Superstore Sales Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from datetime import datetime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\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>order_id</th>\n",
       "      <th>order_date</th>\n",
       "      <th>ship_date</th>\n",
       "      <th>ship_mode</th>\n",
       "      <th>customer_name</th>\n",
       "      <th>segment</th>\n",
       "      <th>state</th>\n",
       "      <th>country</th>\n",
       "      <th>market</th>\n",
       "      <th>region</th>\n",
       "      <th>...</th>\n",
       "      <th>category</th>\n",
       "      <th>sub_category</th>\n",
       "      <th>product_name</th>\n",
       "      <th>sales</th>\n",
       "      <th>quantity</th>\n",
       "      <th>discount</th>\n",
       "      <th>profit</th>\n",
       "      <th>shipping_cost</th>\n",
       "      <th>order_priority</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AG-2011-2040</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>2011-01-06</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>Toby Braunhardt</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>Constantine</td>\n",
       "      <td>Algeria</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Africa</td>\n",
       "      <td>...</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Storage</td>\n",
       "      <td>Tenex Lockers, Blue</td>\n",
       "      <td>408.300</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>106.140</td>\n",
       "      <td>35.46</td>\n",
       "      <td>Medium</td>\n",
       "      <td>2011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>IN-2011-47883</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>2011-01-08</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>Joseph Holt</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>New South Wales</td>\n",
       "      <td>Australia</td>\n",
       "      <td>APAC</td>\n",
       "      <td>Oceania</td>\n",
       "      <td>...</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Supplies</td>\n",
       "      <td>Acme Trimmer, High Speed</td>\n",
       "      <td>120.366</td>\n",
       "      <td>3</td>\n",
       "      <td>0.1</td>\n",
       "      <td>36.036</td>\n",
       "      <td>9.72</td>\n",
       "      <td>Medium</td>\n",
       "      <td>2011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>HU-2011-1220</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>2011-01-05</td>\n",
       "      <td>Second Class</td>\n",
       "      <td>Annie Thurman</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>Budapest</td>\n",
       "      <td>Hungary</td>\n",
       "      <td>EMEA</td>\n",
       "      <td>EMEA</td>\n",
       "      <td>...</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Storage</td>\n",
       "      <td>Tenex Box, Single Width</td>\n",
       "      <td>66.120</td>\n",
       "      <td>4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>29.640</td>\n",
       "      <td>8.17</td>\n",
       "      <td>High</td>\n",
       "      <td>2011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>IT-2011-3647632</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>2011-01-05</td>\n",
       "      <td>Second Class</td>\n",
       "      <td>Eugene Moren</td>\n",
       "      <td>Home Office</td>\n",
       "      <td>Stockholm</td>\n",
       "      <td>Sweden</td>\n",
       "      <td>EU</td>\n",
       "      <td>North</td>\n",
       "      <td>...</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Paper</td>\n",
       "      <td>Enermax Note Cards, Premium</td>\n",
       "      <td>44.865</td>\n",
       "      <td>3</td>\n",
       "      <td>0.5</td>\n",
       "      <td>-26.055</td>\n",
       "      <td>4.82</td>\n",
       "      <td>High</td>\n",
       "      <td>2011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>IN-2011-47883</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>2011-01-08</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>Joseph Holt</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>New South Wales</td>\n",
       "      <td>Australia</td>\n",
       "      <td>APAC</td>\n",
       "      <td>Oceania</td>\n",
       "      <td>...</td>\n",
       "      <td>Furniture</td>\n",
       "      <td>Furnishings</td>\n",
       "      <td>Eldon Light Bulb, Duo Pack</td>\n",
       "      <td>113.670</td>\n",
       "      <td>5</td>\n",
       "      <td>0.1</td>\n",
       "      <td>37.770</td>\n",
       "      <td>4.70</td>\n",
       "      <td>Medium</td>\n",
       "      <td>2011</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          order_id order_date  ship_date       ship_mode    customer_name  \\\n",
       "0     AG-2011-2040 2011-01-01 2011-01-06  Standard Class  Toby Braunhardt   \n",
       "1    IN-2011-47883 2011-01-01 2011-01-08  Standard Class      Joseph Holt   \n",
       "2     HU-2011-1220 2011-01-01 2011-01-05    Second Class    Annie Thurman   \n",
       "3  IT-2011-3647632 2011-01-01 2011-01-05    Second Class     Eugene Moren   \n",
       "4    IN-2011-47883 2011-01-01 2011-01-08  Standard Class      Joseph Holt   \n",
       "\n",
       "       segment            state    country  market   region  ...  \\\n",
       "0     Consumer      Constantine    Algeria  Africa   Africa  ...   \n",
       "1     Consumer  New South Wales  Australia    APAC  Oceania  ...   \n",
       "2     Consumer         Budapest    Hungary    EMEA     EMEA  ...   \n",
       "3  Home Office        Stockholm     Sweden      EU    North  ...   \n",
       "4     Consumer  New South Wales  Australia    APAC  Oceania  ...   \n",
       "\n",
       "          category sub_category                 product_name    sales  \\\n",
       "0  Office Supplies      Storage          Tenex Lockers, Blue  408.300   \n",
       "1  Office Supplies     Supplies     Acme Trimmer, High Speed  120.366   \n",
       "2  Office Supplies      Storage      Tenex Box, Single Width   66.120   \n",
       "3  Office Supplies        Paper  Enermax Note Cards, Premium   44.865   \n",
       "4        Furniture  Furnishings   Eldon Light Bulb, Duo Pack  113.670   \n",
       "\n",
       "   quantity  discount   profit  shipping_cost  order_priority  year  \n",
       "0         2       0.0  106.140          35.46          Medium  2011  \n",
       "1         3       0.1   36.036           9.72          Medium  2011  \n",
       "2         4       0.0   29.640           8.17            High  2011  \n",
       "3         3       0.5  -26.055           4.82            High  2011  \n",
       "4         5       0.1   37.770           4.70          Medium  2011  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_excel('superstore_sales.xlsx')\n",
    "df.head() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Satır ve Sütun Sayısını Ögrenme - Get the number of rows and columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(51290, 21)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Eksik veri olup olmadığını kontrol etme -Count the number of missing values\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "order_id          0\n",
       "order_date        0\n",
       "ship_date         0\n",
       "ship_mode         0\n",
       "customer_name     0\n",
       "segment           0\n",
       "state             0\n",
       "country           0\n",
       "market            0\n",
       "region            0\n",
       "product_id        0\n",
       "category          0\n",
       "sub_category      0\n",
       "product_name      0\n",
       "sales             0\n",
       "quantity          0\n",
       "discount          0\n",
       "profit            0\n",
       "shipping_cost     0\n",
       "order_priority    0\n",
       "year              0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<H4> En çok satan ürünler hangileri?  -  Which are the most selling products?\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>quantity</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>product_name</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Staples</th>\n",
       "      <td>876</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cardinal Index Tab, Clear</th>\n",
       "      <td>337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Eldon File Cart, Single Width</th>\n",
       "      <td>321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rogers File Cart, Single Width</th>\n",
       "      <td>262</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sanford Pencil Sharpener, Water Color</th>\n",
       "      <td>259</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Stockwell Paper Clips, Assorted Sizes</th>\n",
       "      <td>253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Avery Index Tab, Clear</th>\n",
       "      <td>252</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ibico Index Tab, Clear</th>\n",
       "      <td>251</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Smead File Cart, Single Width</th>\n",
       "      <td>250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Stanley Pencil Sharpener, Water Color</th>\n",
       "      <td>242</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                       quantity\n",
       "product_name                                   \n",
       "Staples                                     876\n",
       "Cardinal Index Tab, Clear                   337\n",
       "Eldon File Cart, Single Width               321\n",
       "Rogers File Cart, Single Width              262\n",
       "Sanford Pencil Sharpener, Water Color       259\n",
       "Stockwell Paper Clips, Assorted Sizes       253\n",
       "Avery Index Tab, Clear                      252\n",
       "Ibico Index Tab, Clear                      251\n",
       "Smead File Cart, Single Width               250\n",
       "Stanley Pencil Sharpener, Water Color       242"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "boughts = df.groupby('product_name').agg({'quantity': 'sum'}).sort_values('quantity',ascending=False)[:10]\n",
    "boughts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]),\n",
       " <a list of 10 Text major ticklabel objects>)"
      ]
     },
     "execution_count": 6,
     "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": [
    "sns.barplot(x='product_name',y='quantity',data=boughts.reset_index())\n",
    "plt.xticks(rotation = 'vertical')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " <h4> Satışı en iyi 10 ürün hangileridir? - Which are the Top 10 products by sales?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>sales</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>product_name</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Apple Smart Phone, Full Size</th>\n",
       "      <td>86935.7786</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cisco Smart Phone, Full Size</th>\n",
       "      <td>76441.5306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Motorola Smart Phone, Full Size</th>\n",
       "      <td>73156.3030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nokia Smart Phone, Full Size</th>\n",
       "      <td>71904.5555</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Canon imageCLASS 2200 Advanced Copier</th>\n",
       "      <td>61599.8240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hon Executive Leather Armchair, Adjustable</th>\n",
       "      <td>58193.4841</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Office Star Executive Leather Armchair, Adjustable</th>\n",
       "      <td>50661.6840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Harbour Creations Executive Leather Armchair, Adjustable</th>\n",
       "      <td>50121.5160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Samsung Smart Phone, Cordless</th>\n",
       "      <td>48653.4600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nokia Smart Phone, with Caller ID</th>\n",
       "      <td>47877.7857</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                         sales\n",
       "product_name                                                  \n",
       "Apple Smart Phone, Full Size                        86935.7786\n",
       "Cisco Smart Phone, Full Size                        76441.5306\n",
       "Motorola Smart Phone, Full Size                     73156.3030\n",
       "Nokia Smart Phone, Full Size                        71904.5555\n",
       "Canon imageCLASS 2200 Advanced Copier               61599.8240\n",
       "Hon Executive Leather Armchair, Adjustable          58193.4841\n",
       "Office Star Executive Leather Armchair, Adjustable  50661.6840\n",
       "Harbour Creations Executive Leather Armchair, A...  50121.5160\n",
       "Samsung Smart Phone, Cordless                       48653.4600\n",
       "Nokia Smart Phone, with Caller ID                   47877.7857"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "productSales = pd.DataFrame(df.groupby('product_name').sum()['sales'])\n",
    "productSales.sort_values(by=['sales'], inplace=True, ascending=False)\n",
    "productSales.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h4> Hangi ürün daha çok kar getirmiş ?  Which are the most profitable products?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>profit</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>product_name</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Canon imageCLASS 2200 Advanced Copier</th>\n",
       "      <td>25199.9280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cisco Smart Phone, Full Size</th>\n",
       "      <td>17238.5206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Motorola Smart Phone, Full Size</th>\n",
       "      <td>17027.1130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hoover Stove, Red</th>\n",
       "      <td>11807.9690</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sauder Classic Bookcase, Traditional</th>\n",
       "      <td>10672.0730</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Harbour Creations Executive Leather Armchair, Adjustable</th>\n",
       "      <td>10427.3260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nokia Smart Phone, Full Size</th>\n",
       "      <td>9938.1955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cisco Smart Phone, with Caller ID</th>\n",
       "      <td>9786.6408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nokia Smart Phone, with Caller ID</th>\n",
       "      <td>9465.3257</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Belkin Router, USB</th>\n",
       "      <td>8955.0180</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                        profit\n",
       "product_name                                                  \n",
       "Canon imageCLASS 2200 Advanced Copier               25199.9280\n",
       "Cisco Smart Phone, Full Size                        17238.5206\n",
       "Motorola Smart Phone, Full Size                     17027.1130\n",
       "Hoover Stove, Red                                   11807.9690\n",
       "Sauder Classic Bookcase, Traditional                10672.0730\n",
       "Harbour Creations Executive Leather Armchair, A...  10427.3260\n",
       "Nokia Smart Phone, Full Size                         9938.1955\n",
       "Cisco Smart Phone, with Caller ID                    9786.6408\n",
       "Nokia Smart Phone, with Caller ID                    9465.3257\n",
       "Belkin Router, USB                                   8955.0180"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "profitable_products = pd.DataFrame(df.groupby(['product_name']).sum()['profit'])\n",
    "profitable_products.sort_values(by=['profit'], inplace = True, ascending = False)\n",
    "profitable_products.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]),\n",
       " <a list of 10 Text major ticklabel objects>)"
      ]
     },
     "execution_count": 9,
     "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": [
    "sns.barplot(x='product_name',y='profit',data=profitable_products.head(10).reset_index())\n",
    "plt.xticks(rotation = 'vertical')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h4>Hangi kategori daha çok satış yapmış? -  What category sold the most?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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></th>\n",
       "      <th>quantity</th>\n",
       "      <th>discount</th>\n",
       "      <th>profit</th>\n",
       "      <th>shipping_cost</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>category</th>\n",
       "      <th>sales</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 rowspan=\"5\" valign=\"top\">Furniture</th>\n",
       "      <th>1.892</th>\n",
       "      <td>1</td>\n",
       "      <td>0.6</td>\n",
       "      <td>-0.9933</td>\n",
       "      <td>0.18000</td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.988</th>\n",
       "      <td>1</td>\n",
       "      <td>0.6</td>\n",
       "      <td>-1.4413</td>\n",
       "      <td>0.17000</td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.032</th>\n",
       "      <td>1</td>\n",
       "      <td>0.6</td>\n",
       "      <td>-1.3208</td>\n",
       "      <td>0.10000</td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.328</th>\n",
       "      <td>4</td>\n",
       "      <td>1.2</td>\n",
       "      <td>-1.5132</td>\n",
       "      <td>0.38000</td>\n",
       "      <td>4026</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.784</th>\n",
       "      <td>2</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.4176</td>\n",
       "      <td>0.14000</td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">Technology</th>\n",
       "      <th>10499.970</th>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5039.9856</td>\n",
       "      <td>363.19000</td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11199.968</th>\n",
       "      <td>4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>3919.9888</td>\n",
       "      <td>45.98000</td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13999.960</th>\n",
       "      <td>4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6719.9808</td>\n",
       "      <td>20.00054</td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17499.950</th>\n",
       "      <td>5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8399.9760</td>\n",
       "      <td>349.07000</td>\n",
       "      <td>2013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22638.480</th>\n",
       "      <td>6</td>\n",
       "      <td>0.5</td>\n",
       "      <td>-1811.0784</td>\n",
       "      <td>24.28650</td>\n",
       "      <td>2011</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>29131 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      quantity  discount     profit  shipping_cost  year\n",
       "category   sales                                                        \n",
       "Furniture  1.892             1       0.6    -0.9933        0.18000  2014\n",
       "           1.988             1       0.6    -1.4413        0.17000  2014\n",
       "           2.032             1       0.6    -1.3208        0.10000  2014\n",
       "           2.328             4       1.2    -1.5132        0.38000  4026\n",
       "           2.784             2       0.2     0.4176        0.14000  2014\n",
       "...                        ...       ...        ...            ...   ...\n",
       "Technology 10499.970         3       0.0  5039.9856      363.19000  2014\n",
       "           11199.968         4       0.2  3919.9888       45.98000  2014\n",
       "           13999.960         4       0.0  6719.9808       20.00054  2014\n",
       "           17499.950         5       0.0  8399.9760      349.07000  2013\n",
       "           22638.480         6       0.5 -1811.0784       24.28650  2011\n",
       "\n",
       "[29131 rows x 5 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['category','sales']).sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h4> Hangi kategori daha çok karlı? - Which are the most profitable category?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>profit</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>category</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Technology</th>\n",
       "      <td>663778.73318</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Office Supplies</th>\n",
       "      <td>518473.83430</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Furniture</th>\n",
       "      <td>286782.25380</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       profit\n",
       "category                     \n",
       "Technology       663778.73318\n",
       "Office Supplies  518473.83430\n",
       "Furniture        286782.25380"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "profitable_category = pd.DataFrame(df.groupby(['category']).sum()['profit'])\n",
    "profitable_category.sort_values(by=['profit'], inplace = True, ascending = False)\n",
    "profitable_category"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1224x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(17, 5))\n",
    "plt.bar(df['category'],df['sales'],color = 'b')\n",
    "plt.xticks(size=8)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h4> Kategori ve alt kategorideki toplam satış değerleri - Total sales values by category and subcategory\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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></th>\n",
       "      <th>sales</th>\n",
       "      <th>quantity</th>\n",
       "      <th>discount</th>\n",
       "      <th>profit</th>\n",
       "      <th>shipping_cost</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>category</th>\n",
       "      <th>sub_category</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 rowspan=\"4\" valign=\"top\">Furniture</th>\n",
       "      <th>Bookcases</th>\n",
       "      <td>1.466572e+06</td>\n",
       "      <td>8310</td>\n",
       "      <td>370.710</td>\n",
       "      <td>161924.41950</td>\n",
       "      <td>155481.9670</td>\n",
       "      <td>4852847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chairs</th>\n",
       "      <td>1.501682e+06</td>\n",
       "      <td>12336</td>\n",
       "      <td>560.120</td>\n",
       "      <td>141973.79750</td>\n",
       "      <td>164229.3520</td>\n",
       "      <td>6911889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Furnishings</th>\n",
       "      <td>3.855783e+05</td>\n",
       "      <td>11225</td>\n",
       "      <td>478.880</td>\n",
       "      <td>46967.42550</td>\n",
       "      <td>40746.7660</td>\n",
       "      <td>6380451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tables</th>\n",
       "      <td>7.570419e+05</td>\n",
       "      <td>3083</td>\n",
       "      <td>250.320</td>\n",
       "      <td>-64083.38870</td>\n",
       "      <td>79861.3940</td>\n",
       "      <td>1732979</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"9\" valign=\"top\">Office Supplies</th>\n",
       "      <th>Appliances</th>\n",
       "      <td>1.011064e+06</td>\n",
       "      <td>6078</td>\n",
       "      <td>248.700</td>\n",
       "      <td>141680.58940</td>\n",
       "      <td>108300.5860</td>\n",
       "      <td>3532371</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Art</th>\n",
       "      <td>3.720920e+05</td>\n",
       "      <td>16301</td>\n",
       "      <td>573.080</td>\n",
       "      <td>57953.91090</td>\n",
       "      <td>41287.1420</td>\n",
       "      <td>9828413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Binders</th>\n",
       "      <td>4.619115e+05</td>\n",
       "      <td>21429</td>\n",
       "      <td>1102.480</td>\n",
       "      <td>72449.84600</td>\n",
       "      <td>48181.7120</td>\n",
       "      <td>12382700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Envelopes</th>\n",
       "      <td>1.709043e+05</td>\n",
       "      <td>8380</td>\n",
       "      <td>320.810</td>\n",
       "      <td>29601.11630</td>\n",
       "      <td>18547.4880</td>\n",
       "      <td>4901146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fasteners</th>\n",
       "      <td>8.324232e+04</td>\n",
       "      <td>8390</td>\n",
       "      <td>340.240</td>\n",
       "      <td>11525.42410</td>\n",
       "      <td>9053.3380</td>\n",
       "      <td>4870955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Labels</th>\n",
       "      <td>7.340403e+04</td>\n",
       "      <td>9322</td>\n",
       "      <td>313.890</td>\n",
       "      <td>15010.51200</td>\n",
       "      <td>8059.6750</td>\n",
       "      <td>5245285</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Paper</th>\n",
       "      <td>2.442917e+05</td>\n",
       "      <td>12822</td>\n",
       "      <td>387.300</td>\n",
       "      <td>59207.68270</td>\n",
       "      <td>26660.8450</td>\n",
       "      <td>7121179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Storage</th>\n",
       "      <td>1.127086e+06</td>\n",
       "      <td>16917</td>\n",
       "      <td>700.490</td>\n",
       "      <td>108461.48980</td>\n",
       "      <td>120546.0320</td>\n",
       "      <td>10182612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Supplies</th>\n",
       "      <td>2.430742e+05</td>\n",
       "      <td>8543</td>\n",
       "      <td>310.200</td>\n",
       "      <td>22583.26310</td>\n",
       "      <td>24811.5270</td>\n",
       "      <td>4881018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">Technology</th>\n",
       "      <th>Accessories</th>\n",
       "      <td>7.492370e+05</td>\n",
       "      <td>10946</td>\n",
       "      <td>370.480</td>\n",
       "      <td>129626.30620</td>\n",
       "      <td>83513.3340</td>\n",
       "      <td>6189269</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Copiers</th>\n",
       "      <td>1.509436e+06</td>\n",
       "      <td>7454</td>\n",
       "      <td>260.418</td>\n",
       "      <td>258567.54818</td>\n",
       "      <td>159496.2049</td>\n",
       "      <td>4474471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Machines</th>\n",
       "      <td>7.790601e+05</td>\n",
       "      <td>4906</td>\n",
       "      <td>252.000</td>\n",
       "      <td>58867.87300</td>\n",
       "      <td>79135.8485</td>\n",
       "      <td>2990958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Phones</th>\n",
       "      <td>1.706824e+06</td>\n",
       "      <td>11870</td>\n",
       "      <td>489.610</td>\n",
       "      <td>216717.00580</td>\n",
       "      <td>184902.4920</td>\n",
       "      <td>6756800</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                     sales  quantity  discount        profit  \\\n",
       "category        sub_category                                                   \n",
       "Furniture       Bookcases     1.466572e+06      8310   370.710  161924.41950   \n",
       "                Chairs        1.501682e+06     12336   560.120  141973.79750   \n",
       "                Furnishings   3.855783e+05     11225   478.880   46967.42550   \n",
       "                Tables        7.570419e+05      3083   250.320  -64083.38870   \n",
       "Office Supplies Appliances    1.011064e+06      6078   248.700  141680.58940   \n",
       "                Art           3.720920e+05     16301   573.080   57953.91090   \n",
       "                Binders       4.619115e+05     21429  1102.480   72449.84600   \n",
       "                Envelopes     1.709043e+05      8380   320.810   29601.11630   \n",
       "                Fasteners     8.324232e+04      8390   340.240   11525.42410   \n",
       "                Labels        7.340403e+04      9322   313.890   15010.51200   \n",
       "                Paper         2.442917e+05     12822   387.300   59207.68270   \n",
       "                Storage       1.127086e+06     16917   700.490  108461.48980   \n",
       "                Supplies      2.430742e+05      8543   310.200   22583.26310   \n",
       "Technology      Accessories   7.492370e+05     10946   370.480  129626.30620   \n",
       "                Copiers       1.509436e+06      7454   260.418  258567.54818   \n",
       "                Machines      7.790601e+05      4906   252.000   58867.87300   \n",
       "                Phones        1.706824e+06     11870   489.610  216717.00580   \n",
       "\n",
       "                              shipping_cost      year  \n",
       "category        sub_category                           \n",
       "Furniture       Bookcases       155481.9670   4852847  \n",
       "                Chairs          164229.3520   6911889  \n",
       "                Furnishings      40746.7660   6380451  \n",
       "                Tables           79861.3940   1732979  \n",
       "Office Supplies Appliances      108300.5860   3532371  \n",
       "                Art              41287.1420   9828413  \n",
       "                Binders          48181.7120  12382700  \n",
       "                Envelopes        18547.4880   4901146  \n",
       "                Fasteners         9053.3380   4870955  \n",
       "                Labels            8059.6750   5245285  \n",
       "                Paper            26660.8450   7121179  \n",
       "                Storage         120546.0320  10182612  \n",
       "                Supplies         24811.5270   4881018  \n",
       "Technology      Accessories      83513.3340   6189269  \n",
       "                Copiers         159496.2049   4474471  \n",
       "                Machines         79135.8485   2990958  \n",
       "                Phones          184902.4920   6756800  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['category','sub_category']).sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h4> Alt kategorilerde en çok satan ürünler hangileri? - Which are the most selling products in subcategory? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>category</th>\n",
       "      <th>sub_category</th>\n",
       "      <th>quantity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Furniture</td>\n",
       "      <td>Bookcases</td>\n",
       "      <td>2411</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Furniture</td>\n",
       "      <td>Chairs</td>\n",
       "      <td>3434</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Furniture</td>\n",
       "      <td>Furnishings</td>\n",
       "      <td>3170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Furniture</td>\n",
       "      <td>Tables</td>\n",
       "      <td>861</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Appliances</td>\n",
       "      <td>1755</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Art</td>\n",
       "      <td>4883</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Binders</td>\n",
       "      <td>6152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Envelopes</td>\n",
       "      <td>2435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Fasteners</td>\n",
       "      <td>2420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Labels</td>\n",
       "      <td>2606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Paper</td>\n",
       "      <td>3538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Storage</td>\n",
       "      <td>5059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Supplies</td>\n",
       "      <td>2425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Technology</td>\n",
       "      <td>Accessories</td>\n",
       "      <td>3075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Technology</td>\n",
       "      <td>Copiers</td>\n",
       "      <td>2223</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Technology</td>\n",
       "      <td>Machines</td>\n",
       "      <td>1486</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Technology</td>\n",
       "      <td>Phones</td>\n",
       "      <td>3357</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           category sub_category  quantity\n",
       "0         Furniture    Bookcases      2411\n",
       "1         Furniture       Chairs      3434\n",
       "2         Furniture  Furnishings      3170\n",
       "3         Furniture       Tables       861\n",
       "4   Office Supplies   Appliances      1755\n",
       "5   Office Supplies          Art      4883\n",
       "6   Office Supplies      Binders      6152\n",
       "7   Office Supplies    Envelopes      2435\n",
       "8   Office Supplies    Fasteners      2420\n",
       "9   Office Supplies       Labels      2606\n",
       "10  Office Supplies        Paper      3538\n",
       "11  Office Supplies      Storage      5059\n",
       "12  Office Supplies     Supplies      2425\n",
       "13       Technology  Accessories      3075\n",
       "14       Technology      Copiers      2223\n",
       "15       Technology     Machines      1486\n",
       "16       Technology       Phones      3357"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby([\"category\", \"sub_category\"], as_index=False)[\"quantity\"].count()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h4> Hangi müşteri segmenti daha çok karlı ? - Which customer segments are the most profitable ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>profit</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>segment</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Consumer</th>\n",
       "      <td>749239.78206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Corporate</th>\n",
       "      <td>442785.85866</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Home Office</th>\n",
       "      <td>277009.18056</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   profit\n",
       "segment                  \n",
       "Consumer     749239.78206\n",
       "Corporate    442785.85866\n",
       "Home Office  277009.18056"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "profitable_segment = pd.DataFrame(df.groupby(['segment']).sum()['profit'])\n",
    "profitable_segment.sort_values(by=['profit'], inplace = True, ascending = False)\n",
    "profitable_segment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([0, 1, 2]), <a list of 3 Text major ticklabel objects>)"
      ]
     },
     "execution_count": 16,
     "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": [
    "sns.barplot(x='segment',y='profit',data=profitable_segment.reset_index())\n",
    "plt.xticks(rotation = 'vertical')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h4> Kargo süreçlerinin satışa etkisi - What shipping modes sold the most products?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Pcv8k751uAAAAS1GLhdqOP2tra72+vj53G9/gm4/YasfiP03jnGUw1hmFsc4ojqWxXlXXbPianztYxvLZAAAAxzRBCAAAGI4gBAAADEcQAgAAhiMIAQAAwxGEAACA4QhCAADAcAQhAABgOIIQAAAwHEEIAAAYjiAEAAAMRxACAACGIwgBAADDEYQAAIDhCEIAAMBwBCEAAGA4ghAAADAcQQgAABiOIAQAAAxHEAIAAIYjCAEAAMMRhAAAgOEIQgAAwHAEIQAAYDiCEAAAMBxBCAAAGI4gBAAADEcQAgAAhiMIAQAAwxGEAACA4QhCAADAcAQhAABgOIIQAAAwHEEIAAAYjiAEAAAMRxACAACGIwgBAADDEYQAAIDhCEIAAMBwBCEAAGA4ghAAADAcQQgAABiOIAQAAAxHEAIAAIYjCAEAAMMRhAAAgOEIQgAAwHAEIQAAYDiCEAAAMBxBCAAAGI4gBAAADEcQAgAAhiMIAQAAwxGEAACA4QhCAADAcAQhAABgOIIQAAAwHEEIAAAYjiAEAAAM55gJQlV1XlVdX1V7q+pFc/cDAACsrmMiCFXVCUn+Q5IfSvK4JM+tqsfN2xUAALCqjokglOScJHu7+6+6++tJ3prk/Jl7AgAAVtSOuRuY7EryhQ2P9yX5zkNfVFV7kuyZHv5NVV2/Db2xtU5O8tdzN3E8qJq7A46SsX6EjPXjnrF+hIz1456xfoSOsbH+6Lt64lgJQpv95+o7FbovTXLp8tthWapqvbvX5u4Dls1YZxTGOqMw1lfPsTI1bl+S0zc8Pi3JDTP1AgAArLhjJQh9NMlZVXVmVf29JLuTXDFzTwAAwIo6JqbGdfeBqnphkvclOSHJ67r7kzO3xXKY2sgojHVGYawzCmN9xVT3nS7FAQAAWGnHytQ4AACAbSMIAQAAwxGEAACA4QhCAMA9UlUnz90DwNEShFi6qvrN6f7HqurPquoVc/cEy2CsM5DXVNV/qqoLq+qkuZuBZXFcX22CENvhH0z353X3E5J8z5zNwBIZ6wyhu5+d5HlJOsnvV9UfVNWPVNUJM7cGW81xfYUJQmyH+1TVS5P8j+nxgTmbgSUy1hlCVVWSJyf5R0kqyZVJTknynjn7giVwXF9hvkeIpauqhyR5YpL/ksUX5n5nd//xrE3BEhjrjKKqrkrygSRv6e7PbKhf3N2XzNcZbC3H9dXmjBDb4Se7+8okP5DkT5I8YeZ+YFmMdYbQ3U/v7pdvDEFTXQhi1TiurzBBiO3wA9P9jyZ5ahbzymEVGesMYbpw/I+r6n9V1Weq6pq5e4IlcVxfYYIQ2+F+VXVBkpu7+++SfHXuhmBJjHVG8TNJnpbk2iRnTfewihzXV5ggxHa4IMl9k/xiVd0vye/M3A8si7HOKP62u29L8ndJHpnk7Jn7gWVxXF9hO+ZugCH8VZIzkzwri9WFYFUZ64ziV6YfCv9NFj8Y/tbM/cCyOK6vMKvGsXRV9fYk1yX5kSTvTPLo7jbHlpVjrDOKqjo9ySOS3Njd++buB5bFcX21mRrHdtjZ3b+QxfzalyR50NwNwZIY66y0qvrWqvqjJC/L4jfk/7aqPlhV3zJza7AsjusrzNQ4tsOBqjoxyRenLyXbNXdDsCTGOqvuFUku7u5PHSxU1WOT/PssfmMOq8ZxfYWZGsfSVdWO7j5QVQ9Icm6SD3f3F+fuC7aasc6qq6o/7O4fPNI6HO8c11ebM0IsTVX9+IbtjU+dm+R1294QLImxzkBO2mQaXCU5aY5mYFkc18cgCLFMt83dAGwTY51RXJfkF+6iDqvEcX0AghDL9Ikkt3f3nx0sVNXZsUgHq8dYZwjd/fy5e4Bt4rg+AH+ZLNO/S/KXh9Q+neTXZugFlslYB1gtjusDEIRYpvt0960bC9Nj445VY6wDrBbH9QH4y2SZbqmqx2wsVNW3Jbllpn5gWYx1hlJVj6qq11TVm6vqhKr6ibl7gi3muD4Ay2ezNFV1RpLfTXJDkhuzWHv/4Ul+ors/N19nsLWMdUZTVR9I8sIkr+7up1fVld39jLn7gq3iuD4GiyWwNN392STfX1WPSnJqkhu7+/PzdgVbz1hnQCd093UblhU2w4SV4rg+BmeEAIB7pKp+IYvfkD8tyfuS3NTdL5+1KYB7SBACAO6xqvr2JI9Ncl13Xzt3PwD3lFPZAMA9UlX3T/KYJN+c5MlV9eMztwRLUVU/f8jjPXP1wtZzjRBLU1UfTNJJHpjk0Vmsx/9tST7b3U+eszfYSsY6A3pfkvdkcRE5rJyqenCShyV5dlW9ayrvSPKcJJfO1hhbShBiabr7+5Kkqt6c5Hu7+9aqekAWq7DAyjDWGdBXu/tX524Cluh7kzwzyRlJXpKkknw9yWtm7IktJgixHf5+kgcnuTXJg7L4TTmsImOdUbxm+i35J7I4G5rufum8LcHW6e53J3l3VZ3e3V+oxRKJT07y8ZlbYwsJQmyHn0xyyXSa+X8nuXjmfmBZfirGOmN4SZJfy+I7VmCVvT7JM5K8LMkpSU5L8o/nbIitIwixVNNvUHZ39z+ZuxdYtu7+aBJjnRHs7e63zN0EbIMTpvszuvt5VfUns3bDlhKEWKru7qq6f1Wd2t0uqmWlTd+t8sNJvnqw1t1Pn68jWJqTq+qaJJ+aHnd3XzBnQ7Akn62q9ye5rKp2JLlt7obYOr5HiKWrqquTnJzkpizmknd3f8+8XcHWq6o/7u7vnrsPWLaqevShte7+3By9wLJMs1ouTPKm7j5QVfdJclJ3f2Xm1tgighDAFqmq30zywdzxAvK/mrUpWILpB8SnJ3lkFqtppbvfMGtTsARVdXl3/7O5+2A5BCGWrqpOy2LBhI3/w/Tle6ycqvq9Q0ptrLOKqurtSa5L8iNJ3pnk0d39vHm7gq1XVVcl2Znkz/P/Z7WYBroiXCPEdnhzFqut/HKSl2ZxDQWsnO5+flWdkMXKQjd3t7nkrKqd3f2cqvru7n5JVV0xd0OwJM+fuwGW5z5zN8AQbu/uK5Mc6O4PJPmOuRuCZaiqf5HkT5L8epL/WlU/NnNLsCwHqurEJF+sqpcm2TV3Q7CVquqHps1nbHJjRTgjxHb486q6X5KrquqDSf5m7oZgSV6Y5Luni2rvm+RDSd40c0+wDOdN43xPknOTvHbuhmCLHfwZ+bYspsTVjL2wJK4RYltV1UO7+0tz9wHLMH2/xHOnbyF/VJK3dPdT5+4LtkpVPTHJ9d19a1WdkuTnsphd8qru9uWqrIyquurg1x9U1Wu7+6K5e2LrOSPE0kwXjt8paVeVxRJYVT+d5Heq6iFJvpzkBTP3A1vtt5McXCL+TVmcCdqf5PeyODMEq+jMuRtgOQQhlumXpvt/leS/J7kmyROS+A05q+qz3X3+wQdV9cA5m4El+Hp3315VD0vyiO5+W5JU1Utm7gu22rdU1cuymBJ3cDtJ0t0vna8ttpIgxNIc/HK9qjq7u392Kv9lVf30jG3BMr0zyfcf5jEc726tqucn+a4kb0+SqtqR5Jtm7Qq23oUbtj8wWxcslSDEdvijqnpPko9nsWLch2buB5blfnfzGI53z01yQZKrk7xxqj0yyctn6wiWoLv/aO4eWD6LJbAtquoRSR6V5HPdfdPc/cAyVNUvZzGX/L9l8Rvzz3W3KUMAcAwShFi6qvqOLE4xPzjT8pMWS2BVVdXZSc5K8pfd/edz9wMAbM7UOLbDZVmsnvXFuRuBZZqWzP6pJCcluaCqfqK7f3fmtgCATdxn7gYYwseTfLS7P3fwNndDsCSvS/KbSU7t7tuyuJ4CADgGOSPEdviHST5fVXunx93d3zNnQ7AkJ3T3dVXf+AJyv2wCgGOUIMTSdffZc/cA2+SqqnpNkkdW1W8lef/cDQEAm7NYAktXVd+c5J9nscTqwcUSXnbYneA4VVXfnuSxSa7r7mvn7gcA2JxpG2yHd0z3z0pyQ5JdM/YCW66qnlhVD5ge3pzkiUl+tKoeOWNbAMBhCEJshxO7+7VJbplW0Dp97oZgi/12kq9O229K8rEkf5jk92brCAA4LEGI7XBzVd0vybVV9boslhaGVfL17r69qh6W5BHd/bbuvirJfeduDADYnMUS2A4/3d3/t6p+JsnZWUwdglVya1U9P8l3JXl7klTVjiTfNGtXAMBdckaI7fD2JOnu27v7T5P8xsz9wFZ7bhah5+okvzLVHpnk5bN1BAAcllXjWJqqelaSf5rkB7K4XiJZnIV8aHefN1tjAAAMTxBiaarqQUkenGRPkkun8t8l+Z/dfftsjQEAMDxBiKWpqicmub67b62qU5L8yyy+R+hV3X3DvN0BADAy1wixTJYUBgDgmCQIsUyWFAYA4Jhk+WyWyZLCAAAck1wjxNJU1UlJLkjyt0ne2N0HqupRSc7u7ivm7Q4AgJEJQgAAwHBcIwQAAAxHEAIAAIYjCAEAAMMRhAAAgOH8PxdjbWIzOmW3AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1008x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(14, 6))\n",
    "plt.bar(df['ship_mode'],df['sales'],color = 'b')\n",
    "plt.xticks(rotation='vertical', size=8)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Kargo süreçlerinin kategori bazlı satışa etkisi - Visualize the 'Category' column from the Shipmode column dataset standpoints."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7fb0a2a31640>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x216 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "category_hist = sns.FacetGrid(df, col='ship_mode', palette='rainbow')\n",
    "category_hist.map(plt.hist, 'category')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h4> Hangi ürün pazarı daha çok satış yapıyor? What market sold the most products?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fb0bc618640>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style('whitegrid')\n",
    "sns.countplot(x='market',data=df, palette='rainbow')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h4> En çok satış yapan ilk 10 ülke hangileridir ? - Which are the Top 10 country by sales? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>sales</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>United States</th>\n",
       "      <td>2.297201e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Australia</th>\n",
       "      <td>9.252359e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>France</th>\n",
       "      <td>8.589311e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>China</th>\n",
       "      <td>7.005620e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Germany</th>\n",
       "      <td>6.288400e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mexico</th>\n",
       "      <td>6.225906e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>India</th>\n",
       "      <td>5.896501e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United Kingdom</th>\n",
       "      <td>5.285763e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Indonesia</th>\n",
       "      <td>4.048875e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brazil</th>\n",
       "      <td>3.611064e+05</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       sales\n",
       "country                     \n",
       "United States   2.297201e+06\n",
       "Australia       9.252359e+05\n",
       "France          8.589311e+05\n",
       "China           7.005620e+05\n",
       "Germany         6.288400e+05\n",
       "Mexico          6.225906e+05\n",
       "India           5.896501e+05\n",
       "United Kingdom  5.285763e+05\n",
       "Indonesia       4.048875e+05\n",
       "Brazil          3.611064e+05"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "countries = pd.DataFrame(df.groupby('country').sum()['sales'])\n",
    "countries.sort_values('sales',inplace=True, ascending=False)\n",
    "countries.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h4> En çok satış yapan 10 ülkeyi içeren bir pasta grafik oluşturmak - Create a pie chart with the 10 countries that have the most sales"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "countries = countries.sort_values(by = 'sales',ascending = False)[1:11]\n",
    "countries['sales'].plot(kind='pie',autopct='%1.1f%%',figsize=(14,7))\n",
    "plt.title('10 Countries with most sales')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h4> Ortalama olarak en fazla kargo ücreti ödeyen ilk 10 ülkeler - Which are the average shipping cost for top 10 different countries?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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>shipping_cost</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Taiwan</th>\n",
       "      <td>155.660714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chad</th>\n",
       "      <td>148.970000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lesotho</th>\n",
       "      <td>135.650000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Montenegro</th>\n",
       "      <td>93.937500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Slovenia</th>\n",
       "      <td>61.220000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Republic of the Congo</th>\n",
       "      <td>59.303333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Central African Republic</th>\n",
       "      <td>57.625714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Namibia</th>\n",
       "      <td>50.370000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bangladesh</th>\n",
       "      <td>46.402883</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Estonia</th>\n",
       "      <td>46.070000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          shipping_cost\n",
       "country                                \n",
       "Taiwan                       155.660714\n",
       "Chad                         148.970000\n",
       "Lesotho                      135.650000\n",
       "Montenegro                    93.937500\n",
       "Slovenia                      61.220000\n",
       "Republic of the Congo         59.303333\n",
       "Central African Republic      57.625714\n",
       "Namibia                       50.370000\n",
       "Bangladesh                    46.402883\n",
       "Estonia                       46.070000"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('country').agg({'shipping_cost':'mean'}).sort_values('shipping_cost', ascending=False).head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### En çok kar getiren ilk 10 müşteri kimlerdir ? Who are the top-10 most profitable customers?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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>order_id</th>\n",
       "      <th>customer_name</th>\n",
       "      <th>profit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>29530</th>\n",
       "      <td>CA-2013-118689</td>\n",
       "      <td>Tamara Chand</td>\n",
       "      <td>8399.9760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36239</th>\n",
       "      <td>CA-2014-140151</td>\n",
       "      <td>Raymond Buch</td>\n",
       "      <td>6719.9808</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48097</th>\n",
       "      <td>CA-2014-166709</td>\n",
       "      <td>Hunter Lopez</td>\n",
       "      <td>5039.9856</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33011</th>\n",
       "      <td>CA-2013-117121</td>\n",
       "      <td>Adrian Barton</td>\n",
       "      <td>4946.3700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5522</th>\n",
       "      <td>CA-2011-116904</td>\n",
       "      <td>Sanjit Chand</td>\n",
       "      <td>4630.4755</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43831</th>\n",
       "      <td>ES-2014-1651774</td>\n",
       "      <td>Patrick Jones</td>\n",
       "      <td>3979.0800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46525</th>\n",
       "      <td>CA-2014-127180</td>\n",
       "      <td>Tom Ashbrook</td>\n",
       "      <td>3919.9888</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10265</th>\n",
       "      <td>CA-2012-145352</td>\n",
       "      <td>Christopher Martinez</td>\n",
       "      <td>3177.4750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>548</th>\n",
       "      <td>ID-2011-64599</td>\n",
       "      <td>Carol Adams</td>\n",
       "      <td>2939.3100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24420</th>\n",
       "      <td>IN-2013-50809</td>\n",
       "      <td>Cynthia Arntzen</td>\n",
       "      <td>2817.9900</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              order_id         customer_name     profit\n",
       "29530   CA-2013-118689          Tamara Chand  8399.9760\n",
       "36239   CA-2014-140151          Raymond Buch  6719.9808\n",
       "48097   CA-2014-166709          Hunter Lopez  5039.9856\n",
       "33011   CA-2013-117121         Adrian Barton  4946.3700\n",
       "5522    CA-2011-116904          Sanjit Chand  4630.4755\n",
       "43831  ES-2014-1651774         Patrick Jones  3979.0800\n",
       "46525   CA-2014-127180          Tom Ashbrook  3919.9888\n",
       "10265   CA-2012-145352  Christopher Martinez  3177.4750\n",
       "548      ID-2011-64599           Carol Adams  2939.3100\n",
       "24420    IN-2013-50809       Cynthia Arntzen  2817.9900"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data10 = df.sort_values('profit',ascending=False).head(10)[['order_id','customer_name','profit']]\n",
    "data10"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h4> Sipariş tarihininden ay sutunu oluşturma - Add a ne column (month) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\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>order_id</th>\n",
       "      <th>order_date</th>\n",
       "      <th>ship_date</th>\n",
       "      <th>ship_mode</th>\n",
       "      <th>customer_name</th>\n",
       "      <th>segment</th>\n",
       "      <th>state</th>\n",
       "      <th>country</th>\n",
       "      <th>market</th>\n",
       "      <th>region</th>\n",
       "      <th>...</th>\n",
       "      <th>sub_category</th>\n",
       "      <th>product_name</th>\n",
       "      <th>sales</th>\n",
       "      <th>quantity</th>\n",
       "      <th>discount</th>\n",
       "      <th>profit</th>\n",
       "      <th>shipping_cost</th>\n",
       "      <th>order_priority</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AG-2011-2040</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>2011-01-06</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>Toby Braunhardt</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>Constantine</td>\n",
       "      <td>Algeria</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Africa</td>\n",
       "      <td>...</td>\n",
       "      <td>Storage</td>\n",
       "      <td>Tenex Lockers, Blue</td>\n",
       "      <td>408.300</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>106.1400</td>\n",
       "      <td>35.460</td>\n",
       "      <td>Medium</td>\n",
       "      <td>2011</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>IN-2011-47883</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>2011-01-08</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>Joseph Holt</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>New South Wales</td>\n",
       "      <td>Australia</td>\n",
       "      <td>APAC</td>\n",
       "      <td>Oceania</td>\n",
       "      <td>...</td>\n",
       "      <td>Supplies</td>\n",
       "      <td>Acme Trimmer, High Speed</td>\n",
       "      <td>120.366</td>\n",
       "      <td>3</td>\n",
       "      <td>0.1</td>\n",
       "      <td>36.0360</td>\n",
       "      <td>9.720</td>\n",
       "      <td>Medium</td>\n",
       "      <td>2011</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>HU-2011-1220</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>2011-01-05</td>\n",
       "      <td>Second Class</td>\n",
       "      <td>Annie Thurman</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>Budapest</td>\n",
       "      <td>Hungary</td>\n",
       "      <td>EMEA</td>\n",
       "      <td>EMEA</td>\n",
       "      <td>...</td>\n",
       "      <td>Storage</td>\n",
       "      <td>Tenex Box, Single Width</td>\n",
       "      <td>66.120</td>\n",
       "      <td>4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>29.6400</td>\n",
       "      <td>8.170</td>\n",
       "      <td>High</td>\n",
       "      <td>2011</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>IT-2011-3647632</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>2011-01-05</td>\n",
       "      <td>Second Class</td>\n",
       "      <td>Eugene Moren</td>\n",
       "      <td>Home Office</td>\n",
       "      <td>Stockholm</td>\n",
       "      <td>Sweden</td>\n",
       "      <td>EU</td>\n",
       "      <td>North</td>\n",
       "      <td>...</td>\n",
       "      <td>Paper</td>\n",
       "      <td>Enermax Note Cards, Premium</td>\n",
       "      <td>44.865</td>\n",
       "      <td>3</td>\n",
       "      <td>0.5</td>\n",
       "      <td>-26.0550</td>\n",
       "      <td>4.820</td>\n",
       "      <td>High</td>\n",
       "      <td>2011</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>IN-2011-47883</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>2011-01-08</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>Joseph Holt</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>New South Wales</td>\n",
       "      <td>Australia</td>\n",
       "      <td>APAC</td>\n",
       "      <td>Oceania</td>\n",
       "      <td>...</td>\n",
       "      <td>Furnishings</td>\n",
       "      <td>Eldon Light Bulb, Duo Pack</td>\n",
       "      <td>113.670</td>\n",
       "      <td>5</td>\n",
       "      <td>0.1</td>\n",
       "      <td>37.7700</td>\n",
       "      <td>4.700</td>\n",
       "      <td>Medium</td>\n",
       "      <td>2011</td>\n",
       "      <td>1</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",
       "      <td>...</td>\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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51285</th>\n",
       "      <td>CA-2014-115427</td>\n",
       "      <td>2014-12-31</td>\n",
       "      <td>2015-01-04</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>Erica Bern</td>\n",
       "      <td>Corporate</td>\n",
       "      <td>California</td>\n",
       "      <td>United States</td>\n",
       "      <td>US</td>\n",
       "      <td>West</td>\n",
       "      <td>...</td>\n",
       "      <td>Binders</td>\n",
       "      <td>Cardinal Slant-D Ring Binder, Heavy Gauge Vinyl</td>\n",
       "      <td>13.904</td>\n",
       "      <td>2</td>\n",
       "      <td>0.2</td>\n",
       "      <td>4.5188</td>\n",
       "      <td>0.890</td>\n",
       "      <td>Medium</td>\n",
       "      <td>2014</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51286</th>\n",
       "      <td>MO-2014-2560</td>\n",
       "      <td>2014-12-31</td>\n",
       "      <td>2015-01-05</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>Liz Preis</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>Souss-Massa-Draâ</td>\n",
       "      <td>Morocco</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Africa</td>\n",
       "      <td>...</td>\n",
       "      <td>Binders</td>\n",
       "      <td>Wilson Jones Hole Reinforcements, Clear</td>\n",
       "      <td>3.990</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.4200</td>\n",
       "      <td>0.490</td>\n",
       "      <td>Medium</td>\n",
       "      <td>2014</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51287</th>\n",
       "      <td>MX-2014-110527</td>\n",
       "      <td>2014-12-31</td>\n",
       "      <td>2015-01-02</td>\n",
       "      <td>Second Class</td>\n",
       "      <td>Charlotte Melton</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>Managua</td>\n",
       "      <td>Nicaragua</td>\n",
       "      <td>LATAM</td>\n",
       "      <td>Central</td>\n",
       "      <td>...</td>\n",
       "      <td>Labels</td>\n",
       "      <td>Hon Color Coded Labels, 5000 Label Set</td>\n",
       "      <td>26.400</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12.3600</td>\n",
       "      <td>0.350</td>\n",
       "      <td>Medium</td>\n",
       "      <td>2014</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51288</th>\n",
       "      <td>MX-2014-114783</td>\n",
       "      <td>2014-12-31</td>\n",
       "      <td>2015-01-06</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>Tamara Dahlen</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>Chihuahua</td>\n",
       "      <td>Mexico</td>\n",
       "      <td>LATAM</td>\n",
       "      <td>North</td>\n",
       "      <td>...</td>\n",
       "      <td>Labels</td>\n",
       "      <td>Hon Legal Exhibit Labels, Alphabetical</td>\n",
       "      <td>7.120</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5600</td>\n",
       "      <td>0.199</td>\n",
       "      <td>Medium</td>\n",
       "      <td>2014</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51289</th>\n",
       "      <td>CA-2014-156720</td>\n",
       "      <td>2014-12-31</td>\n",
       "      <td>2015-01-04</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>Jill Matthias</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>Colorado</td>\n",
       "      <td>United States</td>\n",
       "      <td>US</td>\n",
       "      <td>West</td>\n",
       "      <td>...</td>\n",
       "      <td>Fasteners</td>\n",
       "      <td>Bagged Rubber Bands</td>\n",
       "      <td>3.024</td>\n",
       "      <td>3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>-0.6048</td>\n",
       "      <td>0.170</td>\n",
       "      <td>Medium</td>\n",
       "      <td>2014</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>51290 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              order_id order_date  ship_date       ship_mode  \\\n",
       "0         AG-2011-2040 2011-01-01 2011-01-06  Standard Class   \n",
       "1        IN-2011-47883 2011-01-01 2011-01-08  Standard Class   \n",
       "2         HU-2011-1220 2011-01-01 2011-01-05    Second Class   \n",
       "3      IT-2011-3647632 2011-01-01 2011-01-05    Second Class   \n",
       "4        IN-2011-47883 2011-01-01 2011-01-08  Standard Class   \n",
       "...                ...        ...        ...             ...   \n",
       "51285   CA-2014-115427 2014-12-31 2015-01-04  Standard Class   \n",
       "51286     MO-2014-2560 2014-12-31 2015-01-05  Standard Class   \n",
       "51287   MX-2014-110527 2014-12-31 2015-01-02    Second Class   \n",
       "51288   MX-2014-114783 2014-12-31 2015-01-06  Standard Class   \n",
       "51289   CA-2014-156720 2014-12-31 2015-01-04  Standard Class   \n",
       "\n",
       "          customer_name      segment             state        country  market  \\\n",
       "0       Toby Braunhardt     Consumer       Constantine        Algeria  Africa   \n",
       "1           Joseph Holt     Consumer   New South Wales      Australia    APAC   \n",
       "2         Annie Thurman     Consumer          Budapest        Hungary    EMEA   \n",
       "3          Eugene Moren  Home Office         Stockholm         Sweden      EU   \n",
       "4           Joseph Holt     Consumer   New South Wales      Australia    APAC   \n",
       "...                 ...          ...               ...            ...     ...   \n",
       "51285        Erica Bern    Corporate        California  United States      US   \n",
       "51286         Liz Preis     Consumer  Souss-Massa-Draâ        Morocco  Africa   \n",
       "51287  Charlotte Melton     Consumer           Managua      Nicaragua   LATAM   \n",
       "51288     Tamara Dahlen     Consumer         Chihuahua         Mexico   LATAM   \n",
       "51289     Jill Matthias     Consumer          Colorado  United States      US   \n",
       "\n",
       "        region  ... sub_category  \\\n",
       "0       Africa  ...      Storage   \n",
       "1      Oceania  ...     Supplies   \n",
       "2         EMEA  ...      Storage   \n",
       "3        North  ...        Paper   \n",
       "4      Oceania  ...  Furnishings   \n",
       "...        ...  ...          ...   \n",
       "51285     West  ...      Binders   \n",
       "51286   Africa  ...      Binders   \n",
       "51287  Central  ...       Labels   \n",
       "51288    North  ...       Labels   \n",
       "51289     West  ...    Fasteners   \n",
       "\n",
       "                                          product_name    sales quantity  \\\n",
       "0                                  Tenex Lockers, Blue  408.300        2   \n",
       "1                             Acme Trimmer, High Speed  120.366        3   \n",
       "2                              Tenex Box, Single Width   66.120        4   \n",
       "3                          Enermax Note Cards, Premium   44.865        3   \n",
       "4                           Eldon Light Bulb, Duo Pack  113.670        5   \n",
       "...                                                ...      ...      ...   \n",
       "51285  Cardinal Slant-D Ring Binder, Heavy Gauge Vinyl   13.904        2   \n",
       "51286          Wilson Jones Hole Reinforcements, Clear    3.990        1   \n",
       "51287           Hon Color Coded Labels, 5000 Label Set   26.400        3   \n",
       "51288           Hon Legal Exhibit Labels, Alphabetical    7.120        1   \n",
       "51289                              Bagged Rubber Bands    3.024        3   \n",
       "\n",
       "       discount    profit  shipping_cost  order_priority  year month  \n",
       "0           0.0  106.1400         35.460          Medium  2011     1  \n",
       "1           0.1   36.0360          9.720          Medium  2011     1  \n",
       "2           0.0   29.6400          8.170            High  2011     1  \n",
       "3           0.5  -26.0550          4.820            High  2011     1  \n",
       "4           0.1   37.7700          4.700          Medium  2011     1  \n",
       "...         ...       ...            ...             ...   ...   ...  \n",
       "51285       0.2    4.5188          0.890          Medium  2014    12  \n",
       "51286       0.0    0.4200          0.490          Medium  2014    12  \n",
       "51287       0.0   12.3600          0.350          Medium  2014    12  \n",
       "51288       0.0    0.5600          0.199          Medium  2014    12  \n",
       "51289       0.2   -0.6048          0.170          Medium  2014    12  \n",
       "\n",
       "[51290 rows x 22 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "month = df['order_date'].astype(str) \n",
    "month = month.apply(lambda x:datetime.strptime(x,'%Y-%m-%d'))\n",
    "df['month'] = month.map(lambda x: x.month) \n",
    "df\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Yıl ve aylara göre toplam satış değerleri - Total sales values by year and month "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>sales</th>\n",
       "      <th>quantity</th>\n",
       "      <th>discount</th>\n",
       "      <th>profit</th>\n",
       "      <th>shipping_cost</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th>month</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 rowspan=\"12\" valign=\"top\">2011</th>\n",
       "      <th>1</th>\n",
       "      <td>98898.48886</td>\n",
       "      <td>1463</td>\n",
       "      <td>68.758</td>\n",
       "      <td>8321.80096</td>\n",
       "      <td>10544.78800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>91152.15698</td>\n",
       "      <td>1224</td>\n",
       "      <td>52.252</td>\n",
       "      <td>12417.90698</td>\n",
       "      <td>10681.16300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>145729.36736</td>\n",
       "      <td>1836</td>\n",
       "      <td>74.212</td>\n",
       "      <td>15303.56826</td>\n",
       "      <td>13096.18550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>116915.76418</td>\n",
       "      <td>2020</td>\n",
       "      <td>80.782</td>\n",
       "      <td>12902.32438</td>\n",
       "      <td>12954.52000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>146747.83610</td>\n",
       "      <td>2013</td>\n",
       "      <td>82.382</td>\n",
       "      <td>12183.82870</td>\n",
       "      <td>16443.20600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>215207.38022</td>\n",
       "      <td>3112</td>\n",
       "      <td>159.534</td>\n",
       "      <td>23415.24702</td>\n",
       "      <td>23813.10900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>115510.41912</td>\n",
       "      <td>1774</td>\n",
       "      <td>80.086</td>\n",
       "      <td>5585.00352</td>\n",
       "      <td>11844.47600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>207581.49122</td>\n",
       "      <td>3035</td>\n",
       "      <td>121.462</td>\n",
       "      <td>23713.66772</td>\n",
       "      <td>22001.13600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>290214.45534</td>\n",
       "      <td>3707</td>\n",
       "      <td>137.678</td>\n",
       "      <td>35776.88394</td>\n",
       "      <td>29664.85100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>199071.26404</td>\n",
       "      <td>2727</td>\n",
       "      <td>110.192</td>\n",
       "      <td>25963.41834</td>\n",
       "      <td>21380.08200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>298496.53752</td>\n",
       "      <td>4039</td>\n",
       "      <td>178.836</td>\n",
       "      <td>32709.17772</td>\n",
       "      <td>34701.99800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>333925.73460</td>\n",
       "      <td>4493</td>\n",
       "      <td>187.220</td>\n",
       "      <td>40647.98400</td>\n",
       "      <td>37144.83100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"12\" valign=\"top\">2012</th>\n",
       "      <th>1</th>\n",
       "      <td>135780.72024</td>\n",
       "      <td>1845</td>\n",
       "      <td>74.454</td>\n",
       "      <td>10401.63764</td>\n",
       "      <td>13665.74900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>100510.21698</td>\n",
       "      <td>1473</td>\n",
       "      <td>62.784</td>\n",
       "      <td>15000.09618</td>\n",
       "      <td>11393.72600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>163076.77116</td>\n",
       "      <td>2237</td>\n",
       "      <td>101.682</td>\n",
       "      <td>17992.91756</td>\n",
       "      <td>16170.78500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>161052.26952</td>\n",
       "      <td>2250</td>\n",
       "      <td>93.248</td>\n",
       "      <td>17366.96722</td>\n",
       "      <td>16767.86200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>208364.89124</td>\n",
       "      <td>2921</td>\n",
       "      <td>114.272</td>\n",
       "      <td>29876.70374</td>\n",
       "      <td>23801.61700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>256175.69842</td>\n",
       "      <td>3671</td>\n",
       "      <td>168.284</td>\n",
       "      <td>34407.15362</td>\n",
       "      <td>28155.90000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>145236.78512</td>\n",
       "      <td>2321</td>\n",
       "      <td>104.404</td>\n",
       "      <td>15585.38842</td>\n",
       "      <td>17334.43500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>303142.94238</td>\n",
       "      <td>3818</td>\n",
       "      <td>136.166</td>\n",
       "      <td>43573.87858</td>\n",
       "      <td>32038.73000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>289389.16564</td>\n",
       "      <td>4205</td>\n",
       "      <td>169.070</td>\n",
       "      <td>27776.18034</td>\n",
       "      <td>28023.17800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>252939.85020</td>\n",
       "      <td>3563</td>\n",
       "      <td>135.866</td>\n",
       "      <td>30662.88270</td>\n",
       "      <td>25085.74000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>323512.41690</td>\n",
       "      <td>5193</td>\n",
       "      <td>215.868</td>\n",
       "      <td>31820.72180</td>\n",
       "      <td>33489.74100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>338256.96660</td>\n",
       "      <td>4614</td>\n",
       "      <td>172.676</td>\n",
       "      <td>32950.75130</td>\n",
       "      <td>37563.36100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"12\" valign=\"top\">2013</th>\n",
       "      <th>1</th>\n",
       "      <td>199185.90738</td>\n",
       "      <td>2413</td>\n",
       "      <td>91.442</td>\n",
       "      <td>26810.55968</td>\n",
       "      <td>21677.43200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>167239.65040</td>\n",
       "      <td>2102</td>\n",
       "      <td>78.012</td>\n",
       "      <td>25340.02610</td>\n",
       "      <td>16911.85000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>198594.03012</td>\n",
       "      <td>2686</td>\n",
       "      <td>114.384</td>\n",
       "      <td>23433.77462</td>\n",
       "      <td>21268.01000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>177821.31684</td>\n",
       "      <td>2688</td>\n",
       "      <td>116.116</td>\n",
       "      <td>19462.03844</td>\n",
       "      <td>19133.23400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>260498.56470</td>\n",
       "      <td>3808</td>\n",
       "      <td>153.092</td>\n",
       "      <td>28495.69410</td>\n",
       "      <td>28315.21100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>396519.61190</td>\n",
       "      <td>5327</td>\n",
       "      <td>213.642</td>\n",
       "      <td>45478.41340</td>\n",
       "      <td>42814.02600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>229928.95200</td>\n",
       "      <td>3252</td>\n",
       "      <td>125.644</td>\n",
       "      <td>28863.82720</td>\n",
       "      <td>24501.84236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>326488.78936</td>\n",
       "      <td>4934</td>\n",
       "      <td>202.640</td>\n",
       "      <td>31023.66846</td>\n",
       "      <td>35673.08800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>376619.24568</td>\n",
       "      <td>5793</td>\n",
       "      <td>240.674</td>\n",
       "      <td>38905.66778</td>\n",
       "      <td>38488.40000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>293406.64288</td>\n",
       "      <td>3883</td>\n",
       "      <td>160.860</td>\n",
       "      <td>42433.22258</td>\n",
       "      <td>31174.68400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>373989.36010</td>\n",
       "      <td>5556</td>\n",
       "      <td>215.324</td>\n",
       "      <td>48062.99670</td>\n",
       "      <td>41407.16700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>405454.37802</td>\n",
       "      <td>5694</td>\n",
       "      <td>223.692</td>\n",
       "      <td>50202.87112</td>\n",
       "      <td>43183.80000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"12\" valign=\"top\">2014</th>\n",
       "      <th>1</th>\n",
       "      <td>241268.55566</td>\n",
       "      <td>3122</td>\n",
       "      <td>127.928</td>\n",
       "      <td>28001.38626</td>\n",
       "      <td>24870.80100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>184837.35556</td>\n",
       "      <td>2482</td>\n",
       "      <td>111.126</td>\n",
       "      <td>19751.69996</td>\n",
       "      <td>19525.80000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>263100.77262</td>\n",
       "      <td>3722</td>\n",
       "      <td>142.016</td>\n",
       "      <td>37357.26052</td>\n",
       "      <td>26838.63554</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>242771.86130</td>\n",
       "      <td>3594</td>\n",
       "      <td>164.000</td>\n",
       "      <td>23782.30120</td>\n",
       "      <td>26272.71800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>288401.04614</td>\n",
       "      <td>4300</td>\n",
       "      <td>188.986</td>\n",
       "      <td>33953.55774</td>\n",
       "      <td>31882.58300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>401814.06310</td>\n",
       "      <td>6009</td>\n",
       "      <td>251.462</td>\n",
       "      <td>43778.60280</td>\n",
       "      <td>41894.07600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>258705.68048</td>\n",
       "      <td>3637</td>\n",
       "      <td>163.512</td>\n",
       "      <td>28035.87258</td>\n",
       "      <td>29581.73300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>456619.94236</td>\n",
       "      <td>5824</td>\n",
       "      <td>217.672</td>\n",
       "      <td>53542.89496</td>\n",
       "      <td>46759.35300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>481157.24370</td>\n",
       "      <td>6837</td>\n",
       "      <td>272.094</td>\n",
       "      <td>67979.45110</td>\n",
       "      <td>53485.43000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>422766.62916</td>\n",
       "      <td>5876</td>\n",
       "      <td>233.752</td>\n",
       "      <td>58209.83476</td>\n",
       "      <td>44622.41400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>555279.02700</td>\n",
       "      <td>7706</td>\n",
       "      <td>304.384</td>\n",
       "      <td>62856.58790</td>\n",
       "      <td>59918.35500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>503143.69348</td>\n",
       "      <td>7513</td>\n",
       "      <td>335.106</td>\n",
       "      <td>46916.52068</td>\n",
       "      <td>54853.89100</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   sales  quantity  discount       profit  shipping_cost\n",
       "year month                                                              \n",
       "2011 1       98898.48886      1463    68.758   8321.80096    10544.78800\n",
       "     2       91152.15698      1224    52.252  12417.90698    10681.16300\n",
       "     3      145729.36736      1836    74.212  15303.56826    13096.18550\n",
       "     4      116915.76418      2020    80.782  12902.32438    12954.52000\n",
       "     5      146747.83610      2013    82.382  12183.82870    16443.20600\n",
       "     6      215207.38022      3112   159.534  23415.24702    23813.10900\n",
       "     7      115510.41912      1774    80.086   5585.00352    11844.47600\n",
       "     8      207581.49122      3035   121.462  23713.66772    22001.13600\n",
       "     9      290214.45534      3707   137.678  35776.88394    29664.85100\n",
       "     10     199071.26404      2727   110.192  25963.41834    21380.08200\n",
       "     11     298496.53752      4039   178.836  32709.17772    34701.99800\n",
       "     12     333925.73460      4493   187.220  40647.98400    37144.83100\n",
       "2012 1      135780.72024      1845    74.454  10401.63764    13665.74900\n",
       "     2      100510.21698      1473    62.784  15000.09618    11393.72600\n",
       "     3      163076.77116      2237   101.682  17992.91756    16170.78500\n",
       "     4      161052.26952      2250    93.248  17366.96722    16767.86200\n",
       "     5      208364.89124      2921   114.272  29876.70374    23801.61700\n",
       "     6      256175.69842      3671   168.284  34407.15362    28155.90000\n",
       "     7      145236.78512      2321   104.404  15585.38842    17334.43500\n",
       "     8      303142.94238      3818   136.166  43573.87858    32038.73000\n",
       "     9      289389.16564      4205   169.070  27776.18034    28023.17800\n",
       "     10     252939.85020      3563   135.866  30662.88270    25085.74000\n",
       "     11     323512.41690      5193   215.868  31820.72180    33489.74100\n",
       "     12     338256.96660      4614   172.676  32950.75130    37563.36100\n",
       "2013 1      199185.90738      2413    91.442  26810.55968    21677.43200\n",
       "     2      167239.65040      2102    78.012  25340.02610    16911.85000\n",
       "     3      198594.03012      2686   114.384  23433.77462    21268.01000\n",
       "     4      177821.31684      2688   116.116  19462.03844    19133.23400\n",
       "     5      260498.56470      3808   153.092  28495.69410    28315.21100\n",
       "     6      396519.61190      5327   213.642  45478.41340    42814.02600\n",
       "     7      229928.95200      3252   125.644  28863.82720    24501.84236\n",
       "     8      326488.78936      4934   202.640  31023.66846    35673.08800\n",
       "     9      376619.24568      5793   240.674  38905.66778    38488.40000\n",
       "     10     293406.64288      3883   160.860  42433.22258    31174.68400\n",
       "     11     373989.36010      5556   215.324  48062.99670    41407.16700\n",
       "     12     405454.37802      5694   223.692  50202.87112    43183.80000\n",
       "2014 1      241268.55566      3122   127.928  28001.38626    24870.80100\n",
       "     2      184837.35556      2482   111.126  19751.69996    19525.80000\n",
       "     3      263100.77262      3722   142.016  37357.26052    26838.63554\n",
       "     4      242771.86130      3594   164.000  23782.30120    26272.71800\n",
       "     5      288401.04614      4300   188.986  33953.55774    31882.58300\n",
       "     6      401814.06310      6009   251.462  43778.60280    41894.07600\n",
       "     7      258705.68048      3637   163.512  28035.87258    29581.73300\n",
       "     8      456619.94236      5824   217.672  53542.89496    46759.35300\n",
       "     9      481157.24370      6837   272.094  67979.45110    53485.43000\n",
       "     10     422766.62916      5876   233.752  58209.83476    44622.41400\n",
       "     11     555279.02700      7706   304.384  62856.58790    59918.35500\n",
       "     12     503143.69348      7513   335.106  46916.52068    54853.89100"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "date = df.groupby(['year','month']).sum()\n",
    "date"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Yıllara göre toplam satış grafiği - Total sales chart by the year "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([0, 1, 2, 3]), <a list of 4 Text major ticklabel objects>)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x='year',y='sales',data=date.reset_index())\n",
    "plt.xticks(rotation = 'vertical')"
   ]
  }
 ],
 "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": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}