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   "source": [
    "!pip install --quiet pandas plotly requests ipywidgets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3df16db7-6739-454f-8970-197af3aba1a1",
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   "source": [
    "import requests\n",
    "import pandas as pd\n",
    "\n",
    "# --- Students: Change this to your API key ---\n",
    "### API_KEY = \"YOUR_FREE_CENTSUS_API_KEY\"\n",
    "API_KEY = \"---YOUR API KEY---\"\n",
    "STATE_FIPS = \"37\" # North Carolina (can change to any state)\n",
    "\n",
    "# --- THE FIX (Part 1) ---\n",
    "# Using the correct \"Data Profile\" (DP) variable codes\n",
    "variables = \"NAME,DP03_0062E,DP05_0001E,DP04_0089E\"\n",
    "\n",
    "# Build the API call URL (this endpoint is still correct)\n",
    "url = f\"https://api.census.gov/data/2022/acs/acs5/profile?get={variables}&for=county:*&in=state:{STATE_FIPS}&key={API_KEY}\"\n",
    "\n",
    "print(\"Calling Census API...\")\n",
    "response = requests.get(url)\n",
    "\n",
    "if response.status_code == 200:\n",
    "    print(\"Success! Data received.\")\n",
    "    data = response.json()\n",
    "    \n",
    "    # 1. Convert messy JSON list-of-lists to a DataFrame\n",
    "    df = pd.DataFrame(data[1:], columns=data[0])\n",
    "    \n",
    "    # --- THE FIX (Part 2) ---\n",
    "    # 2. Rename columns to be human-readable\n",
    "    df = df.rename(columns={\n",
    "        \"DP03_0062E\": \"Income\",\n",
    "        \"DP05_0001E\": \"Population\",\n",
    "        \"DP04_0089E\": \"Home_Value\",\n",
    "        \"NAME\": \"County\"\n",
    "    })\n",
    "    \n",
    "    # 3. Clean the data (convert from string to number)\n",
    "    cols_to_numeric = ['Income', 'Population', 'Home_Value']\n",
    "    df[cols_to_numeric] = df[cols_to_numeric].apply(pd.to_numeric, errors='coerce')\n",
    "    \n",
    "    # 4. Create our \"Business Impact\" metric\n",
    "    df['Value_to_Income_Ratio'] = df['Home_Value'] / df['Income']\n",
    "    \n",
    "    # 5. Save for our dashboard\n",
    "    df.to_csv(\"nc_county_data.csv\", index=False)\n",
    "    \n",
    "    print(\"Data cleaned, processed, and saved to 'nc_county_data.csv'\")\n",
    "    print(df.head())\n",
    "    \n",
    "else:\n",
    "    print(f\"Error: API call failed with status code {response.status_code}\")\n",
    "    # Print the error message from the server\n",
    "    print(response.text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "81d90d3b-5409-4433-ba2d-15dec857d5d2",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import plotly.graph_objects as go\n",
    "import ipywidgets as widgets\n",
    "from ipywidgets import HBox, VBox\n",
    "from IPython.display import display\n",
    "\n",
    "# --- 1. Load the Data ---\n",
    "df = pd.read_csv(\"nc_county_data.csv\")\n",
    "df['County'] = df['County'].str.replace(\", North Carolina\", \"\")\n",
    "\n",
    "# --- 2. Create the Interactive Sliders ---\n",
    "income_slider = widgets.IntSlider(\n",
    "    value=50000,\n",
    "    min=df['Income'].min(),\n",
    "    max=df['Income'].max(),\n",
    "    step=1000,\n",
    "    description='Min Income ($):',\n",
    "    style={'description_width': 'initial'}\n",
    ")\n",
    "\n",
    "pop_slider = widgets.IntSlider(\n",
    "    value=75000,\n",
    "    min=df['Population'].min(),\n",
    "    max=df['Population'].max(),\n",
    "    step=5000,\n",
    "    description='Min Population:',\n",
    "    style={'description_width': 'initial'}\n",
    ")\n",
    "\n",
    "# --- 3. Create the Chart \"Containers\" ---\n",
    "# We use FigureWidget to make them \"updatable\"\n",
    "fig_bubble = go.FigureWidget(layout=go.Layout(\n",
    "    title=\"Market Analysis (Income vs. Home Value vs. Population)\",\n",
    "    xaxis_title=\"Median Income\",\n",
    "    yaxis_title=\"Median Home Value\"\n",
    "))\n",
    "fig_bubble.add_scatter(x=df['Income'], y=df['Home_Value'], mode='markers')\n",
    "\n",
    "fig_bar = go.FigureWidget(layout=go.Layout(\n",
    "    title=\"Home Value-to-Income Ratio (Lower may be 'cheaper')\"\n",
    "))\n",
    "fig_bar.add_bar(x=df['County'], y=df['Value_to_Income_Ratio'])\n",
    "\n",
    "# --- 4. The \"Brain\" (The Update Function) ---\n",
    "# This function will run EVERY time a slider value changes\n",
    "def update_charts(change):\n",
    "    # Filter the dataframe based on the NEW slider values\n",
    "    min_income = income_slider.value\n",
    "    min_pop = pop_slider.value\n",
    "    \n",
    "    df_filtered = df[\n",
    "        (df['Income'] >= min_income) &\n",
    "        (df['Population'] >= min_pop)\n",
    "    ]\n",
    "    \n",
    "    # Sort for the bar chart\n",
    "    df_bar_sorted = df_filtered.sort_values(\"Value_to_Income_Ratio\")\n",
    "    \n",
    "    # --- Update the charts IN-PLACE (this is fast) ---\n",
    "    # We don't re-create the charts, we just update their data\n",
    "    with fig_bubble.batch_update():\n",
    "        fig_bubble.data[0].x = df_filtered['Income']\n",
    "        fig_bubble.data[0].y = df_filtered['Home_Value']\n",
    "        # We need to add the other properties for the bubble chart\n",
    "        fig_bubble.data[0].marker.size = df_filtered['Population'] / 10000 # Scale size\n",
    "        fig_bubble.data[0].text = df_filtered['County'] # Set hover text\n",
    "        fig_bubble.data[0].marker.color = df_filtered['Income'] # Color by income\n",
    "    \n",
    "    with fig_bar.batch_update():\n",
    "        fig_bar.data[0].x = df_bar_sorted['County']\n",
    "        fig_bar.data[0].y = df_bar_sorted['Value_to_Income_Ratio']\n",
    "\n",
    "# --- 5. Link the Sliders to the Function ---\n",
    "# This says: \"When the 'value' of a slider changes, call the 'update_charts' function\"\n",
    "income_slider.observe(update_charts, names='value')\n",
    "pop_slider.observe(update_charts, names='value')\n",
    "\n",
    "# --- 6. Display the Dashboard ---\n",
    "# VBox = Vertical Box (stacks things)\n",
    "# HBox = Horizontal Box (puts things side-by-side)\n",
    "controls = VBox([income_slider, pop_slider])\n",
    "charts = HBox([fig_bubble, fig_bar])\n",
    "display(VBox([controls, charts]))\n",
    "\n",
    "# --- 7. Run the initial update to sync charts ---\n",
    "update_charts(None) # Call it once to load the default data"
   ]
  },
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