfv27ejTpw+Cg4PVfgEOHjyInj17IjY2Fu+9916V7Tx27BhmzpyJdu3aYdOmTQgJCcHVq1fxt7/9DU+ePIGnpydiYmIAPJ9964svvqiyHJlMBn9/fyQmJmLp0qVYv349UlNT1cLrxIkTCAwMRJcuXfDZZ5/ho48+wvHjxzFv3jzI5XJMnDhROX4eFhamrHvJkiU4cuQIAgICsHv3boSEhODWrVtYuHChxhd6582bh0GDBsHa2hpffPEFPD09kZKSgiVLlsDb2xvx8fEICQnBpUuXsHDhQsFyHj9+jPfeew/JyckIDg7Gtm3b0L59ewQGBuL48eN1ak9VLl26hP/5n/8BAKxbtw5r1qzBw4cPMXnyZGRkZKBHjx7Kn9N7772HL774QjnP9ct69uyJn3/+GXPnzoWOjo7a66Wlpfjzzz/RuXNnleVWVlYwMTHB3bt3AQAZGRno1KmT2vYdOnRQrlMduVyOOXPmYNeuXZg4cSJ27NiBESNGYMuWLfj000+V60VGRiI2NhZ/+9vfsGvXLqxevRr5+fmYP3++cvpiAHj06BFWrVqFadOmIS4uDnZ2dli6dKlyOO/kyZOIiIjABx98gISEBAQGBuLbb7/F6tWra9TexsLhpibGzMwMsbGxyl+YP/74A9u2bUNeXp7azFOvIpPJMGfOHEycOBHA84mPgoODMX36dPj5+QEATE1NMWHCBPz222+wtbVVbvvxxx9j1qxZAICBAwfi3r17iI2NxdChQ/HLL7/g/Pnz2Lx5M0aNGgUA8PDwQElJCSIjI+Hj4wNd3eeH1RtvvCH47lDRxsjISAwYMABRUVHK5S4uLhg1ahQSEhKwZMkSvP322wCe/8I7OztXWda5c+fw66+/Ij4+HgMHDgQAuLu7q1y0lsvliIyMhIeHByIjI5XLO3XqhBkzZiAxMRGenp7Kd6Ndu3ZF9+7dUV5ejqKiIoSGhir32c3NDU+fPsX69evx+PFjWFtbV/cjUdOhQwdYWVlBX19fuV9ff/01DA0NERAQoHznbGFhgbS0NMjlcohEIrVy9uzZg9zcXPz4449o3749AGDQoEGYMWMGNm7cCB8fH43bU5WoqCh07NgRcXFxyuN0wIABGDZsGKKjo7F161bl9ra2tq8sq7prFYWFhQAAExMTtddatWqlfNddWFiIjh07VrlOUVHRK+tQOHfuHH755Rds2rQJo0ePBgD0798fhoaG2Lp1K6ZNm4a33noLOTk5CA4OVjlrNDAwwMcff4wbN24o97ekpARr165VTp3bqVMnDB48GImJibC3t8fly5dhZ2eHDz74AGKxGG5ubjA2NkZBQUGN2ttYeCbRxPTs2VPlHZXij/eL71Bqqk+fPsr/K2YFe3GuWwsLCwDPA+RFij+ECkOHDkV6ejqKiopw8eJFiEQiDBo0CBUVFcqvIUOG4NGjR8ohBwDKP+5C7t69i0ePHqn9EevQoQP69OmDy5cv13hfk5OToaenBw8PD+UyY2NjDBo0SPn9nTt3kJWVhSFDhqi0XSKRwMTEBP/5z3+qLFtfXx8JCQkYNWoUsrOzcenSJRw+fBj//ve/AUDtLpu6kEgkKCkpgY+PD6KiopCcnIwBAwbgo48+qjIgAODy5cvo06ePMiAUxo4di0ePHuHOnTv11r7i4mKkpaVh5MiRKsepmZkZBg8eXKufWU1UNVz4IkWfvOpsTqjfXnb58mXo6upixIgRKssV1zMU+xYVFYXp06cjNzcXycnJOHLkiPKM7eVj4cWAVPwuK4Y2+/Xrh7t378LX1xcxMTFIS0vDmDFjmtzdVjyTaGKMjIxUvheLn+d4db8sVanq3dfL5VelTZs2Kt+3bt0acrkcT58+RX5+PuRyOVxcXKrcNicnRxkO1U18r7hA/HJ9imXp6enVtlWhoKAAFhYWan8QXnyHr6hv5cqVWLlyZZVtF3L+/HmsW7cOd+7cQatWreDo6Kjcv/r8XEGfPn0QFxeHvXv3Ys+ePYiLi0ObNm0wZ84cwT8eBQUFePPNN9WWK/pVKpWqzf2tqcLCQsjlcsGfmeKdf31RHMNVnQ08ffoUpqamyvWqW6c6BQUFsLS0VBv2UhxDin1LS0vDypUrkZaWBiMjI3Tt2hVvvPEGAPVj4cXfN8XvsmKdUaNGQSaT4dChQ4iNjVUOEy5atEjtjZo2MSSaoRfnDwb+751JfSkoKFD5I/D48WPo6OjA3NwcpqamMDY2xr59+6rctqpTfiGKM5nHjx+rvfbo0aNaDa9ZWloiLy8PlZWVKr/kimAAnr/bBZ5fX3Bzc1Mrw9zcvMqy//jjDwQGBmLo0KHYuXMn3nzzTYhEIhw8eBDnz59XWbc+fjYeHh7KIbxLly5h3759WLNmDXr37o1evXpV2e5Hjx6pLVcss7S0VIZnXdtnamoKkUgk+DNT/EzrS6tWrdC2bVvcv39fZfmTJ09QVFSknJqzc+fOKjcRKPzxxx9V9llVzM3NqzyGFG8eLC0t8fTpU/j7+8PBwQHfffcdunTpArFYjMTERPz444+13j8fHx/4+PigsLAQFy5cQHx8PBYvXgxXV9dqh+IaC4ebmhkTExO1C80pKSn1WsfZs2eV/5fJZDh16hR69+4NQ0NDuLm5obi4GHK5HD179lR+3bx5E5999lmV97kL6dy5M6ytrXHy5EmV5Q8ePMC1a9cEz1aq4u7ujoqKCuUFduD5qf+LQ0hdunRB69atkZmZqdL2tm3bIioqSvDM5bfffkNZWRkCAgLQoUMH5R9cRUAo3hlq8rNRvLtU2LBhAyZMmAC5XA4jIyMMHjxYeePCX3/9VWUZEokEV69exZ9//qmy/Pjx47C2tkbHjh2V78hfvEvq2bNn+PXXX1/ZnpcZGxvDyckJP/zwg0rgFBYW4uzZs3B1dX3l9pro378/zp49qzKU8+OPP0JHRwf9+vUD8PyaSEZGBm7fvq1c5/bt28jIyED//v1rVI+bmxsqKipw6tQpleWKoSRXV1fcuXMH+fn5mDZtGrp27arsr3PnzgGo3Rn/J598gsDAQADPw3fkyJGYN28eKioqqv0QYGPimUQzM3jwYPz8888IDw/HkCFDkJycXK+fCAaALVu2oLKyEu3atcM///lP3L17F3v27AHw/IKoRCLBvHnzMG/ePNjb2+PXX39FdHQ0PDw8YGVlVeN6xGIxFixYgJCQECxcuBBjx45FXl4eYmJiYG5urrzAXhPu7u4YMGAAQkND8eTJE7Rv3x779u1Dbm6u8nqMjo4OgoODERYWBh0dHQwePBhSqRSxsbHIzs5Gjx49qiy7R48e0NXVRUREBGbOnIny8nIcPXpUGaaKd+Oa/GzMzMzw+PFjJCYm4u2330a/fv2wZ88eLFu2DGPHjsWzZ8+wa9cuWFhYKP8gvszPzw/Hjx/HjBkz8NFHH8HCwgLHjh3DpUuXsG7dOojFYpibm6NPnz7Yv38/OnbsCHNzc+zbtw+lpaUqw4Ivt6equ5IWLlyIWbNmISAgAO+//z6ePXuGuLg4lJeXK//o1Sd/f39899138Pf3h5+fH+7du4dNmzZh0qRJymGeUaNGYceOHZg9e7byTrCoqCh069YNI0eOrFE9AwcORN++fREaGors7Gw4Ojri8uXLiI+Px/jx49G1a1cUFhbCxMQEO3bsgK6uLnR1dfHjjz/i66+/BlC7a4f9+vXDp59+ig0bNmDgwIGQSqWIiYlBp06d4OjoWMteajg8k2hmJkyYgNmzZ+PkyZMICAjA1atXER0dXa91hIeHY9++fZg3bx6ys7MRHx+vHJ4Ri8WIi4vD6NGjsXPnTsyaNUt5O+zmzZtrXZevry+io6Nx9+5dBAYGYv369ejTpw++/vrrWt8xFBMTg7FjxyI6OhqffPIJbG1tMWnSJJV1Jk6ciKioKKSmpmLOnDlYsWIF7OzssH///irH9YHnQ2hRUVHIzs7G3LlzlbfI7t+/HyKRCMnJyQA0+9n4+voqb1c9duwYBg0ahMjISNy6dQsfffQRFixYACMjI+zbt09wKMfa2hr//Oc/0aNHD6xZswbz58/Hw4cPERsbiwkTJijXW79+PZycnBAaGoqQkBD06NED06dPf2V7quLu7o49e/agtLQUCxYswD/+8Q+0bdsWX375Jbp16/bK/dWEvb09du/ejdLSUgQFBWHPnj2YMWOGymdv9PX1sWfPHvTo0QP/+Mc/sGrVKjg7OyMhIUF5t111RCIRdu7cicmTJ2Pv3r0ICAjAqVOnsGDBAqxbtw7A83f8sbGxkMvlmD9/PpYsWYK//voLBw4cQKtWrZTHQk1MnjwZoaGhOHfuHObMmYOwsDDlvjalT4iL5HyaFxERCeCZBBERCWJIEBGRIIYEEREJYkgQEZEghgQREQliSBARkSCGBBERCWJIEBGRIIYEEREJYkgQEZEghgQREQn6/z4176f7wd5mAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 400x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Seed random number generator\n",
    "np.random.seed(42)\n",
    "\n",
    "# Initialize the number of defaults: n_defaults\n",
    "n_defaults = np.empty(1000)\n",
    "\n",
    "# Compute the number of defaults\n",
    "for i in range(1000):\n",
    "    \"\"\"Compute the number of defaults per 100 loans\"\"\"\n",
    "    n_defaults[i] = perform_bernoulli_trials(100, 0.05)\n",
    "\n",
    "# Plot the histogram with default number of bins; \n",
    "fig = plt.figure(figsize=(4, 3))\n",
    "_ = plt.hist(n_defaults, density=True)\n",
    "_ = plt.xlabel('number of defaults out of 100 loans')\n",
    "_ = plt.ylabel('probability')\n",
    "\n",
    "# Show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1a59432d-fb3d-44d0-ae30-47116b88ad2e",
   "metadata": {},
   "source": [
    "## 🏛💰 Will the bank fail?\n",
    "Let's plot the number of defaults we got from the previous exercise, in our namespace as n_defaults, as a CDF. The`ecdf() function` we wrote is available. If interest rates are such that the bank will lose money if 10 or more of its loans are defaulted upon, what is the probability that the bank will lose money?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "73b96b99-3208-4ff5-84a1-7f1f410c2577",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 500x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Probability of losing money = 0.0249\n"
     ]
    }
   ],
   "source": [
    "# Compute ECDF: x, y\n",
    "x, y = ecdf(n_defaults)\n",
    "\n",
    "# Plot the CDF with labeled axes\n",
    "fig = plt.figure(figsize=(5, 4))\n",
    "_ = plt.plot(x, y, marker='.', linestyle='none')\n",
    "_ = plt.xlabel('number of defaults out of 100')\n",
    "_ = plt.ylabel('CDF')\n",
    "\n",
    "# Show the plot\n",
    "plt.show()\n",
    "\n",
    "# Compute the number of 100-loan simulations with 10 or more defaults: n_lose_money\n",
    "n_lose_money = np.sum(n_defaults >= 10)\n",
    "\n",
    "# Compute and print probability of losing money\n",
    "print('Probability of losing money =', n_lose_money / len(n_defaults))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "25db2485-a14d-4654-8691-057dbffac70a",
   "metadata": {
    "tags": []
   },
   "source": [
    "## 3. Probability distributions and stories: The Binomial distribution\n",
    "In the last chapter, we simulated a story about a person flipping a coin. We did this to get the probability for each possible outcome of the story. That set of probabilities is called **a probability mass function (PMF)**. \n",
    "\n",
    "* **Probability Mass Function**: the set of probabilities of **discrete** outcomes. \n",
    "\n",
    "### Discrete uniform distribution\n",
    "To understand how this works, consider a simpler story, *a person rolling a die once*. The outcomes are discrete because only certain values may be attained; you cannot roll a 3-point-7 with a die. **Each result has the same, or uniform probability, 1/6.** For this reason, the PMF associated with this story is called the ***Discrete Uniform PMF***. Now the PMF is a property of a discrete probability distribution. **A distribution is just a mathematical description of outcomes.** We can match a story to a mathematical description of probabilities, as we have just seen with the Discrete Uniform distribution."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fccd097b-2119-44d9-83d0-45f300606f73",
   "metadata": {},
   "source": [
    "## Binomial didstribution: the story\n",
    "* The number *r* of success in *n* Bernoulli trials with probability *p* of success, is Binomially distributed.\n",
    "* e.g. The number *r* of heads in 4 coin flips with probability 0.5 of heads, is Binomially distributed.\n",
    "\n",
    "## Sampling from the Binomial distribution\n",
    "We call the function `np.random.binomial` with two arguments, the number of Bernoulli trials (coin flips) and the probability of success (heads). We get 2 heads out of four. We want repeat the four-flip experiment over and over again. Again, we can specify the `size` keyword argument, which tells the function how many random numbers to sample out of the Binomial distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "ac67b581-1e48-45eb-bc55-c7f22a6b2c77",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2\n",
      "[2 2 2 2 2 3 3 2 2 0]\n"
     ]
    }
   ],
   "source": [
    "print(f'{np.random.binomial(4, 0.5)}\\n{np.random.binomial(4, 0.5, size=10)}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7783f842-aef2-43cc-a7ac-0404368a9be3",
   "metadata": {},
   "source": [
    "## Sampling out of the Binomial distribution\n",
    "Compute the probability mass function for the number of defaults we would expect for 100 loans as in the last section, but instead of simulating all of the Bernoulli trials, perform the sampling using `np.random.binomial()`. This is identical to the calculation you did in the last set of exercises using your custom-written `perform_bernoulli_trials() function`, but far more computationally efficient. Given this extra efficiency, **we will take 10,000 samples** instead of 1000. After taking the samples, plot the CDF as last time. This CDF that you are plotting is that of the Binomial distribution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "a47f86dc-26a0-4429-9392-32ca0b13111e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 500x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Seed random number generator\n",
    "np.random.seed(42)\n",
    "\n",
    "# Take 10,000 samples out of the binomial distribution: n_defaults\n",
    "n_defaults = np.random.binomial(n=100, p=0.05, size=10000)\n",
    "\n",
    "# Compute CDF: x, y\n",
    "fig = plt.figure(figsize=(5, 4))\n",
    "x,y = ecdf(n_defaults)\n",
    "\n",
    "# Plot the CDF with axis labels\n",
    "plt.plot(x, y, marker='.', linestyle='none')\n",
    "plt.xlabel('Number of defaults out of 100 loans')\n",
    "plt.ylabel('CDF')\n",
    "\n",
    "# Show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f2b807a1-c8d5-4907-9542-bfd8e1d60da8",
   "metadata": {},
   "source": [
    "## Plotting the Binomial PMF\n",
    "Plotting a nice looking PMF requires a bit of `matplotlib` trickery that we will not go into here. Instead, we will plot the PMF of the Binomial distribution *as a histogram*. The trick is setting up the edges of the bins to pass to `plt.hist()` via the `bins` keyword argument. We want the bins centered on the integers. So, the edges of the bins should be -0.5, 0.5, 1.5, 2.5, ... up to `max(n_defaults)` + 1.5. We can generate an array like this using `np.arange()` and then subtracting 0.5 from the array."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "588d3ac3-115f-4bf1-b9b8-da04f1a81203",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 500x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Compute bin edges: edges\n",
    "edges = np.arange(0, max(n_defaults) + 1.5) - 0.5\n",
    "\n",
    "# Generate histogram\n",
    "fig = plt.figure(figsize=(5, 4))\n",
    "plt.hist(n_defaults,density=True,bins=edges)\n",
    "\n",
    "# Label axes\n",
    "plt.xlabel('Number of defaults out of 100 loans')\n",
    "plt.ylabel('CDF')\n",
    "\n",
    "# Show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "90bea3d4-d5e6-42a4-aa2e-cff94fec6de5",
   "metadata": {},
   "source": [
    "## 4. Poisson processes and the Poisson distribution\n",
    "\n",
    "* Poisson process: The timing of the next event is completely independent of when the previous event happened. \n",
    "* Poisson distribution: The number of arrivals of a Poisson process in a given amount of time is Poisson distributed.\n",
    "* The Poisson distribution has \"one\" parameter, **the average number of arrivals (successes) in a given length of time.**\n",
    "\n",
    "## Relationship between Binomial and Poisson distributions\n",
    "**The Poisson distribution is a limit of the Binomial distribution for rare events.** This makes sense if you think about the stories. Say we do a Bernoulli trial every minute for an hour, each with a success probability of 0.1. We would do 60 trials, and the number of successes is Binomially distributed, and we would expect to get about 6 successes. This is just like the Poisson story we discussed in the video, where we get on average 6 hits on a website per hour. \n",
    "* **Poisson distribution** with arrival rate *np* $\\approx$ **Binomial distribution** for *n* Bernoulli trials with *p* probability of success (with *n* large and *p*small).\n",
    "* When we have rare events (low p, high n), the Binomial distribution is Poisson. This has a single parameter, the mean number of successes per time interval.\n",
    "\n",
    "Importantly, the Poisson distribution is often simpler to work with because it has only one parameter instead of two for the Binomial distribution.  \n",
    "\n",
    "Let's explore these two distributions computationally. We will \n",
    "* Compute the mean and standard deviation of samples from a Poisson distribution **with an arrival rate of 10.** \n",
    "* Then, compute the mean and standard deviation of samples from a Binomial distribution with parameters *n* and such *p* that *np=10*. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "0c5f2822-84e4-4ea6-9e9f-753e4a84e6f4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Poisson:      9.9732 3.1276319732347027\n",
      "n = 20 Binom: 9.9999 2.243858282066851\n",
      "n = 100 Binom: 9.9957 2.998346462635698\n",
      "n = 1000 Binom: 9.997 3.124162447761\n"
     ]
    }
   ],
   "source": [
    "# Draw 10,000 samples out of Poisson distribution: samples_poisson\n",
    "samples_poisson = np.random.poisson(10,size=10000)\n",
    "\n",
    "# Print the mean and standard deviation\n",
    "print('Poisson:     ', np.mean(samples_poisson),\n",
    "                       np.std(samples_poisson))\n",
    "\n",
    "# Specify values of n and p to consider for Binomial: n, p\n",
    "n = [20, 100, 1000]\n",
    "p = [0.5, 0.1, 0.01]\n",
    "\n",
    "# Draw 10,000 samples for each n,p pair: samples_binomial\n",
    "for i in range(3):\n",
    "    samples_binomial = np.random.binomial(n[i],p[i],size=10000)\n",
    "\n",
    "    # Print results\n",
    "    print('n =', n[i], 'Binom:', np.mean(samples_binomial),\n",
    "                                 np.std(samples_binomial))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c2a5a913-d58a-48cd-9a10-84987724019f",
   "metadata": {},
   "source": [
    "The means are all about the same, which can be shown to be true by doing some pen-and-paper work. The standard deviation of the Binomial distribution gets closer and **closer to that of the Poisson distribution as the probability *p* gets *lower and lower*.**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "86dd0f47-358a-40d0-87f8-81f989ba4739",
   "metadata": {},
   "source": [
    "---\n",
    "## Chapter 4. Thinking Probabilistically: Continuous Variables<a class=\"anchor\" id=\"fourth-bullet\"></a>\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ce30a707-1530-40df-99c5-1fcd1262be0f",
   "metadata": {},
   "source": [
    "## 1. Probability density functions"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1d8ae5a6-b1a6-4532-b4a9-a15a1886c91f",
   "metadata": {},
   "source": [
    "So far, we have talked about probabilities of discrete quantities, such as die rolls and number of bus arrivals, but what about continuous quantities? Continuous variables can take on any value, not just discrete ones. To understand what the Normal distribution is, let's consider its probability density function, or PDF. This is **the continuous analog to the probability mass function, the PMF.** \n",
    "* **Probability Density Function: Mathematical description of the relative likelihood of observing a value of a continuous variable.**\n",
    "* The probability is given by **the area under the PDF**.\n",
    "* On the other hand, the ***CDF*** shows explicitly the probability that the measured quantity will be less than the value on the x-axis.\n",
    "\n",
    "\n",
    "For example, in the below **PDF** plot, we can see that $x$ is **more likely** to be less than 10 than to be greater than 10. This is because there is **more area** to the left of 10 than to the right. On the right is the **CDF** corresponding to the PDF. Using the CDF, the probability that $x$ is greater than 10 is 0.25."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "a666f713-8ca3-407b-b7e7-f60182a0f302",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Load the first image\n",
    "img1 = mpimg.imread('images/PDF.PNG')\n",
    "\n",
    "# Load the second image\n",
    "img2 = mpimg.imread('images/CDF.PNG')\n",
    "\n",
    "# Display the two images side by side\n",
    "fig, ax = plt.subplots(1, 2, figsize=(10, 10))\n",
    "ax[0].imshow(img1)\n",
    "ax[1].imshow(img2)\n",
    "\n",
    "# Remove the axis labels\n",
    "ax[0].axis('off')\n",
    "ax[1].axis('off')\n",
    "\n",
    "# Show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f350ef9b-4dd1-4228-ab64-fbdeb981c6b2",
   "metadata": {},
   "source": [
    "## 2. Normal distribution\n",
    "## The Normal PDF\n",
    "In this exercise, we will explore the **Normal PDF** and also learn a way to **plot a PDF of a known distribution using *hacker statistics*.** Specifically, we will plot a Normal PDF for various values of the variance. We can see how the different standard deviations result in PDFs of different widths. The peaks are all centered at the mean of 20."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "ffc25d92-f9b0-491b-8e48-13f53ecc89cf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 500x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Draw 100000 samples from Normal distribution with stds of interest: samples_std1, samples_std3, samples_std10\n",
    "samples_std1 = np.random.normal(20, 1, size=100000)\n",
    "samples_std3 = np.random.normal(20, 3, size=100000)\n",
    "samples_std10 = np.random.normal(20, 10, size=100000)\n",
    "\n",
    "# Make histograms\n",
    "fig = plt.figure(figsize=(5, 4))\n",
    "plt.hist(samples_std1, density=True, histtype='step', bins=100)\n",
    "plt.hist(samples_std3, density=True, histtype='step', bins=100)\n",
    "plt.hist(samples_std10, density=True, histtype='step', bins=100)\n",
    "\n",
    "# Make a legend, set limits and show plot\n",
    "_ = plt.legend(('std = 1', 'std = 3', 'std = 10'))\n",
    "plt.ylim(-0.01, 0.42)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f91c2d3f-e4ec-4aca-bd89-003825d278c3",
   "metadata": {},
   "source": [
    "### The Normal CDF\n",
    "Now that we have a feel for how the Normal PDF looks, let's consider its CDF. Using the samples we generated in the last exercise (as `samples_std1`, `samples_std3`, and `samples_std10`), generate and plot the CDFs. The plot presents that the CDFs all pass through the mean at the 50th percentile; the mean and median of a Normal distribution are equal. The width of the CDF varies with the standard deviation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "2a07ee00-add9-4f62-bb23-95c3bdf68965",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 500x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Generate CDFs\n",
    "x_std1, y_std1 = ecdf(samples_std1)\n",
    "x_std3, y_std3 = ecdf(samples_std3)\n",
    "x_std10, y_std10 = ecdf(samples_std10)\n",
    "\n",
    "# Plot CDFs\n",
    "fig = plt.figure(figsize=(5, 4))\n",
    "plt.plot(x_std1, y_std1, marker='.', linestyle='none')\n",
    "plt.plot(x_std3, y_std3, marker='.', linestyle='none')\n",
    "plt.plot(x_std10, y_std10, marker='.', linestyle='none')\n",
    "\n",
    "# Make a legend and show the plot\n",
    "_ = plt.legend(('std = 1', 'std = 3', 'std = 10'), loc='lower right')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d85dbe33-d7dc-4645-be04-81a537ec0189",
   "metadata": {},
   "source": [
    "## 3. The Exponential distribution  \n",
    "Just as there are many named discrete distribution, there are many named continuous distributions as well. For example, let's take a another trip to Poissonville and stand at a bus stop. We know that **the \"number\" of buses** that will arrive per hour are **Poisson distributed**. But **the amount of \"*time*\" between arrivals of buses is *Exponentially* distributed.**\n",
    "\n",
    "* The **Exponential distribution** describes the **waiting times between rare events**.\n",
    "\n",
    "The Exponential distribution has this story: the waiting time between arrivals of a Poisson process are exponentially distributed. It has a **single parameter**, the*mean* waiting time. This distribution is not peaked, as we can see from its PDF. The Exponential and Normal are just two of many examples of continuous distributions. **Importantly, in many cases you can just simulate your story to get the CDF.** Remember, you have the power of a computer. If you can simulate a story, you can get its distribution!"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "87e77a78-b1bf-4baf-98ac-bfcb3902af3e",
   "metadata": {},
   "source": [
    "## If you have a story, you can simulate it!: ⚾Distribution of no-hitters and cycles\n",
    "Sometimes, the story describing our probability distribution does not have a named distribution to go along with it. In these cases, fear not! You can always simulate it. We'll do that in this exercise.  \n",
    "\n",
    "In earlier exercises, we looked at the *rare event of no-hitters in Major League Baseball*. **Hitting the cycle, when a batter gets all four kinds of hits in a single game, is another rare baseball event.** Like no-hitters, **this can be modeled as a \"Poisson process\"**, so the \"time between hits of the cycle\" are also \"Exponentially distributed\".  \n",
    "\n",
    "**How long must we wait to see a no-hitter and then a batter hit the cycle?** The idea is that we have to wait some time for the no-hitter, and then after the no-hitter, we have to wait for hitting the cycle. Stated another way, what is the total waiting time for the arrival of two different Poisson processes in succession? The total waiting time is the time waited for the no-hitter, plus the time waited for the hitting the cycle.\n",
    "\n",
    "* First, we write a function to sample out of the distribution described by this story.\n",
    "* Next, we use our sampling function to *compute the waiting time to observe a no-hitter and hitting of the cycle.* \n",
    "* The mean waiting time for a no-hitter is 764 games, and the mean waiting time for hitting the cycle is 715 games."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "id": "60f83f5e-d507-4062-9ca9-52bdcdc45b14",
   "metadata": {},
   "outputs": [],
   "source": [
    "def successive_poisson(tau1, tau2, size=1):\n",
    "    \"\"\"Compute time for arrival of 2 successive Poisson processes.\"\"\"\n",
    "    # Draw samples out of first exponential distribution: t1\n",
    "    t1 = np.random.exponential(tau1, size=size)\n",
    "\n",
    "    # Draw samples out of second exponential distribution: t2\n",
    "    t2 = np.random.exponential(tau2, size=size)\n",
    "\n",
    "    return t1 + t2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "id": "2f82abd8-187b-47ef-a5ea-153650fc6616",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1000x400 with 2 Axes>"
      ]
     },
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   ],
   "source": [
    "# 1. Generate PDF: Draw samples of waiting times: waiting_times\n",
    "# 100,000 out of the distribution of waiting times for observing a no-hitter and a hitting of the cycle.\n",
    "waiting_times = successive_poisson(764, 715, 100000)\n",
    "\n",
    "# 2. Generate CDF\n",
    "x, y = ecdf(waiting_times)\n",
    "\n",
    "fig, axs = plt.subplots(1, 2, figsize=(10, 4))\n",
    "\n",
    "# Plot the data on each subplot\n",
    "axs[0].hist(waiting_times, density=True, bins=100, histtype='step')\n",
    "axs[0].set_title('PDF')\n",
    "axs[0].set_xlabel('The total waiting time (games)')\n",
    "axs[0].set_ylabel('PDF')\n",
    "\n",
    "axs[1].plot(x, y, marker='.', linestyle='none')\n",
    "axs[1].set_title('CDF')\n",
    "axs[1].set_xlabel('The total waiting time (games)')\n",
    "axs[1].set_ylabel('CDF')\n",
    "\n",
    "# Display the plot\n",
    "plt.show()"
   ]
  },
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   "source": [
    "The CDF says that we need to wait at least 2000 games to see a no-hitter and then a batter hit the cycle with 80% of the probability."
   ]
  },
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   "source": [
    "---\n",
    "## Chapter 5. Final Thoughts<a class=\"anchor\" id=\"fifth-bullet\"></a>\n",
    "---\n",
    "We can now use Python to construct instructive plots and informative summary statistics to explore data. We have also built the intellectual and computational infrastructure to think probabilistically. \n",
    "\n",
    "### Now we can\n",
    "* Construct instructive plots\n",
    "* Compute informative summary statistics\n",
    "* Use hacker statistics\n",
    "* Think probabilistically  \n",
    "\n",
    "The knowledge we learned in this course really shines when we directly apply it to statistical inference problems. In the Part 2,\n",
    "\n",
    "### In the sequel, we will\n",
    "* Estimate parameter values\n",
    "* Perform linear regressions\n",
    "* Compute confidence interval\n",
    "* Perform hypothesis testing\n",
    "\n",
    "we will work with real data sets in Python to infer parameter values by a variety of methods including linear regression. We will use hacker statistics to compute confidence intervals to help you couch the conclusions you draw from your data in the appropriate probabilistic language. You will perform hypothesis tests, such as A/B tests, to help you discern differences between data sets. It's a great opportunity to use and expand upon what we learned here in this first part in statistical thinking. I encourage you to take that opportunity! See you there!"
   ]
  }
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