{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "99001f2d-5344-4d9c-9304-cadabed4a185",
   "metadata": {},
   "source": [
    "# Seaborn Foundations\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a9ba39dd",
   "metadata": {},
   "source": [
    "```{contents}\n",
    ":local:\n",
    ":depth: 2\n",
    "```\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0701-classroom-practice-path",
   "metadata": {},
   "source": [
    "**Recommended classroom practice**\n",
    "\n",
    "Use the exercises below as the core practice path for this section. Additional practice reinforces the same skills; optional extensions explore more advanced tools. These labels guide your study and classroom work. Follow your instructor's directions and the separate Lab and Homework instructions for assigned work.\n",
    "\n",
    "1. **Histogram of Tips**.\n",
    "2. **Map Day to Color**.\n",
    "3. **Reshape Regional Sales**.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a2cd67a5",
   "metadata": {},
   "source": [
    "## Overview\n",
    "\n",
    "Seaborn is a high-level visualization library built on top of Matplotlib. It gives you cleaner defaults and concise plotting functions for statistical graphics, while still letting you use Matplotlib for fine-grained control."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0884a798",
   "metadata": {},
   "source": [
    "In this notebook, we will use the following learning path:\n",
    "\n",
    "1. A first look at Seaborn plots\n",
    "2. Compare Seaborn with pandas and Matplotlib\n",
    "3. The Seaborn data model: semantic mappings (`hue`, `style`, `size`) and tidy, wide, and nested data\n",
    "4. Figure-level vs. axes-level functions\n",
    "\n",
    "The plot families (distribution, relational, categorical) come in the next section, followed by faceting, multivariate views, and styling."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b04fb93c",
   "metadata": {},
   "source": [
    "By convention, Seaborn is imported as `sns`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "203b0313",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-31T06:00:50.391476Z",
     "iopub.status.busy": "2026-08-31T06:00:50.391147Z",
     "iopub.status.idle": "2026-08-31T06:00:51.212886Z",
     "shell.execute_reply": "2026-08-31T06:00:51.212657Z"
    },
    "tags": [
     "hide-input",
     "thebe-init",
     "runnable"
    ]
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "# %pip install seaborn\n",
    "import seaborn as sns\n",
    "\n",
    "# Set consistent defaults for all following plots\n",
    "sns.set_theme(style='whitegrid', context='notebook')\n",
    "\n",
    "tips = sns.load_dataset('tips')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b78413aa",
   "metadata": {},
   "source": [
    "### A First Look\n",
    "\n",
    "Begin with one question and one chart, then add a grouping variable. Once those steps are familiar, use the four-panel comparison below as a reference for how different plotting functions work together.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "58465ede",
   "metadata": {},
   "source": [
    "The syntax for using Seaborn is:\n",
    "\n",
    "```text\n",
    "sns.PLOT-TYPE(data=df, x='col_a', y='col_b', hue='col_c')\n",
    "      ↑           ↑         ↑          ↑          ↑\n",
    "   plot type   DataFrame  x-axis     y-axis     color by\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f9a4bb18",
   "metadata": {},
   "source": [
    "**Read the dataset before choosing the chart**\n",
    "\n",
    "In `tips`, one row records a restaurant bill and its tip, not one customer. A bill can cover several people, so a count of rows is a count of bills rather than a count of diners.\n",
    "\n",
    "| Column | Meaning | Kind of variable |\n",
    "|---|---|---|\n",
    "| `total_bill` | Bill amount in dollars | Numerical amount |\n",
    "| `tip` | Tip amount in dollars | Numerical amount |\n",
    "| `size` | Number of people in the party | Numerical count |\n",
    "| `day` | Recorded day of the week | Categorical group |\n",
    "| `time` | Lunch or Dinner | Categorical group |\n",
    "| `sex` | Recorded sex category | Categorical group |\n",
    "| `smoker` | Recorded smoking-status category | Categorical group |\n",
    "\n",
    "The name `size` refers to party size in the data. Passing `size='size'` to a scatter plot maps that count to marker size; `s=80` instead sets the same marker size for every point.\n",
    "\n",
    "Inspect a few rows below. Before interpreting any plot, identify what its axes measure and what each point, bar, or group represents.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "49f637a4",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-31T06:00:51.457444Z",
     "iopub.status.busy": "2026-08-31T06:00:51.457372Z",
     "iopub.status.idle": "2026-08-31T06:00:51.461197Z",
     "shell.execute_reply": "2026-08-31T06:00:51.461008Z"
    },
    "tags": []
   },
   "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>total_bill</th>\n",
       "      <th>tip</th>\n",
       "      <th>size</th>\n",
       "      <th>day</th>\n",
       "      <th>time</th>\n",
       "      <th>sex</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>16.99</td>\n",
       "      <td>1.01</td>\n",
       "      <td>2</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10.34</td>\n",
       "      <td>1.66</td>\n",
       "      <td>3</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>21.01</td>\n",
       "      <td>3.50</td>\n",
       "      <td>3</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>23.68</td>\n",
       "      <td>3.31</td>\n",
       "      <td>2</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>24.59</td>\n",
       "      <td>3.61</td>\n",
       "      <td>4</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   total_bill   tip  size  day    time     sex\n",
       "0       16.99  1.01     2  Sun  Dinner  Female\n",
       "1       10.34  1.66     3  Sun  Dinner    Male\n",
       "2       21.01  3.50     3  Sun  Dinner    Male\n",
       "3       23.68  3.31     2  Sun  Dinner    Male\n",
       "4       24.59  3.61     4  Sun  Dinner  Female"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# df.loc[row_label, column_label]\n",
    "\n",
    "tips.loc[:, ['total_bill', 'tip', 'size', 'day', 'time', 'sex']].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "tip-measure-choice",
   "metadata": {},
   "source": [
    "**Tip dollars and tip percentage answer different questions**\n",
    "\n",
    "A manager asking “Which customers tip more?” needs to specify the measure. Tip dollars describe the amount left for service; tip percentage describes the tip as a share of the bill. Calculate the percentage as `100 * tip / total_bill`. For example, a $6 tip is larger than a $4 tip, but it can represent a smaller share of a larger bill.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "tip-measure-example",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " total_bill  tip  tip_pct\n",
      "         20    4     20.0\n",
      "         40    6     15.0\n"
     ]
    }
   ],
   "source": [
    "# Two hypothetical bills illustrate the difference between dollars and percentages.\n",
    "import pandas as pd\n",
    "bill_examples = pd.DataFrame({\n",
    "    'total_bill': [20, 40],\n",
    "    'tip': [4, 6]\n",
    "})\n",
    "bill_examples['tip_pct'] = 100 * bill_examples['tip'] / bill_examples['total_bill']\n",
    "print(bill_examples.to_string(index=False))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "tip-measure-interpretation",
   "metadata": {},
   "source": [
    "The $20 bill has a $4 tip (20%); the \\$40 bill has a \\$6 tip (15%). The second bill has the larger dollar tip but the smaller percentage. Neither measure is universally better: choose the one that answers the question, label its units, and explain the comparison.\n",
    "\n",
    "These are hypothetical bills used to illustrate the calculation. The later faceting and heatmap exercises apply tip percentages to the actual `tips` data.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "first-scatter-prediction",
   "metadata": {},
   "source": [
    "**First, draw one relationship**\n",
    "\n",
    "A restaurant manager asks: “Do larger bills tend to come with larger dollar tips?” Both variables are numerical, so start with a scatter plot. Before running the code, predict whether the points will tend to rise, fall, or show no clear direction. Each point represents one recorded bill and its tip.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "first-single-scatter",
   "metadata": {},
   "outputs": [
    {
     "data": {
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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(5, 3))\n",
    "sns.scatterplot(data=tips, x='total_bill', y='tip', ax=ax)\n",
    "ax.set_xlabel('Total bill ($)')\n",
    "ax.set_ylabel('Tip ($)')\n",
    "ax.set_title('Bill Amount and Tip Amount')\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "first-hue-prediction",
   "metadata": {},
   "source": [
    "The points show a broadly positive association: larger bills tend to accompany larger tips, with substantial variation. This chart does not identify meal groups or establish that a larger bill causes a particular tip.\n",
    "\n",
    "**Then, add meal-time colors**\n",
    "\n",
    "The manager now asks: “Does that direction look similar at lunch and dinner?” Keep the same variables on the axes and add `hue='time'`. Seaborn uses the meal-time column to assign colors and create a legend. Predict what extra information the colors will reveal without changing the underlying observations.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "first-hue-scatter",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(5, 3))\n",
    "sns.scatterplot(data=tips, x='total_bill', y='tip', hue='time', ax=ax)\n",
    "ax.set_xlabel('Total bill ($)')\n",
    "ax.set_ylabel('Tip ($)')\n",
    "ax.set_title('Bill Amount and Tip by Meal Time')\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "first-scatter-consolidation",
   "metadata": {},
   "source": [
    "Both meal groups show a broadly positive association, and their points overlap. The colors identify which observations belong to each meal; they do not change the bill or tip values. More points of one color mean more recorded bills in that group, not necessarily larger tips per bill.\n",
    "\n",
    "Read the axis labels and legend before stating a finding. If the overlap makes the comparison difficult, separate panels can help; the faceting section develops that approach.\n",
    "\n",
    "**Reference comparison: several plotting functions**\n",
    "\n",
    "The next example combines four panels. Use it to consolidate the single-plot syntax after the steps above; you do not need to reproduce the whole layout on a first reading.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1d09330d",
   "metadata": {},
   "source": [
    "Using the Matplotlib `subplots()` function, you can place the plots side-by-side. The **`ax=` parameter** tells each plotting function which subplot panel to draw on; without it, axes-level calls can reuse the current axes, so several plots may land on the same panel. pandas `df.plot()` accepts the same `ax=` argument, which is how the comparisons below put a pandas plot and a Seaborn plot side by side. You can see that the Seaborn syntax for calling the plotting functions is familiar but the method names are different. Here we have one plot created by Matplotlib and three by Seaborn.\n",
    "\n",
    "Style note: these plots use the global theme set earlier with `sns.set_theme(style='whitegrid', context='notebook')`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "17ff2cc2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-31T06:00:51.462222Z",
     "iopub.status.busy": "2026-08-31T06:00:51.462167Z",
     "iopub.status.idle": "2026-08-31T06:00:51.610401Z",
     "shell.execute_reply": "2026-08-31T06:00:51.610146Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1600x300 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 4, figsize=(16, 3))\n",
    "\n",
    "### Matplotlib\n",
    "ax[0].hist(tips['total_bill'], bins=25, alpha=0.7)\n",
    "ax[0].set_title('Matplotlib hist()')\n",
    "\n",
    "### Seaborn\n",
    "sns.histplot(data=tips, x='total_bill', bins=25, ax=ax[1])\n",
    "ax[1].set_title('Histogram')\n",
    "\n",
    "### Seaborn with hue\n",
    "sns.scatterplot(data=tips, x='total_bill', y='tip', hue='time', ax=ax[2])\n",
    "ax[2].set_title('Scatter + hue')\n",
    "\n",
    "### Seaborn categorical plot\n",
    "sns.boxplot(data=tips, x='day', y='total_bill', ax=ax[3])\n",
    "ax[3].set_title('Categorical boxplot')\n",
    "\n",
    "plt.tight_layout()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d6f23b6b",
   "metadata": {
    "tags": [
     "thebe-interactive"
    ]
   },
   "outputs": [],
   "source": [
    "### Exercise: Histogram of Tips\n",
    "# Practice path: Core classroom practice.\n",
    "# A restaurant manager wants to see how large tips usually are.\n",
    "# Use sns.histplot() to draw a histogram of the tip column of tips with 20 bins.\n",
    "# Add the title 'Distribution of Tips'.\n",
    "# Then, in a comment, answer: about how much is a typical tip, and are large tips common?\n",
    "# Also explain one limitation: Can this chart tell you whether lunch and dinner customers tip differently?\n",
    "### Your code starts here\n",
    "\n",
    "\n",
    "\n",
    "### Your code ends here\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dd405e63",
   "metadata": {
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [],
   "source": [
    "### Solution\n",
    "ax = sns.histplot(data=tips, x='tip', bins=20)\n",
    "ax.set_title('Distribution of Tips')\n",
    "plt.show()\n",
    "# Answer: Most tips are between about $2 and $4 (the tallest bar is near $2).\n",
    "# Large tips are rare: few tips are above $6.\n",
    "# Limitation: This histogram combines meal times, so it cannot compare lunch with dinner.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "af649175",
   "metadata": {},
   "source": [
    "## Comparing Libraries\n",
    "\n",
    "Pandas plotting is excellent for fast, direct plotting from a `DataFrame`. Seaborn becomes stronger when you need **semantic mappings** (color/style/size by variables), cleaner statistical defaults, and grouped comparisons.\n",
    "\n",
    "Practical rules:\n",
    "- Start with Pandas for quick checks.\n",
    "- Use **Seaborn** for **statistical/grouped** visuals.\n",
    "- Drop to Matplotlib when you need exact low-level control.\n",
    "\n",
    "As a comparison between the three visualization libraries:\n",
    "\n",
    "| Library | Strength | Typical call |\n",
    "|---|---|---|\n",
    "| pandas | Fastest plot straight from a `DataFrame` | `df.plot(kind='hist')` |\n",
    "| Matplotlib | Full control over every element | `ax.hist(df['col'])` |\n",
    "| Seaborn | Statistical defaults and grouping by variables | `sns.histplot(data=df, x='col')` |"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "45420ef2",
   "metadata": {},
   "source": [
    "### Scatter Plots: pandas vs. Seaborn\n",
    "\n",
    "Compare the two plots in the next cell. The Seaborn version shows more information about the data:\n",
    "\n",
    "- `df.plot(kind='scatter')`: fast DataFrame-first plotting.\n",
    "- `sns.scatterplot()`: semantic mappings (`hue`, `style`, `size`) with cleaner statistical **defaults**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "400cabaa",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-31T06:00:51.214193Z",
     "iopub.status.busy": "2026-08-31T06:00:51.214125Z",
     "iopub.status.idle": "2026-08-31T06:00:51.324192Z",
     "shell.execute_reply": "2026-08-31T06:00:51.323964Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 2, figsize=(7, 4))\n",
    "\n",
    "# Pandas: fast DataFrame-first plotting\n",
    "tips.plot(\n",
    "    kind='scatter',\n",
    "    x='total_bill', y='tip',\n",
    "    s=tips['size']*10,          ### s, not size, for scatter plot marker size\n",
    "    alpha=0.7,                  ### add some transparency for better visibility\n",
    "    ax=ax[0],\n",
    "    title='df.plot(kind=\"scatter\")',\n",
    ")\n",
    "# Seaborn: maps more variables (color, marker, size) with about the same amount of code\n",
    "sns.scatterplot(\n",
    "    data=tips,\n",
    "    x='total_bill', y='tip',\n",
    "    hue='time', \n",
    "    style='sex', \n",
    "    size='size',\n",
    "    ax=ax[1]\n",
    ")\n",
    "ax[1].set_title('sns.scatterplot()')\n",
    "ax[1].legend(bbox_to_anchor=(1, 1))  ### move legend outside the plot\n",
    "plt.tight_layout()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "465a3738",
   "metadata": {
    "tags": [
     "thebe-interactive"
    ]
   },
   "outputs": [],
   "source": [
    "### Exercise: Map Day to Color\n",
    "# Practice path: Core classroom practice.\n",
    "# A restaurant manager wants to see whether tipping patterns differ by day of the week.\n",
    "# Using the tips DataFrame, draw a scatter plot of total_bill (x) vs. tip (y)\n",
    "# with the points colored by day. Add the title 'Tips by Day'.\n",
    "# In a comment, describe the overall relationship between bill amount and tip.\n",
    "# Also explain one limitation: Does this chart establish that the day itself causes differences in tips?\n",
    "### Your code starts here\n",
    "\n",
    "\n",
    "\n",
    "### Your code ends here\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c6a9bf46",
   "metadata": {
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [],
   "source": [
    "### Solution\n",
    "ax = sns.scatterplot(data=tips, x='total_bill', y='tip', hue='day')\n",
    "ax.set_title('Tips by Day')\n",
    "plt.show()\n",
    "# Finding: Larger bills generally accompany larger tips, with substantial overlap among days.\n",
    "# Limitation: The chart shows associations; bill amounts, party sizes, and other factors may differ by day.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d5ddbc66",
   "metadata": {},
   "source": [
    "### Histograms: pandas, Matplotlib, and Seaborn"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e471a61d",
   "metadata": {},
   "source": [
    "Compare histogram APIs across pandas, Matplotlib, and Seaborn:\n",
    "\n",
    "- **pandas**: DataFrame-oriented convenience\n",
    "- **Matplotlib**: lower-level direct control\n",
    "- **Seaborn**: statistical defaults and cleaner styling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "1e924da2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-31T06:00:51.325371Z",
     "iopub.status.busy": "2026-08-31T06:00:51.325295Z",
     "iopub.status.idle": "2026-08-31T06:00:51.456207Z",
     "shell.execute_reply": "2026-08-31T06:00:51.455987Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   total_bill   tip\n",
      "0       16.99  1.01\n",
      "1       10.34  1.66\n",
      "2       21.01  3.50\n",
      "3       23.68  3.31\n",
      "4       24.59  3.61\n"
     ]
    },
    {
     "data": {
      "image/png": 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rXMlnRSfHCo51ShSltFgXWPHoUTIppRXqKiI4FinelrdNtagrvmsoko5Blb6KP8HXvmK+ehSptV8FvIoIjste/A01Pl3fP+quqhZufdcoXuv8q8u9WtBDFabLi//EMaB0qjhTjxlvyMyCBQtchZa6Pyth9GbBVpnUK78GUiwQNQipa7ZafVU+Uqurl7AGl1u8OBccoxQXvXJrYEIZGNcCr+PSes+EU3aKRZkreLiP3r/2Exyj9V51f/D5Et1XVkxTj52yJvzVY954cZREgp1gFAy8rhml8cageZPmqIZOF7laFbzxuhWlIBU4mYwncNkAdX/0un6rtlGFPgVHFXK8ccdejZeCrn6UiGtstYKKAm5ZM016k+QEUnIfauZYr+u2WnACk3a1Amk7pbU+e/eXVoBUN011Z+zbt6+rlQ2eUTgc2peORS0igV8AwUsxlLU0THnd6iPlfUkq6H7yySeuG7v+X2qRK21selm8xKAirf5AvFJ3bcVetWIr2VJlmzccJ5B6p6hXjlogNG7Pu2bUZVtrX5cnONapRSaw8Bgc6xTngnn3VeaaC45F3jZDHYeoxUotT4oVao3xKgbUuqVK3kBqqdH29b2khFe9mQJbp/2gbatwrxirVh2Na/znP//pvjMCx1lWNP4Tx4Di1FtRy9jpGg9edkq9DDVEUeVCtYJ689WoS7e6QZdWblNiqbLh448/7nrnqJyjxo3p06eXeE1wpaPX4KMYpe2FKmN5iXw8XceRlrm8iszA96/3qkrdQCp3qzwe2CAWGNeDnx9I58k7Z6EoXsbTuYw3dBFPQgpQuvg0FkQt115tv8b+SjhLOakLi7qABBaSVMsYuL6huvJoxkaNeVYBxmuZ9ZJr1Z5pJsTA2XhVAFKyqtYMb+bz0qhgGljw1P50TN648UBe8FbFQiD9rWDrjZkMboFWIFftplqdQtFjKmSrJUvjVSrTLUbHre5RqqX1qPuT103Ko4qU0n5UeeI3tch5Y3oUlPVeNZZIx+oF2fJa7kN9CSvoR5KcA/FC8UEViKqgVKtoaWN11ZVb8VVzXXifecUdjQcMjL2lXUeBMUG0P7USBI499Kh7pfYXvKKCWtAVr9T6U9a+yuJ9VwTGT8X1UIVjL0arEK3k1Uuu1c1T9wd+3yjZ1cRD6hquBFhjuisycVA49B2j7y0VHhV7VFDX5HE6Pu+7Jtz4TxwDilNyp56Ban31VmgJLoeqt4/ikMp7SnxVbgosx+h6Uq9AtXiL4oWGo2g4odeIUFq5VeOhg2OUenNqf4pTKrcEz6ztDZMMnjsjVipT5gr8rlAZe8aMGe59BTe+qEJYlcH63gocWqiEXufQOxfBrf2iXq/qtRWqBV4VHKr88HrGoiRasJOQJhxQl3F111MA1I8Kal6LaHnLrQRStzq12Kp7pGoktV0ViAKDnboyax8qMGlWaSWLmpTMC4AaA6KEUGNYNHuhWtiViCtRVqu6ZkMsi45XBTcVylRoU1dzTbQTPIOvqEJBY1M0vk+vUyFULc9qNVHQ9sY9ezWqmrxNhVe1umsSDI2VLIvGYKviwJspN9IEWxNMqPVGLVRqadc5VsVFaS1EVUGFUr0vTVSkygTVTuq8aSISdfn3zpu+DDVuR+Miy+v6qi9MdfGq6LriQLxSlz3FKhV4dO2G4k3qo2Evat1RIUQ9eRYtWlQUCxVDdR2pRUHxz5tcRlQIUgxQZaSuMb1WcTdU12VNuqgCo3rTKD6rJUITP2p8s4breAWz4Gu2IlShqomGFGP1u+KpxlcqFpT2vtVCrFZsdftW4VDxUe/Ra53S94KW09IEaqpcVau1Jv5RrydVXqjVS8/RsQZORBYuzdmh7ycVXFXpq54HOq8qUHpjycON/8QxoDglZKq40nWmWKeJunRtq9ylxgLFLrVue9e/4pjmRNDrFCNUvlAPRlVeecPhFEfUG0Z/6/rX9ajejarcCy63am4LXduKG0quVS5TTyE9V+UXlfcUp7V9lV8U/9Q6rPJh4JxCsRRJmcuj96qKDZX31QtIs4F7s6gH08Rs6lGleKiJ6dSjU+dV8TYwwRcNf9L/0VtmTM9TRWhwo47XI0tlWYRGC3YS0oWiwKVaJwU4dctRzb2WqlJAUjfGilJtmC5aJaqabVwJphLEwLFzqjFULaQCmZJgBVEvACrYeftT4FCCqxZgJeIqkKlg6F3gpVHBRvtTgU8FR+1fCWlp3aR1nNqmArUCigK9WvMVXL1CpwpaqkFVgc9LlDWhkArCeh9ljXvRpHLqylPeGt5l0bnQhBsquKoWU18mmhQklrRkg76QVGPsTRCkQKtl17wunPq/qdCsYK0KkrIo+GtSueCJRoBEpN42KuxoEi1dF6GoUKfrRi3LSiKVcKqG31v2xCuUqBu1t6RWYBxRvFZBSa0YqhTVtrzVEIKpxVUxVIVRVVzqtWqBVexXgdcTzjXr0XEpIVUcUKxVBWfwEoCBVGjVa5TI6n0rriluq6JBvan0nrREjib00SRnXjxR7FNMVbd6FfaUmGtpSRUYI6XtqZVc34P6zlClyLfffuvGN6pAG278J44BoWnmb3XfVoOHGnQUY1Q+UqOGGkICY5e6kaucqKRZcUXxRENtVE705pJQvFRipxiheKJhhBpCqCQuuNyqmKcWau1D29S8F2oQEpU7VXGnMo26m+s5eq6OTWXIeFGZMpfOn869ytDqraPXBE6wFkhlZs2RoQnQdA603K56D3j/O1HFrypt1TKuGK4kXNtThW+o5b90zEr4y5o/KdWlFVZk1D4QI95sigrC0aZLQUmvCloKWqgcJQ5akkMBu6KTPAGpSF0ntaap1lBV8o2qV1r8J44BiBfqHXrjjTe6yofSVj3wkxJ3VeSqC7o3/FM9sdRYppZ3b9ldlEQLNvA/Ch6aXEJLjDEzYuWoi6YCswqqFEoBJGL8J44BSGXqUq44GDieXTFSPblUKYzSkWADATQORkHDW3sRkdG66+rCqi5QAJCI8Z84BiCVqWJR8yupy7+G8WiuIHW7V+t18FKzKI4u4gAAAAAA+IAWbAAAAAAAfECCDQAAAACAD0iwAQAAAADwQaYfG0kmWr9Uw9K9NegAJB+t8agJOjp27BjrQ0koxEcg+REfw0dsBJLf7jBiIy3YQRQgS5v3TfdrFj3mhYs+znXVSrXzXdZ1joqft1T73MQDznnVS7VzTnwMH2XH+MC5rjqpeK4Lw4iNtGAH8WofDzrooBKPaXH1hQsXWqtWrax69eoxOLrUwbmuWql2vr/++utYH0JSxMdU+9zEA8551Uu1c058DB9lx/jAua46qXiuvw4jNtKCDQAAAACAD0iwAQAAAADwAQk2AAAAAAA+IMEGAAAAAMAHJNgAAAAAAPiAWcSBCsrPz3dr4CWjnTt3Ft2mp6cnxYyuGRkZsT4MICUkc2xMtvhIbASqTjLHxmSKi9GIjSTYQDm05t2qVatsw4YNlqwKCgosMzPTVq5cmRSBUurWrWtNmjSxtLS0WB8KkJRSITYmY3wkNgLRlQqxMdniot+xkQQbKIcXJBs1auTW+kvGQolqWVULWa1atYRv3dAXm9ZnXLNmjfu7adOmsT4kICmlQmxMpvhIbASqRirExmSJi9GKjSTYQDkBxAuSDRo0sGR+n5KTk5PwgVJyc3PdrYKl/nfJ8J6AeJIqsTHZ4iOxEYiuVImNyRQXoxEbk6NNP8YKCwoSaruoOG/sjGogkVi8/1myjn9KFhWNc8TD+EJsTFzExvhoMYuHbcB/xMbE5WdspAXbB2np6bbm5Qds17qffdtmdoNm1uj0gb5tD5WTjN17kh3/s+SJn8TD+MV1lnj4n8XH/2DLolmWv21TRK/PqF7barbt6vtxwT9cZ6n9PyPB9okKh7tWLYv1YQBAwiF+Akg1Sq7zt6yP9WEAiAK6iAMRKigoTKj9aqbHN954o8LP//e//21Lliyp8PMvueQSGzp0qG/Pnz17trVp08Z+/vn3ls2ePXvaQw895H5/8cUX3WMA4k+sYmOk+yY2AqgKsezWH8m+iY2RowUbiFB6epqNeXqe/bx6c5Xts1njWjbookMieu2QIUNsr732spNPPrnc565YscL69+9vTzzxhLVq1cpioWPHjvbJJ59Y/fr1Y7J/AIkTGysTH4mNAKqqC/JnX620jVt+X0O6qtSpWc0Oa79n2K8jNkaOBBuoBBUgl67YaMkmHiZPyc7OtoYNG8b6MABEgNgYPcRGIHEpuV6/uWoT7KpAbCyOLuJAClC3ms8//9xeeukl12Vmx44d9sADD9gxxxxjBx10kJ1xxhn23nvvueeqa43ul0svvbSoe827775r55xzjh188MHuNWeeeaZ9/PHHlTqurVu32vXXX28dOnSwI444wh588EEr+N9s0cFdfQCgqmPjn/70J5sxY0ZRCw2xEUAqoNxYOSTYQApQsFPXmRNPPNGef/55u+666+zll1+2W265xV599VUXGAcPHuyCYdOmTe25554ret3ll19u33zzjV1zzTWum9Brr71m06dPd11w9Jpdu3ZFfFwquNarV8+NjdG2HnvsMde9CADiITYee+yxdu2117qxhU2aNCE2xgmtM3zrrbda9+7drVOnTnbBBRfY3Llzix6fOXOmK8yrEH7CCSeUGEe6c+dOu+OOO6xbt27u/68C+2+//RaDdwLEJ8qNlUOCDaSAunXrWlZWluXk5Nj69etdreNtt91mPXr0sBYtWtjVV1/tfp84caJlZGQUjV+pU6eO1ahRw92noHrZZZfZ3nvvbQcccICrpVSBZN26dREfV7t27WzYsGHWsmVLO+2009w2p0yZ4uM7B4DIY6MKiGq9UVwiNsYPFfa/+OILu//+++2FF15w5/2KK66wH374wZYuXWr9+vWzI4880hXC1YKmgriSbs/tt9/uxmoqGZg6dap73YABA2L6noB4QrmxchiDDaSY7777zt0eckjxyYDUCvDwww+HfI0Co4LmhAkTXEHkxx9/tEWLFrnH8vPzIz6W4GNo3769jR8/3jZtimxtUADwOzZ27tzZxo4dG/I1xMaqp3P86aef2rRp04rOkwry6nqqljIV3tVNVD0PRAXxBQsW2KRJk1yL9erVq11LnM7noYce6p6jRF0t3Ura1WoH4P9RbgwfLdgAiiaoyMwMXeemcTjHH3+8ff3119a2bVtXczl69OhK7zM9vXgI0jgazbKpWlMAiAeKS8TG+KHuoSq0a0ynR+dGPypkq6u4EulAXbt2tXnz5rnvOd1693nUIte4cWObM2dOFb4TILFRbiwdLdhAivHWAVQh4+ijjy66XzX3qukXBatA6n7zxz/+sWjiCnnyyScrPXPkt99+W+xvHVOzZs0sNzc34m0CgJ+xUX8rARNiY+zVrl3bjjrqqGL3vf32266F7KabbnKTMmm8fKBGjRrZ9u3bXVdXtWArSa9WrVqJ56xatSri49L/e9u2bSXu134Db/UZ0v8xLy/P8nbvjmxfeXlF24yH2ZvjRfC5jgWN71fSp1ba4JZadZvW/8ublKuqeJ+RSFqO9Vpv2S1VQKlbuHe/V27Udr335L33KVOmWJcuXdzEaJ6nnnqq6Dj0o23op6LHpedqbHfg83VMio2aQTz4GLxzHXh8Ze3Le54+P6H+R9pe8HdAaUiwgRShMTGaBVe3KjxqghcFin322cdef/11++CDD1w3Oalevbq7/f777914F01goYks1DKggotmavz73//unlOZySrmz5/vajQ1GY1+V5c/jY0DgHiIjZoc6/3337d77rnHPZfYGH90fm688Ubr1auXK/xrtmMVtgN5f+t/osJz8OOihFvJUaR2795tCxcuLPXx5cuXu1sl1/rsbFi/3nas/zWifeXkpVkDM1u2bFlMk8l45Z3rWFGrbvBnSS2vrmIlP99VrlQl7dP7/IeT3Oua+Omnn9z70ZwGw4cPd5/z5s2bu0otlRsVG3XNqfJANByjRYsWtscee7jHNfeB1ztEM37L5s2b3Wu85Fe/V4Ser6Re+9T46y+//NKeeeYZd/1rG17M1bnX30qIda71u45bytqXXqfnq0t7aULFjlBIsIFKaNa4VsLs7/zzz7chQ4a4oOQl0zfffLPrUte6dWtXmFN3HlHt/llnnWX33nuvaxXQ5C9r1661/v37u8dVm3nXXXfZDTfc4Lr/eC3f4dLkM/oi1HIPmiBDM7mqQAkgsVV1bKzMPsuLjUqYtRyMEBvjiyo3Bg0a5MaCjhkzpigpCK7c8P5WgqNJm0JVfqhwXZkeAuqi6rX0BVICrP/lvvvu67bvtYDVrVfP8jIja33OrFXP3SqRoQW79HMdC/ocrVy50n0O9VkLVr9OrmX+LxmtKrVrZoeVHHouvPBCl7yed955rqJRrdFKspUg77///q7cqFnG9ZlWBaNilJ6zYsUKNweCZvsfOHCg25Zi4ciRI12sXbx4sRujrUoHJeahzlMoev7ZZ5/tluHSsSkea8LDc889t9j78869jkuVA/rd60Je3r70fFUgBPdwkSVLllT43KUVcmUWoy9ECRzb41HXH9VO6kPh1WJ7fp48yHatWubbcWQ3aWHNrvj9yyIVlXWuq5JqulRDrC+x4IuyoKDQ0tMr1lXET9HYr1eDqPfo1UImurL+d2Vd5yhd8Hnz6zotL36mejwMRGwsG/Gxcv+7RIqP6m6qArsmJ1OLlle4Pumkk1xrtlewFy0hpIoPdbV/6623XFKu1q/AhENLfmnt3759+1ZJ2XHj/BmWv2W9RSKjZj2r06lXRK9NZvEQH8u6vsLpYuw3v/edbHHR79jIJGdAhGJVgIzVfgEg3mMU8TE1qMv8iBEj7KKLLnI9DgITZc0MrgmWAs2aNcu1cqsFTLMQq6upN9mZqFCtsdmaMR6Illgl17HedyqiizgAX2lNxHHjxpX5HE1Eoy6QAJAqiI3+UDKs1ujjjjvOrXetLvoetTqpFVpd69VlXLcffviha7XWMl2i8aAnn3yyW0tX21FXYq3vqwmZDj744Bi+MyA1TUzC2EiCDcBXGguj7nlladBA07MAQOogNvpDkytpwqJ33nnH/QRSQj1q1ChXWNf40KlTp7oZhvV74NJdav1Wcq2lg7zu4Uq4AVS9c5MwNpJgA/BVnTp13A8A4P8RG/2hCeW8SeVKo4RZP6XR+Nw777zT/QCIrTpJGBsZgw0AAAAAgA9IsAEAAAAA8AEJNgAAAAAAPiDBBgAAAADAByTYAAAAAAD4gAQbAAAAAAAfkGADESosKEio/a5cudLeeOMN93vPnj3toYce8vnIACB2sTHSfRMbAVSFwsLChNo3sTGB18HesGGD3X///fbBBx/Yli1brE2bNnb99dfboYce6h6fOXOmjR492pYuXWpNmza1a665xk4++eSi1+/cudNGjRplb731lu3YscN9AG6++WarX79+DN8VUkFaerqtefkB27Xu5yrbZ3aDZtbo9IERvXbIkCG21157uevn+eeft2rVqvl+fPAPsRGJKhaxsTLxkdgIoCqkpaXZlkWzLH/bpirdb0b12lazbdewX0dsTOAE+7rrrrNff/3VFSQbNGhgTz75pF1xxRX20ksvudqWfv36We/evV1BUgXNwYMHuwJit27d3Otvv/12mzt3rqtVyc7Otttuu80GDBhgTz31VKzfGlKACpC7Vi2zREOSFf+IjUhkxEYAKEnJdf6W9ZZoiI0JlGD/+OOP9umnn9q0adPskEMOcffdcsst9vHHH9trr71m69atc6021157rXusZcuWtmDBAps0aZIrRK5evdpefvllGz9+fFGrjgqjJ5xwgn3xxRfWsWPHWL49IG5ccskl9vnnn7vfvdszzjjDtXoqAfvss8/sj3/8o/3zn/+0/Px8O+6441xrZ82aNWN85KmJ2AjET2w8/PDDXQUXsRFAqqDcmMBjsOvVq2cTJkywgw46qFj3Cf1s2rTJtb54rTGerl272rx581wLjm69+zwtWrSwxo0b25w5c6rwnQDxTcFQSdWJJ57ouvkE+/rrr12XYyVojzzyiLt+Bg6MrCs6Ko/YCMRPbFRll+KinktsBJAKKDcmcAt27dq17aijjip239tvv+1ab2666SbXFbJJkybFHm/UqJFt377d1q9f71ppVBANHhOg56xatSri41IBddu2bSXu134Db0UF3tzcXIsW7SuWkyLESqhzHQsax1pQUOBq5/QTKCMjI2bHFXws5alVq5ZlZma6a6VOnTruM+W9L93qc3zPPfdYs2bN3O/Dhg1zXZCXLFniErNE5L03fYZ0G0jvX+8zXsVrbAyOj5W9TsONn6kaDwMRG/2NjxWJjffdd597TM9J9tiYCPERQPTVrVvXsrKyLCcnJ2T3cK/cuPfee7uYf+utt1rfvn3thx9+sP32289SXczHYAeaP3++3XjjjdarVy/r0aOHm5hHYwcDeX/v2rXLfTkEPy76EtSXf6R2795tCxcuLPXx5cuXF/2uwmG7du0sWpYtWxbzglQsBZ7rWFHhK/jzlJ6eHtWKlfLo8x+qYFQWr9Co60oFqLy8PPe7bps3b24NGzYsep8HHHCAu/3222/dBFqJSO9F703BPpRQsSNexUtsLC0+Rnqdhhs/Uz0eBiI2+hcfy4uNSq5F7zUVYmOixUcAVW/fffd15UZPp06d3O33339Pgh1PCfa7775rgwYNcv+gMWPGFBUG9UUZyPtbX+CqVQl+3PvyqMwXvGpsWrVqVeJ+FexUqNGHytt+tGt5VUOeii02oc51LOizpGUK9FnU5y1eRFL4UcFXtYx6H/rcqnCs33XrbU/vU495hWY9Hk/vO1x6byogB7fkqvUpUcRTbAyOj5W9TsONn6kaDwMRG/2Pj+XFRq9iKrCCKlljY6LFRwCxiyGheg7FugdTvIiLBFuz2o4cOdJNwKPuBt6Xo2qH16xZU+y5+rt69equW5e6SGopGxUkA79Q9RyNNYyUvmC1j9KoUFPW436KdUtArFXluS6t4OUVvuIpaERyLN4YXr1Wt9770q0K7Js3b3YFRt331VdfudcceOCBcfW+w+G9Ny/hDJQo3R/jLTaWFh+r6jpN9XgYiNhYunCPp7zYqGXyVLGkx5I9NiZSfAQQO4HlRtEEqhLNXr2JJKaTnIlmyR0xYoRddNFFbpbbwMKgZr/1Zq7zzJo1y7Xk6MtBs+uqa5c3oY/XhVDjDzt37lyl7wOIdzVq1LAVK1aEHIOrMbUaP7N48WI3M+Tw4cPtpJNOcusfIjaIjUB8xMahQ4e6Vl1iI4BUQrkxQVuwVeC766673NTumjRk7dq1RY+pRkRTxGtKeHWL1O2HH35ob731lpuxTtQSo8XPNemItqPaWK312qVLFzv44INj+M6QKrIbNEuY/Z1//vk2ZMgQO+2000q0BKrFs3Xr1u6aU+vGqaee6rolIzaIjUh0VR0bK7PPsmKjeou0bdvWrUGvLpHERgCVkVG9dsLsk3JjgibYmhVXE+a888477ieQCo2jRo2ycePG2ejRo23q1KluhmP9Hrg8jVp4VIC8+uqr3d/du3d3hUog2goLCqzR6QNjst+09PA7n2hyrNmzZ4d8TK2eV155pVtXOVG7PSYTYiMSWaxiY6TxsbzYqHVfNTuuN4QGACKhOURqtu0as32HO/yDcmOCJtj9+/d3P2VRoVA/pdEYtDvvvNP9AFUpkiQ3kfeLqkNsRCKLZYwiPgKIV7Gc34C5FaoW30QAAAAAAPiABBtIcer+qKWgAADFY+P7778f68MAgLhCubF8JNgAAAAAAPiABBsAAAAAAB+QYAMVnH0RiYX/GRB9XGeJh/8ZEH1cZ6n9PyPBBsqQlZXlbrdt2xbrQ0GYvP+Z9z8E4B9iY+IiNgLRQ2xMXH7Gxpgu0wXEO63tV7duXVuzZk3R0kfJuNRBfn6+7dy50/2e6OsZqgZSQVL/M/3vEv39APEoVWJjMsVHYiMQfakSG5MlLkYrNpJgA+Vo0qSJu/WCZTIqKCiwvLw8y8zMtPQkWUdWQdL73wHwXyrExmSMj8RGILpSITYmW1z0OzaSYAPlUM1j06ZNrVGjRrZ7925LRtu3b7cffvjBmjdvbrm5uZbo1L0n0WtUgXiXCrEx2eIjsRGIvlSIjckUF6MRG0mwgQrShZesBRPVREq1atUsJycn1ocDIIEkc2wU4iOASCRzbCQuli052vQBAAAAAIgxEmwAAAAAAHxAgg0AAAAAgA9IsAEAAAAA8AEJNgAAAAAAPiDBBgAAAADAByTYAAAAAAD4gAQbAAAACenRRx+1Sy65pNh9w4YNszZt2hT76dmzZ7E1fB988EE78sgj7eCDD7a+ffvaTz/9FIOjB5CMSLABAACQcJ5++ml74IEHStz/3XffWf/+/e2TTz4p+nn++eeLHh83bpxNmzbNRowYYc8884xLuPv06WO7du2q4ncAIBmRYAMAACBhrF692iXQY8aMsX333bfYY4WFhbZkyRI78MADrWHDhkU/9evXd48riZ4yZYoNGDDAevToYW3btrWxY8faqlWrbMaMGTF6RwCSCQk2AAAAEsa3335rWVlZ9uqrr1qHDh2KPfbf//7Xtm3bZvvtt1/I1y5atMi2bt1q3bp1K7qvdu3a1q5dO5szZ07Ujx1A8suM9QEAAAAAFaXx1IFjqgN9//337vbJJ5+0jz76yNLT06179+527bXXWq1atVxLtTRt2rTY6xo1alT0GABUBgk2AAAAkoISbCXVSpjHjx/vWrTvvfdeW7x4sU2dOtW2b9/unpednV3sddWqVbONGzdGvF91TVfLeTBvf95tWlqa5ebmWl5enuXt3h3ZvvLyirap/SL0uUb0pOK5LiwsdNdv1BLs119/3Xr16lUiOAEAAACxcuWVV9qFF15o9erVc3+3bt3ajcE+99xz7euvv7acnJyisdje77Jz506X+EZq9+7dtnDhwlIfX758ubvVPtQdfcP69bZj/a8R7SsnL80amNmyZctSKsGpKO9cI/pS7VxnVzD3jSjBHjx4sN1xxx128skn25lnnmnt27ePZDMAAACAb9R67SXXnv3339/dqgu41zV8zZo11rx586Ln6G8t5xUpjQlv1apVifuVACsJ0WRsSq69FrC69epZXmZkrc+ZtX5/fy1atKAFu4xzjehJxXO9ZMmSCj83ogT7/ffft5deesleeeUVe/bZZ90FftZZZ9lpp53magkBAACAqqZGICXLjz/+eNF9arkWJcB777231axZ02bPnl2UYG/atMkWLFhgF198ccT7VeJcvXr1Uh9XEhL4eGZmpqVlZUW0r4zM34vvqZLYhCv4XCN6Uulcp1Wwe3jEs4g3adLEdcF566233BqEhx56qE2cONGOPvpot2yCljnQ2BIAAACgqhx//PE2c+ZMe/jhh9346w8//NBuuukmO+WUU6xly5aui6cSaS3x9d5777lZxTUBmsq2Gv4IADGf5KxTp07u55xzznGTSHzwwQfuZ4899rA///nPdvnll1tGRkalDxQAAAAoyzHHHGMPPPCATZgwwTX+aObwU0891QYOHFj0HK2BrYagYcOG2Y4dO6xz5842efJk180bAGKaYK9YscJ1E9ePagnV1ea6666zHj16uCT7kUcecf3V77nnnkofKAAAABBo1KhRJe478cQT3U9p1PBzww03uB8AiIsE+7nnnnNJ9fz5892yBieccIKNHDnSdRX3aNbG9evX2zPPPEOCDQAAAABIehEl2Lfccot16NDBbr/9djvppJPcZBGhaDbG8847r7LHCAAAAABA3It4HWzNxJifn180vlpjWLQGoMa6eE4//fSwtvvoo4/aJ598Yk8++WTRfRofoxbzQHvttZebyVwKCgrcRBZ6zubNm904mltvvdXNEgkAyYL4CAAAEP8imkVca57ddtttdu655xbdp+7i3bp1c93BVagLl2Yj16QUwb777js3M7kKlt7P888/X/T4uHHjbNq0aTZixAjXHV377tOnj+3atSuStwYAcYf4CAAAkMQJ9oMPPmivvvqqW/LA065dOxs0aJBNnz7dJk2aVOFtrV692hUQtVyCEvdAhYWFbpK0Aw880K2v7f3Ur1/fPa5C4pQpU9xskJpYrW3btjZ27FhbtWqVWyoMABIZ8REAACAFEuzXXnvNhgwZYr179y66r27dunbZZZe5tQQDW1DK8+2337plEZSwa1x3IM1Mvm3bNttvv/1CvlZrF27dutW1nHtq167tkv05c+ZE8tYAIG4QHwEAAFJgDLZmBy9tDJ8Ke2ohqaiePXu6n1C+//57d6sxhx999JGlp6db9+7dXRKvsd7efpo2bVrsdY0aNQrrGAAgHhEfAQAAUiDBVhL99ttv2+GHH17iMU2us88++/hxbK4AqUKjCoTjx493LTb33nuvLV682KZOnWrbt293z8vOzi72Oi0dtnHjxoj3q66XahkK5u3Pu5W0tDTLzc21aNG+dDypJtS5RvSk2vnWNaVrN5HFQ3ys7Ocm3PiZqvEwla/VeJBq5zwZ4iMAJFyCfemll9rQoUNtw4YNduyxx1qDBg3st99+s3//+9/25ptv2t133+3LwV155ZV24YUXWr169YrW1tYYQ02u9vXXX1tOTk7RWEPvd9m5c2elkl7Nhr5w4cJSH1++fHnR79qPulxGy7Jly1LmS728c43oS6XzHZx4Jpp4io+Rfm7CjZ+pHg9T9VqNF6l0zhM9PgJAwiXYWn5LY/s0Q23gZDkq6GmN7HCX5yqNWme8wqNn//33d7fq4uh1fVyzZo01b9686Dn6W2twR0pjHrUMWTAV7PQFq8mGvAJqtGt5W7RokZItNqHONaIn1c63JgdLdPEQHyv7uQk3fqZqPEzlazUepNo5T4b4CAAJl2DLRRdd5FpP1KKglmxNnqOu4yr0+WXw4MGuMPj4448X3aeWGVEBT+PAa9asabNnzy4qQG7atMkWLFhgF198ccT7VaGvevXqpT6uL9iyHvdTKnyZl6UqzzVS53wnQ/fHeIqPVfW5SfV4mIrXajxJlXMeq/ioisEmTZrEZN8A4Kf0ygZhJdWdOnVyBTo/k2s5/vjjbebMmfbwww+78YUffvih3XTTTW55sJYtW7ouTCooagmb9957z82aqwl+FKB79erl67EAQDwhPgJIJAcccIB99dVXIR+bO3eunXjiiVV+TAAQNy3YGm89cuRI++CDD0JOOqPEW60klXXMMcfYAw88YBMmTLCJEye6mXFPPfVUGzhwYNFztMZrXl6eDRs2zHbs2GGdO3e2yZMnu26MAJCsiI8A4t2UKVOKJkVUWfG5555zqx4E++KLLxj3DSC1E+zhw4e7Cc1OPvlk1xriV8v1qFGjStynGs2yajUzMjLshhtucD8AkKyIjwASjSZVVC8br/FFCXYwlSFVQaiJGwEgZRNs1T6qK+J5553n/xEBAAAg4Slp9hLntm3b2vTp0619+/axPiwAiL8EW90LNYEOAAAAUB7NAwEAqSCiBPu4446z119/3Q477DD/jwgAAABJ59NPP3VDDDV/T0FBQbHH1IX8rrvuitmxAUBME+x27dq5yXV++ukn69Chg+Xk5JQIkldddZVfxwgAAIAEn/Ds3nvvtWrVqln9+vVLLAeWDMsnAkClJjmTOXPmuJ9gJNgAAADwPPXUU26lA61Cw4zhAJJZRAk242gAAABQUWvXrrWzzz6b5BpA0qv0+lqbN2+2pUuX2q5duyw/P9+fowIAAEDS0PDCxYsXx/owACA+W7Bl9uzZNmbMGPvmm2+K1jacOHGiWxd76NCh/h4lAAAAEpaWdx04cKBVr17dzd+Tm5tb4jl77rlnTI4NAGKeYM+cOdP69u1rHTt2tEGDBrlE21vj8MEHH7TGjRtb7969fT1QAAAAJKYLLrjAzRyuRLu0Cc0WLlxY5ccFAHGRYGsG8WOOOcb+/ve/W15eno0ePdrd379/f9u2bZtrzSbBBgAAgIwYMYKZwgGkhIgSbNUwerOEBwfLww8/3KZOnerP0QEAACDhnXnmmbE+BACI3wS7Vq1a9uuvv4Z87JdffnGPAwAAABJqWddgnTt3rpJjAYC4S7DVPXzs2LHWunVrNyuk15K9atUqGz9+vPXo0cPv4wQAAECCuuSSS1xZsbCwsOi+4F6QjMEGkLIJ9vXXX2//+c9/7Nxzz7U99tjD3Xfddde5BLtp06budwAAAECeeOKJEvdp3p65c+faK6+8Yg899FBMjgsA4iLBrlOnjpvI7OWXX7ZZs2bZhg0bXLdw1U5qjE2opRcAAACQmrp06RLyfvV61NJd//jHP+zRRx+t8uMCgLhZBzs7O9u1YOsHAAAAiMShhx5qEydOjPVhAEDsEmy1XJfn9NNPj2TTAAAASCHvv/++1ahRI9aHAQCxS7CHDh0a8n5NVpGRkeF+SLABAAAgl156aYn7CgoK3Pw9K1assL59+8bkuAAgLhLs9957r9SJKtTF55FHHvHj2AAAAJAEAmcP96Snp7sVafr162dnnXVWTI4LAOIiwd5rr71C3r///vvb7t27bcSIETZt2rTKHhsAAACSwJNPPhnrQwCA+J7krDRt2rSx++67z+/NAgAAIMF99NFH9vnnn9umTZusfv36dsghh9iRRx4Z68MCgPhMsHft2mXPP/+8NWjQwM/NAgAAIIGpjPjXv/7VPvnkEzdXT7169Wz9+vVuaa6uXbu6W61QAwApmWD37NnTTWgWPFGFAuXOnTttyJAhfh0fAAAAEtxDDz1k8+bNs3vvvddOPvlkl2Tn5eXZ66+/bnfccYdbB/tvf/tbrA8TAGKTYHfp0qVEgi01a9a0o48+2g477LDKHxkAAACSghLpq6++2k477bSi+zIzM92qM+vWrbN//vOfESXYavlWq3jgGO+FCxfayJEj7ZtvvnHd0C+77LJis5irUejhhx+25557zjZv3mydO3e2W2+91fbee28f3imAVBdRgj1q1Cj/jwQAAABJ6bfffrN27dqFfEz3r169OuxtPv300/bAAw/YoYceWnSfelP27t3b9bZUy/iXX37pbrXOtjdT+bhx49xkvCrPNmnSxEaPHm19+vSx1157jW7qAGKTYK9cuTKs5++5556R7AYAAABJoHnz5q6LeLdu3Uo8NmfOHGvatGmFt6Vk/LbbbrPZs2fbvvvuW+yx6dOnW1ZWlg0fPty1kLds2dJ+/PFHmzBhgkuwNRZ8ypQpNmjQIOvRo4d7zdixY91EazNmzLBTTjnFh3cLIJX5Nga7LOqqAwAAgNR0/vnnuxbjnJwcNwZ7jz32sLVr17qu4xMnTnTdxyvq22+/dUn0q6++ao888oitWLGi6LG5c+e6oYxKrj3eJGranxqJtm7dWizRr127tmtFV6JPgg0gJgm2uuOo5vAPf/iDG0vTuHFj1yXn/ffftzfffNOuvPLKUtfKBgAAQGq54IILbMGCBTZmzJhiy7kWFhbaGWecYX/5y1/CaujRTyirVq2y1q1bF7uvUaNG7vaXX35xj0twi7me4z0WCb2Pbdu2lbh/+/btxW7VQJWbm+smeMvbvTuyfeXlFW1T+0Xoc43oScVzXVhYWOEG5ogS7FdeecVNZhY8Fvukk05yS3TNnz8/rJpIAAAAJC91zdbEY5dffrlbB3vjxo2usHrssce6btx+2bFjR4lx1NWqVXO3WunGSwhCPUfHFKndu3eX2WNz+fLl7lbJtVrLN6xfbzvW/xrRvnLy0kwL4i5btiylEpyK8s41oi/VznV2BedoiCjBnjlzppt9MZTu3bvbM888E8lmAQAAkES+++47u+mmm1wirR6OSqb1s2nTJtd1+1//+pfrGdmiRQtf9qcu6ErmAymxlurVq7vHRc/xfveeo+Q3Uuqy3qpVqxL3KwFWEqKx4tq+1wJWt149y8uMrPU5s1Y9d6tzRgt26eca0ZOK53rJkiUVfm5ECXa9evXsP//5jx1xxBEhk291GQcAAEDq+vnnn93yWEpkgxNoJaSDBw+2xx57zC688EJ7+eWXfSk/albwNWvWFLvP+1vbV9ds7z5NvBb4nDZt2kS8XyXOSuBLoyQk8HGNEU/LyopoXxn/G1+eKolNuILPNaInlc51Whjzj6VHsoOzzz7b/vGPf7hxNOoOrhoMTSpx5513ukCp7j8AAABIXZq5u27duvbSSy/ZCSecUKJgrvWpn3/+edc9W5OQ+UFrWmu28vz8/KL7Zs2a5RJ8DWNs27at1axZ081A7lFrusaH67UAUFkRJdh//etf7eKLL7bHH3/cLrroIjvxxBPd3y+88IJde+21bqbISCi4XnLJJcXu03gWbfvggw92E1o88cQTxR4vKCiwBx980C2voOf07dvXfvrpp4j2DwDxivgIINGoV6PWl65fv36pz2nYsKFrmPn000992aeW4tqyZYvdfPPNrkvniy++6Mqr/fr1KxpDqbipRqL33nvPFi1a5Mquavnu1auXL8cAILWlR9pEPnToUPvss89coe/ee++1SZMm2ccff+wCaSSefvppNwYnkGYm7927t+vCo+T9qquucgFRv3vGjRtn06ZNsxEjRrix3ypQ6hiCx98AQKIiPgJIROp2HbxOdSia9bsyM3gHUiu1yqSaAEyzk2vOIHVF1++eAQMGuN6Yw4YNc7ObZ2Rk2OTJk123dQCorIjGYHvUxcZb+kCtI964lnCsXr3aLfmlrjrBQXj69Oku2A0fPtyNVdGkGD/++KPrcqQaShUSp0yZYoMGDbIePXq414wdO9a11syYMYO1DAEkNOIjgESmluvg8dChqMKwTp06Ee0jeEUbad++vT377LOlvkYJ9Q033OB+ACAuWrC9pbpUaFONYP/+/V3BTq3a11xzTVitI99++60rJL766qvWoUOHYo9pXHeXLl1c4dGjGSc15nvt2rWuW8/WrVutW7duRY/Xrl3bLX8wZ86cSN8aAMQF4iOARKYxzeqiXR5NcKbYBAAp24KtJRWGDBlip512mlsPW2NX5LjjjrM77rjDdUscOHBghbalcYP6CUXdhdRtKJDXYv7LL78UdSdq2rRpiedUpquRljzYtm1biftDLaqu7vLRnMVR+0rFJRhScQH7WEq1861rKpzZIGMl3uNjZT834cbPVI2HqXytxoNUO+d+xkfNG6Eu2GplVlnRW4/aowYZDX/56KOPXO8bAEjZBHv8+PFuIrPbb7+92CyN6pb422+/ua6LFU2wy7Jjx44SC3p7wVnrFXpfdqGes3Hjxoj3u3v3bjd5UEUWVVfhMJq1rhpDlCpf6qGk2gL2sZZK5zs4biSaeIqPkX5uwo2fqR4PU/VajRepdM79io8HHXSQ3XjjjXbXXXe5no/qUdOsWTNXdly5cqUb/qLu4X/729/c8BUASNkEW4UctWCHom6MDz30kPlB6yYGdzdXwVG05poeFz3H+917TmValdUls1WrVhVaVD3arWBaViIVW2xScQH7WEq1862ZZRNdPMTHyn5uwo2fqRoPU/lajQepds79jo9abUZLY2kSMc3a7cWpGjVq2BFHHOFmEA8eAgMAKZdga4bGpUuX2uGHH17iMd2vx/2gJROCJ8fw/m7cuHHRpGq6TzPpBj6nTZs2Ee9Xhb6yFk2vykXVU+HLvCyptIB9PEiV850I3cMTKT5W1ecm1eNhKl6r8SRVznk04uMhhxzifkQ9HTV3hOaEAIBkFNEkZyeddJJbW/Wtt94qakFRQP7mm2/c+OsTTjjBt8kx5s2bV6wb+qxZs1wrhpJ41YhqJnN1MfJs2rTJFixY4F4LAMmK+AggUWcWJ7kGkMwiSrA1vlrLcunWq5HURBbnnHOO60KlsTR+0JjuLVu22M033+y6LGkmyscff9z69etXNEbo4osvdmu/qtuRZs3VJBpq2enVq5cvxwAA8Yj4CAAAkCRdxFVwmzRpkn366aeuxWTDhg1Wq1Ytt2TMUUcd5Vv3IrXCaD8jR450y4E1bNjQBg8e7H73DBgwwHWFHDZsmJv0Ry0zGuejcYIAkKyIjwAAAEmSYF9xxRXWp08fNwY71DjsSGkZh2Dt27e3Z599ttTXZGRk2A033OB+ACBZER8BAACStIv4/Pnzk2KSIAAAAAAAYppga63CV1991a2HCgAAAAAAIuwiXq1aNZdgv/nmm9ayZcsSy1aodXvq1Kl+HSMAAAAAAMmZYK9atco6duxY9HdhYWGxx4P/BgAAAAAg2VU4wZ4xY4Z17drVrV345JNPRveoAAAAAABI1jHYWtt6+fLlxe6bOHGirVu3LhrHBQAAAABAcibYwd2+8/Pz7f7773fdxQEAAAAASHURzSLuYaw1AAAAAAA+JNgAAAAAAOB3JNgAAAAAAMRDgq01rwEAAAAASHVhrYN91VVXWXZ2drH7+vfvb1lZWSWS7nfffdefIwQAAAAAIJkS7DPOOCO6RwIAAAAAQCok2HfffXd0jwQAAAAAgATGJGcAAAAAAPiABBsAAAAAAB+QYAMAAAAA4AMSbAAAAAAAfECCDQAAAACAD0iwAQAAAADwAQk24lJaWprl5ua6WwAAAABIqnWwgYoqKCi09PTKJcZKrtu1axeVbQNALFUkjhHrAABITCTY8J0KhWOenmc/r97s63abNa5lgy46xNdtAkC8xUhiHQAAiYsEG1GhguPSFRtjfRgAEJeIkQAAJCfGYAMAAAAA4AMSbAAAAAAAfECCDQAAAKDKFBYWxvT1QDQxBhsAAABJY/Xq1da9e/cS999999125pln2sKFC23kyJH2zTffWP369e2yyy6zSy+9NCbHmqq0DOtnX620jVt2hv3aOjWr2WHt94zKcQF+IMFOUSwBAwCVi4/EUSA+LVq0yKpVq2bvvvuuS+Q8tWrVsvXr11vv3r2tZ8+edscdd9iXX37pbmvUqGFnnXVWTI871Si5Xr85/AQbiHck2CkqWktpdWrbyC49qeT61QCQTPGRpbSA+PX999/bvvvua40aNSrx2NSpUy0rK8uGDx9umZmZ1rJlS/vxxx9twoQJJNgAfEGCncKisUxMs0Y1fd0eAMQCy2gBieu7775ziXMoc+fOtS5durjk2tO1a1d79NFHbe3atbbHHntU4ZECSEYk2AAAAEiqFux69erZRRddZMuWLbN99tnHrrzySjcue9WqVda6detiz/daun/55ZeIE2xNurVt27YS92/fvr3Yrbqs5+bmWl5enuXt3h3ZvvLyiraZiJN9BZ6D3RGcg7y8jJDvP/hcI3pS8VwXFhYWG3KS8Ak2k1UAQEnERgAoTknbDz/8YK1atbKhQ4dazZo17Y033rC//OUv9thjj9mOHTssOzu72Gs0Xlt27ox8PLASRcXc0ixfvtzdKrFs166dbVi/3nas/zWifeXkpVkDM1d5kIgJjncO1m9Yb7+u2xL269MKfu8tWdr79841oi/VznV2UOxI6ASbySoAoCRiIwAUp67fs2fPtoyMDMvJyXH3HXjggbZ48WKbPHmyu2/Xrl3FXuMl1tWrV494vxrXraQ+mBJAJSEaE67E0ovVdevVs7zMyFqfM2vVc7ctWrRI2BZsqVe3nhWm54b9+nq1c0K+/+BzjehJxXO9ZMmSCj83IRJsJqsAgJKIjQBQkioSg+2///72ySefWJMmTWzNmjXFHvP+bty4caWSxrISdCUhgY8rLqdlZUW0r4z/jR9P9MRG50DfU5G8rqz3H3yuET2pdK7TKtg9XNItSSerUK2KJqsAgGRFbASA4tRS3alTJ9eKHUhDZdTC3LlzZ5s3b57l5+cXPTZr1izXGtqggTpeA0DlJEwLdlVOVlHRiSoCJ2qIlmhMYBHtY462RJ3UI56l2mQV4UxUEc9iPZFPZT834caiqrj2o31M4Ww/1LZT7VqNB6l2zhM9PqrScb/99nO9dzQsRjFy+vTpbpjMCy+84JLoSZMm2c0332x9+vSxr776yh5//HH3XABIiQQ7FpNVVHSiisCJGqIlGhNYRPuYoy1RJ/VIBKk0WUVFJ6qIV/E0kU+kn5twY1FVXPvRPqZwtl/WtlPpWo0XqXTOEzk+pqen2/jx4+2+++6zgQMH2qZNm9w1p7joVToqwdYEkGeccYY1bNjQBg8e7H5H6lTEACmdYMdisoqKTlQh0Q4u0ZjAItEDYqJO6hHPUm2yinAmqohX8TCRT2U/N+HGoqq49qN9TOFsP9S2U+1ajQepds6TIT6qh45WUyhN+/bt7dlnn63SY0o2imWffbXSNm4Jv8J2z4Y1rcP+DaNyXEA8iPsEOxaTVYQ7UUU0pcKXebg4J9GTKpNVJHolU7xN5FNVn5t4vPajeUxlbTtVrtV4kirnPFniI6JPyfX6zeEn2LVrJG4PCSApJjljsgoAKInYCAAAEH/SE2myCs2Ku3TpUtftR5NVaDIfLTezZcsWN1mFujW9+OKLbrKKfv36xfrQASBqiI0AAADxJ+67iDNZBQCURGwEAACIP3GfYAuTVQBAScRGAACA+BL3XcQBAAAAAEgEJNgAAAAAAPiABBsAAAAAAB+QYAMAAAAA4AMSbAAAAAAAfECCDQAAAACAD0iwAQAAAADwAQk2EkbdWtWsoKAwKtuO1nYBoCpVJJYR7wAUFhIHgGjJjNqWAZ/VzM2y9PQ0G/P0PPt59WbfttuscS0bdNEhvm0PAGKlvBhJvAMgaWlp9tlXK23jlp1hv3bPhjWtw/4NLR7fU25urrsFYokEGwlHBcelKzbG+jAAIC4RI4HklpaV41qgK5tIKrlevzn8BLt2jWyLpZzsjJDvX8l1u3btKrwdP84hEAoJNgAAAJAg0jKzXGK4ZdEsy9+2KezXZ9VrYtVbtLdElZ2VEbIFPi8vz9ZvWG/16tazzMyyU5w6NavZYe33rIKjRSoiwQYAJByNI1Z36Mo+BwASlZLr/C3rw35dem4tSwbBLfC7d++2X9dtscL0XMvKyorpsSG1kWDHuSf+tcDmL1rj6zY7tW1kl55U8S40ABBvGGsMAPC7m3k46GKO0pBgx7k1v23zfSxds0Y1fd0eAMQCY40BAH52M68oupijLCTYAAAAAFJOpBO9AWVhHWwAAAAAAHxAgg0AAAAAgA9IsAEAAAAA8AEJNlJe3VrV3HI+0RLNbQNAValILCPeAQBSHZOcIeXVzM0qd8mfSLFUEIBkwdJoAACUjwQb+B+W/AGAshEnAQAoG13EAQAAAADwAQk2AAAAAAA+IMEGAAAAgArKyc6wwsLKTepYUMnXV3b/iB7GYAMAAABABWVnZVhaWpp99tVK27hlZ9iv37NhTeuwf8OIX1+nZjU7rP2eYb8OVYMEG6iCJcA0+240RHPbAFBVKhLLiHcA4o2S4/Wbw0+Qa9fIrtTrEd9IsIEoYgkwACgfS4ABAJIFCTZQBVjaBgDKRpwEACQDJjkDAAAAAMAHtGDHuUb1q1vLver4vk0AAAAAgL9IsONURo26VlhQYJee1M79+E3b1gRcAJCMFN8U59LSy++oVdHnAUA8Sc+tFdnrcmr4fiyIzTJhmsm8MvzYBpI0wS4oKLCHH37YnnvuOdu8ebN17tzZbr31Vtt7770tUSn4qcC35uUHbNe6n33ddnaDZtbo9IFuAi5AFFxzc3MJskkoGeNjRSi+VSSGevEQQGpJ5NiYlpntEqNaB3SL9aEgQZcJE5b6ip6kSLDHjRtn06ZNs1GjRlmTJk1s9OjR1qdPH3vttdcsO/v3afATlQqGu1Yti/VhIMmXAFNy3a7d//eUYDmc5JHM8bEiiKGoaDwj7qWWRI6NaZlZLrn64KMvbMOvv4X9+mYt9rJDO7WNyrGhasVqmS+vYSad3l/JmWDv2rXLpkyZYoMGDbIePXq4+8aOHWtHHnmkzZgxw0455ZRYHyKQUEuAHdCivvX900EWLfkFhZbBuuBVgvgIlL8EWGWXAYvmGt6sDx4dyRIbN27YYuvWbQj7dXX3qBeV40HqdDP3GmYKCgtj2kW9ME67ySd8gr1o0SLbunWrdev2/91kateu7f7pc+bMSZggCcTL0jbNGtWM2trdndo2cnMKsC541SA+AtFfAiyaa3izPnh0EBuBynUzz8vLs7o10u3IQ1pG3E29jg9d1OO1m3zCJ9irVq1yt02bNi12f6NGjYoeAxAfBVIl79HaNuIvPtKyhlQRzZhGvEy+2FiZCcrca7NzK7XvtLSMSr0eySWSbua7d++2tMK0SnVTz/FporZYdZMvS1qh3lkCe+WVV2zw4MG2cOHCYuMAdN+aNWvs8ccfD2t78+fPd//srKySE4DpftXYZGZmFvsw6Pf8bRvN8vPNL2lZ2ZaeU9P37ToZGZZRvY5t25Fnu/P83XZWZoZVz8m0Ldt2W35BQUJsOxGPOZrbTsRjloz0dKtZPctdpxX6YkhLs06dOlkyi3Z8LC0menTf1u36X4f+n2Rlpltutczy49z/Ylbg/1bb3rhll+Xlh/4cVcvKcJ+HaGxbMjPSrU7N3ycaCld52y9r27ovPz/fMjJ+b3nwc9vRPO543naqnvNUjo+xLjv61R11x45dbrK2cOlYsrMzbcfO3VaQH8nrM35//a48V5Ea9usz0l3raTReX9GK3WgeQyK83o9taLhfNR8+B7t251skIUz/Z5UjKnMOtI2c7Ezfy44J34Kdk5NTNJ7G+1127tzpxgeEq6zgp/tKm/hCBbRoiNZ2RYlOtD4CKthGS7S2nYjHHM1tJ+IxV7Tgouekwozp0Y6PZcVET40KrFZQ0TgX/D9TYhHLbYd6XUVVZPulfQ+VN6lMpNuuiGTediqe81SNj/FSdqysnJzKbTenWuW+i5WYJPLr4+EYYv36eDiG7KyMmJ8Dv8uOCZ9ge917VOPYvHnzovv1d5s2bcLeXseOHX09PgCIFeIjAJREbAQQTQk/t3rbtm2tZs2aNnv27KL7Nm3aZAsWLHBrGgJAqiI+AkBJxEYA0ZTwLdjqdnPxxRfbmDFjrH79+rbXXnu5tQy1pmGvXr1ifXgAEDPERwAoidgIIJoSPsGWAQMGuAkkhg0bZjt27HC1j5MnTw452QQApBLiIwCURGwEEC0JP4s4AAAAAADxIOHHYAMAAAAAEA9IsAEAAAAA8AEJNgAAAAAAPiDBBgAAAADAByTYAAAAAAD4gAQbAAAAAAAfkGADAAAAAOADEuwKKCgosAcffNCOPPJIO/jgg61v3772008/xfqwksKGDRvs1ltvte7du1unTp3sggsusLlz5xY9PnPmTDvzzDOtQ4cOdsIJJ9gbb7wR0+NNFsuWLbOOHTvaiy++WHTfwoUL7eKLL3af8Z49e9oTTzwR02NE/CImRh+xMbaIkags4mT0EB9jg7hYcSTYFTBu3DibNm2ajRgxwp555hkXNPv06WO7du2K9aElvOuuu86++OILu//+++2FF16wAw44wK644gr74YcfbOnSpdavXz/35aSL+ZxzzrHBgwe7wInI7d692wYNGmTbtm0rum/9+vXWu3dva968ufs/XHXVVTZmzBj3OxCMmBh9xMbYIUbCD8TJ6CE+Vj3iYngyw3x+ylEgnDJlivtQ9ejRw903duxYd+HOmDHDTjnllFgfYsL68ccf7dNPP3VfQIcccoi775ZbbrGPP/7YXnvtNVu3bp21adPGrr32WvdYy5YtbcGCBTZp0iTr1q1bjI8+cT300ENWs2bNYvdNnz7dsrKybPjw4ZaZmenOtf4/EyZMsLPOOitmx4r4Q0yMPmJjbBEjUVnEyeghPsYGcTE8tGCXY9GiRbZ169ZiF2Xt2rWtXbt2NmfOnJgeW6KrV6+euwgPOuigovvS0tLcz6ZNm1x3n+Bg2LVrV5s3b54VFhbG4IgTnz6zzz77rI0aNarY/TrXXbp0cQEy8FwvX77c1q5dG4MjRbwiJkYfsTF2iJHwA3EyeoiPVY+4GD4S7HKsWrXK3TZt2rTY/Y0aNSp6DJHRl81RRx1l2dnZRfe9/fbbrvZLtbw6v02aNClx3rdv3+66pSA8+uJRN6lhw4aV+DyXdq7ll19+qdLjRHwjJkYfsTE2iJHwC3EyeoiPVYu4GBkS7HLogpTAC1mqVatmO3fujNFRJaf58+fbjTfeaL169XJdqnbs2FHivHt/M4YpfLfffrubnOLUU08t8Vioc63PuPA5RyBiYtUjNlYNYiT8QpysOsTH6CIuRoYx2OXIyckpuii9370PTm5ubgyPLLm8++67bqySZoPUBAneRRocDL2/Offhefnll11XHo1PCkWf7eBz7QXH6tWrV8kxIjEQE6sWsbFqECPhJ+Jk1SA+RhdxMXIk2OXwukOsWbPGzZLn0d+aRAGV99RTT9nIkSPdUgr33HNPUW2Yzr3OcyD9rYu2Vq1aMTraxKQZHTXxhzfZiue2226zf/3rX66LT6hzLY0bN67SY0V8IyZWHWJj1SFGwk/EyegjPkYfcTFyJNjlaNu2rZs1b/bs2UVBUuMRNCOh1n1D5XhLWFxyySV28803u0kqPIceeqh9/vnnxZ4/a9YsV1OZns7ohnCoZlddeQKpO9WAAQPstNNOs1deecUtI5Kfn28ZGRlF57pFixbWoEGDGB014hExsWoQG6sWMRJ+Ik5GF/GxahAXK6EQ5br//vsLu3TpUvjuu+8WLly4sPDyyy8v7NWrV+GuXbtifWgJ7Ycffij8wx/+UHjVVVcVrlmzptjPpk2bCr///nv3+OjRowuXLFlSOHny5MJ27doVfvbZZ7E+9KTQunXrwhdeeMH9vnbt2sLOnTsXDhkypHDx4sXu/oMOOqjwxRdfjPVhIg4RE6OL2BgfiJGoDOJkdBAfY4u4WDG0YFeAamry8vLcDHqqyencubNNnjzZrf2GyGnWRy1c/84777ifQGeccYZbDmDcuHE2evRomzp1qjVr1sz9zjqG/lNNo9aIVHcrnfuGDRu6WSP1OxCMmBhdxMb4Q4xEuIiT0UF8jB/ExdKlKcsu43EAAAAAAFABDEYAAAAAAMAHJNgAAAAAAPiABBsAAAAAAB+QYAMAAAAA4AMSbAAAAAAAfECCDQAAAACAD0iwAQAAAADwAQk2AAAAAAA+IMEGAAAAAMAHJNgAAAAAAPiABBsAAAAAAB+QYAMAAAAAYJX3fw0KKU4t7LCsAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1000x300 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 3, figsize=(10, 3))\n",
    "\n",
    "data = tips[['total_bill', 'tip']]\n",
    "print(data.head())\n",
    "\n",
    "### Pandas histogram\n",
    "data.plot(kind='hist', title='Pandas plot(kind=\"hist\")', ax=ax[0])\n",
    "\n",
    "### Matplotlib histogram\n",
    "ax[1].hist(data, label=['total_bill', 'tip'])\n",
    "ax[1].set_title('Matplotlib ax.hist()')\n",
    "ax[1].legend()\n",
    "\n",
    "### Seaborn histogram\n",
    "sns.histplot(data=data, ax=ax[2])\n",
    "ax[2].set_title('Seaborn histplot()')\n",
    "\n",
    "plt.tight_layout()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "21c8f8d4",
   "metadata": {},
   "source": [
    "The histograms are different because:\n",
    "- pandas and Matplotlib use 10 bins by default, whereas Seaborn chooses the number of bins from the data (NumPy's `'auto'` rule, the larger of the Sturges and Freedman-Diaconis estimates), which may produce more or fewer bins.\n",
    "- Matplotlib bars are narrow because we pass a 2-column DataFrame directly to `ax.hist()`. Matplotlib draws the two columns as side-by-side bars within each bin, so each bar takes half of the bin width.\n",
    "- Seaborn draws both columns on the same bins, one on top of the other, and makes the bars partly transparent. Where a `tip` bar and a `total_bill` bar fall in the same bin, the colors mix (the small gray-purple bar near \\$7). Because tips and bills are on very different scales, almost all tips land in the first two bins, so in practice you would plot variables with different scales separately."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b51bead9",
   "metadata": {
    "tags": [
     "thebe-interactive"
    ]
   },
   "outputs": [],
   "source": [
    "### Exercise: Compare Tip Distributions\n",
    "# Practice path: Additional practice.\n",
    "# The manager now wants to compare tips at lunch and at dinner on one chart.\n",
    "# Use sns.histplot() to draw a histogram of tip with the bars colored by time.\n",
    "# In a comment, explain how you would compare typical tips without confusing counts with tip amounts.\n",
    "# Also explain one limitation: Do taller bars necessarily mean that customers leave larger tips?\n",
    "### Your code starts here\n",
    "\n",
    "\n",
    "\n",
    "### Your code ends here\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1505f12e",
   "metadata": {
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [],
   "source": [
    "### Solution\n",
    "sns.histplot(data=tips, x='tip', hue='time')\n",
    "plt.show()\n",
    "# Finding: Compare where the distributions sit on the horizontal axis; taller bars show more observations.\n",
    "# Limitation: Bar height counts observations. Dinner has more observations, so compare horizontal positions as well as counts.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f1106e77",
   "metadata": {},
   "source": [
    "## Seaborn Data Model and Semantic Mappings\n",
    "\n",
    "Seaborn works best with **tidy data**, which the Seaborn documentation calls **long-form** data:\n",
    "- each column is a variable\n",
    "- each row is one observation\n",
    "\n",
    "Common semantic mappings (you already saw all three in the pandas vs. Seaborn scatter plot: `hue='time'`, `style='sex'`, `size='size'`):\n",
    "- `hue`: map category/value to color\n",
    "- `style`: map category to marker or line style\n",
    "- `size`: map numeric values to marker size\n",
    "\n",
    "Important distinction:\n",
    "- Use `size=...` to map from data\n",
    "- Use `s=...` for a constant marker size\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4c311f1d",
   "metadata": {},
   "source": [
    "### Data Formats\n",
    "\n",
    "Data can be structured in different ways. Understanding these formats is essential because most Python visualization and analysis libraries, including Seaborn and pandas, expect data in a specific format. The common data formats are tidy, wide, and nested. \n",
    "\n",
    "| Format | Best For |\n",
    "|--------|----------|\n",
    "| Tidy | Seaborn, statsmodels, most analysis |\n",
    "| Wide | Excel, quick inspection, some ML models |\n",
    "| Nested | Raw API/JSON data before processing |"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "be3607d1",
   "metadata": {},
   "source": [
    "Seaborn supports both long-form and wide-form data. Long-form data makes explicit mappings such as `x='subject'`, `y='score'`, and `hue='gender'` easier because each variable has a named column. Wide-form plots can also work, as in the histogram comparison above.\n",
    "\n",
    "If the variable you want to map exists only in column headings, reshape it into a column with `melt()` before using that explicit mapping. If a plot fails, check column names, data types, and the requested mapping before deciding that the table needs reshaping. See [Seaborn's data-structure guide](https://seaborn.pydata.org/tutorial/data_structure.html)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "beb7be75",
   "metadata": {},
   "source": [
    "### Tidy (Long-Form) Format\n",
    "\n",
    "In tidy data, \n",
    "\n",
    "- **each row is one observation**, \n",
    "- **each column is one variable**, and \n",
    "- **each cell holds one value**. \n",
    " \n",
    "This is the preferred format for analysis and visualization."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c37039f8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-31T06:00:51.611639Z",
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     "shell.execute_reply": "2026-08-31T06:00:51.614153Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>student</th>\n",
       "      <th>subject</th>\n",
       "      <th>score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Alice</td>\n",
       "      <td>math</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Alice</td>\n",
       "      <td>english</td>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Bob</td>\n",
       "      <td>math</td>\n",
       "      <td>78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Bob</td>\n",
       "      <td>english</td>\n",
       "      <td>88</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  student  subject  score\n",
       "0   Alice     math     90\n",
       "1   Alice  english     85\n",
       "2     Bob     math     78\n",
       "3     Bob  english     88"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "tidy = pd.DataFrame({\n",
    "    'student': ['Alice', 'Alice', 'Bob', 'Bob'],\n",
    "    'subject': ['math', 'english', 'math', 'english'],\n",
    "    'score':   [90, 85, 78, 88]\n",
    "})\n",
    "tidy"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "tidylong1",
   "metadata": {},
   "source": [
    "The observation unit and meaning of each column matter more than whether a table looks long. Keep measurements with different meanings and units, such as score and attendance, separate when summarizing them. Melting them into one `value` column does not make a combined mean meaningful; retain the measurement label and analyze each measurement separately."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "64dc0b36",
   "metadata": {},
   "source": [
    "### Wide Format\n",
    "\n",
    "In wide data, \n",
    "\n",
    "- each row represents one student, and \n",
    "- each subject has its own column. \n",
    " \n",
    "This format is easier for humans to read at a glance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "2692e81d",
   "metadata": {
    "execution": {
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     "shell.execute_reply": "2026-08-31T06:00:51.617627Z"
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   },
   "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>student</th>\n",
       "      <th>math</th>\n",
       "      <th>english</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Alice</td>\n",
       "      <td>90</td>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Bob</td>\n",
       "      <td>78</td>\n",
       "      <td>88</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  student  math  english\n",
       "0   Alice    90       85\n",
       "1     Bob    78       88"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wide = pd.DataFrame({\n",
    "    'student': ['Alice', 'Bob'],\n",
    "    'math':    [90, 78],\n",
    "    'english': [85, 88]\n",
    "})\n",
    "wide"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b2a8a4fa",
   "metadata": {},
   "source": [
    "### Nested Format\n",
    "\n",
    "Nested data stores values inside **dictionaries** or **lists**, common when loading from JSON or APIs. It must be flattened before use."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "6f7219b3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-31T06:00:51.618847Z",
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     "shell.execute_reply": "2026-08-31T06:00:51.620284Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Alice': {'math': 90, 'english': 85}, 'Bob': {'math': 78, 'english': 88}}"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nested = {\n",
    "    'Alice': {'math': 90, 'english': 85},\n",
    "    'Bob':   {'math': 78, 'english': 88}\n",
    "}\n",
    "nested"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a089706c",
   "metadata": {},
   "source": [
    "### Converting Formats\n",
    "\n",
    "**Wide vs. tidy data errors:**\n",
    "If the column you want to pass as `x=` or `hue=` does not exist because its categories are spread across several columns, the data is in wide format. Use `pd.melt()` to convert it to tidy format first.\n",
    "\n",
    "Start with a wide table of student scores:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0380f039",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "students = pd.DataFrame({\n",
    "    'student': ['Alice', 'Bob', 'Carol', 'David', 'Eve'],\n",
    "    'gender':  ['F', 'M', 'F', 'M', 'F'],\n",
    "    'grade':   [10, 10, 11, 11, 12],\n",
    "    'math':    [90, 78, 85, 72, 95],\n",
    "    'english': [85, 88, 80, 76, 92],\n",
    "    'science': [92, 80, 88, 70, 96]\n",
    "})\n",
    "students"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "114c4973",
   "metadata": {},
   "source": [
    "**Wide to tidy:** `pd.melt()` keeps the `id_vars` columns and stacks the subject columns into two new columns, `subject` and `score`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9a0aec16",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "students_tidy = pd.melt(students,\n",
    "                        id_vars=['student', 'gender', 'grade'],\n",
    "                        var_name='subject',\n",
    "                        value_name='score')\n",
    "students_tidy"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f6c04c71",
   "metadata": {},
   "source": [
    "**Tidy to wide:** `pivot()` reverses the process, with one row per student and one column per subject."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f572d2e5",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "students_wide = students_tidy.pivot(index='student',\n",
    "                                    columns='subject', values='score')\n",
    "students_wide"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4c91c161",
   "metadata": {},
   "source": [
    "**Nested to tidy:** build a DataFrame from the dictionary, turn the student names into a column, then melt."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c9c9b324",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "nested_tidy = (\n",
    "    pd.DataFrame(nested).T\n",
    "    .rename_axis('student')\n",
    "    .reset_index()\n",
    "    .melt(id_vars='student', var_name='subject', value_name='score')\n",
    ")\n",
    "nested_tidy"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae006b2b",
   "metadata": {},
   "source": [
    "Once the data is tidy, every column can be passed straight to Seaborn. Here `subject` goes on the x-axis and `gender` is mapped to color:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "63b8f6bb",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "sns.barplot(data=students_tidy, x='subject', y='score', hue='gender',\n",
    "            errorbar=None)   ### hide error bars; we only have a few students\n",
    "plt.title('Average Score by Subject and Gender')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fb4dd870",
   "metadata": {
    "tags": [
     "thebe-interactive"
    ]
   },
   "outputs": [],
   "source": [
    "### Exercise: Reshape Regional Sales\n",
    "# Practice path: Core classroom practice.\n",
    "# The sales team keeps quarterly revenue (in $1,000s) in the wide table below.\n",
    "# 1. Use pd.melt() to make a tidy DataFrame named sales_tidy\n",
    "#    with the columns region, quarter, and revenue.\n",
    "# 2. Draw a bar plot with quarter on the x-axis, revenue on the y-axis,\n",
    "#    and the bars colored by region.\n",
    "# In a comment, identify the region with the highest Q4 revenue.\n",
    "# Also explain one limitation: Can one revenue value per region and quarter show variation among individual orders?\n",
    "sales = pd.DataFrame({\n",
    "    'region': ['North', 'South', 'East', 'West'],\n",
    "    'Q1': [240, 215, 260, 230],\n",
    "    'Q2': [255, 220, 250, 245],\n",
    "    'Q3': [270, 210, 275, 250],\n",
    "    'Q4': [290, 230, 280, 265]\n",
    "})\n",
    "### Your code starts here\n",
    "\n",
    "\n",
    "\n",
    "### Your code ends here\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4b588976",
   "metadata": {
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [],
   "source": [
    "### Solution\n",
    "sales = pd.DataFrame({\n",
    "    'region': ['North', 'South', 'East', 'West'],\n",
    "    'Q1': [240, 215, 260, 230],\n",
    "    'Q2': [255, 220, 250, 245],\n",
    "    'Q3': [270, 210, 275, 250],\n",
    "    'Q4': [290, 230, 280, 265]\n",
    "})\n",
    "sales_tidy = pd.melt(sales, id_vars='region',\n",
    "                     var_name='quarter', value_name='revenue')\n",
    "print(sales_tidy.head())\n",
    "\n",
    "sns.barplot(data=sales_tidy, x='quarter', y='revenue', hue='region')\n",
    "plt.show()\n",
    "# Finding: North has the highest Q4 revenue: 290 thousand dollars.\n",
    "# Limitation: These quarterly totals do not show the distribution of individual order values.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cd74886e",
   "metadata": {},
   "source": [
    "## Figure vs. Axes-Level APIs\n",
    "\n",
    "Seaborn has two common usage patterns:\n",
    "\n",
    "- **Figure-level** functions (e.g., `displot`, `relplot`, `catplot`) manage an entire figure/grid and return grid objects (`FacetGrid`, `JointGrid`, `PairGrid`).\n",
    "- **Axes-level** functions (e.g., `histplot`, `scatterplot`, `lineplot`, `boxplot`) draw on a specific Matplotlib `ax` and return an `Axes`. Because they draw onto one Matplotlib `Axes`, you can combine them with other plots in the same figure.\n",
    "\n",
    "Rule of thumb:\n",
    "- Use figure-level when **faceting** is the main goal.\n",
    "- Use axes-level for **custom** subplot composition.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d049fec0-39c0-4543-a715-1b9b16242e82",
   "metadata": {},
   "source": [
    "```{figure} ../../figures/sns_plot_chart.png \n",
    ":name: sns_plot_chart\n",
    ":width: 375\n",
    ":align: center\n",
    ":alt: Seaborn Plots Chart\n",
    "Seaborn Plots [Chart](https://seaborn.pydata.org/tutorial/function_overview.html)\n",
    "``` "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9933f304",
   "metadata": {},
   "source": [
    "### API Differences\n",
    "| Aspect | Figure-level | Axes-level |\n",
    "|---|---|---|\n",
    "| **Returns** | `FacetGrid` · `JointGrid` · `PairGrid` | `Axes` |\n",
    "| **Faceting** | Built-in via `row=`, `col=`, `col_wrap=` | Not available — use `plt.subplots()` + loop |\n",
    "| **Sizing** | `height=` + `aspect=` | `plt.subplots(figsize=(w, h))` |\n",
    "| **Multiple panels** | **Automatic grid** from data | `fig, axes = plt.subplots()` + pass `ax=` |\n",
    "| **Layering plots** | Hard | Easy: multiple functions on the same `ax` |\n",
    "| **When to use** | Grouped/faceted views, small multiples | Custom layouts, overlaying, single-axes control |"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cfe9711b",
   "metadata": {},
   "source": [
    "Here is the same scatter plot, split by meal time, drawn both ways. The figure-level `relplot()` builds the panels from the `col=` variable; the axes-level version needs `plt.subplots()`, a loop, and `ax=`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "31d5b3e7",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "### Figure-level: one call, panels built from the data\n",
    "sns.relplot(data=tips, x='total_bill', y='tip', hue='sex',\n",
    "            col='time', height=3)\n",
    "plt.show()\n",
    "\n",
    "### Axes-level: you create the panels and fill each one\n",
    "fig, axes = plt.subplots(1, 2, figsize=(7, 3), sharey=True)\n",
    "for ax, meal in zip(axes, ['Lunch', 'Dinner']):\n",
    "    sns.scatterplot(data=tips[tips['time'] == meal],\n",
    "                    x='total_bill', y='tip', hue='sex', ax=ax)\n",
    "    ax.set_title(f'time = {meal}')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c9efbaef",
   "metadata": {},
   "source": [
    "### Function Reference\n",
    "\n",
    "The table below summarizes common figure-level functions and their typical axes-level backends.\n",
    "\n",
    "| Figure-level | Axes-level Functions | Typical use |\n",
    "|---|---|---|\n",
    "| `displot()` | `histplot()`, `kdeplot()`, `ecdfplot()` | Distributions, histograms, KDE, ECDF |\n",
    "| `relplot()` | `scatterplot()`, `lineplot()` | Relationships and trends |\n",
    "| `catplot()` | `stripplot()`, `swarmplot()`, `boxplot()`, `violinplot()`, `boxenplot()`, `barplot()`, `countplot()`, `pointplot()` | Categorical comparisons |\n",
    "| `jointplot()` | `scatterplot()`, `kdeplot()`, `histplot()`, `regplot()`, `residplot()`, `kind=\"hex\"` *(Matplotlib hexbin)* | Bivariate plots with marginals and/or regression |\n",
    "| `pairplot()` | `scatterplot()`, `kdeplot()`, `histplot()` | Pairwise relationships across variables |\n",
    "\n",
    "Box plots read the same way as in [Pandas Chart Types](../05-visualization/0502-pandas-chart-types.ipynb): the median line, the box for the middle 50% of values, whiskers for the usual range, and dots for possible outliers. Section 7.2 returns to them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "76fdcb39",
   "metadata": {
    "tags": [
     "thebe-interactive"
    ]
   },
   "outputs": [],
   "source": [
    "### Exercise: Facet a Box Plot\n",
    "# Practice path: Additional practice.\n",
    "# A manager wants to compare bill sizes across days, separately for lunch and dinner.\n",
    "# Use the figure-level function sns.catplot() to draw box plots of total_bill by day,\n",
    "# with one panel per meal time. Keep each panel small (about 3 inches tall).\n",
    "# Then, in a comment, answer: at dinner, which day has the highest median bill?\n",
    "# Are lunch bills or dinner bills more spread out?\n",
    "# Also explain one limitation: Can a box plot tell you how many bills each group contains?\n",
    "### Your code starts here\n",
    "\n",
    "\n",
    "\n",
    "### Your code ends here\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "68bd9a4e",
   "metadata": {
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [],
   "source": [
    "### Solution\n",
    "sns.catplot(data=tips, x='day', y='total_bill', col='time',\n",
    "            kind='box', height=3)\n",
    "plt.show()\n",
    "# Answer: Sunday has the highest median dinner bill (about $19.60).\n",
    "# On Friday, the dinner box is wider in its middle-half spread than the lunch box.\n",
    "# Thursday dinner has only one observation, so do not generalize from its collapsed box.\n",
    "# Limitation: Box heights summarize spread, not sample size; check the group counts separately.\n"
   ]
  }
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