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",
    "import matplotlib as mpl\n",
    "\n",
    "mpl.rcParams['figure.dpi'] = 400\n",
    "mpl.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": 6,
   "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": 8,
   "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": 8,
     "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": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\hp\\Anaconda3\\lib\\site-packages\\pandas\\plotting\\_matplotlib\\tools.py:307: MatplotlibDeprecationWarning: \n",
      "The rowNum attribute was deprecated in Matplotlib 3.2 and will be removed two minor releases later. Use ax.get_subplotspec().rowspan.start instead.\n",
      "  layout[ax.rowNum, ax.colNum] = ax.get_visible()\n",
      "C:\\Users\\hp\\Anaconda3\\lib\\site-packages\\pandas\\plotting\\_matplotlib\\tools.py:307: MatplotlibDeprecationWarning: \n",
      "The colNum attribute was deprecated in Matplotlib 3.2 and will be removed two minor releases later. Use ax.get_subplotspec().colspan.start instead.\n",
      "  layout[ax.rowNum, ax.colNum] = ax.get_visible()\n",
      "C:\\Users\\hp\\Anaconda3\\lib\\site-packages\\pandas\\plotting\\_matplotlib\\tools.py:313: MatplotlibDeprecationWarning: \n",
      "The rowNum attribute was deprecated in Matplotlib 3.2 and will be removed two minor releases later. Use ax.get_subplotspec().rowspan.start instead.\n",
      "  if not layout[ax.rowNum + 1, ax.colNum]:\n",
      "C:\\Users\\hp\\Anaconda3\\lib\\site-packages\\pandas\\plotting\\_matplotlib\\tools.py:313: MatplotlibDeprecationWarning: \n",
      "The colNum attribute was deprecated in Matplotlib 3.2 and will be removed two minor releases later. Use ax.get_subplotspec().colspan.start instead.\n",
      "  if not layout[ax.rowNum + 1, ax.colNum]:\n"
     ]
    },
    {
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Js1prty5YdVYDd8rQiFpkUb1cleEPK+Pla+a11g4kuXqdeVfNbfPn55YvyzuQ5EtHkHffrIz89H/i6P1QhsbjW6rq2nEkeU5f51/1eR/r0+pQHW6Gvx39e/+SdWbzZ99IV4tqcWo/kqSSXN5au2Z+YWvthiQf7ZOP7o/qUB0CbInW2gtaa7WBeM+in1dVT0vyhxn6jv+stfZHqzz9qseC3s84c27dNfPmli3KO9JeyZYel5mcHtkOo5/lnHmd9J4GaubI6BMN1A1bxmAE2L0+leFAd2KSR84vrKq9SR7TJ/9mC7eLLVJVd81w2eeHJ/lYkn/cWlt2CaWrk1zb//0jS9aZzf9uvbTWbstwKbQjypub3qy8T7TWDm4g7yuL/lDEhuxJcs8FMbsH2V379Pf0aXWoDjfDp7Jyqbm1Poj9XX9Ui2pxaievY53ZtytmvyPVoToE2HGq6seSvCnDfZh/tbX2mjVSZseCf9D7FPMelaHxfyDJpxfkPbCq7rlgO+6V4Y+Y43WTjfdKtuO4zHT0yHYQ/SznzEdI70nNHCl9ooG6YcsYjAC7VP+G3exbCv9kwSrPSnJKkm8m+d9btFlskaq6U5KLkzw2yWeTPKW1duOy9ftl7v6yT96uXqrqcUkemuHD+1vmFv9Ff3zh6D7Xs7wzkzypT755Sd6zq+qwP9L06Wf1yTctyfuJ/vPHeXuS/MKSvLdkuFfp2UvubTp73fPbyQa01k5d9g2qJL/fV3tFn3dWz1GH6nByrbWbMjSykpX98l29Uf3kPvm+nqMW1eLUZt8aeHBVnTG/sKpOyUoD/PJEHXbqEGAHqapHJfmfSe6c5D+01v7jOtLen+FSv6ckeeaC5bNjwTvHn2lba5dl5RLKv7ggbzbvM621y0d5G+qVbNNxmYnoke0c+lkL85wzL6H3pGY2Qp9I3bANWmtCiF0aGUarHUpyMMl5o/nnZBjJ15Kcv93bKSbf73synBy1JF9Icu915n1vhktQtSQvS1J9/v0z3D+rJXnNgrxTknyjL39Vkr19/ulJPtTnv33Jdl7al78pyV36/JP6dEvymSTHLch9V1/+wSSn93l7k/x2n/+1JHddkHdhX35pkvv3edVfb0tyc5Izt3sf7vZIclF/vy9Qh+pwi2runAwfqA4m+YXR/FOTvKO/71ckOUEtqsVNqsG7jurir5OcNVp2jwx/uJm95/dVh+pQCCF2WiR5yOjY8+ojzP21nvfVJOeM5j8vw/nboSSPXZD3vJ737SRPGM1/Qp/Xkjx7Qd6GeiVbfVwWk9eoHtkdPKKf5Zx52nq6KHpPamZ5fegTqRuxhbHtGyCE2NxI8uv9QDE7gF7SD7ItyVuT7NnubRST7/PzRvv88n5Ssyj+fEHuz4/q48sZLiN1oE9/PMlJS57zif1kpCX5el/3pj79xST3WpL38CTX9/X29bx9ffqbSb5/Sd59Mtxzq/Xn+Xh/3tlJ0Y8vyTu5v6bWTx4/2V9ny3AC+vzt3n/HQmSVD4TqUB1uYt29JEPzsWW4J97HRvXxjSSPUItqcZNr8KmjurgtwzH6s1lpZtyaURNEHapDIYTYSZHknf1396EMA++WfQ79xQW5xyf5Xz3/YIa+xRVZ+Vz78lWe98LRep/rMZt+7Sp5G+qVbPVxWUxep3pkd+CIfpZz5mnr6aLoPamZ1WtEn0jdiC2Kbd8AIcTmR5KfSvLefqC6KcN9Fl8aH7J2ZSR5YVY+vK0WVy3Jf1yGb2h+M8P9sy5L8m+SnLjG8z4syZ9kGFm5P8mVSf5LktPWyDsjyRv6ic3+/nhhkvutkXdakv/an2d/f94/WXYSNsq7c389l/XXd12Gy1Cdu9377liJrPGBUB1u/z7arZHkR/v7/I2+v76Y5Hcy+ia6WlSLm1yDD07y+gy3bbil76urkvxBkkeqQ3UohBA7NTJc2n49n0P/7ZL8PRn6FJ/O0LfYl6GP8ZPreO6fT/KRJDf2+EiSn1tH3oZ6JVt9XBaT16oe2R00op/lnHnaerooek9qZu060SdSN2ILYnYZEQAAAAAAAACASRy33RsAAAAAAAAAAOwuBiMAAAAAAAAAAJMyGAEAAAAAAAAAmJTBCAAAAAAAAADApAxGAAAAAAAAAAAmZTACAAAAAAAAADApgxEAAAAAAAAAgEkZjAAAAAAAAAAATMpgBAAAAAAAAABgUgYjAAAAAAAAAACTMhgBAAAAAAAAAJiUwQgAAAAAAAAAwKQMRgAAAAAAAAAAJmUwAgAAAAAAAAAwKYMRAAAAAAAAAIBJGYwAAAAAAAAAAEzKYAQAAAAAAAAAYFIGIwAAAAAAAAAAkzIYAQAAAAAAAACYlMEIAAAAAAAAAMCkDEYAAAAAAAAAACZlMAIAAAAAAAAAMCmDEQAAAAAAAACASRmMAAAAAAAAAABMymAEAAAAAAAAAGBSBiMAAAAAAAAAAJMyGAEAAAAAAAAAmJTBCAAAAAAAAADApAxGAAAAAAAAAAAmZTACAAAAAAAAADApgxEAAAAAAAAAgEkZjAAAAAAAAAAATMpgBAAAAAAAAABgUgYjAAAAAAAAAACTMhgBAAAAAAAAAJiUwQgAAAAAAAAAwKQMRgAAAAAAAAAAJmUwAgAAAAAAAAAwKYMRAAAAAAAAAIBJGYwAAAAAAAAAAEzKYAQAAAAAAAAAYFIGIwAAAAAAAAAAkzIYAQAAAAAAAACYlMEIAAAAAAAAAMCk/j+04tT3w/C94QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 2400x1600 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[bill_feats].hist(bins = 20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "________________________________________________________________________\n",
    "**Next, obtain the `.describe()` summary of the payment amount features.**\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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": 11,
     "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": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2400x1600 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[pay_amt_feats].hist(xrot=20)\n",
    "plt.show()\n"
   ]
  },
  {
   "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": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create Boolean mask\n",
    "payment_amt_mask = df[pay_amt_feats] == 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Use `.head()`\n",
    "payment_amt_mask.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Use `.sum()`\n",
    "payment_amt_mask.sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "     Does this make sense given the histogram in the previous step?"
   ]
  },
  {
   "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": 21,
   "metadata": {
    "eid": "b0455"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<AxesSubplot:title={'center':'PAY_AMT1'}>,\n",
       "        <AxesSubplot:title={'center':'PAY_AMT2'}>],\n",
       "       [<AxesSubplot:title={'center':'PAY_AMT3'}>,\n",
       "        <AxesSubplot:title={'center':'PAY_AMT4'}>],\n",
       "       [<AxesSubplot:title={'center':'PAY_AMT5'}>,\n",
       "        <AxesSubplot:title={'center':'PAY_AMT6'}>]], dtype=object)"
      ]
     },
     "execution_count": 21,
     "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][~payment_amt_mask].apply(lambda x: np.log10(x)).hist()"
   ]
  },
  {
   "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": "3.7.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}