syam.ipynb

{
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   "execution_count": 1,
   "id": "5f28831c-1806-45ec-9e53-b91501fab3ec",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "1648ff79-9de0-42c2-a012-262ab1b4244e",
   "metadata": {},
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    {
     "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>Store</th>\n",
       "      <th>Date</th>\n",
       "      <th>Weekly_Sales</th>\n",
       "      <th>Holiday_Flag</th>\n",
       "      <th>Temperature</th>\n",
       "      <th>Fuel_Price</th>\n",
       "      <th>CPI</th>\n",
       "      <th>Unemployment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>05-02-2010</td>\n",
       "      <td>1643690.90</td>\n",
       "      <td>0</td>\n",
       "      <td>42.31</td>\n",
       "      <td>2.572</td>\n",
       "      <td>211.096358</td>\n",
       "      <td>8.106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>12-02-2010</td>\n",
       "      <td>1641957.44</td>\n",
       "      <td>1</td>\n",
       "      <td>38.51</td>\n",
       "      <td>2.548</td>\n",
       "      <td>211.242170</td>\n",
       "      <td>8.106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>19-02-2010</td>\n",
       "      <td>1611968.17</td>\n",
       "      <td>0</td>\n",
       "      <td>39.93</td>\n",
       "      <td>2.514</td>\n",
       "      <td>211.289143</td>\n",
       "      <td>8.106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>26-02-2010</td>\n",
       "      <td>1409727.59</td>\n",
       "      <td>0</td>\n",
       "      <td>46.63</td>\n",
       "      <td>2.561</td>\n",
       "      <td>211.319643</td>\n",
       "      <td>8.106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>05-03-2010</td>\n",
       "      <td>1554806.68</td>\n",
       "      <td>0</td>\n",
       "      <td>46.50</td>\n",
       "      <td>2.625</td>\n",
       "      <td>211.350143</td>\n",
       "      <td>8.106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6430</th>\n",
       "      <td>45</td>\n",
       "      <td>28-09-2012</td>\n",
       "      <td>713173.95</td>\n",
       "      <td>0</td>\n",
       "      <td>64.88</td>\n",
       "      <td>3.997</td>\n",
       "      <td>192.013558</td>\n",
       "      <td>8.684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6431</th>\n",
       "      <td>45</td>\n",
       "      <td>05-10-2012</td>\n",
       "      <td>733455.07</td>\n",
       "      <td>0</td>\n",
       "      <td>64.89</td>\n",
       "      <td>3.985</td>\n",
       "      <td>192.170412</td>\n",
       "      <td>8.667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6432</th>\n",
       "      <td>45</td>\n",
       "      <td>12-10-2012</td>\n",
       "      <td>734464.36</td>\n",
       "      <td>0</td>\n",
       "      <td>54.47</td>\n",
       "      <td>4.000</td>\n",
       "      <td>192.327265</td>\n",
       "      <td>8.667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6433</th>\n",
       "      <td>45</td>\n",
       "      <td>19-10-2012</td>\n",
       "      <td>718125.53</td>\n",
       "      <td>0</td>\n",
       "      <td>56.47</td>\n",
       "      <td>3.969</td>\n",
       "      <td>192.330854</td>\n",
       "      <td>8.667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6434</th>\n",
       "      <td>45</td>\n",
       "      <td>26-10-2012</td>\n",
       "      <td>760281.43</td>\n",
       "      <td>0</td>\n",
       "      <td>58.85</td>\n",
       "      <td>3.882</td>\n",
       "      <td>192.308899</td>\n",
       "      <td>8.667</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6435 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Store        Date  Weekly_Sales  Holiday_Flag  Temperature  Fuel_Price  \\\n",
       "0         1  05-02-2010    1643690.90             0        42.31       2.572   \n",
       "1         1  12-02-2010    1641957.44             1        38.51       2.548   \n",
       "2         1  19-02-2010    1611968.17             0        39.93       2.514   \n",
       "3         1  26-02-2010    1409727.59             0        46.63       2.561   \n",
       "4         1  05-03-2010    1554806.68             0        46.50       2.625   \n",
       "...     ...         ...           ...           ...          ...         ...   \n",
       "6430     45  28-09-2012     713173.95             0        64.88       3.997   \n",
       "6431     45  05-10-2012     733455.07             0        64.89       3.985   \n",
       "6432     45  12-10-2012     734464.36             0        54.47       4.000   \n",
       "6433     45  19-10-2012     718125.53             0        56.47       3.969   \n",
       "6434     45  26-10-2012     760281.43             0        58.85       3.882   \n",
       "\n",
       "             CPI  Unemployment  \n",
       "0     211.096358         8.106  \n",
       "1     211.242170         8.106  \n",
       "2     211.289143         8.106  \n",
       "3     211.319643         8.106  \n",
       "4     211.350143         8.106  \n",
       "...          ...           ...  \n",
       "6430  192.013558         8.684  \n",
       "6431  192.170412         8.667  \n",
       "6432  192.327265         8.667  \n",
       "6433  192.330854         8.667  \n",
       "6434  192.308899         8.667  \n",
       "\n",
       "[6435 rows x 8 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('./datas/Walmart_Sales.csv')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "93cdeb5c-9066-4c16-9d38-008ad9abf487",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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