Visualization.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "4229b99c-3c44-474b-afda-7138eeb424b2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "        <script type=\"text/javascript\">\n",
       "        window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
       "        if (window.MathJax && window.MathJax.Hub && window.MathJax.Hub.Config) {window.MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
       "        if (typeof require !== 'undefined') {\n",
       "        require.undef(\"plotly\");\n",
       "        requirejs.config({\n",
       "            paths: {\n",
       "                'plotly': ['https://cdn.plot.ly/plotly-2.18.2.min']\n",
       "            }\n",
       "        });\n",
       "        require(['plotly'], function(Plotly) {\n",
       "            window._Plotly = Plotly;\n",
       "        });\n",
       "        }\n",
       "        </script>\n",
       "        "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as py\n",
    "import matplotlib.pyplot as plt\n",
    "import cufflinks as cf\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "fbc92e35-5a4e-4ae9-ab42-0530501dd1eb",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "        vertical-align: middle;\n",
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       "\n",
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       "        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>SN</th>\n",
       "      <th>Commodity</th>\n",
       "      <th>Date</th>\n",
       "      <th>Unit</th>\n",
       "      <th>Minimum</th>\n",
       "      <th>Maximum</th>\n",
       "      <th>Average</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>Tomato Big(Nepali)</td>\n",
       "      <td>2013-06-16</td>\n",
       "      <td>Kg</td>\n",
       "      <td>35.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>37.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Tomato Small(Local)</td>\n",
       "      <td>2013-06-16</td>\n",
       "      <td>Kg</td>\n",
       "      <td>26.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>29.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>Potato Red</td>\n",
       "      <td>2013-06-16</td>\n",
       "      <td>Kg</td>\n",
       "      <td>20.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>20.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>Potato White</td>\n",
       "      <td>2013-06-16</td>\n",
       "      <td>Kg</td>\n",
       "      <td>15.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>15.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>Onion Dry (Indian)</td>\n",
       "      <td>2013-06-16</td>\n",
       "      <td>Kg</td>\n",
       "      <td>28.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>29.0</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197156</th>\n",
       "      <td>197156</td>\n",
       "      <td>Garlic Dry Nepali</td>\n",
       "      <td>2021-05-13</td>\n",
       "      <td>Kg</td>\n",
       "      <td>100.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>110.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197157</th>\n",
       "      <td>197157</td>\n",
       "      <td>Fish Fresh(Rahu)</td>\n",
       "      <td>2021-05-13</td>\n",
       "      <td>KG</td>\n",
       "      <td>270.0</td>\n",
       "      <td>280.0</td>\n",
       "      <td>275.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197158</th>\n",
       "      <td>197158</td>\n",
       "      <td>Fish Fresh(Bachuwa)</td>\n",
       "      <td>2021-05-13</td>\n",
       "      <td>KG</td>\n",
       "      <td>225.0</td>\n",
       "      <td>235.0</td>\n",
       "      <td>230.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197159</th>\n",
       "      <td>197159</td>\n",
       "      <td>Fish Fresh(Chhadi)</td>\n",
       "      <td>2021-05-13</td>\n",
       "      <td>KG</td>\n",
       "      <td>220.0</td>\n",
       "      <td>230.0</td>\n",
       "      <td>225.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197160</th>\n",
       "      <td>197160</td>\n",
       "      <td>Fish Fresh(Mungari)</td>\n",
       "      <td>2021-05-13</td>\n",
       "      <td>KG</td>\n",
       "      <td>240.0</td>\n",
       "      <td>250.0</td>\n",
       "      <td>245.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>197161 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            SN            Commodity        Date Unit  Minimum  Maximum  \\\n",
       "0            0   Tomato Big(Nepali)  2013-06-16   Kg     35.0     40.0   \n",
       "1            1  Tomato Small(Local)  2013-06-16   Kg     26.0     32.0   \n",
       "2            2           Potato Red  2013-06-16   Kg     20.0     21.0   \n",
       "3            3         Potato White  2013-06-16   Kg     15.0     16.0   \n",
       "4            4   Onion Dry (Indian)  2013-06-16   Kg     28.0     30.0   \n",
       "...        ...                  ...         ...  ...      ...      ...   \n",
       "197156  197156    Garlic Dry Nepali  2021-05-13   Kg    100.0    120.0   \n",
       "197157  197157     Fish Fresh(Rahu)  2021-05-13   KG    270.0    280.0   \n",
       "197158  197158  Fish Fresh(Bachuwa)  2021-05-13   KG    225.0    235.0   \n",
       "197159  197159   Fish Fresh(Chhadi)  2021-05-13   KG    220.0    230.0   \n",
       "197160  197160  Fish Fresh(Mungari)  2021-05-13   KG    240.0    250.0   \n",
       "\n",
       "        Average  \n",
       "0          37.5  \n",
       "1          29.0  \n",
       "2          20.5  \n",
       "3          15.5  \n",
       "4          29.0  \n",
       "...         ...  \n",
       "197156    110.0  \n",
       "197157    275.0  \n",
       "197158    230.0  \n",
       "197159    225.0  \n",
       "197160    245.0  \n",
       "\n",
       "[197161 rows x 7 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset = pd.read_csv(\"kalimati_tarkari_dataset.csv\")\n",
    "dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "9facc0d3-413d-4047-833f-e9a68d76c0f1",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 197161 entries, 0 to 197160\n",
      "Data columns (total 7 columns):\n",
      " #   Column     Non-Null Count   Dtype  \n",
      "---  ------     --------------   -----  \n",
      " 0   SN         197161 non-null  int64  \n",
      " 1   Commodity  197161 non-null  object \n",
      " 2   Date       197161 non-null  object \n",
      " 3   Unit       197161 non-null  object \n",
      " 4   Minimum    197161 non-null  float64\n",
      " 5   Maximum    197161 non-null  float64\n",
      " 6   Average    197161 non-null  float64\n",
      "dtypes: float64(3), int64(1), object(3)\n",
      "memory usage: 10.5+ MB\n"
     ]
    }
   ],
   "source": [
    "dataset.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "88f31255-06c8-4a02-b780-bddd8d59ca8f",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "\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>SN</th>\n",
       "      <th>Date</th>\n",
       "      <th>Unit</th>\n",
       "      <th>Minimum</th>\n",
       "      <th>Maximum</th>\n",
       "      <th>Average</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Commodity</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Tomato Big(Nepali)</th>\n",
       "      <td>0</td>\n",
       "      <td>2013-06-16</td>\n",
       "      <td>Kg</td>\n",
       "      <td>35.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>37.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tomato Small(Local)</th>\n",
       "      <td>1</td>\n",
       "      <td>2013-06-16</td>\n",
       "      <td>Kg</td>\n",
       "      <td>26.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>29.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Potato Red</th>\n",
       "      <td>2</td>\n",
       "      <td>2013-06-16</td>\n",
       "      <td>Kg</td>\n",
       "      <td>20.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>20.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Potato White</th>\n",
       "      <td>3</td>\n",
       "      <td>2013-06-16</td>\n",
       "      <td>Kg</td>\n",
       "      <td>15.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>15.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Onion Dry (Indian)</th>\n",
       "      <td>4</td>\n",
       "      <td>2013-06-16</td>\n",
       "      <td>Kg</td>\n",
       "      <td>28.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>29.0</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Garlic Dry Nepali</th>\n",
       "      <td>197156</td>\n",
       "      <td>2021-05-13</td>\n",
       "      <td>Kg</td>\n",
       "      <td>100.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>110.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fish Fresh(Rahu)</th>\n",
       "      <td>197157</td>\n",
       "      <td>2021-05-13</td>\n",
       "      <td>KG</td>\n",
       "      <td>270.0</td>\n",
       "      <td>280.0</td>\n",
       "      <td>275.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fish Fresh(Bachuwa)</th>\n",
       "      <td>197158</td>\n",
       "      <td>2021-05-13</td>\n",
       "      <td>KG</td>\n",
       "      <td>225.0</td>\n",
       "      <td>235.0</td>\n",
       "      <td>230.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fish Fresh(Chhadi)</th>\n",
       "      <td>197159</td>\n",
       "      <td>2021-05-13</td>\n",
       "      <td>KG</td>\n",
       "      <td>220.0</td>\n",
       "      <td>230.0</td>\n",
       "      <td>225.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fish Fresh(Mungari)</th>\n",
       "      <td>197160</td>\n",
       "      <td>2021-05-13</td>\n",
       "      <td>KG</td>\n",
       "      <td>240.0</td>\n",
       "      <td>250.0</td>\n",
       "      <td>245.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>197161 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                         SN        Date Unit  Minimum  Maximum  Average\n",
       "Commodity                                                              \n",
       "Tomato Big(Nepali)        0  2013-06-16   Kg     35.0     40.0     37.5\n",
       "Tomato Small(Local)       1  2013-06-16   Kg     26.0     32.0     29.0\n",
       "Potato Red                2  2013-06-16   Kg     20.0     21.0     20.5\n",
       "Potato White              3  2013-06-16   Kg     15.0     16.0     15.5\n",
       "Onion Dry (Indian)        4  2013-06-16   Kg     28.0     30.0     29.0\n",
       "...                     ...         ...  ...      ...      ...      ...\n",
       "Garlic Dry Nepali    197156  2021-05-13   Kg    100.0    120.0    110.0\n",
       "Fish Fresh(Rahu)     197157  2021-05-13   KG    270.0    280.0    275.0\n",
       "Fish Fresh(Bachuwa)  197158  2021-05-13   KG    225.0    235.0    230.0\n",
       "Fish Fresh(Chhadi)   197159  2021-05-13   KG    220.0    230.0    225.0\n",
       "Fish Fresh(Mungari)  197160  2021-05-13   KG    240.0    250.0    245.0\n",
       "\n",
       "[197161 rows x 6 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset = pd.read_csv(\"kalimati_tarkari_dataset.csv\", index_col=1)\n",
    "dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "09443e41-77e7-4006-9f63-806388488f76",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 197161 entries, Tomato Big(Nepali) to Fish Fresh(Mungari)\n",
      "Data columns (total 6 columns):\n",
      " #   Column   Non-Null Count   Dtype  \n",
      "---  ------   --------------   -----  \n",
      " 0   SN       197161 non-null  int64  \n",
      " 1   Date     197161 non-null  object \n",
      " 2   Unit     197161 non-null  object \n",
      " 3   Minimum  197161 non-null  float64\n",
      " 4   Maximum  197161 non-null  float64\n",
      " 5   Average  197161 non-null  float64\n",
      "dtypes: float64(3), int64(1), object(2)\n",
      "memory usage: 10.5+ MB\n"
     ]
    }
   ],
   "source": [
    "dataset.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "87b79345-dcc6-48f7-b225-516cf7e396de",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "        if (typeof require !== 'undefined') {\n",
       "        require.undef(\"plotly\");\n",
       "        requirejs.config({\n",
       "            paths: {\n",
       "                'plotly': ['https://cdn.plot.ly/plotly-2.18.2.min']\n",
       "            }\n",
       "        });\n",
       "        require(['plotly'], function(Plotly) {\n",
       "            window._Plotly = Plotly;\n",
       "        });\n",
       "        }\n",
       "        </script>\n",
       "        "
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     },
     "metadata": {},
     "output_type": "display_data"
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     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SN</th>\n",
       "      <th>Date</th>\n",
       "      <th>Unit</th>\n",
       "      <th>Minimum</th>\n",
       "      <th>Maximum</th>\n",
       "      <th>Average</th>\n",
       "      <th>SMA-50</th>\n",
       "      <th>SMA-200</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Commodity</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Potato Red</th>\n",
       "      <td>2</td>\n",
       "      <td>2013-06-16</td>\n",
       "      <td>Kg</td>\n",
       "      <td>20.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>20.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Potato Red</th>\n",
       "      <td>76</td>\n",
       "      <td>2013-06-17</td>\n",
       "      <td>Kg</td>\n",
       "      <td>20.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>20.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Potato Red</th>\n",
       "      <td>149</td>\n",
       "      <td>2013-06-18</td>\n",
       "      <td>Kg</td>\n",
       "      <td>19.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>19.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Potato Red</th>\n",
       "      <td>222</td>\n",
       "      <td>2013-06-19</td>\n",
       "      <td>Kg</td>\n",
       "      <td>20.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>20.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Potato Red</th>\n",
       "      <td>295</td>\n",
       "      <td>2013-06-20</td>\n",
       "      <td>Kg</td>\n",
       "      <td>20.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>20.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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>Potato Red</th>\n",
       "      <td>196712</td>\n",
       "      <td>2021-05-09</td>\n",
       "      <td>Kg</td>\n",
       "      <td>35.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>36.5</td>\n",
       "      <td>31.74</td>\n",
       "      <td>44.290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Potato Red</th>\n",
       "      <td>196803</td>\n",
       "      <td>2021-05-10</td>\n",
       "      <td>Kg</td>\n",
       "      <td>35.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>36.5</td>\n",
       "      <td>31.94</td>\n",
       "      <td>43.955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Potato Red</th>\n",
       "      <td>196894</td>\n",
       "      <td>2021-05-11</td>\n",
       "      <td>Kg</td>\n",
       "      <td>35.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>36.5</td>\n",
       "      <td>32.14</td>\n",
       "      <td>43.630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Potato Red</th>\n",
       "      <td>196985</td>\n",
       "      <td>2021-05-12</td>\n",
       "      <td>Kg</td>\n",
       "      <td>35.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>32.36</td>\n",
       "      <td>43.305</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Potato Red</th>\n",
       "      <td>197076</td>\n",
       "      <td>2021-05-13</td>\n",
       "      <td>Kg</td>\n",
       "      <td>35.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>32.56</td>\n",
       "      <td>42.960</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2746 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                SN        Date Unit  Minimum  Maximum  Average  SMA-50  \\\n",
       "Commodity                                                                \n",
       "Potato Red       2  2013-06-16   Kg     20.0     21.0     20.5     NaN   \n",
       "Potato Red      76  2013-06-17   Kg     20.0     21.0     20.5     NaN   \n",
       "Potato Red     149  2013-06-18   Kg     19.0     20.0     19.5     NaN   \n",
       "Potato Red     222  2013-06-19   Kg     20.0     21.0     20.5     NaN   \n",
       "Potato Red     295  2013-06-20   Kg     20.0     21.0     20.5     NaN   \n",
       "...            ...         ...  ...      ...      ...      ...     ...   \n",
       "Potato Red  196712  2021-05-09   Kg     35.0     38.0     36.5   31.74   \n",
       "Potato Red  196803  2021-05-10   Kg     35.0     38.0     36.5   31.94   \n",
       "Potato Red  196894  2021-05-11   Kg     35.0     38.0     36.5   32.14   \n",
       "Potato Red  196985  2021-05-12   Kg     35.0     37.0     36.0   32.36   \n",
       "Potato Red  197076  2021-05-13   Kg     35.0     37.0     36.0   32.56   \n",
       "\n",
       "            SMA-200  \n",
       "Commodity            \n",
       "Potato Red      NaN  \n",
       "Potato Red      NaN  \n",
       "Potato Red      NaN  \n",
       "Potato Red      NaN  \n",
       "Potato Red      NaN  \n",
       "...             ...  \n",
       "Potato Red   44.290  \n",
       "Potato Red   43.955  \n",
       "Potato Red   43.630  \n",
       "Potato Red   43.305  \n",
       "Potato Red   42.960  \n",
       "\n",
       "[2746 rows x 8 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cf.go_offline()\n",
    "\n",
    "datavisual = dataset.loc[\"Potato Red\"]\n",
    "datavisual[\"SMA-50\"] = datavisual[\"Minimum\"].rolling(50).mean()\n",
    "datavisual[\"SMA-200\"] = datavisual[\"Minimum\"].rolling(200).mean()\n",
    "datavisual"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "aa747abf-67bf-4d4e-a370-29f17227aa8c",
   "metadata": {
    "tags": []
   },
   "outputs": [
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   ],
   "source": [
    "\n",
    "olahGrafik = datavisual.loc[\"Potato Red\", (\"Minimum\", \"Maximum\", \"Average\", \"Date\")]\n",
    "olahGrafik.iplot(x=\"Date\")"
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