0103_Discretisation_Custom_Binning.ipynb

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    "%load_ext nb_black"
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       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 2;\n",
       "                var nbb_unformatted_code = \"import random\\nimport pandas as pd\\nimport numpy as np\\nfrom scipy import stats\\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",
    "from scipy import stats\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.style.use(\"fivethirtyeight\")"
   ]
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       "                var nbb_cell_id = 3;\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": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><div id=7f50e762-de44-45b4-ae99-262559a5a20b 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('7f50e762-de44-45b4-ae99-262559a5a20b').style.display = 'flex' </script> <table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>customer_id</th>\n",
       "      <th>age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>customer_id_0</td>\n",
       "      <td>58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>customer_id_1</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>customer_id_2</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>customer_id_3</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>customer_id_4</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>customer_id_5</td>\n",
       "      <td>92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>customer_id_6</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>customer_id_7</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>customer_id_8</td>\n",
       "      <td>92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>customer_id_9</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table></div>"
      ],
      "text/plain": [
       "     customer_id  age\n",
       "0  customer_id_0   58\n",
       "1  customer_id_1   24\n",
       "2  customer_id_2   50\n",
       "3  customer_id_3   72\n",
       "4  customer_id_4   30\n",
       "5  customer_id_5   92\n",
       "6  customer_id_6    4\n",
       "7  customer_id_7   33\n",
       "8  customer_id_8   92\n",
       "9  customer_id_9   22"
      ]
     },
     "execution_count": 4,
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       "                var nbb_unformatted_code = \"age = np.random.randint(0, 100, 10000)\\ncustomer_id = [\\\"customer_id_\\\" + str(i) for i in range(len(age))]\\ndf = pd.DataFrame({\\\"customer_id\\\": customer_id, \\\"age\\\": age})\\ndf.head(10)\";\n",
       "                var nbb_formatted_code = \"age = np.random.randint(0, 100, 10000)\\ncustomer_id = [\\\"customer_id_\\\" + str(i) for i in range(len(age))]\\ndf = pd.DataFrame({\\\"customer_id\\\": customer_id, \\\"age\\\": age})\\ndf.head(10)\";\n",
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   "source": [
    "age = np.random.randint(0, 100, 10000)\n",
    "customer_id = [\"customer_id_\" + str(i) for i in range(len(age))]\n",
    "df = pd.DataFrame({\"customer_id\": customer_id, \"age\": age})\n",
    "df.head(10)"
   ]
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   "execution_count": 5,
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     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 5;\n",
       "                var nbb_unformatted_code = \"bin_ranges = [0, 3, 12, 19, 60, 100]\\nbin_names_str = [\\\"toddler\\\", \\\"child\\\", \\\"teen\\\", \\\"adult\\\", \\\"senior\\\"]\";\n",
       "                var nbb_formatted_code = \"bin_ranges = [0, 3, 12, 19, 60, 100]\\nbin_names_str = [\\\"toddler\\\", \\\"child\\\", \\\"teen\\\", \\\"adult\\\", \\\"senior\\\"]\";\n",
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   "source": [
    "bin_ranges = [0, 3, 12, 19, 60, 100]\n",
    "bin_names_str = [\"toddler\", \"child\", \"teen\", \"adult\", \"senior\"]"
   ]
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   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><div id=a872308f-e99d-4281-8a57-9c25c56fad13 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('a872308f-e99d-4281-8a57-9c25c56fad13').style.display = 'flex' </script> <table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>customer_id</th>\n",
       "      <th>age</th>\n",
       "      <th>age_bin_custom_range</th>\n",
       "      <th>age_bin_str_label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>customer_id_0</td>\n",
       "      <td>58</td>\n",
       "      <td>(19, 60]</td>\n",
       "      <td>adult</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>customer_id_1</td>\n",
       "      <td>24</td>\n",
       "      <td>(19, 60]</td>\n",
       "      <td>adult</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>customer_id_2</td>\n",
       "      <td>50</td>\n",
       "      <td>(19, 60]</td>\n",
       "      <td>adult</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>customer_id_3</td>\n",
       "      <td>72</td>\n",
       "      <td>(60, 100]</td>\n",
       "      <td>senior</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>customer_id_4</td>\n",
       "      <td>30</td>\n",
       "      <td>(19, 60]</td>\n",
       "      <td>adult</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>customer_id_5</td>\n",
       "      <td>92</td>\n",
       "      <td>(60, 100]</td>\n",
       "      <td>senior</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>customer_id_6</td>\n",
       "      <td>4</td>\n",
       "      <td>(3, 12]</td>\n",
       "      <td>child</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>customer_id_7</td>\n",
       "      <td>33</td>\n",
       "      <td>(19, 60]</td>\n",
       "      <td>adult</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>customer_id_8</td>\n",
       "      <td>92</td>\n",
       "      <td>(60, 100]</td>\n",
       "      <td>senior</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>customer_id_9</td>\n",
       "      <td>22</td>\n",
       "      <td>(19, 60]</td>\n",
       "      <td>adult</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table></div>"
      ],
      "text/plain": [
       "     customer_id  age age_bin_custom_range age_bin_str_label\n",
       "0  customer_id_0   58             (19, 60]             adult\n",
       "1  customer_id_1   24             (19, 60]             adult\n",
       "2  customer_id_2   50             (19, 60]             adult\n",
       "3  customer_id_3   72            (60, 100]            senior\n",
       "4  customer_id_4   30             (19, 60]             adult\n",
       "5  customer_id_5   92            (60, 100]            senior\n",
       "6  customer_id_6    4              (3, 12]             child\n",
       "7  customer_id_7   33             (19, 60]             adult\n",
       "8  customer_id_8   92            (60, 100]            senior\n",
       "9  customer_id_9   22             (19, 60]             adult"
      ]
     },
     "execution_count": 6,
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       "                var nbb_cell_id = 6;\n",
       "                var nbb_unformatted_code = \"df[\\\"age_bin_custom_range\\\"] = pd.cut(df[\\\"age\\\"], bins=bin_ranges)\\ndf[\\\"age_bin_str_label\\\"] = pd.cut(df[\\\"age\\\"], bins=bin_ranges, labels=bin_names_str)\\n\\ndf.head(10)\";\n",
       "                var nbb_formatted_code = \"df[\\\"age_bin_custom_range\\\"] = pd.cut(df[\\\"age\\\"], bins=bin_ranges)\\ndf[\\\"age_bin_str_label\\\"] = pd.cut(df[\\\"age\\\"], bins=bin_ranges, labels=bin_names_str)\\n\\ndf.head(10)\";\n",
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   "source": [
    "df[\"age_bin_custom_range\"] = pd.cut(df[\"age\"], bins=bin_ranges)\n",
    "df[\"age_bin_str_label\"] = pd.cut(df[\"age\"], bins=bin_ranges, labels=bin_names_str)\n",
    "\n",
    "df.head(10)"
   ]
  },
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     "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 = 7;\n",
       "                var nbb_unformatted_code = \"plt.subplot(1, 2, 1)\\nplt.hist(df[\\\"age\\\"], bins=20, edgecolor=\\\"black\\\")\\nplt.xlabel(\\\"Age\\\")\\nplt.ylabel(\\\"Count\\\")\\nplt.subplot(1, 2, 2)\\ndf[\\\"age_bin_str_label\\\"].value_counts().plot(kind=\\\"bar\\\", rot=0, edgecolor=\\\"black\\\")\\nplt.xlabel(\\\"Age Group\\\")\\nplt.ylabel(\\\"Count\\\")\\n\\n# _ = plt.hist(df[\\\"Age_bin_ceil\\\"], edgecolor=\\\"black\\\")\\n# plt.grid()\";\n",
       "                var nbb_formatted_code = \"plt.subplot(1, 2, 1)\\nplt.hist(df[\\\"age\\\"], bins=20, edgecolor=\\\"black\\\")\\nplt.xlabel(\\\"Age\\\")\\nplt.ylabel(\\\"Count\\\")\\nplt.subplot(1, 2, 2)\\ndf[\\\"age_bin_str_label\\\"].value_counts().plot(kind=\\\"bar\\\", rot=0, edgecolor=\\\"black\\\")\\nplt.xlabel(\\\"Age Group\\\")\\nplt.ylabel(\\\"Count\\\")\\n\\n# _ = plt.hist(df[\\\"Age_bin_ceil\\\"], edgecolor=\\\"black\\\")\\n# plt.grid()\";\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"
    }
   ],
   "source": [
    "plt.subplot(1, 2, 1)\n",
    "plt.hist(df[\"age\"], bins=20, edgecolor=\"black\")\n",
    "plt.xlabel(\"Age\")\n",
    "plt.ylabel(\"Count\")\n",
    "plt.subplot(1, 2, 2)\n",
    "df[\"age_bin_str_label\"].value_counts().plot(kind=\"bar\", rot=0, edgecolor=\"black\")\n",
    "plt.xlabel(\"Age Group\")\n",
    "plt.ylabel(\"Count\")\n",
    "\n",
    "# _ = plt.hist(df[\"Age_bin_ceil\"], edgecolor=\"black\")\n",
    "# plt.grid()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><div id=d0eafea5-15d3-439d-831a-ccbb2cb1c300 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('d0eafea5-15d3-439d-831a-ccbb2cb1c300').style.display = 'flex' </script> <table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>customer_id</th>\n",
       "      <th>income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>customer_id_0</td>\n",
       "      <td>100456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>customer_id_1</td>\n",
       "      <td>61808</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>customer_id_2</td>\n",
       "      <td>89050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>customer_id_3</td>\n",
       "      <td>152811</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>customer_id_4</td>\n",
       "      <td>62159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>customer_id_5</td>\n",
       "      <td>78272</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>customer_id_6</td>\n",
       "      <td>114207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>customer_id_7</td>\n",
       "      <td>197605</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>customer_id_8</td>\n",
       "      <td>42618</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>customer_id_9</td>\n",
       "      <td>35115</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table></div>"
      ],
      "text/plain": [
       "     customer_id  income\n",
       "0  customer_id_0  100456\n",
       "1  customer_id_1   61808\n",
       "2  customer_id_2   89050\n",
       "3  customer_id_3  152811\n",
       "4  customer_id_4   62159\n",
       "5  customer_id_5   78272\n",
       "6  customer_id_6  114207\n",
       "7  customer_id_7  197605\n",
       "8  customer_id_8   42618\n",
       "9  customer_id_9   35115"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 8;\n",
       "                var nbb_unformatted_code = \"income = np.concatenate(\\n    [\\n        np.random.randint(20000, 80000, 600),\\n        np.random.randint(80000, 120000, 200),\\n        np.random.randint(120000, 180000, 150),\\n        np.random.randint(180000, 250000, 50),\\n    ]\\n)\\n\\nnp.random.shuffle(income)\\n\\ncustomer_id = [\\\"customer_id_\\\" + str(i) for i in range(len(income))]\\ndf = pd.DataFrame({\\\"customer_id\\\": customer_id, \\\"income\\\": income})\\ndf.head(10)\";\n",
       "                var nbb_formatted_code = \"income = np.concatenate(\\n    [\\n        np.random.randint(20000, 80000, 600),\\n        np.random.randint(80000, 120000, 200),\\n        np.random.randint(120000, 180000, 150),\\n        np.random.randint(180000, 250000, 50),\\n    ]\\n)\\n\\nnp.random.shuffle(income)\\n\\ncustomer_id = [\\\"customer_id_\\\" + str(i) for i in range(len(income))]\\ndf = pd.DataFrame({\\\"customer_id\\\": customer_id, \\\"income\\\": income})\\ndf.head(10)\";\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"
    }
   ],
   "source": [
    "income = np.concatenate(\n",
    "    [\n",
    "        np.random.randint(20000, 80000, 600),\n",
    "        np.random.randint(80000, 120000, 200),\n",
    "        np.random.randint(120000, 180000, 150),\n",
    "        np.random.randint(180000, 250000, 50),\n",
    "    ]\n",
    ")\n",
    "\n",
    "np.random.shuffle(income)\n",
    "\n",
    "customer_id = [\"customer_id_\" + str(i) for i in range(len(income))]\n",
    "df = pd.DataFrame({\"customer_id\": customer_id, \"income\": income})\n",
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 9;\n",
       "                var nbb_unformatted_code = \"bin_ranges = [20000, 80000, 120000, 180000, 250000]\\nbin_names_str = [\\\"low\\\", \\\"medium\\\", \\\"high\\\", \\\"very high\\\"]\";\n",
       "                var nbb_formatted_code = \"bin_ranges = [20000, 80000, 120000, 180000, 250000]\\nbin_names_str = [\\\"low\\\", \\\"medium\\\", \\\"high\\\", \\\"very high\\\"]\";\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"
    }
   ],
   "source": [
    "bin_ranges = [20000, 80000, 120000, 180000, 250000]\n",
    "bin_names_str = [\"low\", \"medium\", \"high\", \"very high\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><div id=8c57f422-39bc-4a9c-a1e8-89609735bef0 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('8c57f422-39bc-4a9c-a1e8-89609735bef0').style.display = 'flex' </script> <table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>customer_id</th>\n",
       "      <th>income</th>\n",
       "      <th>income_bin_custom_range</th>\n",
       "      <th>income_bin_str_label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>customer_id_0</td>\n",
       "      <td>100456</td>\n",
       "      <td>(80000, 120000]</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>customer_id_1</td>\n",
       "      <td>61808</td>\n",
       "      <td>(20000, 80000]</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>customer_id_2</td>\n",
       "      <td>89050</td>\n",
       "      <td>(80000, 120000]</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>customer_id_3</td>\n",
       "      <td>152811</td>\n",
       "      <td>(120000, 180000]</td>\n",
       "      <td>high</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>customer_id_4</td>\n",
       "      <td>62159</td>\n",
       "      <td>(20000, 80000]</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>customer_id_5</td>\n",
       "      <td>78272</td>\n",
       "      <td>(20000, 80000]</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>customer_id_6</td>\n",
       "      <td>114207</td>\n",
       "      <td>(80000, 120000]</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>customer_id_7</td>\n",
       "      <td>197605</td>\n",
       "      <td>(180000, 250000]</td>\n",
       "      <td>very high</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>customer_id_8</td>\n",
       "      <td>42618</td>\n",
       "      <td>(20000, 80000]</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>customer_id_9</td>\n",
       "      <td>35115</td>\n",
       "      <td>(20000, 80000]</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table></div>"
      ],
      "text/plain": [
       "     customer_id  income income_bin_custom_range income_bin_str_label\n",
       "0  customer_id_0  100456         (80000, 120000]               medium\n",
       "1  customer_id_1   61808          (20000, 80000]                  low\n",
       "2  customer_id_2   89050         (80000, 120000]               medium\n",
       "3  customer_id_3  152811        (120000, 180000]                 high\n",
       "4  customer_id_4   62159          (20000, 80000]                  low\n",
       "5  customer_id_5   78272          (20000, 80000]                  low\n",
       "6  customer_id_6  114207         (80000, 120000]               medium\n",
       "7  customer_id_7  197605        (180000, 250000]            very high\n",
       "8  customer_id_8   42618          (20000, 80000]                  low\n",
       "9  customer_id_9   35115          (20000, 80000]                  low"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 10;\n",
       "                var nbb_unformatted_code = \"df[\\\"income_bin_custom_range\\\"] = pd.cut(df[\\\"income\\\"], bins=bin_ranges)\\ndf[\\\"income_bin_str_label\\\"] = pd.cut(df[\\\"income\\\"], bins=bin_ranges, labels=bin_names_str)\\n\\ndf.head(10)\";\n",
       "                var nbb_formatted_code = \"df[\\\"income_bin_custom_range\\\"] = pd.cut(df[\\\"income\\\"], bins=bin_ranges)\\ndf[\\\"income_bin_str_label\\\"] = pd.cut(df[\\\"income\\\"], bins=bin_ranges, labels=bin_names_str)\\n\\ndf.head(10)\";\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"
    }
   ],
   "source": [
    "df[\"income_bin_custom_range\"] = pd.cut(df[\"income\"], bins=bin_ranges)\n",
    "df[\"income_bin_str_label\"] = pd.cut(df[\"income\"], bins=bin_ranges, labels=bin_names_str)\n",
    "\n",
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Count')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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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 = 11;\n",
       "                var nbb_unformatted_code = \"plt.subplot(1, 2, 1)\\nplt.hist(df[\\\"income\\\"], bins=20, edgecolor=\\\"black\\\")\\nplt.xlabel(\\\"income\\\")\\nplt.ylabel(\\\"Count\\\")\\nplt.subplot(1, 2, 2)\\ndf[\\\"income_bin_str_label\\\"].value_counts().plot(kind=\\\"bar\\\", rot=0, edgecolor=\\\"black\\\")\\nplt.xlabel(\\\"income\\\")\\nplt.ylabel(\\\"Count\\\")\";\n",
       "                var nbb_formatted_code = \"plt.subplot(1, 2, 1)\\nplt.hist(df[\\\"income\\\"], bins=20, edgecolor=\\\"black\\\")\\nplt.xlabel(\\\"income\\\")\\nplt.ylabel(\\\"Count\\\")\\nplt.subplot(1, 2, 2)\\ndf[\\\"income_bin_str_label\\\"].value_counts().plot(kind=\\\"bar\\\", rot=0, edgecolor=\\\"black\\\")\\nplt.xlabel(\\\"income\\\")\\nplt.ylabel(\\\"Count\\\")\";\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"
    }
   ],
   "source": [
    "plt.subplot(1, 2, 1)\n",
    "plt.hist(df[\"income\"], bins=20, edgecolor=\"black\")\n",
    "plt.xlabel(\"income\")\n",
    "plt.ylabel(\"Count\")\n",
    "plt.subplot(1, 2, 2)\n",
    "df[\"income_bin_str_label\"].value_counts().plot(kind=\"bar\", rot=0, edgecolor=\"black\")\n",
    "plt.xlabel(\"income\")\n",
    "plt.ylabel(\"Count\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.8.13"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}