Exploring_Remaining_Financial_Features_in_the_Dataset.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Run the two cells below before you begin. These will set the notebook to autosave every 10 seconds, import the necessary libraries for this challenge, and set figure appearance.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "IPython.notebook.set_autosave_interval(10000)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Autosaving every 10 seconds\n"
     ]
    }
   ],
   "source": [
    "%autosave 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "plt.rcParams['figure.dpi'] = 400\n",
    "plt.rcParams['font.size'] = 4"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**To begin, import data set `cleaned_data.csv` and create lists of feature names for the remaining financial features: `'BILL_AMT1'`, `'BILL_AMT2'`, `'BILL_AMT3'`, `'BILL_AMT4'`, `'BILL_AMT5'`, `'BILL_AMT6'`,  `'PAY_AMT1'`, `'PAY_AMT2'`, `'PAY_AMT3'`, `'PAY_AMT4'`, `'PAY_AMT5'`, and `'PAY_AMT6'`.**\n",
    "\n",
    "**Name the features `bill_feats` and `pay_amt_feats`, respectively.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import the dataset\n",
    "df = pd.read_csv('cleaned_data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create lists `bill_feats` and `pay_amt_feats`\n",
    "bill_feats=['BILL_AMT1', 'BILL_AMT2', 'BILL_AMT3', 'BILL_AMT4', 'BILL_AMT5', 'BILL_AMT6']\n",
    "pay_amt_feats=['PAY_AMT1', 'PAY_AMT2', 'PAY_AMT3', 'PAY_AMT4', 'PAY_AMT5', 'PAY_AMT6']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "____________________________________________________________________________________\n",
    "**Next, use `.describe()` to examine statistical summaries of the bill amount features**\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>BILL_AMT1</th>\n",
       "      <th>BILL_AMT2</th>\n",
       "      <th>BILL_AMT3</th>\n",
       "      <th>BILL_AMT4</th>\n",
       "      <th>BILL_AMT5</th>\n",
       "      <th>BILL_AMT6</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>26664.000000</td>\n",
       "      <td>26664.000000</td>\n",
       "      <td>26664.000000</td>\n",
       "      <td>26664.000000</td>\n",
       "      <td>26664.000000</td>\n",
       "      <td>26664.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>51405.730723</td>\n",
       "      <td>49300.001500</td>\n",
       "      <td>47026.340047</td>\n",
       "      <td>43338.894539</td>\n",
       "      <td>40338.136701</td>\n",
       "      <td>38889.872337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>73633.687106</td>\n",
       "      <td>70934.549534</td>\n",
       "      <td>68705.359524</td>\n",
       "      <td>64275.250740</td>\n",
       "      <td>60705.944083</td>\n",
       "      <td>59432.541657</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-165580.000000</td>\n",
       "      <td>-69777.000000</td>\n",
       "      <td>-157264.000000</td>\n",
       "      <td>-170000.000000</td>\n",
       "      <td>-81334.000000</td>\n",
       "      <td>-339603.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>3580.000000</td>\n",
       "      <td>2999.750000</td>\n",
       "      <td>2627.250000</td>\n",
       "      <td>2341.750000</td>\n",
       "      <td>1745.000000</td>\n",
       "      <td>1256.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>22361.000000</td>\n",
       "      <td>21150.000000</td>\n",
       "      <td>20079.500000</td>\n",
       "      <td>19037.000000</td>\n",
       "      <td>18066.000000</td>\n",
       "      <td>17005.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>67649.750000</td>\n",
       "      <td>64395.500000</td>\n",
       "      <td>60360.000000</td>\n",
       "      <td>54727.500000</td>\n",
       "      <td>50290.500000</td>\n",
       "      <td>49253.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>746814.000000</td>\n",
       "      <td>671563.000000</td>\n",
       "      <td>855086.000000</td>\n",
       "      <td>706864.000000</td>\n",
       "      <td>823540.000000</td>\n",
       "      <td>699944.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           BILL_AMT1      BILL_AMT2      BILL_AMT3      BILL_AMT4  \\\n",
       "count   26664.000000   26664.000000   26664.000000   26664.000000   \n",
       "mean    51405.730723   49300.001500   47026.340047   43338.894539   \n",
       "std     73633.687106   70934.549534   68705.359524   64275.250740   \n",
       "min   -165580.000000  -69777.000000 -157264.000000 -170000.000000   \n",
       "25%      3580.000000    2999.750000    2627.250000    2341.750000   \n",
       "50%     22361.000000   21150.000000   20079.500000   19037.000000   \n",
       "75%     67649.750000   64395.500000   60360.000000   54727.500000   \n",
       "max    746814.000000  671563.000000  855086.000000  706864.000000   \n",
       "\n",
       "           BILL_AMT5      BILL_AMT6  \n",
       "count   26664.000000   26664.000000  \n",
       "mean    40338.136701   38889.872337  \n",
       "std     60705.944083   59432.541657  \n",
       "min    -81334.000000 -339603.000000  \n",
       "25%      1745.000000    1256.000000  \n",
       "50%     18066.000000   17005.000000  \n",
       "75%     50290.500000   49253.750000  \n",
       "max    823540.000000  699944.000000  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[bill_feats].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Reflect on what you see. Does it make sense?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "___________________________________________________________________________________\n",
    "**Then you should visualize the bill amount features using a 2 by 3 grid of histogram plots.**\n",
    "> Hint: You can use 20 bins for this visualization."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x000002706A87EA08>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000002706B22AF88>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000002706B25D5C8>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x000002706B293D08>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000002706B2CF488>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000002706B303D88>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 2400x1600 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[bill_feats].hist(bins=20,layout=(2,3))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "________________________________________________________________________\n",
    "**Next, obtain the `.describe()` summary of the payment amount features.**\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>PAY_AMT1</th>\n",
       "      <th>PAY_AMT2</th>\n",
       "      <th>PAY_AMT3</th>\n",
       "      <th>PAY_AMT4</th>\n",
       "      <th>PAY_AMT5</th>\n",
       "      <th>PAY_AMT6</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>26664.000000</td>\n",
       "      <td>2.666400e+04</td>\n",
       "      <td>26664.000000</td>\n",
       "      <td>26664.000000</td>\n",
       "      <td>26664.000000</td>\n",
       "      <td>26664.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5704.085771</td>\n",
       "      <td>5.881110e+03</td>\n",
       "      <td>5259.514964</td>\n",
       "      <td>4887.048717</td>\n",
       "      <td>4843.729973</td>\n",
       "      <td>5257.843047</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>16699.398632</td>\n",
       "      <td>2.121431e+04</td>\n",
       "      <td>17265.439561</td>\n",
       "      <td>15956.349371</td>\n",
       "      <td>15311.721795</td>\n",
       "      <td>17635.468185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1000.000000</td>\n",
       "      <td>8.020000e+02</td>\n",
       "      <td>390.000000</td>\n",
       "      <td>294.750000</td>\n",
       "      <td>242.750000</td>\n",
       "      <td>111.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2114.500000</td>\n",
       "      <td>2.007000e+03</td>\n",
       "      <td>1822.000000</td>\n",
       "      <td>1500.000000</td>\n",
       "      <td>1500.000000</td>\n",
       "      <td>1500.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>5027.000000</td>\n",
       "      <td>5.000000e+03</td>\n",
       "      <td>4556.250000</td>\n",
       "      <td>4050.500000</td>\n",
       "      <td>4082.750000</td>\n",
       "      <td>4015.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>873552.000000</td>\n",
       "      <td>1.227082e+06</td>\n",
       "      <td>889043.000000</td>\n",
       "      <td>621000.000000</td>\n",
       "      <td>426529.000000</td>\n",
       "      <td>528666.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            PAY_AMT1      PAY_AMT2       PAY_AMT3       PAY_AMT4  \\\n",
       "count   26664.000000  2.666400e+04   26664.000000   26664.000000   \n",
       "mean     5704.085771  5.881110e+03    5259.514964    4887.048717   \n",
       "std     16699.398632  2.121431e+04   17265.439561   15956.349371   \n",
       "min         0.000000  0.000000e+00       0.000000       0.000000   \n",
       "25%      1000.000000  8.020000e+02     390.000000     294.750000   \n",
       "50%      2114.500000  2.007000e+03    1822.000000    1500.000000   \n",
       "75%      5027.000000  5.000000e+03    4556.250000    4050.500000   \n",
       "max    873552.000000  1.227082e+06  889043.000000  621000.000000   \n",
       "\n",
       "            PAY_AMT5       PAY_AMT6  \n",
       "count   26664.000000   26664.000000  \n",
       "mean     4843.729973    5257.843047  \n",
       "std     15311.721795   17635.468185  \n",
       "min         0.000000       0.000000  \n",
       "25%       242.750000     111.000000  \n",
       "50%      1500.000000    1500.000000  \n",
       "75%      4082.750000    4015.000000  \n",
       "max    426529.000000  528666.000000  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[pay_amt_feats].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "    Does this make sense?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "_______________________________________________________________________________________\n",
    "**Next, plot a histogram of the bill payment features similar to the bill amount features, \n",
    "but also apply some rotation to the x-axis labels with the `xrot` keyword argument \n",
    "so that they don't overlap. In any plotting function, you can include the `xrot=<angle>`\n",
    "keyword argument to rotate x-axis labels by a given angle in degrees.**\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x000002706B1E0C48>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000002706B201C08>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000002706B608188>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x000002706D61D808>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000002706D651E48>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000002706D68C4C8>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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E45Pkzv1r/szE6zi4f7xNuq7clXRfDm47Me8J/fJzk3zvWq+9P5Gs5vqB6+VoSHmTL7mSr+UI+RJiPuF9PryIOniQ4bw2zHDchhmOmxDLHf4+ZppbdeLsc+wcI8eDDvmV57HFjsCAtP6vk/36tiR3TfK5JO+qqh1VdVCStNbekeTZ6e49dXKS35mY9+wkX+vn/kRV3TBJqqpWV2itfT3Jt5LsSnKjflmbfByYO/WPH2+ttao6pLW2J0laa2cm+aN095vZmeT3q2pnP/byJB9Ol4MnJ/mOZG+uqurg1truJOf12z+6n3eNHA0sb/K1cXK1OfK1OfIFc+B9Pkjq4GFyXhsmx22YHDdYYv4+ZkqdOHvOMbMnx7Mlv/Mhz3OiCQDG6fZJDkqy0lq7uLW20lq7YqI4fW+S5/c/P7WqTkmS1tql6S5JlST/K8kjq+rQ/oP4oKo6uB/b2T/+x+xfyoGbLMrXWHZp/3ir/iRx+eR6rbXPJXlhuk7eByZ5+MTwLyfZneQhSX6hqm7W52pna21PVR2W5MR+3U9t3SuaLfnaOLnaHPnaHPkCuFbUwUvKeW2YHLdhctwA1qRO3ALOMbMnx7Mlv/Mhz8tBEwCMQFVN/y2fn65D9TpVddLqwokO1CuTvDnJG/uhX5tY5y+SvDpdl9SpSX62X35F/yF6QpL7JflGkk/M5AVtoaq6UZLDq+rwiWU10el1SZIL03Xq3m6tbbTWPpTk9/unvzmx/CPp7j9zXpLHJfmTqrpja+2qqrp1kj9L9wXjFa2107fydc2KfG2cXG2OfG2OfAFsjDp4GJzXhslxGybHDaCjTtx6zjGzJ8ezJb/zIc9LpC3BPQmEEBuPJMemKzR/Nt0lT64/MbZ6/5TvSPLZJBckecg+tnW3dF1XK0keMLH8+CQv6ZevJHldug/UZyY5p1/2/EXnYj95um6SP05yRrqOr/9I8qtJbjC13u3S3UNmT5KH7WN7t0ryhf61P25i+eFJHp3ky/3YpUn+NcnF/fNPJPnORedDvuRKvuRrjPkSQmyviDp4cOG8Nsxw3IYZjpsQYjuHOnHm+XWOkeNBh/zK83aNhe+AEGLjkeTnknyz/yC7qn/8YrpOqMOn1n17P/5/khy8zvYO7j+UV5K8Y2rsmCTPSfLV7C1uV/oP5mck2bHofOwjTw9J1wm2ku5+XF9IcmX//N1JTpla/9X92FuSHLqPXD27X+/vp3Oa5Hv6XH+zP8l9NsmzkuxcdD7kS67kS77GmC8hxPaKqIMHF85rwwzHbZjhuAkhtnOoE2eeX+cYOR50yK88b+dY+A4IITYWSX6j/7D7WpIXJfnpJG+dKDb/OMm3T6x//375ZUnuto/tPjjJ15N8LslJa4zfPN1lrn4lXdfWTRadi/3k6cgk/9i/9ucmuV6S6/T5OLtf/tEk95uYc/uJPD54H9v+oSRfSXJaktuus87RSW6a5LhF50K+5Eq+5Gus+RJCbK+IOnhw4bw2zHDchhmOmxBiO4c6ceb5dY6R40GH/Mrzdo+F74AQYv+R7nJT/5XuEil3mxo7Nd09rfYkednU2Dv6D9HXZ/3u1u9Id1mWrya569TY4LpXk/xY/5rfNLFsV/94/yTv7cf/ORMdZtnb4fvRXPPyNKuXDbtdui62i5LcbB/7UIvOg3zJ1aJDvuRLCCG2IqIOHmQ4rw0zHLdhhuMmhNiuoU6cS46dY+R40CG/8rzdY0eAITgpyY2TvK+19rGqOqiqDurH/jjJ76X7IHxCVT12Yt7v9I+PTPLwqto5veHW2qf6uddNcpOpsZWtfRlzca/+8aNJUlWHtNauTJLW2nvS5eoz6e7v9ZsT8343XYfv3ZP8dFUd28+v1lqrqp2ttf9IdxmbXeku/7Wm1p91BkK+Nk6uNke+Nke+ANamDh4m57VhctyGyXEDtit14uw5x8yeHM+W/M6HPC8pTQAwDHfsH/ckSWvtitbaFf2H4WVJXpfkVf06v1dVh/XrfSjJH/TLn57kvqsb7AvjXVV1cL/oqnT3TBmEqtqxzvNv9Y83S5LW2uX9ePXL35fkT/ufn1ZVt+nXOzvd5buS5ClJHltVR0ycbK6qqjsmuW3/O87a8hc1Q/K1cXK1OfK1OfIFsGnq4CXmvDZMjtswOW4A16BO3CLOMbMnx7Mlv/Mhz8OjCQCW0OqH58SH5JeSXJHkyKq63up6q91NrbXz0xW1H09y/SS/NbG5pyf5t3SF8W9U1QP7OVf03VjfneSEdJ1YX5rdq9oaVXWj1R8nltVEF+6e/vE6VXXU6joTubo4yV8neXs/9JyJdV6R5NXp7mHznCS/VVXX6082Jyd5cZKDkzyvtbZ6Yltq8rVxcrU58rU58gWwMergYXBeGybHbZgcN4COOnHrOcfMnhzPlvzOhzwPWFuCexIIsZ0jyU2T/FSSRyd5aJKdE2Or9z35kXT3Rvm3JLdYZzsH9dtZSdcVdcuJse9J8k8TY89L8sAkz093L5WVJE9YdC72k6dvT/LyJJ9Id1mZdyZ5WJKD+vEd/eMD+9dz3mquVvM4sa0dSR6R5JJ0J6h7TB2P56fr9F1Jd2+xDyW5tH/+/yU5ftH5kC+5ki/5GmO+hBDbK6IOHlw4rw0zHLdhhuMmhNjOEXXirPPrHCPHgw75lWexwWO46B0QYrtGkp1JnjXxQbYa70/y8Kl1dyU5PxPFZyaK34n1Tkh3aZWVJM+YGjs5yVsmfs+V/ePuJE9adD72k6sn9yeH1f3dPfE6/k+SG0yt/4l+7Jn7yNXxSf7ffr3fXmP8Cf1J7ar+pPPZJL+46FzIl1zJl3yNNV9CiO0TUQcPMpzXhhmO2zDDcRNCbNdQJ84lx84xcjzokF95Fps4joveASG2YyQ5Iskr+w+7zyd5QZLfTvIv/bJLkjwpyZETc361HzsrezusprupDkvy7HSXwvqrJMdNjR+U5J7puqqel+RXkhyz6HzsJ1d3THJ2ksuSPDXJUenuAfNzEyedlye51cScx2Vv4X7DftmOqe3u7HO+kuQVSQ5Jdzmb6Zx+W5I7JDli0bmQL7mSL/kaa76EENsnog4eZDivDTMct2GG4yaE2K4RdeI8cuwcI8eDDvmVZ7HJY7noHRBiO0aS+6W75MnHMnG5qnRF6V/0H4JfTPJzE2O3SfLpfuzp/bIda2z7Ef06n0hy+NRYbeXrmFOufq9/PU+bfh1JfibJuUkuT/KnE+M3TfKuft5r1tjm6vwH9et8eoi5kS+5kq/lDfkSQoi1I+rgQYbz2jDDcRtmOG5CiO0a6sS55Ng5Ro4HHfIrz2JzsSPAXFVVJfn+dJes+svW2her6uCq2tVauzTJqUn+MsmNk5xaVd/ZTz03yUv6n59TVbdrra1MbTdJ/i7Jxek6pW42+btb/yk7FFV1aJLv7p/+S7/s4HTdYUny50n+d7pO4MdX1aP65V9Jd0maJPmxqnrwOr/iM+kuY3N5kiOratCfifK1cXK1fxOfKfK1Qas5ky+AtamDh8l5bZgct2Fy3IDtSp04e84xsyfHsyW/s+PfgcdLcmHO+sLytv3TK/ple1prV1ZVtda+lORPkvxDuqL0Gf06lyR5c5L/m+5v9/lVdZOJTe/sH2+avZ2xn5/xy5mZPheXJflGunvAXD/571yt9ON7krwhyVvTXbrr16rqOv3ydyX5435zv1NV95vcfP94t3SXstndWts9+SVhaKpqh3ytrapuPfXce2sfqurHkr1fguVr36rq3lV1StLlzN8iwPrUwcOjDhgm9cgw+J4CsJc6cbacY2ZP/bW11Enz4d+Bx08TAMxZVe1K8vV090Y5rKpWi9HJztN/TvKaJF9N8sDVbqrW2vlJfi1JS/KQJL9eVSf0Y1f2c++b5Mgkn0py5bJ3UlXVd1TVnfs4bnV5/x/Tjkj3ObUzydH9+jtWx/vHLyR5fZIzkpyY5Mn98ovT3cPr9HRdvn9UVY+rqp39Ceu7kzwzybfS3fNrEKrqXlX12Kp6ZFXdtaoO6Yfka0pVPaqqzk/y7Kq6cb+svLfW1ufrv5K8pqruv7pcvtZWVQ+vqg8neU+Sl1fVvZOkf03yBbAGdfDyUpMPk+8Gw+R7CsA1qRO3hppu9tRfs6VOmg//DryNtCW4J4EQY4okd033QXZC//wa9zVJ9wG3kuSt0+us/pzu8lYv7dd7b5LDJtZ5UrrLq6wk+dckP5Xke9J1Wa30Y/dddC72k6c7J3l7kgvSXf5lJclHk/xokoMn1nt6P/bhNbaxmqtjk7xgYhs3mljnnkne0Y9dnu4Lw/v7n1f6HB+31nFapkhycpJ39vu8Gl9O8tpM3MtMvq72Oi7p9/lzSX50YmynXF3ttd4lyT/1+/z1JC9LcsuJ8R3ydbXXerMkb+z3+Yr+NTw2ya3lSwix3SPq4MFF1OSDjPhuMNiI7ylCiG0aUSfOOr9qutnnWP01+xyrk2afY/8OvM1i4TsgxFgiyeHZW6xeleRnpz/EJj5E7zBRLNylX7ZzjW0+LMnZ6TpcHzw19iNJ/m2q8FhJdy+sH1p0PvaRp8OSPGtif89O8r4k5/XPv5b8/+3df9BldV3A8fdnd/mpuDCAgSBGEFgimPwKRYRKkYQAkRiiEIxQsGmEiD+MoIaNsUmNkBkE3DQKaREXhRJGoBIEEQYI1FCQIYL4YeEuuyA/n+fTH9/vZc8+7g7OuPeee855v2Y+c+9zzr3PnvPZe8/9fM/9POfLqY3H71j3f3a0X2vJ1XuA+4HHgffNWbdp/b+5t/6eZfX+B9rOx0/5ujqL0gk9S+lYPgf4GvBYXfZx8/UT+/fmuu3fAV6gDIR2rusWmKuXC7XPNt6LVwIHrOFxo8Ju0PkavXYoxfEspXB9l/kyDMOwDu5iYE3eycCxQecDxymGYQwsrBPHnl9rusm8hq2/JpNr66Tx5dbzwAON1jfAMPoQ9aB4UeMg+lItuPao6+cWtxtTugRngSvX8PtGB9ttga/WD73TKfOmzGs8bmvgaOAC4ELgo6MPxGmMeuD/RN3vh4ETGut+BbiirnsE2L4u3ww4ry6/rZGbubebAdfXx324LpvP6l3DmwLbA3ut6UNr2gJ4Q/1/naUUlX/Q2N9NgPfVdcuB7eryhUPN15zc7UfpHL0EuBV4Gjil+f6gzD00yFzV48aLjX0/Zk5u1l/Dcwabr8Z2v5/SsXo1r3Cs9b1oGMZQAuvgzgXW5J0MHBv0InCcYhjGgALrxHHn15pu/Dm2/ppsvq2TxpNXzwMPOHo59400SRGxCXAScALwXUrxeTPwNuDQiHhVZmZERONpz1G6rVbUxxxSf9cCWG1ulUcol69aALylLs/RL8nMxzLzssw8CTg5M8/NVXNdTaPDgVMpH+L7Z+Znoex3Zt4FfAq4nVKsHwKQmcsoX7j9F7AHpXCHMi8NNbfz6uP+va47oK6bGeWy/rw8Mx/MzNsyc2acO7qOvBM4FrgR2DMzL677uyAzV1I6Tq+jfMiuB5CZTzHcfDHnfbY+8E/Al4ENKN3ge45WZuYKhpurN1KOQw8Cp2Xmpc1jR2a+MPcJNV//zADz1XhdHUg5Hl/YzFdEbBMRW0TEdqNl9b04yHxJGg7r4M6yJu8mxwYd5jhF0tBYJ06ENd34WX9NgHXS2HkeeMBsApB+dkk5ED5MufTPJ4DLgWcp3YD7wqpCtd4fdVAtrYsWRUTk6l8qza93r663O0XEa5q/Z7WNmPIDaETMo8ylDXB2Zj4QEfPm7PfdlEI/KN28I3cA/1jvL4qIN6xlfx+st081/s0u+xblMj2/n5mPRMT8WhSN9v3VwC9SLs2zvPG8Oxlmvprvs80on3FB6R79BqVgPCwiXtN4ylBfWxdT5s7bHtgrIjYCiIitI+L4iDgjIi6LiNMjYu+I2Lg+725KVz4MKF+jwhZ4B+UYdRtARGwVER+j5OQm4HsRcU1EHFqfegtwWb0/mHxJGhTr4I6xJu80xwYd5jhF0gBZJ46RNd3EWH9NgHXS2HkeeMD8z5B+Rpn5NHA2cFxmjg6KVwA3AL8EHBERW8HqXW2Z+d/APwD3Uea7WbSWf+Ln6+1DmbliTmdcZ9RC/u8oc8hcM1o2+pCPiPmZ+Qzwg/qUrRvP/RGlA/B6ylxXn46I19XnRf3dALvV26cb/2ZnZeb3gT/PzAfqzzOUS+aMCqN9KJdI+gzwqsbznqQMrAaVL1jtPfZMvV2ZmY9SXj8rKF3S+9XHzq+vrSUMLFdZuua/ADwJHA/sHRG/TemyXUyZT+4oyrxN1wCLa3fnE5Rc3sCA8lWL1w0pc+mtADIitgfOpxy796R0fM9QrhZwcUScThlkL6YU2oPJl6ThsA7uHmvy7nJs0G2OUyQNjXXieFnTTYb112RYJ42X54EHLqdgTgLD6FNQ5zUBjqDMufQocMxaHrsR8EFWzY31YWDhnMf8bV13Vv05xrHdLeTpJ/aDcrmfq+r+HtXMZ72/J6WwmgW+CLy9se5QSsflD4Ad296/CeTvDykfrLPAsrrfNwDHNx6z11DzRZmjaxY4sv68OaVomQX+Hnj9nMfvPbRcUQYni+s+30gpBEf5+RBwGvD1xvK/BtYb6muL0gRwZ93nNwMn1vsXA68FtgR2Aa5l1VxxJwz19WUYxjAD6+DOxZpyijV55wLHBp0JHKcYhjHQsE4ce36t6Safc+uvdZ9T66Tx5dbzwAON1jfAMPoWo6KLMmfNBfXg+BVg5+b6Oc85vz5uOaWL7d3A24HP1eX/AfxC2/s2obzdCrwI/MZa1v8ecH/Ny+PAlyh/ZTtTl51Zc9/L4p/S6Xx33dcZ4F/q6+ybrBoc/Q6wcX38B4aUL2BevT2y7t/ujXW/RZnH6Im6fi9KF/oedf3xQ8pV3edfo8zNN0u5lNY+c9ZvXvMyCzwPHNx4Lx47wHydU/ftcuDblMuSbVjXjfKyA6WTdpYy994OdflxQ8uXYRjDC6yDOx9Yk3cqcGzQmcBximEYAw/rxNbybU237nNr/bXuc2qdNJk8ex54gNH6BhhGH6PxwfU24B5gJXAKsGAtj3t1PVCuaBQLo/gOsG/b+zShvG1VC9P/ATadsy4a93epHzSjfL1AKfwPbnsfJpCjvSl/abwE2GvOur+s+bgVeNOQ81Xfb7PAgWvI0UwtHkfvsQsGnqtzKAOW3evP8xrHptHtkpqPK+c8d1D5qsXyU5QTELPA39Tlo79oGBXGhwHfr8f+jww1X4ZhDDOsg7sf1uTdCccG3QvHKYZhDDmsEyeeb2u68eTV+mt8ubVOGn+OPQ88sBidrJY0JhFxJvAx4C7g1Mz85pz1kfWNGBG7AwcBO1Hml/63zLxowpvcmog4mHKZqqsz89A6x89MnYNmdk6uNgC2pQwINsnMb7S46RNT5yZ/fWY+1Fi2Xma+GBGbA9cBbwGOyMwrGzkcRL5Gr5GIOIlyabhfzcw7G+vPBT4CzKcUj2dk5l/N+R2DyNVIROwEbJGZt6xh3ei9txvlGAZlEHNvI9eDyVdEbAZcRLl84QzwKeBPM/Olun6Uk02ASykds4sy88yIWJCZLw0pX5JkHdxN1uTd4digOxynSNLqrBPHz5puPKy/1j3rpMnxPPDwLGh7A6S+Gh00gcuA/YH9gMMi4ruZuWL0hdCo2ALIzDsol2JhtL6NbZ+0Rq52q4vurrfzgZm6jkZhGpn5PPDAxDe2ZTUXDwGMishawPwRZwAAB3JJREFUZM7PzCcj4lpKobk/pVtvpj5vEPlqvJ+2oXzG/RggIg4HzgJ2retnKF3jV9X1L7/fhpKrkcy8D7hvLetm693nKHM7bQP8HHDvKNdDyldmLouIKyhd39sC21FOQIxeO1mPZysj4k5KE8Bb67pBvr4kDZN1cDdZk3ePY4PucJwiSYV14vhZ042X9de6Z500OZ4HHp55bW+A1FeNgup+SmG7AjgcOKAufyki3hoR+6zl+YMpaBsfMPvX2zvq8hcAImLHiPhkRPxmXR+T3cLpNCoiIyLq7YaUrmiA29rarimxknJJojdGxFLKpYp2Bb4OHApcU38+MSI2r+9HX1dzRMT8enc9YAvKe++x9rZoKlwFfLneP4pyGUPg5XyNaqtN6+2dSNLAWAd3kzV5tzk26AzHKZIGzTpx/KzpJsf6a52zTmqR54H7ySsBSGPUuKzSUuBdwPuB90TEg8AxwJ8AT0XEdpm5ssVNbV1EbAHsDjwDfK0uWwh8EDgJ2BH4ZeCrjWJ28EYdp8BMROwJ/DrwOHBP87JeA/RjYH3gi5RO50eBP87MJQD18lx7AEcCtwNfGHCu1qjx2gI4kPKl9s3AD9vbqvZl5rMR8RlgZ8px/YyIWJ6ZdzQGfwuBd1IGLje3t7WS1B7r4G6yJu82xwad4DhF0uBZJ46fNd3kWH+tU9ZJLfE8cH95JQBpjOqlodfPzCeB8yiXCjoKuIlS0M4CF1rQArALsBFwbf2S7WjKX9x+klKYLsrMg9rcwGnU+NLxOOBaynw852TmtwdeBF0MJKVg/IvM3DYzlzS6Q5dSisXXUb6o1RyN19bxwDmUAcxZmbms1Q2bApn5n8DpwDLKl/2LI+LkiNgpIn4XuJ5y2b0lwL+2t6WS1B7r4M6yJu8wxwad4DhF0uBZJ06ENd2EWH+tU9ZJLfE8cH95JQBpzEaXWgIWAhuw6hLRSyidbI+2smFTotERuSslP9tGxKXA0fUhXwJOycxH2trGaRQRW2bm/0bEgcCHgMPqqkXA51vbsCmRmc9FxL7AI5n5MKyaJ6rOjbYiIv4MODEzn2h3a6dLRLw2M38YEe+mvLYOr6s+DdzS3pZNl8y8OyKOBU4GDgLOp8yZtWF9yOeA0zPzxZY2UZJaZx3cHdbk3ebYoDscp0hSYZ04HtZ0k2P9te5ZJ02e54H7L2xGksYrInYAzgXeWxfdBXw0M29qb6umT0RcTrn817OUTtV7gD/KzBtb3bApFBHbAWdTLpm2VV38LeC0zPTS43PU+Yxm7b59ZRHxJuBMyjz329TFt1MGiBZ+axARG1D+YuEdlAH288DizLy11Q2TpClgHdw91uTd49iguxynSBoy68TxsqYbL+uv8bNOGj/PAw+DVwKQxigi5lE6Ld8L/B/lr0I/3+pGTaH6of6j+uOzlA+ai1rcpGn3OPA9yqW9rgOWZuZX2t2k6dWYz0iv7EHgAcq8cUspc2stbXeTpltmPg9cAlwyZ/4sSRo06+DusSbvLMcGHWXdKGmorBPHy5puIqy/xsw6aSI8DzwAXglAGrOI2B44hDKX1fNtb8+0qpf62Qc4zzy9svrXx1sCj2bmbNvbo/6IiIWUubXus+D+6TQutydJarAO7h5r8m5ybCBJ6hrrxPGyphs/6y/1geeB+88mAEmSJEmSJEmSJEmSemJe2xsgSZIkSZIkSZIkSZLWDZsAJEmSJEmSJEmSJEnqCZsAJEmSJEmSJEmSJEnqCZsAJEmSJEmSJEmSJEnqCZsAJEmSJEmSJEmSJEnqCZsAJEmSJEmSJEmSJEnqCZsAJEmSJEmSJEmSJEnqCZsAJEmSJEmSJEmSJEnqCZsAJEmSJEmSJEmSJEnqCZsAJEmSJEmSJEmSJEnqCZsAJEmSJEmSJEmSJEnqCZsAJEmSJEmSJEmSJEnqCZsAJEmSJEmSJEmSJEnqCZsAJEmSJEmSJEmSJEnqCZsAJEmSJEmSJEmSJEnqCZsAJEmSJEmSJEmSJEnqCZsAJEmSJEmSJEmSJEnqCZsAJEmSJEmSJEmSJEnqCZsAJEmSJEmSJEmSJEnqCZsAJEmSJEmSJEmSJEnqCZsAJEmSJEmSJEmSJEnqCZsAJEmSJEmSJEmSJEnqCZsAJEmSJEmSJEmSJEnqCZsAJEmSJEmSJEmSJEnqCZsAJEmSJEmSJEmSJEnqCZsAJEmSJEmSJEmSJEnqCZsAJEmSJEmSJEmSJEnqCZsAJEmSJEmSJEmSJEnqCZsAJEmSJEmSJEmSJEnqCZsAJEmSJEmSJEmSJEnqCZsAJEmSJEmSJEmSJEnqCZsAJEmSJEmSJEmSJEnqCZsAJEmSJEmSJEmSJEnqCZsAJEmSJEmSJEmSJEnqCZsAJEmSJEmSJEmSJEnqCZsAJEmSJEmSJEmSJEnqCZsAJEmSJEmSJEmSJEnqCZsAJEmSJEmSJEmSJEnqCZsAJEmSJEmSJEmSJEnqCZsAJEmSJEmSJEmSJEnqCZsAJEmSJEmSJEmSJEnqCZsAJEmSJEmSJEmSJEnqCZsAJEmSJEmSJEmSJEnqif8HKOdRP9nsZTIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 2400x1600 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[pay_amt_feats].hist(layout=(2,3), xrot=30)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "    Consider these results."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "______________________________________________________________________________________________\n",
    "**Then, use a Boolean mask to see how many of the payment amount data are exactly equal to 0. Review the results of the generated mask using `.head()` and `.sum()`.**\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create Boolean mask\n",
    "pay_zero_mask=df[pay_amt_feats]==0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>PAY_AMT1</th>\n",
       "      <th>PAY_AMT2</th>\n",
       "      <th>PAY_AMT3</th>\n",
       "      <th>PAY_AMT4</th>\n",
       "      <th>PAY_AMT5</th>\n",
       "      <th>PAY_AMT6</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PAY_AMT1  PAY_AMT2  PAY_AMT3  PAY_AMT4  PAY_AMT5  PAY_AMT6\n",
       "0      True     False      True      True      True      True\n",
       "1      True     False     False     False      True     False\n",
       "2     False     False     False     False     False     False\n",
       "3     False     False     False     False     False     False\n",
       "4     False     False     False     False     False     False"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Use `.head()`\n",
    "pay_zero_mask.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PAY_AMT1    4656\n",
       "PAY_AMT2    4833\n",
       "PAY_AMT3    5293\n",
       "PAY_AMT4    5697\n",
       "PAY_AMT5    5981\n",
       "PAY_AMT6    6373\n",
       "dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Use `.sum()`\n",
    "pay_zero_mask.sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "     Does this make sense given the histogram in the previous step?\n",
    "     \n",
    "     Yes it makes sense."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "_______________________________________________________________________________________________________________________________\n",
    "**Finally, ignoring the payments of 0 using the mask you created in the previous step, use pandas `.apply()` and NumPy's `np.log10()` to plot histograms of logarithmic transformations of the non-zero payments.**\n",
    "> Hint: You can use `.apply()` to apply any function, including `log10`, to all the elements of a DataFrame or a column using the following syntax: `.apply(<function_name>)`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "eid": "b0455"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x000002706A7C8548>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000002706B0CAA48>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000002706B53D848>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x000002706BD7F608>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000002706BDB8488>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000002706BDF1308>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 2400x1600 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "df[pay_amt_feats][~pay_zero_mask].apply(np.log10).hist(layout=(2,3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}