Descriptive-statistics.ipynb

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    "Copyright 2020 Dale Bowman, Andrew M. Olney and made available under [CC BY-SA](https://creativecommons.org/licenses/by-sa/4.0) for text and [Apache-2.0](http://www.apache.org/licenses/LICENSE-2.0) for code."
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    "# Descriptive statistics\n",
    "\n",
    "One of the most important components of a data science project is to examine your data using descriptive statistics. \n",
    "We call this phase of the project *Exploratory Data Analysis (EDA)*. \n",
    "\n",
    "There are two types of EDA, *graphical* EDA and *numerical* EDA. \n",
    "The graphical EDA involves plotting the features (variables) in the data to look for symmetry, skewness and multiple modality. \n",
    "**We will discuss numerical EDA in this session.**\n",
    "\n",
    "## What you will learn\n",
    "\n",
    "In the sections that follow you will learn about descriptive statistics, in particular numerical EDA, and how they can help us learn about our data and what types of analyses may be appropriate.  We will study the following:\n",
    "\n",
    "- level of measure\n",
    "- measures of central tendency\n",
    "- measures of dispersion\n",
    "- sampling\n",
    "\n",
    "## When to use numerical EDA\n",
    "\n",
    "Descriptive statistics, both numerical and graphical, are useful when you begin a data science project and you want to explore the data.  Often insights will be gained that can be useful in further analyses.\n",
    "\n",
    "## Level of Measure\n",
    "\n",
    "The type of features (variables) you have in the data determines which graphical and numerical techniques are appropriate. \n",
    "One characteristic of the features is their level of measure. \n",
    "There are four levels: nominal, ordinal, interval, and ratio. \n",
    "The characteristics of these levels are detailed in the table below.\n",
    "\n",
    "| Level    | Description                              | Example                      |\n",
    "|:---------|:-----------------------------------------|:-----------------------------|\n",
    "| nominal  | categorical data that can be *named*     | eye color                    |\n",
    "| ordinal  | categorical data with a natural ordering |  grades: A, B, C, D, F       |\n",
    "| interval | numerical data without a true zero       | Fahrenheit temperature scale |\n",
    "| ratio    | numerical data with a true zero          | Kelvin temperature scale     |\n",
    "\n",
    "\n",
    "## Descriptive Statistics, Numerical EDA\n",
    "\n",
    "Descriptive measures fall into one of two categories:\n",
    "\n",
    "- Measuring central tendencies of a variable\n",
    "- Measuring the spread of a variable\n",
    "\n",
    "To explore these, we'll use the `iris` dataset:\n",
    "\n",
    "| Variable    | Type    | Description           |\n",
    "|:-------------|:---------|:-----------------------|\n",
    "| SepalLength | Ratio   | the sepal length (cm) |\n",
    "| SepalWidth  | Ratio   | the sepal width (cm)  |\n",
    "| PetalLength | Ratio   | the petal length (cm) |\n",
    "| PetalWidth  | Ratio   | the petal width (cm)  |\n",
    "| Species     | Nominal | the flower species    |\n",
    "\n",
    "<div style=\"text-align:center;font-size: smaller\">\n",
    " <b>Source:</b> This dataset was taken from the <a href=\"https://archive.ics.uci.edu/ml/datasets/iris\">UCI Machine Learning Repository library\n",
    "    </a></div>\n",
    "<br>\n",
    "\n",
    "\n",
    "We can calculate central tendency and spread using `pandas dataframes`.\n",
    "Let's start by importing `pandas`:\n",
    "\n",
    "- `import pandas as pd`"
   ]
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   "source": [
    "import pandas as pd\n",
    "\n",
    "#<xml xmlns=\"https://developers.google.com/blockly/xml\"><variables><variable id=\"zq]t,,UR`GWj6?Hub9%a\">pd</variable></variables><block type=\"importAs_Python\" id=\"o[3wY[w:R*b$p^Ow/ZZw\" x=\"125\" y=\"352\"><field name=\"libraryName\">pandas</field><field name=\"libraryAlias\" id=\"zq]t,,UR`GWj6?Hub9%a\">pd</field></block></xml>"
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    "Now let's load a dataset into a dataframe:\n",
    "\n",
    "- Create variable `dataframe`\n",
    "- Set it to `with pd do read_csv using \"datasets/iris.csv\"`"
   ]
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    "dataframe = pd.read_csv('datasets/iris.csv')\n",
    "\n",
    "#<xml xmlns=\"https://developers.google.com/blockly/xml\"><variables><variable id=\"[V~uW+0L/4GW;45ulv+l\">dataframe</variable><variable id=\"zq]t,,UR`GWj6?Hub9%a\">pd</variable></variables><block type=\"variables_set\" id=\"i*U..F)p9r]#n*e(./*x\" x=\"-9\" y=\"195\"><field name=\"VAR\" id=\"[V~uW+0L/4GW;45ulv+l\">dataframe</field><value name=\"VALUE\"><block type=\"varDoMethod_Python\" id=\":~$/ssC/m-m@jj8K*O4W\"><field name=\"VAR\" id=\"zq]t,,UR`GWj6?Hub9%a\">pd</field><field name=\"MEMBER\">read_csv</field><data>pd:read_csv</data><value name=\"INPUT\"><block type=\"text\" id=\"~Mspn1jJRE!J8ISd3!#V\"><field name=\"TEXT\">datasets/iris.csv</field></block></value></block></value></block></xml>"
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    "Now we're ready to calculate the measures of central tendency in the next section."
   ]
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    "### Measures of Central Tendency\n",
    "\n",
    "The measures of central tendency most commonly used to describe data are **mean, median and mode**. \n",
    "\n",
    "\n",
    "#### Mean\n",
    "The **mean** is the numerical average of the variables. \n",
    "Let Let $X_1, X_2, \\ldots, X_n$ represent the data;\n",
    "then the mean is found as $\\bar{X} = \\frac{1}{n} \\sum_{i=1}^n X_i.$\n",
    "\n",
    "A dataframe will do this calculation for you:\n",
    "\n",
    "- `with dataframe do mean using`"
   ]
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    {
     "data": {
      "text/plain": [
       "SepalLength    5.843333\n",
       "SepalWidth     3.054000\n",
       "PetalLength    3.758667\n",
       "PetalWidth     1.198667\n",
       "dtype: float64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataframe.mean()\n",
    "\n",
    "#<xml xmlns=\"https://developers.google.com/blockly/xml\"><variables><variable id=\"[V~uW+0L/4GW;45ulv+l\">dataframe</variable></variables><block type=\"varDoMethod_Python\" id=\"]I:]}wMu=(Gnu*RD$$~9\" x=\"8\" y=\"188\"><field name=\"VAR\" id=\"[V~uW+0L/4GW;45ulv+l\">dataframe</field><field name=\"MEMBER\">mean</field><data>dataframe:mean</data></block></xml>"
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    "This gave us the mean of each column, i.e. the mean of each variable in the dataframe\n",
    "\n",
    "#### Median\n",
    "The **median** is the number in the middle of the data. \n",
    "By definition, one half of the data points are below the median and one half are above. \n",
    "\n",
    "We can get this from a dataframe to:\n",
    "\n",
    "- `with dataframe do median using`"
   ]
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      "text/plain": [
       "SepalLength    5.80\n",
       "SepalWidth     3.00\n",
       "PetalLength    4.35\n",
       "PetalWidth     1.30\n",
       "dtype: float64"
      ]
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    "dataframe.median()\n",
    "\n",
    "#<xml xmlns=\"https://developers.google.com/blockly/xml\"><variables><variable id=\"[V~uW+0L/4GW;45ulv+l\">dataframe</variable></variables><block type=\"varDoMethod_Python\" id=\"]I:]}wMu=(Gnu*RD$$~9\" x=\"8\" y=\"188\"><field name=\"VAR\" id=\"[V~uW+0L/4GW;45ulv+l\">dataframe</field><field name=\"MEMBER\">median</field><data>dataframe:median</data></block></xml>"
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    "Just like before we have a median for each variable.\n",
    "Notice that the mean and the median are almost the same for the first two variables but very different for the third variable.\n",
    "What do you think that means?\n",
    "\n",
    "#### Mode\n",
    "\n",
    "The **mode** is the value in the data that shows up the most:\n",
    "\n",
    "- `with dataframe do mode using`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
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    {
     "data": {
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       "<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>SepalLength</th>\n",
       "      <th>SepalWidth</th>\n",
       "      <th>PetalLength</th>\n",
       "      <th>PetalWidth</th>\n",
       "      <th>Species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   SepalLength  SepalWidth  PetalLength  PetalWidth     Species\n",
       "0          5.0         3.0          1.5         0.2      setosa\n",
       "1          NaN         NaN          NaN         NaN  versicolor\n",
       "2          NaN         NaN          NaN         NaN   virginica"
      ]
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   "source": [
    "dataframe.mode()\n",
    "\n",
    "#<xml xmlns=\"https://developers.google.com/blockly/xml\"><variables><variable id=\"[V~uW+0L/4GW;45ulv+l\">dataframe</variable></variables><block type=\"varDoMethod_Python\" id=\"]I:]}wMu=(Gnu*RD$$~9\" x=\"8\" y=\"188\"><field name=\"VAR\" id=\"[V~uW+0L/4GW;45ulv+l\">dataframe</field><field name=\"MEMBER\">mode</field><data>dataframe:mode</data></block></xml>"
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    "The output here is a little more difficult to understand.\n",
    "The first row gives us the mode for `SepalLength`, `SepalWidth`, `PetalLength`, and `PetalWidth`.\n",
    "However `Species` has three modes: each of the species occurs **exactly** the same number of times in the data.\n",
    "Because of that, `pandas` displays three rows here, with `NaN` (Not a Number) everywhere else on the second two rows.\n",
    "<!-- To get a more clear display, do the following:\n",
    "\n",
    "- Create a variable `species`\n",
    "- In menu LISTS, get a `dictVariable` block\n",
    "- Put `\"Species\"` inside it and change `dictVariable` to `dataframe`\n",
    "\n",
    "TODO: intellisense not working on series variables -->"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<!-- species = dataframe['Species']\n",
    "-->"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<!-- Now do this:\n",
    "\n",
    "- `with species do mode using` -->"
   ]
  },
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   "metadata": {},
   "source": [
    "#### Summary\n",
    "\n",
    "The type of variable determines the measures of central tendency available.\n",
    "\n",
    "For categorical data, the mode is the only measure of central tendency that can be computed unless the data are ordinal, in which case you can use either the mode or the median.\n",
    "\n",
    "In the table below, X indicates where a variable type and a measure of central tendency can be used together.\n",
    "\n",
    "|          | mode | median | mean |\n",
    "|----------|------|--------|------|\n",
    "| nominal  | X    |        |      |\n",
    "| ordinal  | X    | X      |      |\n",
    "| interval | X    | X      | X    |\n",
    "| ratio    | X    | X      | X    |"
   ]
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    "### Example Categorical Data \n",
    "\n",
    "The grades in a large statistics course occurred with the following frequency.\n",
    "\n",
    "<!-- | Grade     | A | B  | C  | D  | F  |\n",
    "|:-----------|---|----|----|----|----|\n",
    "| Frequency | 5 | 15 | 25 | 10 | 45 | -->\n",
    "\n",
    "<!-- AO: seems more natural: -->\n",
    "\n",
    "| Grade     | F | D  | C  | B  | A  |\n",
    "|:-----------|---|----|----|----|----|\n",
    "| Frequency | 45 | 10 | 25 | 15 | 5 |\n",
    "\n",
    "The mode of this data is the grade with the highest frequency, in this case F. \n",
    "Since this data is ordinal, we can also compute the median. \n",
    "There are a total of 100 grades, so the median will be the grade with 50 grades above and 50 below. \n",
    "This puts the median grade at **D**.\n",
    "\n",
    "For numerical data, the mode, median and mean can all be used to measure central tendency. \n",
    "Sometimes one measure will be more useful than another. \n",
    "For example, when outliers exist in the data, the mean can be skewed towards the outliers. \n",
    "Think of measuring incomes where one of the incomes is that of a professional basketball player. \n",
    "The extremely higher income of the player is much different than most of the other incomes. \n",
    "It is called an *outlier* and will affect the mean more than the median. \n",
    "As a simple example consider the following incomes.\n",
    "\n",
    "$30,000 ~~ 40,000 ~~ 50,000~~60,000~~4,000,000$\n",
    "\n",
    "The mean of these incomes is \n",
    "\n",
    "$\\frac{30000+40000+50000+60000+4000000}{5} = \\$836,000.$\n",
    "\n",
    "Notice that \\\\$4 million is more than 10 times greater than the next highest value, \\\\$60 thousand.\n",
    "As a result, the mean is pulled between \\\\$60 thousand and \\\\$4 million\n",
    "\n",
    "In contrast, the median of the incomes is the number with 2 incomes below and 2\n",
    "incomes above, $50,000, which is a much more reasonable estimate of the\n",
    "central tendency of the majority of these incomes.\n",
    "\n",
    "Let's take a closer look at this with dataframes:\n",
    "\n",
    "- Create a variable `salary`\n",
    "- Set it to `with pd create Dataframe using` a **list containing a list containing**:\n",
    "    - 30000\n",
    "    - 40000\n",
    "    - 50000\n",
    "    - 60000\n",
    "    - 4000000\n",
    "    \n",
    "**Note it is `create` and not `do`** because we are creating a dataframe using the values in the list.\n",
    "\n",
    "This is what the lists look like:"
   ]
  },
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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "salary = pd.DataFrame([30000, 40000, 50000, 60000, 4000000])\n",
    "\n",
    "#<xml xmlns=\"https://developers.google.com/blockly/xml\"><variables><variable id=\":CSTVHy4liG@-G^A}4h7\">salary</variable><variable id=\"zq]t,,UR`GWj6?Hub9%a\">pd</variable></variables><block type=\"variables_set\" id=\"^,Y2E?u8,{P0eK?4*v(^\" x=\"109\" y=\"412\"><field name=\"VAR\" id=\":CSTVHy4liG@-G^A}4h7\">salary</field><value name=\"VALUE\"><block type=\"varCreateObject_Python\" id=\".`?GJi9%01TcebUauJK?\"><field name=\"VAR\" id=\"zq]t,,UR`GWj6?Hub9%a\">pd</field><field name=\"MEMBER\">DataFrame</field><data>pd:DataFrame</data><value name=\"INPUT\"><block type=\"lists_create_with\" id=\":X:+~0D).,#BVd$qbNJy\"><mutation items=\"1\"></mutation><value name=\"ADD0\"><block type=\"lists_create_with\" id=\"Ec(e*=i[/o*JY1wPJ3b)\"><mutation items=\"5\"></mutation><value name=\"ADD0\"><block type=\"math_number\" id=\"rcV5tFvv,4uV3n%R]~7%\"><field name=\"NUM\">30000</field></block></value><value name=\"ADD1\"><block type=\"math_number\" id=\"sb#uTkds?[6oppKnWe6a\"><field name=\"NUM\">40000</field></block></value><value name=\"ADD2\"><block type=\"math_number\" id=\"]5VLtY%^_Vy#5w;#T_V0\"><field name=\"NUM\">50000</field></block></value><value name=\"ADD3\"><block type=\"math_number\" id=\"wPfzb`0n;_XZ~_;v|w{H\"><field name=\"NUM\">60000</field></block></value><value name=\"ADD4\"><block type=\"math_number\" id=\"WvJsD9l8`Y]rNR~fMT/N\"><field name=\"NUM\">4000000</field></block></value></block></value></block></value></block></value></block></xml>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now try the following:\n",
    "\n",
    "- In TEXT get `print`\n",
    "- `print with salary do median using`\n",
    "- `print with salary do mean using`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    50000.0\n",
      "dtype: float64\n",
      "0    836000.0\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print(salary.median())\n",
    "print(salary.mean())\n",
    "\n",
    "#<xml xmlns=\"https://developers.google.com/blockly/xml\"><variables><variable id=\":CSTVHy4liG@-G^A}4h7\">salary</variable></variables><block type=\"text_print\" id=\"`jn6J~kaejV!Cx!s{zP-\" x=\"111\" y=\"117\"><value name=\"TEXT\"><shadow type=\"text\" id=\"X+4}*qR3]Q;+8cTr}T0?\"><field name=\"TEXT\">abc</field></shadow><block type=\"varDoMethod_Python\" id=\"Pl:^oR,*DCGup|yt@jhP\"><field name=\"VAR\" id=\":CSTVHy4liG@-G^A}4h7\">salary</field><field name=\"MEMBER\">median</field><data>salary:median</data></block></value><next><block type=\"text_print\" id=\",V)HMF*rq4(p2J4=%*g^\"><value name=\"TEXT\"><shadow type=\"text\"><field name=\"TEXT\">abc</field></shadow><block type=\"varDoMethod_Python\" id=\"{^HKlDM0I%-#.W.[A$z.\"><field name=\"VAR\" id=\":CSTVHy4liG@-G^A}4h7\">salary</field><field name=\"MEMBER\">mean</field><data>salary:mean</data></block></value></block></next></block></xml>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see, this matches the example above.\n",
    "The mean is much much bigger than the median.\n",
    "\n",
    "Now go back up to your list and take out the $4 million block (remember to take the blank spot out of your list as well) and run the mean/median again.\n",
    "\n",
    "When you do this, you should find that the mean and the median are **exactly** the same.\n",
    "What does this mean?\n",
    "When they are the same, the data is **symmetric** (like the plot below)."
   ]
  },
  {
   "attachments": {
    "spread2.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Measures of Dispersion (spread)\n",
    "\n",
    "Even when two different variables have similar means (or medians, or modes) they can still be quite different depending on how the data are spread out around the center. \n",
    "In Figure 1 below both distributions have the same mean (0) but different spreads. \n",
    "The red curve has most of its points close to the center while the blue curve has points spread further from the mean.\n",
    "\n",
    "\n",
    "![spread2.png](attachment:spread2.png)\n",
    "\n",
    "**Figure 1:** Two distributions with the same center but different\n",
    "spread\n",
    "\n",
    "One measure of dispersion that can be used with ordered categorical data (ordinal level) or numerical data (interval/ratio level) is the **five number summary**.\n",
    "The five number summary is useful for comparing the center and spread of multiple variables. \n",
    "You use the numbers in the five number summary to construct a box and whiskers plot. \n",
    "The five numbers are: \n",
    "\n",
    "- minimum\n",
    "- first quartile\n",
    "- median\n",
    "- third quartile\n",
    "- maximum\n",
    "\n",
    "The first quartile is the median of the values below the median and the third quartile is the median of the\n",
    "values above the median.\n",
    "\n",
    "To use a football analogy, quartiles are like the 4 quarters in a game, and the median is like halftime.\n",
    "\n",
    "We can get the five number summary easily from `pandas`:\n",
    "\n",
    "- `with dataframe do describe using`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SepalLength</th>\n",
       "      <th>SepalWidth</th>\n",
       "      <th>PetalLength</th>\n",
       "      <th>PetalWidth</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5.843333</td>\n",
       "      <td>3.054000</td>\n",
       "      <td>3.758667</td>\n",
       "      <td>1.198667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.828066</td>\n",
       "      <td>0.433594</td>\n",
       "      <td>1.764420</td>\n",
       "      <td>0.763161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>4.300000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>5.100000</td>\n",
       "      <td>2.800000</td>\n",
       "      <td>1.600000</td>\n",
       "      <td>0.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>5.800000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.350000</td>\n",
       "      <td>1.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.400000</td>\n",
       "      <td>3.300000</td>\n",
       "      <td>5.100000</td>\n",
       "      <td>1.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>7.900000</td>\n",
       "      <td>4.400000</td>\n",
       "      <td>6.900000</td>\n",
       "      <td>2.500000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       SepalLength  SepalWidth  PetalLength  PetalWidth\n",
       "count   150.000000  150.000000   150.000000  150.000000\n",
       "mean      5.843333    3.054000     3.758667    1.198667\n",
       "std       0.828066    0.433594     1.764420    0.763161\n",
       "min       4.300000    2.000000     1.000000    0.100000\n",
       "25%       5.100000    2.800000     1.600000    0.300000\n",
       "50%       5.800000    3.000000     4.350000    1.300000\n",
       "75%       6.400000    3.300000     5.100000    1.800000\n",
       "max       7.900000    4.400000     6.900000    2.500000"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataframe.describe()\n",
    "\n",
    "#<xml xmlns=\"https://developers.google.com/blockly/xml\"><variables><variable id=\"[V~uW+0L/4GW;45ulv+l\">dataframe</variable></variables><block type=\"varDoMethod_Python\" id=\"O3QcP4Y+iT~?:8x5/Mld\" x=\"8\" y=\"188\"><field name=\"VAR\" id=\"[V~uW+0L/4GW;45ulv+l\">dataframe</field><field name=\"MEMBER\">describe</field><data>dataframe:describe</data></block></xml>"
   ]
  },
  {
   "attachments": {
    "summary.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note we've also got a few extras here: count, mean, and std.\n",
    "\n",
    "### Example Five Number Summary \n",
    "\n",
    "For the data shown below the minimum is 3, the median is 9 and the maximum is 22. \n",
    "We find the first quartile as the median of the lower five numbers: here it is 6. \n",
    "The third quartile is 13, the median of the numbers above the median. \n",
    "So the five number summary for this data is $\\{ 3,6,9,13,22\\}$.\n",
    "\n",
    "![summary.png](attachment:summary.png)\n",
    "\n",
    "Other measures of the spread for numerical data include the range, the interquartile range, and the variance. \n",
    "\n",
    "The **range** is simply the maximum value minus the minimum. \n",
    "When outliers are present they may inflate the range. \n",
    "For example in our income example the range would be $4000000-30000=3,970,000$ which is not representative of the spread of the majority of incomes. \n",
    "\n",
    "To reduce the effect of outliers on the measure of dispersion, the interquartile range is often used. \n",
    "The **interquartile range** is defined as the third quartile minus the first quartile.\n",
    "\n",
    "The most commonly used measures of dispersion for numerical data are the **variance** and its square root, the **standard deviation**. \n",
    "The variance measures the sum of squared differences of the data about the mean.\n",
    "Squaring the differences may seem complicated but makes sense when you realize that the sum of differences about the mean is zero.\n",
    "\n",
    "Again, let $X_1, X_2, \\ldots, X_n$ be the variables you want to compute the variance of. \n",
    "The formula for the variance is given by $S^2 = \\frac{\\sum_{i=1}^n (X_i  - \\bar{X})^2}{n-1}.$ \n",
    "The standard deviation is the square root of the variance.\n",
    "\n",
    "When we did `describe` above, it gave us standard deviation already (`std`)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Sampling\n",
    "\n",
    "The descriptive statistics discussed here all assume that the data we have is a **random sample** from some larger population. \n",
    "The population mean, $\\mu$, and the population variance, $\\sigma^2$ are unknown and the sample is typically taken to gain information about them. \n",
    "The population mean and variance are **parameters** while the sample mean ($\\bar{X}$) and sample variance ($S^2$) are called **statistics**. \n",
    "Since the sample mean and sample variance are computed from a random sample from the population, each time we take a different sample, we expect to get different values of the sample mean and sample variance. \n",
    "\n",
    "We would like to know how much difference there would be in say $\\bar{X}$ over different samples. \n",
    "The *standard error* can be used to estimate the variance about a statistic. \n",
    "For the sample mean, it is known that the variation in $\\bar{X}$ will vary in direct proportion to the population variance, $\\sigma^2$ and inversely with the sample size. \n",
    "So we can reduce the variation in $\\bar{X}$ by increasing our sample size, $n$. \n",
    "The standard error of $\\bar{X}$ can be estimated by $\\displaystyle \\sqrt{\\frac{S^2}{n}}.$\n",
    "\n",
    "The best way to begin to understand this is to sample some rows from your dataframe (remember the rows are just data points) and calculate the mean of that same.\n",
    "\n",
    "When we do this in `pandas`, the sample is just another (smaller) dataframe:\n",
    "\n",
    "- Create variable `sample`\n",
    "- Set `sample` to `with dataframe do sample using 10`\n",
    "- `sample`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SepalLength</th>\n",
       "      <th>SepalWidth</th>\n",
       "      <th>PetalLength</th>\n",
       "      <th>PetalWidth</th>\n",
       "      <th>Species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>134</th>\n",
       "      <td>6.1</td>\n",
       "      <td>2.6</td>\n",
       "      <td>5.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>5.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.8</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>5.6</td>\n",
       "      <td>2.7</td>\n",
       "      <td>4.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.1</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.5</td>\n",
       "      <td>4.9</td>\n",
       "      <td>1.5</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>5.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.2</td>\n",
       "      <td>1.5</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>4.8</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.3</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>4.4</td>\n",
       "      <td>2.9</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>5.4</td>\n",
       "      <td>3.9</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.4</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.3</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     SepalLength  SepalWidth  PetalLength  PetalWidth     Species\n",
       "134          6.1         2.6          5.6         1.4   virginica\n",
       "149          5.9         3.0          5.1         1.8   virginica\n",
       "94           5.6         2.7          4.2         1.3  versicolor\n",
       "34           4.9         3.1          1.5         0.1      setosa\n",
       "72           6.3         2.5          4.9         1.5  versicolor\n",
       "61           5.9         3.0          4.2         1.5  versicolor\n",
       "45           4.8         3.0          1.4         0.3      setosa\n",
       "8            4.4         2.9          1.4         0.2      setosa\n",
       "16           5.4         3.9          1.3         0.4      setosa\n",
       "145          6.7         3.0          5.2         2.3   virginica"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample = dataframe.sample(10)\n",
    "\n",
    "sample\n",
    "\n",
    "#<xml xmlns=\"https://developers.google.com/blockly/xml\"><variables><variable id=\"X~V70-15t9L^b}[IjBoW\">sample</variable><variable id=\"[V~uW+0L/4GW;45ulv+l\">dataframe</variable></variables><block type=\"variables_set\" id=\"X2Q0lhlIuB;M-}%}kt0@\" x=\"114\" y=\"312\"><field name=\"VAR\" id=\"X~V70-15t9L^b}[IjBoW\">sample</field><value name=\"VALUE\"><block type=\"varDoMethod_Python\" id=\"}THa7gMDOBGv~{O0J-}v\"><field name=\"VAR\" id=\"[V~uW+0L/4GW;45ulv+l\">dataframe</field><field name=\"MEMBER\">sample</field><data>dataframe:sample</data><value name=\"INPUT\"><block type=\"math_number\" id=\"x6aHWTYmPfX=;4W1C.io\"><field name=\"NUM\">10</field></block></value></block></value></block><block type=\"variables_get\" id=\"*XWdVPs3p=}r`r6WYNWV\" x=\"112\" y=\"371\"><field name=\"VAR\" id=\"X~V70-15t9L^b}[IjBoW\">sample</field></block></xml>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Rerun the sampling cell above a couple of times to see how it randomly draws 10 rows from the original dataframe.\n",
    "You can get a sense of the randomness by looking at the index column on the left hand side - this originally was orderd from 1 to 150 in the dataframe.\n",
    "\n",
    "Once you're ready do this:\n",
    "\n",
    "- Set `sample` to `with dataframe do sample using 10`\n",
    "- `with sample do mean using`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SepalLength    5.81\n",
       "SepalWidth     2.97\n",
       "PetalLength    3.34\n",
       "PetalWidth     0.91\n",
       "dtype: float64"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample = dataframe.sample(10)\n",
    "\n",
    "sample.mean()\n",
    "\n",
    "#<xml xmlns=\"https://developers.google.com/blockly/xml\"><variables><variable id=\"X~V70-15t9L^b}[IjBoW\">sample</variable><variable id=\"[V~uW+0L/4GW;45ulv+l\">dataframe</variable></variables><block type=\"variables_set\" id=\"x.L#2+Kmq01S1!FI|%*2\" x=\"16\" y=\"135\"><field name=\"VAR\" id=\"X~V70-15t9L^b}[IjBoW\">sample</field><value name=\"VALUE\"><block type=\"varDoMethod_Python\" id=\"!L!ZRT9j}{ngeakBRIE!\"><field name=\"VAR\" id=\"[V~uW+0L/4GW;45ulv+l\">dataframe</field><field name=\"MEMBER\">sample</field><data>dataframe:sample</data><value name=\"INPUT\"><block type=\"math_number\" id=\"VWJ^n9kSRcY:)U5Kmi;8\"><field name=\"NUM\">10</field></block></value></block></value></block><block type=\"varDoMethod_Python\" id=\"p9K6asIXwdef{s%{s[|Q\" x=\"8\" y=\"188\"><field name=\"VAR\" id=\"X~V70-15t9L^b}[IjBoW\">sample</field><field name=\"MEMBER\">mean</field><data>sample:mean</data></block></xml>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Run this code cell a couple of times (trick: do Ctrl + Enter)\n",
    "\n",
    "Notice that the means change each time, just a bit.\n",
    "\n",
    "That difference in means over different samples is what we mean by standard error of the mean."
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "xpython",
   "language": "python",
   "name": "xpython"
  },
  "language_info": {
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}