exploratory_data_analysis.ipynb

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  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np \n",
    "import plotly.express as px\n",
    "import statsmodels\n",
    "import plotly.graph_objects as go"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>item_name</th>\n",
       "      <th>item_category</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>11/29/2021</td>\n",
       "      <td>tea</td>\n",
       "      <td>food</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>11/29/2021</td>\n",
       "      <td>cold coffe</td>\n",
       "      <td>food</td>\n",
       "      <td>35.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>11/29/2021</td>\n",
       "      <td>flat coffe</td>\n",
       "      <td>food</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11/29/2021</td>\n",
       "      <td>biscuits</td>\n",
       "      <td>food</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>11/29/2021</td>\n",
       "      <td>medicine</td>\n",
       "      <td>medicine</td>\n",
       "      <td>500.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      "text/plain": [
       "        date   item_name  item_category   price \n",
       "0  11/29/2021        tea            food    10.0\n",
       "1  11/29/2021  cold coffe           food    35.0\n",
       "2  11/29/2021  flat coffe           food    10.0\n",
       "3  11/29/2021    biscuits           food    10.0\n",
       "4  11/29/2021   medicine        medicine   500.0"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('data/bangalore  - item.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
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     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1299 entries, 0 to 1298\n",
      "Data columns (total 4 columns):\n",
      " #   Column          Non-Null Count  Dtype  \n",
      "---  ------          --------------  -----  \n",
      " 0   date            1299 non-null   object \n",
      " 1   item_name       1299 non-null   object \n",
      " 2   item_category   1299 non-null   object \n",
      " 3   price           1298 non-null   float64\n",
      "dtypes: float64(1), object(3)\n",
      "memory usage: 40.7+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "(1299, 4)"
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     "execution_count": 59,
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   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['date ', 'item_name ', 'item_category ', 'price '], dtype='object')"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.rename({\n",
    "    'date ' : 'date',\n",
    "    'item_name ' : 'item_name',\n",
    "    'item_category ' : 'item_category',\n",
    "    'price ' : 'price'    \n",
    "    }, axis = 1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
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       "0"
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     "execution_count": 62,
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   ],
   "source": [
    "df.item_name.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.dropna(inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['food', 'medicine', 'utilities', 'study_materials', 'wants',\n",
       "       'charity', 'travel', 'clothes'], dtype=object)"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.item_category.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "date = df.date.str.split('/', expand =True)\n",
    "df['month'] = date[0]\n",
    "df['day'] = date[1]\n",
    "df['year'] = date[2]\n",
    "\n",
    "df.year = pd.to_numeric(df.year)\n",
    "df.month = pd.to_numeric(df.month)\n",
    "df.day = pd.to_numeric(df.day)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.date = pd.to_datetime(df.date)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
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       "      <td>2021-11-29</td>\n",
       "      <td>tea</td>\n",
       "      <td>food</td>\n",
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       "      <td>11</td>\n",
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       "      <td>2021</td>\n",
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       "      <th>1</th>\n",
       "      <td>2021-11-29</td>\n",
       "      <td>cold coffe</td>\n",
       "      <td>food</td>\n",
       "      <td>35.0</td>\n",
       "      <td>11</td>\n",
       "      <td>29</td>\n",
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       "      <th>2</th>\n",
       "      <td>2021-11-29</td>\n",
       "      <td>flat coffe</td>\n",
       "      <td>food</td>\n",
       "      <td>10.0</td>\n",
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       "      <td>2021-11-29</td>\n",
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       "      <th>4</th>\n",
       "      <td>2021-11-29</td>\n",
       "      <td>medicine</td>\n",
       "      <td>medicine</td>\n",
       "      <td>500.0</td>\n",
       "      <td>11</td>\n",
       "      <td>29</td>\n",
       "      <td>2021</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "        date   item_name item_category  price  month  day  year\n",
       "0 2021-11-29        tea           food   10.0     11   29  2021\n",
       "1 2021-11-29  cold coffe          food   35.0     11   29  2021\n",
       "2 2021-11-29  flat coffe          food   10.0     11   29  2021\n",
       "3 2021-11-29    biscuits          food   10.0     11   29  2021\n",
       "4 2021-11-29   medicine       medicine  500.0     11   29  2021"
      ]
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     "execution_count": 67,
     "metadata": {},
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    "    fig = px.bar(dummy_df, x = value, y = 'price')\n",
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   "source": [
    "df['month_year'] = df['date'].dt.to_period('M')\n",
    "dummy_df = df.groupby(['month_year', 'item_category'])['price'].sum().reset_index()\n",
    "dummy_df.month_year = dummy_df.month_year.astype(str)\n",
    "fig = px.bar(dummy_df, x=\"month_year\", y=\"price\", color=\"item_category\")\n",
    "fig.show()\n",
    "\n",
    "# fig = go.Figure()\n",
    "# counter, color_list = 0, ['#f71616', '#f71616', '#f76c16', '#bf7411', '#bfb911', '#94bf11', '#54bf11',  '#bf7411', '#bfb911', '#94bf11', '#54bf11']\n",
    "# for category in dummy_df.item_category.unique() :\n",
    "#     temp_df = dummy_df[dummy_df.item_category == category]\n",
    "#     fig.add_trace(go.Bar(\n",
    "#         x=temp_df.month_year,\n",
    "#         y=temp_df.price,\n",
    "#         name= category,\n",
    "#         # marker_color= color_list[counter]\n",
    "#     ))\n",
    "#     counter += 1 \n",
    "\n",
    "# fig.update_layout(plot_bgcolor=\"white\", barmode='group')"
   ]
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  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "category_df = df.groupby(['item_category'])['price'].sum().reset_index()"
   ]
  },
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    "fig = px.treemap(\n",
    "    dummy_df, path = [px.Constant('total spending'), 'item_name'], \n",
    "    values = 'total spending'\n",
    ")\n",
    "\n",
    "fig.show()"
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  },
  {
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   "outputs": [],
   "source": []
  },
  {
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