lineaire_regression.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Pro\\miniconda3\\envs\\fromscratch\\lib\\site-packages\\ipykernel_launcher.py:10: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  # Remove the CWD from sys.path while we load stuff.\n",
      "C:\\Users\\Pro\\miniconda3\\envs\\fromscratch\\lib\\site-packages\\ipykernel_launcher.py:32: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "    }\n",
       "\n",
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       "    }\n",
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       "<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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>42.42</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>32.67</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.90</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9.11</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>12.24</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>17.16</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3.48</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>42.45</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>46.51</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>4.41</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>22.59</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>6.89</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>39.60</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>4.39</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>27.76</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>34.28</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>12.49</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>9.88</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>60.16</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>24.37</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>21.73</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>28.11</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>10.91</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>29.18</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>26.19</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>13.59</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>9.60</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>20.59</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2.56</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>13.29</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        0   1\n",
       "0   42.42   5\n",
       "1   32.67  15\n",
       "2    1.90   1\n",
       "3    9.11   2\n",
       "4   12.24   4\n",
       "5   17.16   2\n",
       "6    3.48   1\n",
       "7   42.45   5\n",
       "8   46.51   5\n",
       "9    4.41   8\n",
       "10  22.59   6\n",
       "11   6.89   2\n",
       "12  39.60  12\n",
       "13   4.39   4\n",
       "14  27.76   3\n",
       "15  34.28   7\n",
       "16  12.49   2\n",
       "17   9.88  15\n",
       "18  60.16  12\n",
       "19  24.37   6\n",
       "20  21.73   7\n",
       "21  28.11  10\n",
       "22  10.91   5\n",
       "23  29.18   6\n",
       "24  26.19   3\n",
       "25  13.59   4\n",
       "26   9.60   7\n",
       "27  20.59   7\n",
       "28   2.56   2\n",
       "29  13.29   6"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import datetime as dt\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import random\n",
    "import scipy.stats as st\n",
    "from matplotlib import pyplot as plot\n",
    "data=pd.read_csv('operations.csv',parse_dates=[0])\n",
    "groceries=data[data.categ==\"GROCERIES\"]\n",
    "groceries.sort_values(\"transaction_date\")\n",
    "groceries[\"amount\"]=-groceries[\"amount\"]\n",
    "r=[]\n",
    "def number_day_month(num):\n",
    "    if num in [1,3,5,7,8,10,12]:\n",
    "        return 31\n",
    "    elif num in [4,6,8,9,11]:\n",
    "        return 30\n",
    "    else:\n",
    "        return 28 \n",
    "last_date=dt.datetime.now()\n",
    "last_day=last_date.strftime(\"%d\")    \n",
    "last_month=last_date.strftime(\"%m\")    \n",
    "for i,row in groceries.iterrows():\n",
    "    month=row[\"month\"]\n",
    "    day=row[\"day\"]\n",
    "    if month==last_month:\n",
    "        r.append(day-last_day)\n",
    "    else:\n",
    "        r.append((number_day_month(last_month)-int(last_day))+day+(month-int(last_month)-1)*30)\n",
    "    last_day=row[\"day\"]\n",
    "    last_month=row[\"month\"]\n",
    "r[0]=0\n",
    "groceries['wait']=r\n",
    "groceries=groceries.iloc[1:,]\n",
    "a=groceries.groupby('transaction_date')['amount'].sum()\n",
    "b=groceries.groupby('transaction_date')['wait'].first()\n",
    "wait_amount_groceries=pd.DataFrame([a for a in zip(a,b)])\n",
    "wait_amount_groceries.to_csv(\"grocerie.csv\")\n",
    "wait_amount_groceries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "plot.scatter(wait_amount_groceries[0],wait_amount_groceries[1],c='blue')\n",
    "plot.xlabel(\"amount\")\n",
    "plot.ylabel('wait')\n",
    "plot.title('wait=f(amount)')\n",
    "plot.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Pro\\miniconda3\\envs\\fromscratch\\lib\\site-packages\\ipykernel_launcher.py:7: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  import sys\n"
     ]
    },
    {
     "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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>wait</th>\n",
       "      <td>2.567119</td>\n",
       "      <td>0.121678</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             0         1\n",
       "wait  2.567119  0.121678"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#estimate a and b for the lenear regression\n",
    "import statsmodels.api as sm\n",
    "\n",
    "Y = groceries[['amount']]\n",
    "X = groceries[['wait']]\n",
    "X=X.copy()\n",
    "Y[\"intercept\"]=1.\n",
    "\n",
    "# X = X.copy() # X will be modified, so a copy is created\n",
    "# Y['intercept'] = 1.\n",
    "result = sm.OLS(Y, X).fit() # OLS = Ordinary Least Squares \n",
    "#a,b = result.params['wait'],result.params['intercept']\n",
    "a,b = result.params[0],result.params[1]\n",
    "result.params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "plot.plot(groceries.wait,groceries.amount, \"o\")\n",
    "plot.plot(np.arange(15),[a*x+b for x in np.arange(15)])\n",
    "plot.xlabel(\"wait\")\n",
    "plot.ylabel(\"amount\")\n",
    "plot.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.5225788781770362\n",
      "10.013545135845753\n"
     ]
    }
   ],
   "source": [
    "groceries=groceries[groceries.wait<12]\n",
    "Y = groceries[['amount']]\n",
    "X = groceries[['wait']]\n",
    "X=X.copy()\n",
    "X[\"intercept\"]=1.\n",
    "result = sm.OLS(Y, X).fit() # OLS = Ordinary Least Squares\n",
    "#a,b = result.params['wait'],result.params['intercept']\n",
    "a,b = result.params[0],result.params[1]\n",
    "print(a)\n",
    "print(b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "plot.plot(groceries.wait,groceries.amount, \"o\")\n",
    "plot.plot(np.arange(15),[a*x+b for x in np.arange(15)])\n",
    "plot.xlabel(\"wait\")\n",
    "plot.ylabel(\"amount\")\n",
    "plot.show()"
   ]
  },
  {
   "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": "3.7.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}