mathematical-models/dataset-visualization.ipynb

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Chance the Rapper & PnB Rock)\", \"Artist.Name\": \"Ed Sheeran\", \"Genre\": \"pop\", \"Beats.Per.Minute\": 95, \"Energy\": 79, \"Danceability\": 75, \"Loudness..dB..\": -6, \"Liveness\": 7, \"Valence.\": 61, \"Length.\": 206, \"Acousticness..\": 21, \"Speechiness.\": 12, \"Popularity\": 82, \"Valence\": \"ok\", \"BeatsPerMinute\": 95}]}}, {\"mode\": \"vega-lite\"});\n",
       "</script>"
      ],
      "text/plain": [
       "alt.Chart(...)"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import altair as alt\n",
    "\n",
    "data = pd.read_csv('top50.csv')\n",
    "\n",
    "def genre_transform(genre):\n",
    "    if 'pop' in genre:\n",
    "        return 'pop'\n",
    "    if 'rap' in genre or 'hip hop' in genre:\n",
    "        return 'rap/hip hop'\n",
    "    return 'other'\n",
    "\n",
    "def valence_transform(valence):\n",
    "    if valence > 70:\n",
    "        return 'happy'\n",
    "    if valence < 30:\n",
    "        return 'sad'\n",
    "    return 'ok'\n",
    "\n",
    "data['Genre'] = [genre_transform(i) for i in data['Genre']]\n",
    "data['Valence'] = [valence_transform(i) for i in data['Valence.']]\n",
    "data['BeatsPerMinute'] = data['Beats.Per.Minute']\n",
    "\n",
    "alt.Chart(data).mark_point(filled=True).encode(\n",
    "    alt.OpacityValue(0.5),\n",
    "    alt.X(\n",
    "        'Energy:Q',\n",
    "        scale=alt.Scale(domain=(30, 90)),\n",
    "    ),\n",
    "    alt.Y(\n",
    "        'Danceability:Q',\n",
    "        scale=alt.Scale(domain=(25, 95)),\n",
    "    ),\n",
    "    size='BeatsPerMinute:Q',\n",
    "    color='Genre:N',\n",
    "    shape='Valence:N',\n",
    ")"
   ]
  }
 ],
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