08.07-Domain-knowledge-discretisation.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Domain knowledge discretisation\n",
    "\n",
    "Frequently, when engineering variables in a business setting, the business experts determine the intervals in which they think the variable should be divided so that it makes sense for the business. Typical examples are the discretisation of variables like Age and Income. \n",
    "\n",
    "Income for example is usually capped at a certain maximum value, and all incomes above that value fall into the last bucket. As per Age, it is usually divided in certain groups according to the business need, for example division into  0-21 (for under-aged), 20-30 (for young adults), 30-40, 40-60, and > 60 (for retired or close to) are frequent.\n",
    "\n",
    "## In this demo\n",
    "\n",
    "We will learn how to divide a variable into pre-defined buckets using the titanic and lending club datasets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>survived</th>\n",
       "      <th>age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>29.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.9167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>2.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>30.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>25.0000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   survived      age\n",
       "0         1  29.0000\n",
       "1         1   0.9167\n",
       "2         0   2.0000\n",
       "3         0  30.0000\n",
       "4         0  25.0000"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# load the Titanic Dataset\n",
    "\n",
    "data = pd.read_csv('../titanic.csv',\n",
    "                   usecols=['age', 'survived'])\n",
    "\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The variable Age contains missing data, that I will fill by extracting a random sample of the variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def impute_na(data, variable):\n",
    "    df = data.copy()\n",
    "\n",
    "    # random sampling\n",
    "    df[variable+'_random'] = df[variable]\n",
    "\n",
    "    # extract the random sample to fill the na\n",
    "    random_sample = data[variable].dropna().sample(\n",
    "        df[variable].isnull().sum(), random_state=0)\n",
    "\n",
    "    # pandas needs to have the same index in order to merge datasets\n",
    "    random_sample.index = df[df[variable].isnull()].index\n",
    "    df.loc[df[variable].isnull(), variable+'_random'] = random_sample\n",
    "\n",
    "    return df[variable+'_random']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# let's fill the missing data\n",
    "data['age'] = impute_na(data, 'age')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>survived</th>\n",
       "      <th>age</th>\n",
       "      <th>Age_buckets_labels</th>\n",
       "      <th>Age_buckets</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>29.0000</td>\n",
       "      <td>20-40</td>\n",
       "      <td>(20.0, 40.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.9167</td>\n",
       "      <td>0-20</td>\n",
       "      <td>(-0.001, 20.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>2.0000</td>\n",
       "      <td>0-20</td>\n",
       "      <td>(-0.001, 20.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>20-40</td>\n",
       "      <td>(20.0, 40.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>25.0000</td>\n",
       "      <td>20-40</td>\n",
       "      <td>(20.0, 40.0]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   survived      age Age_buckets_labels     Age_buckets\n",
       "0         1  29.0000              20-40    (20.0, 40.0]\n",
       "1         1   0.9167               0-20  (-0.001, 20.0]\n",
       "2         0   2.0000               0-20  (-0.001, 20.0]\n",
       "3         0  30.0000              20-40    (20.0, 40.0]\n",
       "4         0  25.0000              20-40    (20.0, 40.0]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# let's divide Age into the buckets that we described in the intro cell\n",
    "# of this notebook\n",
    "\n",
    "# bucket boundaries\n",
    "buckets = [0, 20, 40, 60, 1000]\n",
    "\n",
    "# bucket labels\n",
    "labels = ['0-20', '20-40', '40-60', '>60']\n",
    "\n",
    "# discretisation\n",
    "data['Age_buckets_labels'] = pd.cut(\n",
    "    data['age'], bins=buckets, labels=labels, include_lowest=True)\n",
    "\n",
    "data['Age_buckets'] = pd.cut(data['age'], bins=buckets, include_lowest=True)\n",
    "\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "    }\n",
       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>survived</th>\n",
       "      <th>age</th>\n",
       "      <th>Age_buckets_labels</th>\n",
       "      <th>Age_buckets</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1304</th>\n",
       "      <td>0</td>\n",
       "      <td>14.5</td>\n",
       "      <td>0-20</td>\n",
       "      <td>(-0.001, 20.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1305</th>\n",
       "      <td>0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>20-40</td>\n",
       "      <td>(20.0, 40.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1306</th>\n",
       "      <td>0</td>\n",
       "      <td>26.5</td>\n",
       "      <td>20-40</td>\n",
       "      <td>(20.0, 40.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1307</th>\n",
       "      <td>0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>20-40</td>\n",
       "      <td>(20.0, 40.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1308</th>\n",
       "      <td>0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>20-40</td>\n",
       "      <td>(20.0, 40.0]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      survived   age Age_buckets_labels     Age_buckets\n",
       "1304         0  14.5               0-20  (-0.001, 20.0]\n",
       "1305         0  39.0              20-40    (20.0, 40.0]\n",
       "1306         0  26.5              20-40    (20.0, 40.0]\n",
       "1307         0  27.0              20-40    (20.0, 40.0]\n",
       "1308         0  29.0              20-40    (20.0, 40.0]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Above we can observe the buckets into which each Age observation was placed. For example, age 14 was placed into the 0-20 bucket.\n",
    "\n",
    "Let's explore the number of observations and survival rate per bucket after this arbitrary discretisation method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7ed4ce3eb8>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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kewBVVQ9OupYeuD/HJ8k+wFKGCzzvrqp7J1zS2HjWzSyS7AicDLwa+Fnaf3zgIuDsqvrxBMtbVJL8HPBHwBHAQwxXT+8CfA5YU1V3TK66xcf9OV5JDgL+AtgV+IfWvIxh3/7HqvrypGobF4N+Fkk+zvAfei3D1b4w/MdfBexRVa+bVG2LTZK/A94PnF9VP2ltOwDHAe+oqkMnWd9i4/4cryTXA2+uqms2aT8U+F9V9cLJVDY+Bv0sktxSVQfOsu3Wqvr57V3TYpXktqpaMd9tmpn7c7zm2J/rq+qA7V3TuDlGP7sHkxwHXFBVPwVI8iSGoybHQ+fnuiR/zvDX0V2tbT+Gv46+MrGqFi/353j9dZJLgHN5/P48EbhsYlWNkUf0s0iyHHgfcDiPBftuwJUM46B/P5nKFp92z6OTGeYlWMowpryB4R5IZ1fVP02wvEXH/Tl+SY5khv1ZVZdOtLAxMehHkGRPhn21GO5PLUmP43n0W5BklyT7V9UD00M+yQsmWVcPktw66RoWqySvbqdVkmRJkrVJvpbkk0mWTbq+xSbJqUn2asv7J/lCkgeTXJPkX0+6vnEw6GeR5LXAzcAFSW5M8pJpm8+ZTFWLU5LvJflue/xeku8B+0+1T7q+Rei9VfWdtvynwPUMczT/NfDhiVW1eL112oHcGcDpVbU78E6G0y4XPYN+du8CXlxVBwEnAR9J8u/btplm0dLszgH+Cjigqnauqp2BO9vyLpMtbVHaYdryAVV1elVtqKpzgDmnldNmpp+UsndVXQhQVVcBO0+kojEz6Ge3Q1XdA1BVXwJeBrw7yW8xw9SIml1VvQ34APDxJL/Vzl5yH269q5K8J8nT2vKxAEleBjw82dIWpfOTnJPk2cCFSd6R5OeSnATcOenixsEvY2eR5IvA66vqm9PadgEuBH6pqp4yseIWqRbwpzKcorp/Vf3shEtalJLsBLwbeGNrWgb8APi/DGeEdRFO21OSNwBvBfYHnsJwmuVfAe+rqkX/4WnQzyLJC4EfVNX6Tdp3Al5bVR+dTGWLX5J9gRf1curaJCXZFdixqh6YdC164nLoZhZV9dUZQv6VVfVjQ37btCGxYyddRw+q6uGqeiDJ70+6ll4kWdmuVeiGQT8/75l0AR1ZOekCOvOqSRfQg/bX5heB1066lnEy6OfHs23G575JF9AZ/98cj1UMt5Z406QLGSfH6LcgyXN47LLoAu5nuGPgTRMtTNpEkidN3ZNJWy/JjcC/ZbidxONOxljMPKKfRZJ3Ap9gOFL6EnAt8GOGUwTXTLK2xSbJrklOS3Jzkgfaz02tbbdJ17fYJNkxyZuTXJbkhiRfBS5J8pZ2soC2Qjs99eZ28dSHGe4n1AWP6GfRLtF//qYTjLQvaW70VrCjS/IZhkkx1lbVt1vbv2L4M/nlVfWrk6xvsXGuhIWR5P8AH6uqS9up1NcBB/bwl5JBP4skNwP/rqq+tUn7M4HPznavem1ujnv7z7pNM3OuhPFrf1muA1ZUC8UkHwE+WVWfnmhxY+D96Gf3DuCKJLfx2D2qfw44gOGiH43uW0l+l+GI/l745/k538Bj+1ajc66EMauqhxj+bU9ve/2Eyhk7j+i3oP3jOYTH36P62qnp2zSaJLsDaxi+2N6H4Yvtexm+8HrftBt0aQQzzJUQhvlOnStBMzLotV20M5iWAVdX1fentb+iqrqYxWcSpuZKAN5fVb8x6Xr0xGTQa8G1G8GdAtwEHAS8vaouatu+XFUHT7K+xSbJxTM0H87whTdV5cVTehzH6LU9/CbDLZ+/34Ydzk+yvKo+gBf6bI1lwDeADzIMgwV4CfA/J1mUnrg8j17bww5TwzVVdQfwK8CRSf4Yg35rrGQ49e/dwMPtvun/WFWfr6rPT7QyPSEZ9Noevp3koKmVFvqvBPYCupiqbXuqqp9W1ekME+K8O8mf4l/n2gLH6LXg2jymj05dLLXJtsOq6m8nUFY3khwNHFZV75p0LXpiMuglqXMO3UhS5wx6SeqcQS9JnTPotV0keXWSalfIjvN1r0qyzbNVJTknyWvm0f8NSeY9uXmS30/yO2OuZXmSr8+3Fv3LYdBrezkB+Bvg+EkXMiZvAOYd9NIkGPRacEmeARzGMJHD8a3tSUn+PMmNST6d5NKpo9gkL07y+STXJflMm8dzS34jyReTfD3JIe01Hnfk3LYtb8snTk3Y0W5Fu2m9f9COqp80Uy2tzpXAR5Ncn+RpbRKVb7TX/R8j7pffTHJtq+OCJD8zbfPLk/y/JLcmeWXrv0OS/96ec0OSN8/wms9P8qVW1w1JnDdBXmSh7eJY4LKqujXJd5IcDDwbWM5wwdTeDPfB+VCbIelPgGOqamOS1wHvBd64hdd/elX9myS/DHwI+IXZOiZ5PsMVpYdV1f1J9thk+x8x3AnyJIZ/H5vVUlVvTHIq8DtVta69xquB51RVZfRZs/6yqv53e98/ZPgg/JO2bTnDlHb7A1cmOQA4keFK2JckeQrwt0k+y3AbhClvAT5QVR9tk+TsMGIt6phBr+3hBOD9bfkTbX0n4FPtfurfTnJl234gQ1BfngSGoLpnjtf/OEBVfSHJLnME7eEM8/7e354z/RbJvwdcU1WrAZKMWst3gR8CH0xyCTDqRBW/0AJ+N+AZwGembTuv7ZvbktwOPAf4NeAF08bvdwVWALdOe97fMVwtu4zhg+S2EWtRxwx6Lah2G93DGUKtGMKygAtnewrDVI0vncfbbHrVXwGP8vihyadOe/3ZrhK8Fnhxkj3aB8BItVTVo23I6AiGoalTGX7nuZwDHFtVX03yBoZ7AE3/HdhkPcDbqmr6B8LU/emnavlYkmuAo4HPJHlTVX1uhFrUMcfotdBeA5xbVc+squVVtR/w98D9wH9o4+D78FjI3QIsSfJSgCQ7teGWLXld6/tLDEMbDwN3AAe39oOBZ7W+VwCvbR9AbDJ0cxlwGsNE2zvPUcv3gJ1b+zOAXavqUoaZyQ5iNDsD97Thql/fZNtxbd/szzDMdQvDEf9bW3+S/HySp09/UpJnA7dX1RkME7u8YMRa1DGP6LXQTmAIz+kuAJ7LMGPX1xmGHq5hCOkftaGJM5LsyvD/6PuBG7fwHg8m+SKwC4+N5V8AnJjkeoYj9VsBqurGJO8FPp/kJ8BXGM6goW3/VAv5i4GjGD6oZqrlHOAvkvwjcCRwUZKnMhx1/6cR983vtd/7W8DXaB8czS3A5xlm5HpLVf0wyQcZxu6/nGEsaSPD9x/TvY7hy+kfA98G3jNiLeqY97rRxCR5RrtH/Z7Alxi+IN3sxmeSto1H9JqkT7cvTp8M/IEhLy0Mj+i1KCT5M4Zz8af7QFV9eBL1zCXJu4HjNmn+VFW9dxL16F82g16SOudZN5LUOYNekjpn0EtS5wx6Serc/wdY6h6RpFHbHAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# number of passengers per age bucket\n",
    "\n",
    "data.groupby('Age_buckets_labels')['age'].count().plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7ed502dac8>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# survival rate per age bucket\n",
    "\n",
    "data.groupby('Age_buckets_labels')['survived'].mean().plot.bar()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Lending Club\n",
    "\n",
    "Let's explore discretisation using domain knowledge in a different business scenario. I will use the loan book from the peer to peer lending company Lending Club. This dataset contains information on loans given to people, and the financial characteristics of those people as well as the loan performance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>annual_inc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>24000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>30000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12252.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>49200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>80000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   annual_inc\n",
       "0     24000.0\n",
       "1     30000.0\n",
       "2     12252.0\n",
       "3     49200.0\n",
       "4     80000.0"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# I will load only the income declared by the borrower for the demonstration\n",
    "\n",
    "data = pd.read_csv('../loan.csv', usecols=['annual_inc'])\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    8.873750e+05\n",
       "mean     7.502759e+04\n",
       "std      6.469830e+04\n",
       "min      0.000000e+00\n",
       "25%      4.500000e+04\n",
       "50%      6.500000e+04\n",
       "75%      9.000000e+04\n",
       "max      9.500000e+06\n",
       "Name: annual_inc, dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.annual_inc.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7ed50accf8>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# let's inspect the distribution of Incomes\n",
    "\n",
    "data.annual_inc.hist(bins=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7ed515c898>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# and now let's look at the lower incomes in more detail\n",
    "\n",
    "data[data.annual_inc<500000].annual_inc.hist(bins=100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that the majority of the population earns below 150,000. So we may want to make a cap there."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>annual_inc</th>\n",
       "      <th>Income_buckets</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>24000.0</td>\n",
       "      <td>0-45k</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>30000.0</td>\n",
       "      <td>0-45k</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12252.0</td>\n",
       "      <td>0-45k</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>49200.0</td>\n",
       "      <td>45-65k</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>80000.0</td>\n",
       "      <td>65-90k</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   annual_inc Income_buckets\n",
       "0     24000.0          0-45k\n",
       "1     30000.0          0-45k\n",
       "2     12252.0          0-45k\n",
       "3     49200.0         45-65k\n",
       "4     80000.0         65-90k"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# and now let's divide into arbitrary buckets, assuming that these make business sense\n",
    "\n",
    "# bucket interval\n",
    "buckets = [0, 45000, 65000, 90000, 150000, 1e10]\n",
    "\n",
    "# bucket labels\n",
    "labels = ['0-45k', '45-65k', '65-90k', '90-150k', '>150k']\n",
    "\n",
    "# discretisation\n",
    "data['Income_buckets'] = pd.cut(\n",
    "    data.annual_inc, bins=buckets, labels=labels, include_lowest=True)\n",
    "\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>annual_inc</th>\n",
       "      <th>Income_buckets</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>887374</th>\n",
       "      <td>31000.0</td>\n",
       "      <td>0-45k</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887375</th>\n",
       "      <td>79000.0</td>\n",
       "      <td>65-90k</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887376</th>\n",
       "      <td>35000.0</td>\n",
       "      <td>0-45k</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887377</th>\n",
       "      <td>64400.0</td>\n",
       "      <td>45-65k</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887378</th>\n",
       "      <td>100000.0</td>\n",
       "      <td>90-150k</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        annual_inc Income_buckets\n",
       "887374     31000.0          0-45k\n",
       "887375     79000.0         65-90k\n",
       "887376     35000.0          0-45k\n",
       "887377     64400.0         45-65k\n",
       "887378    100000.0        90-150k"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([0, 1, 2, 3, 4]), <a list of 5 Text xticklabel objects>)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.groupby(['Income_buckets'])['annual_inc'].count().plot.bar()\n",
    "plt.xticks(rotation=45)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([0, 1, 2, 3, 4]), <a list of 5 Text xticklabel objects>)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "(data.groupby(['Income_buckets'])['annual_inc'].count()/len(data)).plot.bar()\n",
    "plt.xticks(rotation=45)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "We  have captured ~equal amount of borrowers in each of the first 3 buckets, and we see clearly, that a smaller percentage of the loans were disbursed to high earners.\n",
    "\n",
    "**That is all for this demonstration. I hope you enjoyed the notebook, and see you in the next one.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
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