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   "source": [
    "# Lesson 11 activity: data visualization\n",
    "\n",
    "In this activity, you'll use matplotlib, seaborn, and plotly to explore a housing sales dataset through a series of plots.\n",
    "\n",
    "## Problem statement:\n",
    "Analyze the housing dataset using various types of plots to gain insights into the data.\n",
    "\n",
    "Housing sales dataset available here: [housing_data.csv](https://media.githubusercontent.com/media/gperdrizet/fullstack-2605/refs/heads/main/data/housing_data.csv)\n",
    "\n",
    "## Steps to complete:\n",
    "1. Create a line plot to visualize the trend of house prices over the years.\n",
    "2. Use a scatter plot to visualize the relationship between `LotArea` and `SalePrice`.\n",
    "3. Create a bar chart to show the count of houses in each `Neighborhood`.\n",
    "4. Use a box plot to visualize the distribution of `SalePrice` in each `Neighborhood`.\n",
    "5. Create a pie chart to visualize the proportion of houses in each `MSZoning` category.\n",
    "6. Use a 3D scatter plot to visualize `LotArea`, `OverallQual`, and `SalePrice` together."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f4533d40",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.ticker import FuncFormatter\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "81325909",
   "metadata": {},
   "source": [
    "## Load the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "78d3abbd",
   "metadata": {},
   "outputs": [],
   "source": [
    "url = 'https://media.githubusercontent.com/media/gperdrizet/fullstack-2605/refs/heads/main/data/housing_data.csv'\n",
    "df = pd.read_csv(url)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e2411c29",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1ea17f5f",
   "metadata": {},
   "source": [
    "## 1. House price trends over time\n",
    "\n",
    "Create a line plot showing how mean sale price changed each year. Include error bars showing the standard deviation.\n",
    "\n",
    "**Hints:**\n",
    "- Convert `YrSold` to string so it's treated as a categorical axis\n",
    "- Use `groupby` to calculate mean and standard deviation per year\n",
    "- `plt.errorbar()` can draw the error bars; `plt.plot()` draws the line\n",
    "- Use `FuncFormatter` to scale the y-axis to thousands"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b470ec57",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert sale year to string so it's treated as a categorical variable\n",
    "df['YrSold'] = df['YrSold'].astype(str)\n",
    "\n",
    "# Group by year and calculate mean and standard deviation of sale price\n",
    "# Your code here\n",
    "\n",
    "# Create a line plot with error bars\n",
    "# Your code here\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8d1196f5",
   "metadata": {},
   "source": [
    "## 2. LotArea vs SalePrice\n",
    "\n",
    "Create a scatter plot showing the relationship between lot area and sale price.\n",
    "\n",
    "**Hints:**\n",
    "- Use `plt.scatter()` or `sns.scatterplot()`\n",
    "- The data has outliers — experiment with axis limits or log scale to make the relationship clearer\n",
    "- Use `FuncFormatter` to scale both axes to thousands"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7b6aca64",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a scatter plot of LotArea vs SalePrice\n",
    "# Your code here\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5ea498ab",
   "metadata": {},
   "source": [
    "## 3. House count by neighborhood\n",
    "\n",
    "Create a bar chart showing how many houses are in each neighborhood.\n",
    "\n",
    "**Hints:**\n",
    "- Use `value_counts()` to count houses per neighborhood\n",
    "- A horizontal bar chart (`plt.barh()` or `sns.barplot()`) works well here since neighborhood names are long\n",
    "- Sort the bars by count to make the chart easier to read"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9c9311cf",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Count houses by neighborhood\n",
    "# Your code here\n",
    "\n",
    "# Create a bar chart\n",
    "# Your code here\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e93603d0",
   "metadata": {},
   "source": [
    "## 4. SalePrice distribution by neighborhood\n",
    "\n",
    "Create a box plot showing the distribution of sale prices in each neighborhood.\n",
    "\n",
    "**Hints:**\n",
    "- Use `sns.boxplot()` with `x='Neighborhood'` and `y='SalePrice'`\n",
    "- Order neighborhoods by median sale price using `groupby` + `sort_values`\n",
    "- Rotate x-axis labels so they don't overlap (`plt.xticks(rotation=45, ha='right')`)\n",
    "- Use `FuncFormatter` to scale the y-axis to thousands"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a1c461e8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get list of neighborhoods ordered by median sale price\n",
    "# Your code here\n",
    "\n",
    "# Create a box plot\n",
    "# Your code here\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "81456ac8",
   "metadata": {},
   "source": [
    "## 5. Proportion of houses by MSZoning\n",
    "\n",
    "Create a pie chart showing what proportion of houses fall into each zoning category.\n",
    "\n",
    "**Hints:**\n",
    "- Use `value_counts()` to count houses per zoning category\n",
    "- Use `plt.pie()` with a legend showing category labels and percentages\n",
    "- Calculate percentages as `counts / counts.sum() * 100`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fa0f44d5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Count houses by MSZoning\n",
    "# Your code here\n",
    "\n",
    "# Create a pie chart\n",
    "# Your code here\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2d620c7b",
   "metadata": {},
   "source": [
    "## 6. 3D scatter plot: LotArea, OverallQual, and SalePrice\n",
    "\n",
    "Create a 3D scatter plot with `LotArea` on the x-axis, `OverallQual` on the y-axis, and `SalePrice` on the z-axis. Color the points by sale price.\n",
    "\n",
    "**Hints:**\n",
    "- For matplotlib: use `plt.axes(projection='3d')` then `ax.scatter()`\n",
    "- For plotly: use `px.scatter_3d()` for an interactive version\n",
    "- Use `FuncFormatter` to scale the lot area and sale price axes to thousands\n",
    "- Use `cmap='viridis'` and add a colorbar"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b5b51b69",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a 3D scatter plot\n",
    "# Your code here\n",
    "\n",
    "plt.show()"
   ]
  }
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