{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "a84cb8e6",
   "metadata": {},
   "source": [
    "# Statistics fundamentals demo: data types"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "aa9132ba",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "05323f71",
   "metadata": {},
   "source": [
    "## Load weather dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "86841bcc",
   "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>weather_condition</th>\n",
       "      <th>wind_strength</th>\n",
       "      <th>temperature_c</th>\n",
       "      <th>rainfall_inches</th>\n",
       "      <th>humidity_percent</th>\n",
       "      <th>pressure_hpa</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Sunny</td>\n",
       "      <td>Light Breeze</td>\n",
       "      <td>8.2</td>\n",
       "      <td>0.13</td>\n",
       "      <td>48.8</td>\n",
       "      <td>1016.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Snowy</td>\n",
       "      <td>Gale</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.29</td>\n",
       "      <td>89.6</td>\n",
       "      <td>1009.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Rainy</td>\n",
       "      <td>Strong Wind</td>\n",
       "      <td>7.3</td>\n",
       "      <td>0.01</td>\n",
       "      <td>100.0</td>\n",
       "      <td>1003.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Cloudy</td>\n",
       "      <td>Light Breeze</td>\n",
       "      <td>21.6</td>\n",
       "      <td>0.62</td>\n",
       "      <td>49.3</td>\n",
       "      <td>1006.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Sunny</td>\n",
       "      <td>Calm</td>\n",
       "      <td>12.0</td>\n",
       "      <td>1.09</td>\n",
       "      <td>38.6</td>\n",
       "      <td>1016.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  weather_condition wind_strength  temperature_c  rainfall_inches  \\\n",
       "0             Sunny  Light Breeze            8.2             0.13   \n",
       "1             Snowy          Gale            1.6             0.29   \n",
       "2             Rainy   Strong Wind            7.3             0.01   \n",
       "3            Cloudy  Light Breeze           21.6             0.62   \n",
       "4             Sunny          Calm           12.0             1.09   \n",
       "\n",
       "   humidity_percent  pressure_hpa  \n",
       "0              48.8        1016.5  \n",
       "1              89.6        1009.4  \n",
       "2             100.0        1003.3  \n",
       "3              49.3        1006.9  \n",
       "4              38.6        1016.0  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "url = 'https://gperdrizet.github.io/FSA_devops/assets/data/unit2/weather.csv'\n",
    "df = pd.read_csv(url)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "1a72ba8e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 365 entries, 0 to 364\n",
      "Data columns (total 6 columns):\n",
      " #   Column             Non-Null Count  Dtype  \n",
      "---  ------             --------------  -----  \n",
      " 0   weather_condition  365 non-null    object \n",
      " 1   wind_strength      365 non-null    object \n",
      " 2   temperature_c      365 non-null    float64\n",
      " 3   rainfall_inches    365 non-null    float64\n",
      " 4   humidity_percent   365 non-null    float64\n",
      " 5   pressure_hpa       365 non-null    float64\n",
      "dtypes: float64(4), object(2)\n",
      "memory usage: 17.2+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "07cc9df8",
   "metadata": {},
   "source": [
    "## Statistical data types\n",
    "\n",
    "### 1. Discrete vs continuous random variables\n",
    "\n",
    "Before discussing measurement scales, let's understand the fundamental distinction between discrete and continuous variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3b86bc46",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1400x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def variables_plot():\n",
    "    fig, axes = plt.subplots(1, 2, figsize=(14, 5))\n",
    "\n",
    "    # Bar chart for weather condition (categorical discrete)\n",
    "    weather_counts = df['weather_condition'].value_counts().sort_index()\n",
    "\n",
    "    axes[0].set_title('Discrete variable: weather condition\\n(countable categories)')\n",
    "    axes[0].bar(weather_counts.index, weather_counts.values, color='grey', edgecolor='black')\n",
    "    axes[0].set_xlabel('Weather Condition')\n",
    "    axes[0].set_ylabel('Frequency (Number of Days)')\n",
    "    axes[0].grid(True, alpha=0.3, axis='y')\n",
    "\n",
    "    # Add text annotation\n",
    "    axes[0].text(0.2, 0.95, 'Only 4 distinct values possible', \n",
    "                transform=axes[0].transAxes, ha='center', \n",
    "                bbox=dict(boxstyle='round', facecolor='lightgrey', alpha=0.5))\n",
    "\n",
    "    # KDE for temperature (continuous variable)\n",
    "    axes[1].set_title('Continuous variable: temperature\\n (infinite possible values)')\n",
    "    sns.kdeplot(x='temperature_c', data=df, ax=axes[1], color='black', alpha=0.5)\n",
    "    axes[1].set_xlabel('Temperature (°C)')\n",
    "    axes[1].set_ylabel('Relative frequency (number of days)')\n",
    "    axes[1].grid(True, alpha=0.3, axis='y')\n",
    "    axes[1].axvline(0, color='blue', linestyle=':', linewidth=2, alpha=0.7, label='0°C (freezing)')\n",
    "    axes[1].legend()\n",
    "\n",
    "    # Add text annotation\n",
    "    axes[1].text(0.2, 0.95, 'Can take any value in the range', \n",
    "                transform=axes[1].transAxes, ha='center',\n",
    "                bbox=dict(boxstyle='round', facecolor='lightgrey', alpha=0.5))\n",
    "\n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "\n",
    "variables_plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7a706638",
   "metadata": {},
   "source": [
    "### 2. Measurement scales (types of data)\n",
    "\n",
    "**Nominal data**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c89908af",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "weather_condition\n",
       "Sunny     153\n",
       "Cloudy    107\n",
       "Rainy      71\n",
       "Snowy      34\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['weather_condition'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eb805728",
   "metadata": {},
   "source": [
    "**Ordinal data**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "488e65cc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "wind_strength\n",
       "Light Breeze     121\n",
       "Moderate Wind     98\n",
       "Calm              66\n",
       "Strong Wind       55\n",
       "Gale              25\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['wind_strength'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b9f23a63",
   "metadata": {},
   "source": [
    "**Interval data**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "74c8b05b",
   "metadata": {},
   "source": [
    "Temperature in Celsius is interval data because 0°C doesn't mean \"no temperature\" - it's just the freezing point of water. The temperature can go below 0°C (negative values are valid), and ratios are not meaningful: 20°C is not \"twice as hot\" as 10°C."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ee92adb2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "min    -10.000000\n",
       "mean     5.753425\n",
       "max     25.000000\n",
       "Name: temperature_c, dtype: float64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['temperature_c'].describe().loc[['min', 'mean', 'max']]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7b9ba800",
   "metadata": {},
   "source": [
    "**Ratio data**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "3fc1d015",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "min     0.000000\n",
       "mean    0.303699\n",
       "max     2.230000\n",
       "Name: rainfall_inches, dtype: float64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['rainfall_inches'].describe().loc[['min', 'mean', 'max']]"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}