0108_Discretisation_Rounding.ipynb

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    "%load_ext nb_black"
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       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 3;\n",
       "                var nbb_unformatted_code = \"import random\\nimport pandas as pd\\nimport numpy as np\\nimport matplotlib.pyplot as plt\\n\\nplt.style.use(\\\"fivethirtyeight\\\")\";\n",
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   "source": [
    "import random\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.style.use(\"fivethirtyeight\")"
   ]
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   "execution_count": 4,
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       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 4;\n",
       "                var nbb_unformatted_code = \"plt.rcParams[\\\"figure.figsize\\\"] = (12, 4)\\nplt.rcParams.update({\\\"font.size\\\": 14})\";\n",
       "                var nbb_formatted_code = \"plt.rcParams[\\\"figure.figsize\\\"] = (12, 4)\\nplt.rcParams.update({\\\"font.size\\\": 14})\";\n",
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    "plt.rcParams[\"figure.figsize\"] = (12, 4)\n",
    "plt.rcParams.update({\"font.size\": 14})"
   ]
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   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><div id=9981f2ed-9ac9-4677-9257-18024cc0d489 style=\"display:none; background-color:#9D6CFF; color:white; width:200px; height:30px; padding-left:5px; border-radius:4px; flex-direction:row; justify-content:space-around; align-items:center;\" onmouseover=\"this.style.backgroundColor='#BA9BF8'\" onmouseout=\"this.style.backgroundColor='#9D6CFF'\" onclick=\"window.commands?.execute('create-mitosheet-from-dataframe-output');\">See Full Dataframe in Mito</div> <script> if (window.commands?.hasCommand('create-mitosheet-from-dataframe-output')) document.getElementById('9981f2ed-9ac9-4677-9257-18024cc0d489').style.display = 'flex' </script> <table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>feat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.354748</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.322974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.994180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.539885</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.406180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.943614</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.345952</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.000003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.739474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.193036</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table></div>"
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      "text/plain": [
       "       feat\n",
       "0  0.354748\n",
       "1  0.322974\n",
       "2  0.994180\n",
       "3  0.539885\n",
       "4  0.406180\n",
       "5  0.943614\n",
       "6  0.345952\n",
       "7  0.000003\n",
       "8  0.739474\n",
       "9  0.193036"
      ]
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     "execution_count": 5,
     "metadata": {},
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       "                var nbb_unformatted_code = \"df = pd.DataFrame({\\\"feat\\\": np.random.random(10000)})\\ndf.head(10)\";\n",
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   "source": [
    "df = pd.DataFrame({\"feat\": np.random.random(10000)})\n",
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
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    {
     "data": {
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       "<div><div id=e1c48332-2487-4517-ba0d-67be481a9b60 style=\"display:none; background-color:#9D6CFF; color:white; width:200px; height:30px; padding-left:5px; border-radius:4px; flex-direction:row; justify-content:space-around; align-items:center;\" onmouseover=\"this.style.backgroundColor='#BA9BF8'\" onmouseout=\"this.style.backgroundColor='#9D6CFF'\" onclick=\"window.commands?.execute('create-mitosheet-from-dataframe-output');\">See Full Dataframe in Mito</div> <script> if (window.commands?.hasCommand('create-mitosheet-from-dataframe-output')) document.getElementById('e1c48332-2487-4517-ba0d-67be481a9b60').style.display = 'flex' </script> <table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>feat</th>\n",
       "      <th>feat_10</th>\n",
       "      <th>feat_100</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.354748</td>\n",
       "      <td>4</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.322974</td>\n",
       "      <td>3</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.994180</td>\n",
       "      <td>10</td>\n",
       "      <td>99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.539885</td>\n",
       "      <td>5</td>\n",
       "      <td>54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.406180</td>\n",
       "      <td>4</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.943614</td>\n",
       "      <td>9</td>\n",
       "      <td>94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.345952</td>\n",
       "      <td>3</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.000003</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.739474</td>\n",
       "      <td>7</td>\n",
       "      <td>74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.193036</td>\n",
       "      <td>2</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table></div>"
      ],
      "text/plain": [
       "       feat  feat_10  feat_100\n",
       "0  0.354748        4        35\n",
       "1  0.322974        3        32\n",
       "2  0.994180       10        99\n",
       "3  0.539885        5        54\n",
       "4  0.406180        4        41\n",
       "5  0.943614        9        94\n",
       "6  0.345952        3        35\n",
       "7  0.000003        0         0\n",
       "8  0.739474        7        74\n",
       "9  0.193036        2        19"
      ]
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     "execution_count": 6,
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       "                var nbb_cell_id = 6;\n",
       "                var nbb_unformatted_code = \"df[\\\"feat_10\\\"] = np.round(df[\\\"feat\\\"] * 10).astype(\\\"int\\\")\\ndf[\\\"feat_100\\\"] = np.round(df[\\\"feat\\\"] * 100).astype(\\\"int\\\")\\ndf.head(10)\";\n",
       "                var nbb_formatted_code = \"df[\\\"feat_10\\\"] = np.round(df[\\\"feat\\\"] * 10).astype(\\\"int\\\")\\ndf[\\\"feat_100\\\"] = np.round(df[\\\"feat\\\"] * 100).astype(\\\"int\\\")\\ndf.head(10)\";\n",
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   "source": [
    "df[\"feat_10\"] = np.round(df[\"feat\"] * 10).astype(\"int\")\n",
    "df[\"feat_100\"] = np.round(df[\"feat\"] * 100).astype(\"int\")\n",
    "df.head(10)"
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     "text": [
      "feat 10000\n",
      "feat_10 11\n",
      "feat_100 101\n"
     ]
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 7;\n",
       "                var nbb_unformatted_code = \"for col in df.columns:\\n    print(col, df[col].nunique())\";\n",
       "                var nbb_formatted_code = \"for col in df.columns:\\n    print(col, df[col].nunique())\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
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   "source": [
    "for col in df.columns:\n",
    "    print(col, df[col].nunique())"
   ]
  },
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   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 864x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 28;\n",
       "                var nbb_unformatted_code = \"plt.subplot(1, 2, 1)\\nplt.hist(df[\\\"feat\\\"], bins=100, edgecolor=\\\"black\\\")\\nplt.xlabel(\\\"Feat\\\")\\nplt.ylabel(\\\"Histogram (Count)\\\")\\n\\nplt.subplot(1, 2, 2)\\nplt.hist(df[\\\"feat_10\\\"], bins=100, edgecolor=\\\"black\\\")\\nplt.xlabel(\\\"Feat_10\\\")\\nplt.ylabel(\\\"Histogram (Count)\\\")\\nplt.tight_layout()\";\n",
       "                var nbb_formatted_code = \"plt.subplot(1, 2, 1)\\nplt.hist(df[\\\"feat\\\"], bins=100, edgecolor=\\\"black\\\")\\nplt.xlabel(\\\"Feat\\\")\\nplt.ylabel(\\\"Histogram (Count)\\\")\\n\\nplt.subplot(1, 2, 2)\\nplt.hist(df[\\\"feat_10\\\"], bins=100, edgecolor=\\\"black\\\")\\nplt.xlabel(\\\"Feat_10\\\")\\nplt.ylabel(\\\"Histogram (Count)\\\")\\nplt.tight_layout()\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
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   ],
   "source": [
    "plt.subplot(1, 2, 1)\n",
    "plt.hist(df[\"feat\"], bins=100, edgecolor=\"black\")\n",
    "plt.xlabel(\"Feat\")\n",
    "plt.ylabel(\"Histogram (Count)\")\n",
    "\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.hist(df[\"feat_10\"], bins=100, edgecolor=\"black\")\n",
    "plt.xlabel(\"Feat_10\")\n",
    "plt.ylabel(\"Histogram (Count)\")\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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