Untitled0.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = np.matrix([[1,2,3,4],[5,6,7,8],[9,10,-11,12],[13,14,15,-16]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[30]\n",
      " [70]\n",
      " [44]\n",
      " [22]]\n"
     ]
    }
   ],
   "source": [
    "x = a*np.matrix([1,2,3,4]).T\n",
    "print x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "matrix([[ 1.],\n",
       "        [ 2.],\n",
       "        [ 3.],\n",
       "        [ 4.]])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.I * x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "31.709791983764585"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "10**9/(60.0*60.0*24*365)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.95162581964040482"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "10*(1-np.exp(-0.1))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def mean_factor(depth, coef = 0.5):\n",
    "    x = coef * depth\n",
    "    return (1.0/x) * (1-np.exp(-x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.1986524106001829"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_factor(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "247.5"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "554.5 - 307"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.41312709387821822"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.exp(-0.884)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12.745695229401848"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "0.884+np.log(60*60.0/(2.54e-2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x105323690>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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TXq1Wi4iICPFzeHg4KioqUF1d3WM9Go3GpKyDgwP8/PzEdHN1m2sDAGRmZsJo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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x104c3f0d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "def f1(sand):\n",
    "    return np.exp(0.0153*sand - 0.884 - np.log(60*60/(2.54e-2) ))\n",
    "\n",
    "\n",
    "def f3(sand):\n",
    "    return np.exp(0.0126*sand - 0.6 - np.log(60*60/(2.54e-2) ))\n",
    "\n",
    "\n",
    "def f2(sand):\n",
    "    return 7.0556E-6*(np.exp(0.0352*sand-2.035))\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "s = np.linspace(0,100,100)\n",
    "plt.plot(s, f1(s), label=\"Sasha1\", lw = 2)\n",
    "plt.plot(s, f3(s), label=\"Sasha2\", lw = 2)\n",
    "\n",
    "plt.plot(s, f2(s), label=\"Diana\", lw = 2)\n",
    "plt.legend()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7.05555555556e-06\n"
     ]
    }
   ],
   "source": [
    "print 2.54e-2/(60.0*60.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6.641509433962264"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "0.352/0.053"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8640000000000001"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "1e-8*24*60*60*1000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = [1,23,45,6,7]\n",
    "y = np.cumsum(x)\n",
    "y.tolist()\n",
    "len(y) == len(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0060479999999999996"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "7e-8*24*60*60"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "52.440000000000005"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "1200*0.0437"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "<div style=\"font-size:300%;\">\n",
    "$$\n",
    "d_t (\\delta h) = P - E\n",
    "$$\n",
    "\n",
    "<br/>\n",
    "\n",
    "$$\n",
    "\\delta h = h(t) - \\bar{h}\n",
    "$$\n",
    "</div>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"font-size:300%;\">\n",
    "$$\n",
    "\\delta h = \\frac{\\delta S}{A_{\\rm lake}}\n",
    "$$\n",
    "\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "172800"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "24*3600 * 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "114048"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "9504 * 12"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "expenses = [-44.52, -273.31, -351.84, -274.75, -137.97, -112.53, -50.98, -88.70]\n",
    "payments = [394.55]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-940.05"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(expenses) + sum(payments)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from datetime import datetime\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "df = pd.read_csv(\"/home/huziy/Downloads/cibc_all.csv\", \n",
    "                 converters={0:lambda tok: datetime.strptime(tok, \"%Y-%m-%d\")}, header=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2022-08-16</td>\n",
       "      <td>COSTCO WHOLESALE W505 LAVAL, QC</td>\n",
       "      <td>273.31</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5223********7179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2022-08-16</td>\n",
       "      <td>COSTCO ESSENCE W505 LAVAL, QC</td>\n",
       "      <td>44.52</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5223********7179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2022-08-15</td>\n",
       "      <td>COSTCO WHOLESALE W505 LAVAL, QC</td>\n",
       "      <td>351.84</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5223********7179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2022-08-02</td>\n",
       "      <td>PAYMENT THANK YOU/PAIEMEN T MERCI</td>\n",
       "      <td>NaN</td>\n",
       "      <td>394.55</td>\n",
       "      <td>5223********0513</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2022-08-02</td>\n",
       "      <td>COSTCO ENTREPRISE #144 ANJOU, QC</td>\n",
       "      <td>274.75</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5223********7179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>2021-09-13</td>\n",
       "      <td>COSTCO WHOLESALE W546 BOISBRIAND, QC</td>\n",
       "      <td>72.78</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5223********7179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>2021-09-11</td>\n",
       "      <td>COSTCO ESSENCE W505 LAVAL, QC</td>\n",
       "      <td>39.58</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5223********7179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>2021-09-11</td>\n",
       "      <td>COSTCO WHOLESALE W505 LAVAL, QC</td>\n",
       "      <td>291.51</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5223********7179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>2021-09-08</td>\n",
       "      <td>PAYMENT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>540.70</td>\n",
       "      <td>5223********0513</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>2021-09-03</td>\n",
       "      <td>COSTCO WHOLESALE W546 BOISBRIAND, QC</td>\n",
       "      <td>260.14</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5223********7179</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>102 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             0                                     1       2       3  \\\n",
       "0   2022-08-16       COSTCO WHOLESALE W505 LAVAL, QC  273.31     NaN   \n",
       "1   2022-08-16         COSTCO ESSENCE W505 LAVAL, QC   44.52     NaN   \n",
       "2   2022-08-15       COSTCO WHOLESALE W505 LAVAL, QC  351.84     NaN   \n",
       "3   2022-08-02     PAYMENT THANK YOU/PAIEMEN T MERCI     NaN  394.55   \n",
       "4   2022-08-02      COSTCO ENTREPRISE #144 ANJOU, QC  274.75     NaN   \n",
       "..         ...                                   ...     ...     ...   \n",
       "97  2021-09-13  COSTCO WHOLESALE W546 BOISBRIAND, QC   72.78     NaN   \n",
       "98  2021-09-11         COSTCO ESSENCE W505 LAVAL, QC   39.58     NaN   \n",
       "99  2021-09-11       COSTCO WHOLESALE W505 LAVAL, QC  291.51     NaN   \n",
       "100 2021-09-08                               PAYMENT     NaN  540.70   \n",
       "101 2021-09-03  COSTCO WHOLESALE W546 BOISBRIAND, QC  260.14     NaN   \n",
       "\n",
       "                    4  \n",
       "0    5223********7179  \n",
       "1    5223********7179  \n",
       "2    5223********7179  \n",
       "3    5223********0513  \n",
       "4    5223********7179  \n",
       "..                ...  \n",
       "97   5223********7179  \n",
       "98   5223********7179  \n",
       "99   5223********7179  \n",
       "100  5223********0513  \n",
       "101  5223********7179  \n",
       "\n",
       "[102 rows x 5 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(12403.119999999999, 11349.08, 1054.039999999999)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[2].sum(), df[3].sum(), df[2].sum() - df[3].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='0'>"
      ]
     },
     "execution_count": 25,
     "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": [
    "ax = df.plot(x=0, y=2, label=\"Expenses\")\n",
    "df.plot(x=0,y=3, label=\"Payments\", ax=ax)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2022-08-02</td>\n",
       "      <td>PAYMENT THANK YOU/PAIEMEN T MERCI</td>\n",
       "      <td>NaN</td>\n",
       "      <td>394.55</td>\n",
       "      <td>5223********0513</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2022-07-21</td>\n",
       "      <td>PAYMENT THANK YOU/PAIEMEN T MERCI</td>\n",
       "      <td>NaN</td>\n",
       "      <td>345.70</td>\n",
       "      <td>5223********0513</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2022-07-15</td>\n",
       "      <td>PAYMENT THANK YOU/PAIEMEN T MERCI</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24.35</td>\n",
       "      <td>5223********0513</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2022-07-11</td>\n",
       "      <td>PAYMENT THANK YOU/PAIEMEN T MERCI</td>\n",
       "      <td>NaN</td>\n",
       "      <td>379.06</td>\n",
       "      <td>5223********0513</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2022-06-30</td>\n",
       "      <td>PAYMENT THANK YOU/PAIEMEN T MERCI</td>\n",
       "      <td>NaN</td>\n",
       "      <td>190.85</td>\n",
       "      <td>5223********0513</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2022-06-23</td>\n",
       "      <td>PAYMENT THANK YOU/PAIEMEN T MERCI</td>\n",
       "      <td>NaN</td>\n",
       "      <td>216.66</td>\n",
       "      <td>5223********0513</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2022-06-09</td>\n",
       "      <td>PAYMENT THANK YOU/PAIEMEN T MERCI</td>\n",
       "      <td>NaN</td>\n",
       "      <td>349.29</td>\n",
       "      <td>5223********0513</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>2022-05-26</td>\n",
       "      <td>PAYMENT THANK YOU/PAIEMEN T MERCI</td>\n",
       "      <td>NaN</td>\n",
       "      <td>390.37</td>\n",
       "      <td>5223********0513</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>2022-05-17</td>\n",
       "      <td>PRIME DE BIENVENUE</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20.00</td>\n",
       "      <td>5223********0513</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>2022-05-17</td>\n",
       "      <td>PAYMENT THANK YOU/PAIEMEN T MERCI</td>\n",
       "      <td>NaN</td>\n",
       "      <td>385.88</td>\n",
       "      <td>5223********0513</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>2022-04-27</td>\n",
       "      <td>PAYMENT THANK YOU/PAIEMEN T MERCI</td>\n",
       "      <td>NaN</td>\n",
       "      <td>180.13</td>\n",
       "      <td>5223********0513</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>2022-04-22</td>\n",
       "      <td>PAYMENT THANK YOU/PAIEMEN T MERCI</td>\n",
       "      <td>NaN</td>\n",
       "      <td>616.51</td>\n",
       "      <td>5223********0513</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>2022-04-06</td>\n",
       "      <td>PAYMENT THANK YOU/PAIEMEN T MERCI</td>\n",
       "      <td>NaN</td>\n",
       "      <td>456.34</td>\n",
       "      <td>5223********0513</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>2022-03-24</td>\n",
       "      <td>PAYMENT THANK YOU/PAIEMEN T MERCI</td>\n",
       "      <td>NaN</td>\n",
       "      <td>345.97</td>\n",
       "      <td>5223********0513</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>2022-03-17</td>\n",
       "      <td>PAYMENT THANK YOU/PAIEMEN T MERCI</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1218.45</td>\n",
       "      <td>5223********0513</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>2022-03-14</td>\n",
       "      <td>PAYMENT THANK YOU/PAIEMEN T MERCI</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.00</td>\n",
       "      <td>5223********0513</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>2022-02-14</td>\n",
       "      <td>PAYMENT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>604.21</td>\n",
       "      <td>5223********0513</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>2022-01-31</td>\n",
       "      <td>PAYMENT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>596.47</td>\n",
       "      <td>5223********0513</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>2022-01-10</td>\n",
       "      <td>PAYMENT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>428.04</td>\n",
       "      <td>5223********0513</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>2021-12-29</td>\n",
       "      <td>PAYMENT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>327.06</td>\n",
       "      <td>5223********0513</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>2021-12-13</td>\n",
       "      <td>PAYMENT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>244.14</td>\n",
       "      <td>5223********0513</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>2021-12-04</td>\n",
       "      <td>COSTCO WHOLESALE W505 LAVAL, QC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22.98</td>\n",
       "      <td>5223********7179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>2021-11-29</td>\n",
       "      <td>PAYMENT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>393.24</td>\n",
       "      <td>5223********0513</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>2021-11-22</td>\n",
       "      <td>PAYMENT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>372.16</td>\n",
       "      <td>5223********0513</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>2021-11-08</td>\n",
       "      <td>PAYMENT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>597.71</td>\n",
       "      <td>5223********0513</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>2021-10-14</td>\n",
       "      <td>PAYMENT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>344.57</td>\n",
       "      <td>5223********0513</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>2021-10-07</td>\n",
       "      <td>PAYMENT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>189.88</td>\n",
       "      <td>5223********0513</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>2021-09-27</td>\n",
       "      <td>PAYMENT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>368.58</td>\n",
       "      <td>5223********0513</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>2021-09-20</td>\n",
       "      <td>COSTCO WHOLESALE W505 LAVAL, QC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>43.67</td>\n",
       "      <td>5223********7179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>2021-09-20</td>\n",
       "      <td>COSTCO WHOLESALE W505 LAVAL, QC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15.00</td>\n",
       "      <td>5223********7179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>2021-09-15</td>\n",
       "      <td>PAYMENT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>736.56</td>\n",
       "      <td>5223********0513</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>2021-09-08</td>\n",
       "      <td>PAYMENT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>540.70</td>\n",
       "      <td>5223********0513</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             0                                  1   2        3  \\\n",
       "3   2022-08-02  PAYMENT THANK YOU/PAIEMEN T MERCI NaN   394.55   \n",
       "12  2022-07-21  PAYMENT THANK YOU/PAIEMEN T MERCI NaN   345.70   \n",
       "15  2022-07-15  PAYMENT THANK YOU/PAIEMEN T MERCI NaN    24.35   \n",
       "17  2022-07-11  PAYMENT THANK YOU/PAIEMEN T MERCI NaN   379.06   \n",
       "20  2022-06-30  PAYMENT THANK YOU/PAIEMEN T MERCI NaN   190.85   \n",
       "22  2022-06-23  PAYMENT THANK YOU/PAIEMEN T MERCI NaN   216.66   \n",
       "24  2022-06-09  PAYMENT THANK YOU/PAIEMEN T MERCI NaN   349.29   \n",
       "27  2022-05-26  PAYMENT THANK YOU/PAIEMEN T MERCI NaN   390.37   \n",
       "30  2022-05-17                 PRIME DE BIENVENUE NaN    20.00   \n",
       "31  2022-05-17  PAYMENT THANK YOU/PAIEMEN T MERCI NaN   385.88   \n",
       "35  2022-04-27  PAYMENT THANK YOU/PAIEMEN T MERCI NaN   180.13   \n",
       "37  2022-04-22  PAYMENT THANK YOU/PAIEMEN T MERCI NaN   616.51   \n",
       "41  2022-04-06  PAYMENT THANK YOU/PAIEMEN T MERCI NaN   456.34   \n",
       "44  2022-03-24  PAYMENT THANK YOU/PAIEMEN T MERCI NaN   345.97   \n",
       "46  2022-03-17  PAYMENT THANK YOU/PAIEMEN T MERCI NaN  1218.45   \n",
       "47  2022-03-14  PAYMENT THANK YOU/PAIEMEN T MERCI NaN    10.00   \n",
       "52  2022-02-14                            PAYMENT NaN   604.21   \n",
       "57  2022-01-31                            PAYMENT NaN   596.47   \n",
       "61  2022-01-10                            PAYMENT NaN   428.04   \n",
       "65  2021-12-29                            PAYMENT NaN   327.06   \n",
       "68  2021-12-13                            PAYMENT NaN   244.14   \n",
       "70  2021-12-04    COSTCO WHOLESALE W505 LAVAL, QC NaN    22.98   \n",
       "71  2021-11-29                            PAYMENT NaN   393.24   \n",
       "75  2021-11-22                            PAYMENT NaN   372.16   \n",
       "79  2021-11-08                            PAYMENT NaN   597.71   \n",
       "84  2021-10-14                            PAYMENT NaN   344.57   \n",
       "86  2021-10-07                            PAYMENT NaN   189.88   \n",
       "90  2021-09-27                            PAYMENT NaN   368.58   \n",
       "91  2021-09-20    COSTCO WHOLESALE W505 LAVAL, QC NaN    43.67   \n",
       "92  2021-09-20    COSTCO WHOLESALE W505 LAVAL, QC NaN    15.00   \n",
       "95  2021-09-15                            PAYMENT NaN   736.56   \n",
       "100 2021-09-08                            PAYMENT NaN   540.70   \n",
       "\n",
       "                    4  \n",
       "3    5223********0513  \n",
       "12   5223********0513  \n",
       "15   5223********0513  \n",
       "17   5223********0513  \n",
       "20   5223********0513  \n",
       "22   5223********0513  \n",
       "24   5223********0513  \n",
       "27   5223********0513  \n",
       "30   5223********0513  \n",
       "31   5223********0513  \n",
       "35   5223********0513  \n",
       "37   5223********0513  \n",
       "41   5223********0513  \n",
       "44   5223********0513  \n",
       "46   5223********0513  \n",
       "47   5223********0513  \n",
       "52   5223********0513  \n",
       "57   5223********0513  \n",
       "61   5223********0513  \n",
       "65   5223********0513  \n",
       "68   5223********0513  \n",
       "70   5223********7179  \n",
       "71   5223********0513  \n",
       "75   5223********0513  \n",
       "79   5223********0513  \n",
       "84   5223********0513  \n",
       "86   5223********0513  \n",
       "90   5223********0513  \n",
       "91   5223********7179  \n",
       "92   5223********7179  \n",
       "95   5223********0513  \n",
       "100  5223********0513  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[~df[3].isna(),:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='0'>"
      ]
     },
     "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": [
    "ax = df.loc[~df[2].isna(),:].plot(x=0,y=2, label=\"Expenses\")\n",
    "df.loc[~df[3].isna(),:].plot(x=0,y=3, label=\"Payments\", ax=ax)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12403.119999999999"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[2].sum()"
   ]
  },
  {
   "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": "3.10.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}