diff --git a/02_activities/assignments/assigment_3_done/Python/AppendixPython.ipynb b/02_activities/assignments/assigment_3_done/Python/AppendixPython.ipynb
new file mode 100644
index 000000000..4f7af5d58
--- /dev/null
+++ b/02_activities/assignments/assigment_3_done/Python/AppendixPython.ipynb
@@ -0,0 +1,393 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "516a78e0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd\n",
+    "import scipy\n",
+    "import PIL\n",
+    "import requests\n",
+    "import io\n",
+    "import seaborn as sns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f348debf",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "ename": "ValueError",
+     "evalue": "Could not interpret value `total` for `y`. An entry with this name does not appear in `data`.",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
+      "\u001b[31mValueError\u001b[39m                                Traceback (most recent call last)",
+      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[15]\u001b[39m\u001b[32m, line 52\u001b[39m\n\u001b[32m     48\u001b[39m plt.tight_layout()\n\u001b[32m     49\u001b[39m plt.show()\n\u001b[32m---> \u001b[39m\u001b[32m52\u001b[39m daysplot = \u001b[43msns\u001b[49m\u001b[43m.\u001b[49m\u001b[43mrelplot\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m     53\u001b[39m \u001b[43m            \u001b[49m\u001b[43mdata\u001b[49m\u001b[43m=\u001b[49m\u001b[43mdf\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m     54\u001b[39m \u001b[43m            \u001b[49m\u001b[43mx\u001b[49m\u001b[43m=\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mREPORT_YEAR\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[32m     55\u001b[39m \u001b[43m            \u001b[49m\u001b[43my\u001b[49m\u001b[43m=\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mtotal\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[32m     56\u001b[39m \u001b[43m            \u001b[49m\u001b[43mhue\u001b[49m\u001b[43m=\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mFAMILY_VIOLENCE_FLAG\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[32m     57\u001b[39m \u001b[43m            \u001b[49m\u001b[43mcol\u001b[49m\u001b[43m=\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mREPORT_DOW\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[32m     58\u001b[39m \u001b[43m            \u001b[49m\u001b[43mkind\u001b[49m\u001b[43m=\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mscatter\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[32m     59\u001b[39m \u001b[43m            \u001b[49m\u001b[43mcol_wrap\u001b[49m\u001b[43m=\u001b[49m\u001b[32;43m2\u001b[39;49m\u001b[43m)\u001b[49m\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~/Desktop/visualization/visualization-env/lib/python3.11/site-packages/seaborn/relational.py:748\u001b[39m, in \u001b[36mrelplot\u001b[39m\u001b[34m(data, x, y, hue, size, style, units, weights, row, col, col_wrap, row_order, col_order, palette, hue_order, hue_norm, sizes, size_order, size_norm, markers, dashes, style_order, legend, kind, height, aspect, facet_kws, **kwargs)\u001b[39m\n\u001b[32m    746\u001b[39m         msg = \u001b[33m\"\u001b[39m\u001b[33mThe `weights` parameter has no effect with kind=\u001b[39m\u001b[33m'\u001b[39m\u001b[33mscatter\u001b[39m\u001b[33m'\u001b[39m\u001b[33m.\u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m    747\u001b[39m         warnings.warn(msg, stacklevel=\u001b[32m2\u001b[39m)\n\u001b[32m--> \u001b[39m\u001b[32m748\u001b[39m p = \u001b[43mPlotter\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m    749\u001b[39m \u001b[43m    \u001b[49m\u001b[43mdata\u001b[49m\u001b[43m=\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    750\u001b[39m \u001b[43m    \u001b[49m\u001b[43mvariables\u001b[49m\u001b[43m=\u001b[49m\u001b[43mvariables\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    751\u001b[39m \u001b[43m    \u001b[49m\u001b[43mlegend\u001b[49m\u001b[43m=\u001b[49m\u001b[43mlegend\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    752\u001b[39m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m    753\u001b[39m p.map_hue(palette=palette, order=hue_order, norm=hue_norm)\n\u001b[32m    754\u001b[39m p.map_size(sizes=sizes, order=size_order, norm=size_norm)\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~/Desktop/visualization/visualization-env/lib/python3.11/site-packages/seaborn/relational.py:396\u001b[39m, in \u001b[36m_ScatterPlotter.__init__\u001b[39m\u001b[34m(self, data, variables, legend)\u001b[39m\n\u001b[32m    387\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34m__init__\u001b[39m(\u001b[38;5;28mself\u001b[39m, *, data=\u001b[38;5;28;01mNone\u001b[39;00m, variables={}, legend=\u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[32m    388\u001b[39m \n\u001b[32m    389\u001b[39m     \u001b[38;5;66;03m# TODO this is messy, we want the mapping to be agnostic about\u001b[39;00m\n\u001b[32m    390\u001b[39m     \u001b[38;5;66;03m# the kind of plot to draw, but for the time being we need to set\u001b[39;00m\n\u001b[32m    391\u001b[39m     \u001b[38;5;66;03m# this information so the SizeMapping can use it\u001b[39;00m\n\u001b[32m    392\u001b[39m     \u001b[38;5;28mself\u001b[39m._default_size_range = (\n\u001b[32m    393\u001b[39m         np.r_[\u001b[32m.5\u001b[39m, \u001b[32m2\u001b[39m] * np.square(mpl.rcParams[\u001b[33m\"\u001b[39m\u001b[33mlines.markersize\u001b[39m\u001b[33m\"\u001b[39m])\n\u001b[32m    394\u001b[39m     )\n\u001b[32m--> \u001b[39m\u001b[32m396\u001b[39m     \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m.\u001b[49m\u001b[34;43m__init__\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m=\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvariables\u001b[49m\u001b[43m=\u001b[49m\u001b[43mvariables\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m    398\u001b[39m     \u001b[38;5;28mself\u001b[39m.legend = legend\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~/Desktop/visualization/visualization-env/lib/python3.11/site-packages/seaborn/_base.py:634\u001b[39m, in \u001b[36mVectorPlotter.__init__\u001b[39m\u001b[34m(self, data, variables)\u001b[39m\n\u001b[32m    629\u001b[39m \u001b[38;5;66;03m# var_ordered is relevant only for categorical axis variables, and may\u001b[39;00m\n\u001b[32m    630\u001b[39m \u001b[38;5;66;03m# be better handled by an internal axis information object that tracks\u001b[39;00m\n\u001b[32m    631\u001b[39m \u001b[38;5;66;03m# such information and is set up by the scale_* methods. The analogous\u001b[39;00m\n\u001b[32m    632\u001b[39m \u001b[38;5;66;03m# information for numeric axes would be information about log scales.\u001b[39;00m\n\u001b[32m    633\u001b[39m \u001b[38;5;28mself\u001b[39m._var_ordered = {\u001b[33m\"\u001b[39m\u001b[33mx\u001b[39m\u001b[33m\"\u001b[39m: \u001b[38;5;28;01mFalse\u001b[39;00m, \u001b[33m\"\u001b[39m\u001b[33my\u001b[39m\u001b[33m\"\u001b[39m: \u001b[38;5;28;01mFalse\u001b[39;00m}  \u001b[38;5;66;03m# alt., used DefaultDict\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m634\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43massign_variables\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvariables\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m    636\u001b[39m \u001b[38;5;66;03m# TODO Lots of tests assume that these are called to initialize the\u001b[39;00m\n\u001b[32m    637\u001b[39m \u001b[38;5;66;03m# mappings to default values on class initialization. I'd prefer to\u001b[39;00m\n\u001b[32m    638\u001b[39m \u001b[38;5;66;03m# move away from that and only have a mapping when explicitly called.\u001b[39;00m\n\u001b[32m    639\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m var \u001b[38;5;129;01min\u001b[39;00m [\u001b[33m\"\u001b[39m\u001b[33mhue\u001b[39m\u001b[33m\"\u001b[39m, \u001b[33m\"\u001b[39m\u001b[33msize\u001b[39m\u001b[33m\"\u001b[39m, \u001b[33m\"\u001b[39m\u001b[33mstyle\u001b[39m\u001b[33m\"\u001b[39m]:\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~/Desktop/visualization/visualization-env/lib/python3.11/site-packages/seaborn/_base.py:679\u001b[39m, in \u001b[36mVectorPlotter.assign_variables\u001b[39m\u001b[34m(self, data, variables)\u001b[39m\n\u001b[32m    674\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m    675\u001b[39m     \u001b[38;5;66;03m# When dealing with long-form input, use the newer PlotData\u001b[39;00m\n\u001b[32m    676\u001b[39m     \u001b[38;5;66;03m# object (internal but introduced for the objects interface)\u001b[39;00m\n\u001b[32m    677\u001b[39m     \u001b[38;5;66;03m# to centralize / standardize data consumption logic.\u001b[39;00m\n\u001b[32m    678\u001b[39m     \u001b[38;5;28mself\u001b[39m.input_format = \u001b[33m\"\u001b[39m\u001b[33mlong\u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m--> \u001b[39m\u001b[32m679\u001b[39m     plot_data = \u001b[43mPlotData\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvariables\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m    680\u001b[39m     frame = plot_data.frame\n\u001b[32m    681\u001b[39m     names = plot_data.names\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~/Desktop/visualization/visualization-env/lib/python3.11/site-packages/seaborn/_core/data.py:58\u001b[39m, in \u001b[36mPlotData.__init__\u001b[39m\u001b[34m(self, data, variables)\u001b[39m\n\u001b[32m     51\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34m__init__\u001b[39m(\n\u001b[32m     52\u001b[39m     \u001b[38;5;28mself\u001b[39m,\n\u001b[32m     53\u001b[39m     data: DataSource,\n\u001b[32m     54\u001b[39m     variables: \u001b[38;5;28mdict\u001b[39m[\u001b[38;5;28mstr\u001b[39m, VariableSpec],\n\u001b[32m     55\u001b[39m ):\n\u001b[32m     57\u001b[39m     data = handle_data_source(data)\n\u001b[32m---> \u001b[39m\u001b[32m58\u001b[39m     frame, names, ids = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_assign_variables\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvariables\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m     60\u001b[39m     \u001b[38;5;28mself\u001b[39m.frame = frame\n\u001b[32m     61\u001b[39m     \u001b[38;5;28mself\u001b[39m.names = names\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~/Desktop/visualization/visualization-env/lib/python3.11/site-packages/seaborn/_core/data.py:232\u001b[39m, in \u001b[36mPlotData._assign_variables\u001b[39m\u001b[34m(self, data, variables)\u001b[39m\n\u001b[32m    230\u001b[39m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m    231\u001b[39m         err += \u001b[33m\"\u001b[39m\u001b[33mAn entry with this name does not appear in `data`.\u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m--> \u001b[39m\u001b[32m232\u001b[39m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(err)\n\u001b[32m    234\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m    235\u001b[39m \n\u001b[32m    236\u001b[39m     \u001b[38;5;66;03m# Otherwise, assume the value somehow represents data\u001b[39;00m\n\u001b[32m    237\u001b[39m \n\u001b[32m    238\u001b[39m     \u001b[38;5;66;03m# Ignore empty data structures\u001b[39;00m\n\u001b[32m    239\u001b[39m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(val, Sized) \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(val) == \u001b[32m0\u001b[39m:\n",
+      "\u001b[31mValueError\u001b[39m: Could not interpret value `total` for `y`. An entry with this name does not appear in `data`."
+     ]
+    }
+   ],
+   "source": [
+    "# 1) Get package + pick a datastore_active resource\n",
+    "base_url = \"https://ckan0.cf.opendata.inter.prod-toronto.ca\"\n",
+    "package_id = \"intimate-partner-and-family-violence\"\n",
+    "\n",
+    "pkg = requests.get(\n",
+    "    f\"{base_url}/api/3/action/package_show\",\n",
+    "    params={\"id\": package_id},\n",
+    "    timeout=60\n",
+    ").json()\n",
+    "\n",
+    "resources = pkg[\"result\"][\"resources\"]\n",
+    "active = [r for r in resources if r.get(\"datastore_active\")]\n",
+    "\n",
+    "# choose the first active resource (change index if needed)\n",
+    "resource_id = active[0][\"id\"]\n",
+    "\n",
+    "# 2) Load into pandas\n",
+    "csv_text = requests.get(f\"{base_url}/datastore/dump/{resource_id}\", timeout=120).text\n",
+    "df = pd.read_csv(io.StringIO(csv_text))\n",
+    "\n",
+    "# 3) Aggregate by relation\n",
+    "rel = (\n",
+    "    df.groupby(\"FAMILY_VIOLENCE_RELATION\", dropna=False)[\"COUNT_\"]\n",
+    "      .sum()\n",
+    "      .sort_values(ascending=False)\n",
+    "      .reset_index()\n",
+    "      .rename(columns={\"COUNT_\": \"total\"})\n",
+    ")\n",
+    "\n",
+    "#keep only top N to make the chart readable\n",
+    "top_n = 15\n",
+    "rel_plot = rel.head(top_n)\n",
+    "\n",
+    "# 4) Plot\n",
+    "sns.set(style=\"whitegrid\")\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "\n",
+    "ax = sns.barplot(\n",
+    "    data=rel_plot,\n",
+    "    y=\"FAMILY_VIOLENCE_RELATION\",\n",
+    "    x=\"total\"\n",
+    ")\n",
+    "\n",
+    "ax.set_title(\"Intimate Partner & Family Violence by Relation (Top 15)\")\n",
+    "ax.set_xlabel(\"Total count\")\n",
+    "ax.set_ylabel(\"Relation\")\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "ea71b332",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(8,5))\n",
+    "year_totals = (\n",
+    "    df.groupby(\"REPORT_YEAR\")[\"COUNT_\"]\n",
+    "      .sum()\n",
+    "      .reset_index()\n",
+    ")\n",
+    "\n",
+    "yeargraph = sns.lineplot(\n",
+    "    data=year_totals,\n",
+    "    x=\"REPORT_YEAR\",\n",
+    "    y=\"COUNT_\",\n",
+    "    color=\"hotpink\",\n",
+    "    linestyle=\"--\",\n",
+    "    linewidth=3,\n",
+    "    marker=\"o\",\n",
+    "    markerfacecolor=\"indigo\"\n",
+    ")\n",
+    "\n",
+    "plt.title(\"Total Intimate Partner & Family Violence by Year\")\n",
+    "plt.xlabel(\"Report Year\")\n",
+    "plt.ylabel(\"Total Count\")\n",
+    "\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "a49ac8df",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "rel = (\n",
+    "    df.groupby(\"FAMILY_VIOLENCE_RELATION\")[\"COUNT_\"]\n",
+    "      .sum()\n",
+    "      .sort_values(ascending=False)\n",
+    "      .reset_index()\n",
+    ")\n",
+    "\n",
+    "plt.figure(figsize=(10,6))\n",
+    "\n",
+    "sns.barplot(\n",
+    "    data=rel,\n",
+    "    x=\"FAMILY_VIOLENCE_RELATION\",\n",
+    "    y=\"COUNT_\"\n",
+    ")\n",
+    "\n",
+    "plt.title(\"Family/Intimate Partner Violence by Relation\")\n",
+    "plt.xlabel(\"Relation\")\n",
+    "plt.ylabel(\"Total Count\")\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "b1222068",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "year = (\n",
+    "    df.groupby(\"REPORT_YEAR\")[\"COUNT_\"]\n",
+    "      .sum()\n",
+    "      .reset_index()\n",
+    ")\n",
+    "\n",
+    "plt.figure(figsize=(8,5))\n",
+    "\n",
+    "sns.barplot(\n",
+    "    data=year,\n",
+    "    x=\"REPORT_YEAR\",\n",
+    "    y=\"COUNT_\"\n",
+    ")\n",
+    "\n",
+    "plt.title(\"Family/Intimate Partner Violence by Year\")\n",
+    "plt.ylabel(\"Total Count\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "daa63232",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "dow = (\n",
+    "    df.groupby(\"REPORT_DOW\")[\"COUNT_\"]\n",
+    "      .sum()\n",
+    "      .reset_index()\n",
+    ")\n",
+    "\n",
+    "# optional: order days properly\n",
+    "day_order = [\"Monday\",\"Tuesday\",\"Wednesday\",\"Thursday\",\"Friday\",\"Saturday\",\"Sunday\"]\n",
+    "dow[\"REPORT_DOW\"] = pd.Categorical(dow[\"REPORT_DOW\"], categories=day_order, ordered=True)\n",
+    "dow = dow.sort_values(\"REPORT_DOW\")\n",
+    "\n",
+    "plt.figure(figsize=(8,5))\n",
+    "\n",
+    "sns.barplot(\n",
+    "    data=dow,\n",
+    "    x=\"REPORT_DOW\",\n",
+    "    y=\"COUNT_\"\n",
+    ")\n",
+    "\n",
+    "plt.title(\"Violence Incidents by Day of Week\")\n",
+    "plt.ylabel(\"Total Count\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "0670fbb0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# make sure counts are numeric\n",
+    "df[\"COUNT_\"] = pd.to_numeric(df[\"COUNT_\"], errors=\"coerce\")\n",
+    "\n",
+    "# aggregate\n",
+    "rel_year = (\n",
+    "    df.groupby([\"REPORT_YEAR\",\"FAMILY_VIOLENCE_RELATION\"])[\"COUNT_\"]\n",
+    "      .sum()\n",
+    "      .reset_index()\n",
+    ")\n",
+    "\n",
+    "pivot = rel_year.pivot(\n",
+    "    index=\"REPORT_YEAR\",\n",
+    "    columns=\"FAMILY_VIOLENCE_RELATION\",\n",
+    "    values=\"COUNT_\"\n",
+    ").fillna(0)\n",
+    "\n",
+    "pivot.plot(\n",
+    "    kind=\"bar\",\n",
+    "    stacked=True,\n",
+    "    figsize=(12,6),\n",
+    "    colormap=\"viridis\"\n",
+    ")\n",
+    "\n",
+    "plt.title(\"Family/Intimate Partner Violence by Relation and Year\")\n",
+    "plt.xlabel(\"Year\")\n",
+    "plt.ylabel(\"Total Count\")\n",
+    "plt.legend(title=\"Relation\", bbox_to_anchor=(1.05,1))\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "e6500e5b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# aggregate\n",
+    "rel_year = (\n",
+    "    df.groupby([\"REPORT_DOW\",\"FAMILY_VIOLENCE_RELATION\"])[\"COUNT_\"]\n",
+    "      .sum()\n",
+    "      .reset_index()\n",
+    ")\n",
+    "\n",
+    "pivot = rel_year.pivot(\n",
+    "    index=\"REPORT_DOW\",\n",
+    "    columns=\"FAMILY_VIOLENCE_RELATION\",\n",
+    "    values=\"COUNT_\"\n",
+    ").fillna(0)\n",
+    "\n",
+    "pivot.plot(\n",
+    "    kind=\"bar\",\n",
+    "    stacked=True,\n",
+    "    figsize=(12,6),\n",
+    "    colormap=\"viridis\"\n",
+    ")\n",
+    "\n",
+    "plt.title(\"Family/Intimate Partner Violence by Relation and Day\")\n",
+    "plt.xlabel(\"Day of Week\")\n",
+    "plt.ylabel(\"Total Count\")\n",
+    "plt.legend(title=\"Relation\", bbox_to_anchor=(1.05,1))\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f4c335d0",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "visualization-env (3.11.13)",
+   "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": "3.11.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/02_activities/assignments/assigment_3_done/Python/Python markdown.ipynb b/02_activities/assignments/assigment_3_done/Python/Python markdown.ipynb
new file mode 100644
index 000000000..171b38401
--- /dev/null
+++ b/02_activities/assignments/assigment_3_done/Python/Python markdown.ipynb	
@@ -0,0 +1,58 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "f0dea6ae",
+   "metadata": {},
+   "source": [
+    "Assignment 3 - Python"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dc23d96c",
+   "metadata": {},
+   "source": [
+    " > What software did you use to create your data visualization? \n",
+    " I created my data visualizations using Python, specifically the libraries pandas, seaborn, and matplotlib.\n",
+    "\n",
+    "    > Who is your intended audience? \n",
+    "    My intended audience includes researchers studying social issues, ssocial workers, government and policy organizations that are interested in learning more about the intimate partner and family violence.\n",
+    "\n",
+    "    > What information or message are you trying to convey with your visualization? \n",
+    "    The visualization aims to communicate patterns in reported intimate partner and family violence over time and across relationship categories. The goal is to show how incidents are distributed and how trends change year-to-year, rather than to make claims about true prevalence.\n",
+    "\n",
+    "    > What aspects of design did you consider when making your visualization? How did you apply them? With what elements of your plots? \n",
+    "    I was interested in clarity & simplicity so I avoided clutter and limited the number of variables shown at once. For example, aggregating by year reduced noise and made trends clearer. Furthermore, I considered color choice I used color-blind friendly palettes (e.g., viridis) to ensure distinctions between categories are visible to most viewers. Also, readability which prompted me to have clear axis labels, descriptive titles and logical ordering of categories (e.g., years in ascending order)\n",
+    "\n",
+    "    > How did you ensure that your data visualizations are reproducible? If the tool you used to make your data visualization is not reproducible, how will this impact your data visualization? \n",
+    "    The visualization is reproducible because because I pulled the code data directly from the public OpenData link for developers. Furthermore, all transformations are scripted and no manual edits were made to the dataset to ensure that anyone can rerun the code to recreate results.\n",
+    "    \n",
+    "    > How did you ensure that your data visualization is accessible?  \n",
+    "    One of the main consideration I made for accessibility purposes for my data visualization was around using color-blind friendly palletes. \n",
+    "\n",
+    "    > Who are the individuals and communities who might be impacted by your visualization?  \n",
+    "    Potentially impacted groups include survivors of violence, affected families and communities, advocacy organizations,policymakers, and law enforcement and service providers. As this data is directly tied to some community members lived experiences.\n",
+    "\n",
+    "    > How did you choose which features of your chosen dataset to include or exclude from your visualization? \n",
+    "    I was hoping to simply identify if rates of IPFV were changing over the year and also if certain days of the week had higher reports of IPFV. So, this helped me to hone in on the features of that data that made the most sense to visualize that. Then I thought it might be interesting to see the relationship of the reports to see if there are any trends there as well.\n",
+    "\n",
+    "    > What ‘underwater labour’ contributed to your final data visualization product?\n",
+    "    The “underwater labour” included learning how to query the provided data from OpenData Toronto, cleaning and converting data types, aggregating counts correctly, testing different visualizations, debugging code errors, deciding how to present sensitive data responsibly and iterating on design choices.\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "visualization-env (3.11.13)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "name": "python",
+   "version": "3.11.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/02_activities/assignments/assigment_3_done/Python/PythonVisual.png b/02_activities/assignments/assigment_3_done/Python/PythonVisual.png
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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "f0dea6ae",
+   "metadata": {},
+   "source": [
+    "Assignment 3 - Python: https://open.toronto.ca/dataset/intimate-partner-and-family-violence/"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dc23d96c",
+   "metadata": {},
+   "source": [
+    " > What software did you use to create your data visualization? \n",
+    " I created my data visualizations using Python, specifically the libraries pandas, seaborn, and matplotlib.\n",
+    "\n",
+    "> Who is your intended audience? \n",
+    "    My intended audience includes researchers studying social issues, ssocial workers, government and policy organizations that are interested in learning more about the intimate partner and family violence.\n",
+    "\n",
+    "> What information or message are you trying to convey with your visualization? \n",
+    "    The visualization aims to communicate patterns in reported intimate partner and family violence over time and across relationship categories. The goal is to show how incidents are distributed and how trends change year-to-year, rather than to make claims about true prevalence.\n",
+    "\n",
+    "> What aspects of design did you consider when making your visualization? How did you apply them? With what elements of your plots? \n",
+    "    I was interested in clarity & simplicity so I avoided clutter and limited the number of variables shown at once. For example, aggregating by year reduced noise and made trends clearer. Furthermore, I considered color choice I used color-blind friendly palettes (e.g., viridis) to ensure distinctions between categories are visible to most viewers. Also, readability which prompted me to have clear axis labels, descriptive titles and logical ordering of categories (e.g., years in ascending order)\n",
+    "\n",
+    "> How did you ensure that your data visualizations are reproducible? If the tool you used to make your data visualization is not reproducible, how will this impact your data visualization? \n",
+    "    The visualization is reproducible because because I pulled the code data directly from the public OpenData link for developers. Furthermore, all transformations are scripted and no manual edits were made to the dataset to ensure that anyone can rerun the code to recreate results.\n",
+    "    \n",
+    "> How did you ensure that your data visualization is accessible?  \n",
+    "    One of the main consideration I made for accessibility purposes for my data visualization was around using color-blind friendly palletes. \n",
+    "\n",
+    "> Who are the individuals and communities who might be impacted by your visualization?  \n",
+    "    Potentially impacted groups include survivors of violence, affected families and communities, advocacy organizations,policymakers, and law enforcement and service providers. As this data is directly tied to some community members lived experiences.\n",
+    "\n",
+    "> How did you choose which features of your chosen dataset to include or exclude from your visualization? \n",
+    "    I was hoping to simply identify if rates of IPFV were changing over the year and also if certain days of the week had higher reports of IPFV. So, this helped me to hone in on the features of that data that made the most sense to visualize that. Then I thought it might be interesting to see the relationship of the reports to see if there are any trends there as well. I avoided overly granular geographic data that could stigmatize specific neighborhoods without context.\n",
+    "\n",
+    "> What ‘underwater labour’ contributed to your final data visualization product?\n",
+    "    The “underwater labour” included learning how to query the provided data from OpenData Toronto, cleaning and converting data types, aggregating counts correctly, testing different visualizations, debugging code errors, deciding how to present sensitive data responsibly and iterating on design choices.\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "visualization-env (3.11.13)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "name": "python",
+   "version": "3.11.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/02_activities/assignments/assigment_3_done/R/AppendixR notebook.pdf b/02_activities/assignments/assigment_3_done/R/AppendixR notebook.pdf
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+++ b/02_activities/assignments/assigment_3_done/R/R markdown.ipynb	
@@ -0,0 +1,44 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "bb5d7848",
+   "metadata": {},
+   "source": [
+    "    > What software did you use to create your data visualization?\n",
+    "    I created my data visualizations using RStudio with the R programming language. I primarily used the packages ggplot2 for visualization and dplyr for data cleaning and aggregation. The dataset was accessed from the Toronto Open Data portal and processed within R.\n",
+    "\n",
+    "    > Who is your intended audience? \n",
+    "    The intended audience includes researchers studying social and public health issues, policy makers and service organizations, community groups focused on violence prevention and a general audience interested in understanding trends in reported violence.\n",
+    "    \n",
+    "    > What information or message are you trying to convey with your visualization? \n",
+    "    The visualization aims to communicate patterns in reported intimate partner and family violence over time and across relationship categories or temporal grouping (day of the week). The goal is to highlight trends and distributions rather than make claims about true prevalence.\n",
+    "    \n",
+    "    > What aspects of design did you consider when making your visualization? How did you apply them? With what elements of your plots? \n",
+    "    Similar to my other visualization, I prioritized clarity and simplicity by using aggregated data and avoided overcrowded plots. For example, grouping by day of week or relation made patterns easier to interpret. Color and visual factors as I used accessible color palettes (e.g., viridis) to support viewers with color vision differences. Lastly, I aimed to support readability with the use of clear titles and axis labels, logical ordering of categorical variables, and minimal visual clutter through clean themes (e.g., theme_minimal())\n",
+    "    \n",
+    "    > How did you ensure that your data visualizations are reproducible? If the tool you used to make your data visualization is not reproducible, how will this impact your data visualization? \n",
+    "    Reproducibility was ensured by writing all steps in R scripts, using publicly accessible datasets, documenting data transformations and aggregation steps and avoiding manual edits to the data\n",
+    "\n",
+    "    > How did you ensure that your data visualization is accessible?  \n",
+    "    One of the main consideration I made for accessibility purposes for my data visualization was around using color-blind friendly palletes. \n",
+    "    \n",
+    "    > Who are the individuals and communities who might be impacted by your visualization?  \n",
+    "    Potentially impacted groups include survivors of violence, affected families and communities, advocacy organizations,policymakers, and law enforcement and service providers. As this data is directly tied to some community members lived experiences.\n",
+    "    \n",
+    "    > How did you choose which features of your chosen dataset to include or exclude from your visualization? \n",
+    "    I selected variables that directly supported the goal of showing trends in the counts of reports by relation and one certain days. I avoided overly granular geographic data that could stigmatize specific neighborhoods without context.\n",
+    "    \n",
+    "    > What ‘underwater labour’ contributed to your final data visualization product?\n",
+    "    Similar to my other visualization the “underwater labour” included learning to work with the dataset, cleaning and transforming variables, converting date and categorical fields, testing multiple visualization types, debugging R code, reflecting on ethical considerations, and iterating on design decisions."
+   ]
+  }
+ ],
+ "metadata": {
+  "language_info": {
+   "name": "python"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/02_activities/assignments/assigment_3_done/R/R visualization 2.png b/02_activities/assignments/assigment_3_done/R/R visualization 2.png
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+++ b/02_activities/assignments/assigment_3_done/R/Rmarkdown.ipynb
@@ -0,0 +1,52 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "1283f3e0",
+   "metadata": {},
+   "source": [
+    "Assignment 3 - R: https://open.toronto.ca/dataset/intimate-partner-and-family-violence/"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bb5d7848",
+   "metadata": {},
+   "source": [
+    "> What software did you use to create your data visualization?\n",
+    "    I created my data visualizations using RStudio with the R programming language. I primarily used the packages ggplot2 for visualization and dplyr for data cleaning and aggregation. The dataset was accessed from the Toronto Open Data portal and processed within R.\n",
+    "\n",
+    "> Who is your intended audience? \n",
+    "    The intended audience includes researchers studying social and public health issues, policy makers and service organizations, community groups focused on violence prevention and a general audience interested in understanding trends in reported violence.\n",
+    "    \n",
+    "> What information or message are you trying to convey with your visualization? \n",
+    "    The visualization aims to communicate patterns in reported intimate partner and family violence over time and across relationship categories or temporal grouping (day of the week). The goal is to highlight trends and distributions rather than make claims about true prevalence.\n",
+    "    \n",
+    "> What aspects of design did you consider when making your visualization? How did you apply them? With what elements of your plots? \n",
+    "    Similar to my other visualization, I prioritized clarity and simplicity by using aggregated data and avoided overcrowded plots. For example, grouping by day of week or relation made patterns easier to interpret. Color and visual factors as I used accessible color palettes (e.g., viridis) to support viewers with color vision differences. Lastly, I aimed to support readability with the use of clear titles and axis labels, logical ordering of categorical variables, and minimal visual clutter through clean themes (e.g., theme_minimal())\n",
+    "    \n",
+    "> How did you ensure that your data visualizations are reproducible? If the tool you used to make your data visualization is not reproducible, how will this impact your data visualization? \n",
+    "    Reproducibility was ensured by writing all steps in R scripts, using publicly accessible datasets, documenting data transformations and aggregation steps and avoiding manual edits to the data\n",
+    "\n",
+    "> How did you ensure that your data visualization is accessible?  \n",
+    "    One of the main consideration I made for accessibility purposes for my data visualization was around using color-blind friendly palletes. \n",
+    "    \n",
+    "> Who are the individuals and communities who might be impacted by your visualization?  \n",
+    "    Potentially impacted groups include survivors of violence, affected families and communities, advocacy organizations,policymakers, and law enforcement and service providers. As this data is directly tied to some community members lived experiences.\n",
+    "    \n",
+    "> How did you choose which features of your chosen dataset to include or exclude from your visualization? \n",
+    "    I selected variables that directly supported the goal of showing trends in the counts of reports by relation and one certain days. I avoided overly granular geographic data that could stigmatize specific neighborhoods without context.\n",
+    "    \n",
+    "> What ‘underwater labour’ contributed to your final data visualization product?\n",
+    "    Similar to my other visualization the “underwater labour” included learning to work with the dataset, cleaning and transforming variables, converting date and categorical fields, testing multiple visualization types, debugging R code, reflecting on ethical considerations, and iterating on design decisions."
+   ]
+  }
+ ],
+ "metadata": {
+  "language_info": {
+   "name": "python"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/02_activities/assignments/assigment_3_done/assignment_3.md b/02_activities/assignments/assigment_3_done/assignment_3.md
new file mode 100644
index 000000000..99341bc82
--- /dev/null
+++ b/02_activities/assignments/assigment_3_done/assignment_3.md
@@ -0,0 +1,69 @@
+# Data Visualization
+
+## Assignment 3: Final Project
+
+### Requirements:
+- We will finish this class by giving you the chance to use what you have learned in a practical context, by creating data visualizations from raw data. 
+- Choose a dataset of interest from the [City of Toronto’s Open Data Portal](https://www.toronto.ca/city-government/data-research-maps/open-data/) or [Ontario’s Open Data Catalogue](https://data.ontario.ca/). 
+- Using Python and one other data visualization software (Excel or free alternative, Tableau Public, any other tool you prefer), create two distinct visualizations from your dataset of choice.  
+- For each visualization, describe and justify: 
+    > What software did you use to create your data visualization?
+
+    > Who is your intended audience? 
+    
+    > What information or message are you trying to convey with your visualization? 
+    
+    > What aspects of design did you consider when making your visualization? How did you apply them? With what elements of your plots? 
+    
+    > How did you ensure that your data visualizations are reproducible? If the tool you used to make your data visualization is not reproducible, how will this impact your data visualization? 
+    
+    > How did you ensure that your data visualization is accessible?  
+    
+    > Who are the individuals and communities who might be impacted by your visualization?  
+    
+    > How did you choose which features of your chosen dataset to include or exclude from your visualization? 
+    
+    > What ‘underwater labour’ contributed to your final data visualization product?
+
+- This assignment is intentionally open-ended - you are free to create static or dynamic data visualizations, maps, or whatever form of data visualization you think best communicates your information to your audience of choice! 
+- Total word count should not exceed **(as a maximum) 1000 words** 
+ 
+### Why am I doing this assignment?:  
+- This ongoing assignment ensures active participation in the course, and assesses the learning outcomes: 
+* Create and customize data visualizations from start to finish in Python
+* Apply general design principles to create accessible and equitable data visualizations
+* Use data visualization to tell a story  
+- This would be a great project to include in your GitHub Portfolio – put in the effort to make it something worthy of showing prospective employers!
+
+### Rubric:
+
+| Component         | Scoring  | Requirement                                                                 |
+|-------------------|----------|-----------------------------------------------------------------------------|
+| Data Visualizations | Complete/Incomplete | - Data visualizations are distinct from each other<br>- Data visualizations are clearly identified<br>- Different sources/rationales (text with two images of data, if visualizations are labeled)<br>- High-quality visuals (high resolution and clear data)<br>- Data visualizations follow best practices of accessibility |
+| Written Explanations | Complete/Incomplete | - All questions from assignment description are answered for each visualization<br>- Explanations are supported by course content or scholarly sources, where needed |
+| Code              | Complete/Incomplete | - All code is included as an appendix with your final submissions<br>- Code is clearly commented and reproducible |
+
+## Submission Information
+
+🚨 **Please review our [Assignment Submission Guide](https://github.com/UofT-DSI/onboarding/blob/main/onboarding_documents/submissions.md)** 🚨 for detailed instructions on how to format, branch, and submit your work. Following these guidelines is crucial for your submissions to be evaluated correctly.
+
+### Submission Parameters:
+* Submission Due Date: `23:59 - 11/02/2025`
+* The branch name for your repo should be: `assignment-3`
+* What to submit for this assignment:
+    * A folder/directory containing:
+        * This file (assignment_3.md)
+        * Two data visualizations 
+        * Two markdown files for each both visualizations with their written descriptions.
+        * Link to your dataset of choice.
+        * Complete and commented code as an appendix (for your visualization made with Python, and for the other, if relevant) 
+* What the pull request link should look like for this assignment: `https://github.com/<your_github_username>/visualization/pull/<pr_id>`
+    * Open a private window in your browser. Copy and paste the link to your pull request into the address bar. Make sure you can see your pull request properly. This helps the technical facilitator and learning support staff review your submission easily.
+
+Checklist:
+- [ ] Create a branch called `assignment-3`.
+- [ ] Ensure that the repository is public.
+- [ ] Review [the PR description guidelines](https://github.com/UofT-DSI/onboarding/blob/main/onboarding_documents/submissions.md#guidelines-for-pull-request-descriptions) and adhere to them.
+- [ ] Verify that the link is accessible in a private browser window.
+
+If you encounter any difficulties or have questions, please don't hesitate to reach out to our team via our Slack. Our Technical Facilitators and Learning Support staff are here to help you navigate any challenges.