Merge branch 'main' of https://github.com/epfl-ada/ada-2024-project-a…

…lpacadreamagency

data_analysis/interactive_plots.ipynb

@@ -6713,9 +6713,9 @@
     "fig.update_layout(\n",
     "    title=\"Themes in genres\",\n",
     "    legend=dict(\n",
-    "        x=-0.8,  # Horizontal position (0 = far left, 1 = far right)\n",
-    "        y=0.9,  # Vertical position (0 = bottom, 1 = top)\n",
-    "        title=\"Topic\"  # Optional: Add a legend title\n",
+    "        x=-0.8,  \n",
+    "        y=0.9, \n",
+    "        title=\"Topic\"  \n",
     "    ),\n",
     "    width=800,\n",
     "    height=800\n",
@@ -11333,7 +11333,7 @@
     "                    label=age,\n",
     "                    method='update',\n",
     "                    args=[\n",
-    "                        {'visible': [i == idx for i in range(len(df_age.index.values))]},  # Toggle visibility\n",
+    "                        {'visible': [i == idx for i in range(len(df_age.index.values))]},  \n",
     "                        {'title': f\"Genre: {age}\"} \n",
     "                    ]\n",
     "                )\n",
@@ -365986,20 +365986,17 @@
     "from collections import Counter\n",
     "import heapq\n",
     "\n",
-    "# Assuming MOVIES DataFrame and required columns are already defined\n",
     "column = ['vote_average','revenue', 'budget']\n",
     "unique_genre = []\n",
     "\n",
-    "# Extract unique genres\n",
     "MOVIES[\"new_genres\"].apply(lambda x: unique_genre.extend(ast.literal_eval(x)))\n",
     "\n",
     "counted_genre = Counter(unique_genre)\n",
     "unique_genre = heapq.nsmallest(5, counted_genre, key=counted_genre.get)\n",
     "fig = go.Figure()\n",
     "unique_genre=['Romance', 'Horror']\n",
-    "# Create a dictionary to store traces by genre\n",
+    "\n",
     "for idx, genre in enumerate(unique_genre):\n",
-    "    # Filter data for the specific genre\n",
     "    df_temp = MOVIES[MOVIES[\"new_genres\"].apply(lambda x: genre in ast.literal_eval(x))]\n",
     "    df_temp = df_temp[column+[\"name\"]]\n",
     "    df_temp = df_temp[(df_temp != 0).all(axis=1)]\n",
@@ -366012,7 +366009,6 @@
     "        ))\n",
     "\n",
     "sizeref = 10\n",
-    "# Tune marker appearance and layout\n",
     "fig.update_traces(mode='markers', marker=dict(sizemode='area', line_width=2, sizeref=sizeref, line=dict(width=2,color='DarkSlateGrey')))\n",
     "\n",
     "fig.update_layout(\n",
@@ -366034,7 +366030,7 @@
     ")\n",
     "\n",
     "fig.update_layout(\n",
-    "    clickmode=\"event+select\"  # Allows selecting a point and its corresponding trace\n",
+    "    clickmode=\"event+select\" \n",
     "\n",
     ")\n",
     "fig.show()\n",
@@ -546802,7 +546798,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 62,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -547095,30 +547091,28 @@
     "genre_df['group'] = genre_df['group'].apply(lambda x: int(x))\n",
     "\n",
     "# https://python-graph-gallery.com/231-chord-diagram-with-bokeh/\n",
-    "# libraries\n",
+    "\n",
     "import pandas as pd\n",
     "import holoviews as hv\n",
     "from holoviews import opts, dim\n",
     "from bokeh.sampledata.les_mis import data\n",
     "from bokeh.plotting import figure, output_file, save\n",
     "\n",
-    "# Should the diagram be plotted with 'bokeh' or 'matplotlib'?\n",
+    "\n",
     "hv.extension('bokeh')\n",
-    "# How large should be the diagram?\n",
+    "\n",
     "hv.output(size=300)\n",
     "\n",
-    "# Data set\n",
     "nodes = hv.Dataset(genre_df, 'index')\n",
     "links = df\n",
     "\n",
-    "# Chord diagram\n",
+    "\n",
     "chord = hv.Chord((links, nodes)).select(value=(5, None))\n",
     "chord.opts(\n",
     "    opts.Chord(cmap='Category20', edge_cmap='Category20', edge_color=dim('source').str(), \n",
     "               labels='name', node_color=dim('index').str()))\n",
     "\n",
-    "# Not needed in a jupyter notebook\n",
-    "# It shows the diagram when run in another IDE or from a python script:\n",
+    "\n",
     "from bokeh.plotting import show\n",
     "output_file('chord_plot.html', mode='inline')\n",
     "save(hv.render(chord))\n",