kunskapskontroll_Lagerholm.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Del 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1. Beskriv kort hur en relationsdatabas fungerar\n",
    "\n",
    "En relationsdatabas där man kan lagra data i tabeller som är relaterade till varandra med hjälp av primärnycklar (som är unika för varje rad) och främmande nycklar (som skapar förhållanden mellan tabeller). På så sätt kan man skapa små tabeller och därmed reducera redundans och komplexiteten. Man använder SQL för att interagera med en sådan databas.\n",
    "\n",
    "#### 2. Vad menas med “CRUD”-flödet?\n",
    "\n",
    "CRUD står för Create (lägga till information), Read (läsa), Update (uppdatera) och Delete (radera). Det är de fyra grundläggande operationerna som man ska kunna utföra på data i en relationsdatabas.\n",
    "\n",
    "#### 3. Beskriv kort vad en “left join” och en “inner join” är. Varför använder man det?\n",
    "\n",
    "En left join hämtar alla rader från den första tabellen och alla rader från den andra tabellen som har ett matchande värde i den första tabellen. En inner join däremot hämtar BARA rader från den första tabellen som har ett matchande värde i den andra tabellen. Man använder det för att kunna kombinera data från två tabeller baserat på ett gemensamt fält.\n",
    "\n",
    "#### 4. Beskriv kort vad indexering i SQL innebär.\n",
    "\n",
    "Indexering används för att snabba upp sökningar i en db. Ett index skapas på en eller flera kolumner för att göra det enklare att hitta specifika rader utan att behöva söka igenom hela tabellen.\n",
    "\n",
    "#### 6. Beskriv kort vad en vy i SQL är.\n",
    "\n",
    "En vy i SQL är som en låtsastabell som visar data från en eller flera tabeller. Den gör det lättare att arbeta med data utan att ändra något i de riktiga tabellerna.\n",
    "\n",
    "#### 7. Beskriv kort vad en lagrad procedur i SQL är.\n",
    "En lagrad procedur i SQL är ett sparat program i SQL-kod som kan köras när som helst för att utföra en uppgift i DB.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "#!pip install sqlalchemy pandas tabulate matplotlib ipykernel jupyter notebook pyodbc scipy seaborn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pyodbc\n",
    "from sqlalchemy import create_engine\n",
    "\n",
    "# Create connection string\n",
    "conn_str = (\n",
    "    'DRIVER={SQL Server};'\n",
    "    'SERVER=OMEN;'\n",
    "    'DATABASE=AdventureWorks2022;'\n",
    "    'Trusted_Connection=yes;'\n",
    ")\n",
    "\n",
    "# Create SQLAlchemy engine\n",
    "engine = create_engine(f'mssql+pyodbc:///?odbc_connect={conn_str}')\n",
    "\n",
    "# Create pyodbc connection\n",
    "aw = pyodbc.connect(conn_str)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Först utforskar jag strukturen av db AdventureWorks2022\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "|    | TABLE_CATALOG      | TABLE_SCHEMA   | TABLE_NAME                            | TABLE_TYPE   |\n",
      "|---:|:-------------------|:---------------|:--------------------------------------|:-------------|\n",
      "|  0 | AdventureWorks2022 | Sales          | SalesTaxRate                          | BASE TABLE   |\n",
      "|  1 | AdventureWorks2022 | Sales          | PersonCreditCard                      | BASE TABLE   |\n",
      "|  2 | AdventureWorks2022 | Person         | vAdditionalContactInfo                | VIEW         |\n",
      "|  3 | AdventureWorks2022 | Person         | PersonPhone                           | BASE TABLE   |\n",
      "|  4 | AdventureWorks2022 | HumanResources | vEmployee                             | VIEW         |\n",
      "|  5 | AdventureWorks2022 | Sales          | SalesTerritory                        | BASE TABLE   |\n",
      "|  6 | AdventureWorks2022 | HumanResources | vEmployeeDepartment                   | VIEW         |\n",
      "|  7 | AdventureWorks2022 | Person         | PhoneNumberType                       | BASE TABLE   |\n",
      "|  8 | AdventureWorks2022 | HumanResources | vEmployeeDepartmentHistory            | VIEW         |\n",
      "|  9 | AdventureWorks2022 | Sales          | vIndividualCustomer                   | VIEW         |\n",
      "| 10 | AdventureWorks2022 | Production     | Product                               | BASE TABLE   |\n",
      "| 11 | AdventureWorks2022 | Sales          | vPersonDemographics                   | VIEW         |\n",
      "| 12 | AdventureWorks2022 | HumanResources | vJobCandidate                         | VIEW         |\n",
      "| 13 | AdventureWorks2022 | HumanResources | vJobCandidateEmployment               | VIEW         |\n",
      "| 14 | AdventureWorks2022 | HumanResources | vJobCandidateEducation                | VIEW         |\n",
      "| 15 | AdventureWorks2022 | Production     | vProductAndDescription                | VIEW         |\n",
      "| 16 | AdventureWorks2022 | Production     | vProductModelCatalogDescription       | VIEW         |\n",
      "| 17 | AdventureWorks2022 | Production     | vProductModelInstructions             | VIEW         |\n",
      "| 18 | AdventureWorks2022 | Sales          | vSalesPerson                          | VIEW         |\n",
      "| 19 | AdventureWorks2022 | Sales          | SalesTerritoryHistory                 | BASE TABLE   |\n",
      "| 20 | AdventureWorks2022 | Sales          | vSalesPersonSalesByFiscalYears        | VIEW         |\n",
      "| 21 | AdventureWorks2022 | Person         | vStateProvinceCountryRegion           | VIEW         |\n",
      "| 22 | AdventureWorks2022 | Sales          | vStoreWithDemographics                | VIEW         |\n",
      "| 23 | AdventureWorks2022 | Sales          | vStoreWithContacts                    | VIEW         |\n",
      "| 24 | AdventureWorks2022 | Production     | ScrapReason                           | BASE TABLE   |\n",
      "| 25 | AdventureWorks2022 | Sales          | vStoreWithAddresses                   | VIEW         |\n",
      "| 26 | AdventureWorks2022 | Purchasing     | vVendorWithContacts                   | VIEW         |\n",
      "| 27 | AdventureWorks2022 | HumanResources | Shift                                 | BASE TABLE   |\n",
      "| 28 | AdventureWorks2022 | Purchasing     | vVendorWithAddresses                  | VIEW         |\n",
      "| 29 | AdventureWorks2022 | Production     | ProductCategory                       | BASE TABLE   |\n",
      "| 30 | AdventureWorks2022 | Purchasing     | ShipMethod                            | BASE TABLE   |\n",
      "| 31 | AdventureWorks2022 | Production     | ProductCostHistory                    | BASE TABLE   |\n",
      "| 32 | AdventureWorks2022 | Production     | ProductDescription                    | BASE TABLE   |\n",
      "| 33 | AdventureWorks2022 | Sales          | ShoppingCartItem                      | BASE TABLE   |\n",
      "| 34 | AdventureWorks2022 | Production     | ProductDocument                       | BASE TABLE   |\n",
      "| 35 | AdventureWorks2022 | dbo            | DatabaseLog                           | BASE TABLE   |\n",
      "| 36 | AdventureWorks2022 | Production     | ProductInventory                      | BASE TABLE   |\n",
      "| 37 | AdventureWorks2022 | Sales          | SpecialOffer                          | BASE TABLE   |\n",
      "| 38 | AdventureWorks2022 | dbo            | ErrorLog                              | BASE TABLE   |\n",
      "| 39 | AdventureWorks2022 | Production     | ProductListPriceHistory               | BASE TABLE   |\n",
      "| 40 | AdventureWorks2022 | Person         | Address                               | BASE TABLE   |\n",
      "| 41 | AdventureWorks2022 | Sales          | SpecialOfferProduct                   | BASE TABLE   |\n",
      "| 42 | AdventureWorks2022 | Production     | ProductModel                          | BASE TABLE   |\n",
      "| 43 | AdventureWorks2022 | Person         | AddressType                           | BASE TABLE   |\n",
      "| 44 | AdventureWorks2022 | Person         | StateProvince                         | BASE TABLE   |\n",
      "| 45 | AdventureWorks2022 | Production     | ProductModelIllustration              | BASE TABLE   |\n",
      "| 46 | AdventureWorks2022 | dbo            | AWBuildVersion                        | BASE TABLE   |\n",
      "| 47 | AdventureWorks2022 | Production     | ProductModelProductDescriptionCulture | BASE TABLE   |\n",
      "| 48 | AdventureWorks2022 | Production     | BillOfMaterials                       | BASE TABLE   |\n",
      "| 49 | AdventureWorks2022 | Sales          | Store                                 | BASE TABLE   |\n",
      "| 50 | AdventureWorks2022 | Production     | ProductPhoto                          | BASE TABLE   |\n",
      "| 51 | AdventureWorks2022 | Production     | ProductProductPhoto                   | BASE TABLE   |\n",
      "| 52 | AdventureWorks2022 | Production     | TransactionHistory                    | BASE TABLE   |\n",
      "| 53 | AdventureWorks2022 | Production     | ProductReview                         | BASE TABLE   |\n",
      "| 54 | AdventureWorks2022 | Person         | BusinessEntity                        | BASE TABLE   |\n",
      "| 55 | AdventureWorks2022 | Production     | TransactionHistoryArchive             | BASE TABLE   |\n",
      "| 56 | AdventureWorks2022 | Production     | ProductSubcategory                    | BASE TABLE   |\n",
      "| 57 | AdventureWorks2022 | Person         | BusinessEntityAddress                 | BASE TABLE   |\n",
      "| 58 | AdventureWorks2022 | Purchasing     | ProductVendor                         | BASE TABLE   |\n",
      "| 59 | AdventureWorks2022 | Person         | BusinessEntityContact                 | BASE TABLE   |\n",
      "| 60 | AdventureWorks2022 | Production     | UnitMeasure                           | BASE TABLE   |\n",
      "| 61 | AdventureWorks2022 | Purchasing     | Vendor                                | BASE TABLE   |\n",
      "| 62 | AdventureWorks2022 | Person         | ContactType                           | BASE TABLE   |\n",
      "| 63 | AdventureWorks2022 | Sales          | CountryRegionCurrency                 | BASE TABLE   |\n",
      "| 64 | AdventureWorks2022 | Person         | CountryRegion                         | BASE TABLE   |\n",
      "| 65 | AdventureWorks2022 | Production     | WorkOrder                             | BASE TABLE   |\n",
      "| 66 | AdventureWorks2022 | Purchasing     | PurchaseOrderDetail                   | BASE TABLE   |\n",
      "| 67 | AdventureWorks2022 | Sales          | CreditCard                            | BASE TABLE   |\n",
      "| 68 | AdventureWorks2022 | Production     | Culture                               | BASE TABLE   |\n",
      "| 69 | AdventureWorks2022 | Production     | WorkOrderRouting                      | BASE TABLE   |\n",
      "| 70 | AdventureWorks2022 | Sales          | Currency                              | BASE TABLE   |\n",
      "| 71 | AdventureWorks2022 | Purchasing     | PurchaseOrderHeader                   | BASE TABLE   |\n",
      "| 72 | AdventureWorks2022 | Sales          | CurrencyRate                          | BASE TABLE   |\n",
      "| 73 | AdventureWorks2022 | Sales          | Customer                              | BASE TABLE   |\n",
      "| 74 | AdventureWorks2022 | HumanResources | Department                            | BASE TABLE   |\n",
      "| 75 | AdventureWorks2022 | Production     | Document                              | BASE TABLE   |\n",
      "| 76 | AdventureWorks2022 | Sales          | SalesOrderDetail                      | BASE TABLE   |\n",
      "| 77 | AdventureWorks2022 | Person         | EmailAddress                          | BASE TABLE   |\n",
      "| 78 | AdventureWorks2022 | HumanResources | Employee                              | BASE TABLE   |\n",
      "| 79 | AdventureWorks2022 | dbo            | sysdiagrams                           | BASE TABLE   |\n",
      "| 80 | AdventureWorks2022 | Sales          | SalesOrderHeader                      | BASE TABLE   |\n",
      "| 81 | AdventureWorks2022 | HumanResources | EmployeeDepartmentHistory             | BASE TABLE   |\n",
      "| 82 | AdventureWorks2022 | HumanResources | EmployeePayHistory                    | BASE TABLE   |\n",
      "| 83 | AdventureWorks2022 | Sales          | SalesOrderHeaderSalesReason           | BASE TABLE   |\n",
      "| 84 | AdventureWorks2022 | Sales          | SalesPerson                           | BASE TABLE   |\n",
      "| 85 | AdventureWorks2022 | Production     | Illustration                          | BASE TABLE   |\n",
      "| 86 | AdventureWorks2022 | HumanResources | JobCandidate                          | BASE TABLE   |\n",
      "| 87 | AdventureWorks2022 | Production     | Location                              | BASE TABLE   |\n",
      "| 88 | AdventureWorks2022 | Person         | Password                              | BASE TABLE   |\n",
      "| 89 | AdventureWorks2022 | Sales          | SalesPersonQuotaHistory               | BASE TABLE   |\n",
      "| 90 | AdventureWorks2022 | Person         | Person                                | BASE TABLE   |\n",
      "| 91 | AdventureWorks2022 | Sales          | SalesReason                           | BASE TABLE   |\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "query = \"\"\"SELECT * \n",
    "FROM INFORMATION_SCHEMA.TABLES;\"\"\"\n",
    "df = pd.read_sql(query, engine)\n",
    "print(df.to_markdown())\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Det finns 92 tabeller och vyer i db:n som är uppdelade i scheman. Base tabeller är de som innehåller data. Det som vi är intresserade av är de 5 scheman nämligen humanresources, production, sales, person och purchasing."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Vilka är de som jobbar på företaget?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   TotalEmployees\n",
      "0             290\n",
      "  FirstName LastName                 JobTitle\n",
      "0       Ken  Sánchez  Chief Executive Officer\n"
     ]
    }
   ],
   "source": [
    "query = \"\"\"\n",
    "SELECT COUNT(*) AS TotalEmployees\n",
    "FROM HumanResources.Employee;\n",
    "\"\"\"\n",
    "df1 = pd.read_sql(query, engine)\n",
    "print(df1)\n",
    "\n",
    "\n",
    "query = \"\"\"\n",
    "SELECT \n",
    "    A.FirstName,\n",
    "    A.LastName, \n",
    "    B.JobTitle\n",
    "FROM Person.Person A\n",
    "JOIN HumanResources.Employee B\n",
    "ON A.BusinessEntityID = B.BusinessEntityID\n",
    "WHERE B.JobTitle = 'Chief Executive Officer';\n",
    "\"\"\"\n",
    "df2 = pd.read_sql(query, engine)\n",
    "print(df2)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ken Sánchez  [https://www.linkedin.com/in/ken-sanchez-1755867a/]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "#### vi kan analysera kunddata\n",
    "#### t.ex. skapa ett stapeldiagram för antal kunder per territorium"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "|    | Name           |   CC |\n",
      "|---:|:---------------|-----:|\n",
      "|  0 | Southwest      | 4696 |\n",
      "|  1 | Australia      | 3665 |\n",
      "|  2 | Northwest      | 3520 |\n",
      "|  3 | United Kingdom | 1991 |\n",
      "|  4 | France         | 1884 |\n",
      "|  5 | Germany        | 1852 |\n",
      "|  6 | Canada         | 1791 |\n",
      "|  7 | Southeast      |  176 |\n",
      "|  8 | Central        |  132 |\n",
      "|  9 | Northeast      |  113 |\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "query = \"\"\"\n",
    "SELECT \n",
    "    B.Name,\n",
    "    COUNT(*) AS CC\n",
    "FROM sales.customer A\n",
    "LEFT JOIN sales.SalesTerritory B\n",
    "ON A.TerritoryID = B.TerritoryID\n",
    "GROUP BY B.Name\n",
    "ORDER BY CC DESC;\n",
    "\"\"\"\n",
    "df = pd.read_sql(query, engine)\n",
    "print(df.to_markdown())\n",
    "\n",
    "\n",
    "plt.figure()\n",
    "bar_plot = sns.barplot(x='Name', y='CC', data=df, palette=\"pastel\", hue='Name', edgecolor='lightgray')\n",
    "\n",
    "plt.xlabel(''), plt.ylabel(''), plt.title('Antal kunder per territorium'), plt.xticks(rotation=45, ha='right')\n",
    "\n",
    "#text over\n",
    "for index, row in df.iterrows():\n",
    "    plt.text(row.name, row.CC, row.CC, ha='center', va='bottom', fontsize=10)\n",
    "\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### De flesta kunder kommer från Southwest i USA. Det är kanske där vi ska anlägga vår nya fabrik."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Jag vet fortfarande inte vad företaget säljer.\n",
    "#### Därför ska jag skapa ett cirkeldiagram för produktkategorier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "|    | Name        |   C |\n",
      "|---:|:------------|----:|\n",
      "|  0 | Accessories |   8 |\n",
      "|  1 | Bikes       |  32 |\n",
      "|  2 | Clothing    |  22 |\n",
      "|  3 | Components  |  43 |\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Product Categories')"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "query = \"\"\"\n",
    "SELECT A.Name, COUNT(*) AS C\n",
    "FROM production.product B\n",
    "JOIN production.ProductCategory A\n",
    "ON B.ProductSubcategoryID = A.ProductCategoryID \n",
    "GROUP BY A.Name\n",
    "\"\"\"\n",
    "df = pd.read_sql(query, engine)\n",
    "print(df.to_markdown())\n",
    "\n",
    "plt.pie(x=df['C'], labels=df['Name'], autopct='%1.1f%%')\n",
    "plt.title('Product Categories')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Bikes och Components är de produktkategorier som företaget fokuserar mest på. Lite mindre på kläder och accessories."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "#### hur mycket känner företaget per år? \n",
    "#### det går att visualisera försäljningen över tid med hjälp av en linjediagram men också andra diagram t.ex. ett stapeldiagram\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Year    TotalSales\n",
      "0  2011  1.264167e+07\n",
      "1  2012  3.352430e+07\n",
      "2  2013  4.362248e+07\n",
      "3  2014  2.005793e+07\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "query = \"\"\" SELECT \n",
    "    YEAR(OrderDate) AS Year, \n",
    "    SUM(SubTotal) AS TotalSales\n",
    "FROM sales.salesorderheader\n",
    "\n",
    "GROUP BY YEAR(OrderDate)\n",
    "ORDER BY Year;\n",
    "\"\"\"\n",
    "df = pd.read_sql(query, engine)\n",
    "print(df)\n",
    "\n",
    "plt.plot(df['Year'], df['TotalSales'])\n",
    "plt.title('Försäljning per år (USD)')\n",
    "#plt.xticks(df['Year'])\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### år 2013 sålde företaget för mer än 40 miljoner USD. Det är nästan 400% ökning jämfört med år 2012. Men år 2014 var försäljningen halverad. Kanske något som vi bör undersöka mer senare, i andra delen av uppgiften då vi ska göra statistisk analys på försäljningen under 2014."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Del 2\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Nu tänker jag med hjälp av statistisk analys undersöka varför försäljningen sjönk under 2014.\n",
    "\n",
    "#### En möjlig orsak kan vara att försäljningen minskade på grund av pricerna på produkterna blev för höga under år 2014 så kunderna valde en konkurrent istället.\n",
    "\n",
    "#### Jag börjar med att hämta alla produkter som lanserades under 2013."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "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>ProductID</th>\n",
       "      <th>Name</th>\n",
       "      <th>StartDate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>707</td>\n",
       "      <td>Sport-100 Helmet, Red</td>\n",
       "      <td>2013-05-30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>708</td>\n",
       "      <td>Sport-100 Helmet, Black</td>\n",
       "      <td>2013-05-30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>711</td>\n",
       "      <td>Sport-100 Helmet, Blue</td>\n",
       "      <td>2013-05-30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>712</td>\n",
       "      <td>AWC Logo Cap</td>\n",
       "      <td>2013-05-30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>713</td>\n",
       "      <td>Long-Sleeve Logo Jersey, S</td>\n",
       "      <td>2013-05-30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>190</th>\n",
       "      <td>995</td>\n",
       "      <td>ML Bottom Bracket</td>\n",
       "      <td>2013-05-30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191</th>\n",
       "      <td>996</td>\n",
       "      <td>HL Bottom Bracket</td>\n",
       "      <td>2013-05-30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>192</th>\n",
       "      <td>997</td>\n",
       "      <td>Road-750 Black, 44</td>\n",
       "      <td>2013-05-30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193</th>\n",
       "      <td>998</td>\n",
       "      <td>Road-750 Black, 48</td>\n",
       "      <td>2013-05-30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194</th>\n",
       "      <td>999</td>\n",
       "      <td>Road-750 Black, 52</td>\n",
       "      <td>2013-05-30</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>195 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     ProductID                        Name  StartDate\n",
       "0          707       Sport-100 Helmet, Red 2013-05-30\n",
       "1          708     Sport-100 Helmet, Black 2013-05-30\n",
       "2          711      Sport-100 Helmet, Blue 2013-05-30\n",
       "3          712                AWC Logo Cap 2013-05-30\n",
       "4          713  Long-Sleeve Logo Jersey, S 2013-05-30\n",
       "..         ...                         ...        ...\n",
       "190        995           ML Bottom Bracket 2013-05-30\n",
       "191        996           HL Bottom Bracket 2013-05-30\n",
       "192        997          Road-750 Black, 44 2013-05-30\n",
       "193        998          Road-750 Black, 48 2013-05-30\n",
       "194        999          Road-750 Black, 52 2013-05-30\n",
       "\n",
       "[195 rows x 3 columns]"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "query = \"\"\"\n",
    "SELECT\n",
    "    A.ProductID,\n",
    "    B.Name,\n",
    "    A.StartDate\n",
    "FROM \n",
    "    Production.ProductListPriceHistory A \n",
    "    JOIN Production.Product B\n",
    "    ON A.ProductID = B.ProductID\n",
    "WHERE \n",
    "    YEAR(A.StartDate) = 2013;\n",
    "\"\"\"\n",
    "sales_df = pd.read_sql(query, engine)\n",
    "sales_df\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Nu vill jag hämta alla produkter som lanserades under år 2014."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "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>ProductID</th>\n",
       "      <th>Name</th>\n",
       "      <th>StartDate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [ProductID, Name, StartDate]\n",
       "Index: []"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "query = \"\"\"\n",
    "SELECT \n",
    "    A.ProductID,\n",
    "    B.Name,\n",
    "    A.StartDate\n",
    "FROM \n",
    "    Production.ProductListPriceHistory A\n",
    "    JOIN Production.Product B\n",
    "    ON A.ProductID = B.ProductID\n",
    "WHERE \n",
    "    YEAR(A.StartDate) = 2014;\n",
    "\"\"\"\n",
    "\n",
    "df = pd.read_sql(query, engine)\n",
    "df\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Jag kan inte komma vidare då företaget inte lanserade nya produkter under 2014 vilket kan vara en anledning till den stora försäljningsnedgången.\n",
    "\n",
    "#### Men jag kan ändå analysera om det fanns skillnader i antalet sålda enheter för produkter mellan 2013 och 2014. Dvs att antalet sålda enheter av varje produkt minskade år 2014 respektive år 2013.\n",
    "\n",
    "#### Jag började med att hämta data om försäljningen för alla produkter under 2013 och sorterade dem efter antalet sålda enheter. Därefter sparade jag **ProductID** i en lista för att jämföra antalet sålda enheter för samma produkter mellan de två åren och testa min hypotes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "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>ProductID</th>\n",
       "      <th>Name</th>\n",
       "      <th>TotalSold_2013</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>870</td>\n",
       "      <td>Water Bottle - 30 oz.</td>\n",
       "      <td>2918</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>712</td>\n",
       "      <td>AWC Logo Cap</td>\n",
       "      <td>2183</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>864</td>\n",
       "      <td>Classic Vest, S</td>\n",
       "      <td>2006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>708</td>\n",
       "      <td>Sport-100 Helmet, Black</td>\n",
       "      <td>1893</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>711</td>\n",
       "      <td>Sport-100 Helmet, Blue</td>\n",
       "      <td>1832</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194</th>\n",
       "      <td>843</td>\n",
       "      <td>Cable Lock</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>195</th>\n",
       "      <td>726</td>\n",
       "      <td>LL Road Frame - Red, 48</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>826</td>\n",
       "      <td>LL Road Rear Wheel</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>763</td>\n",
       "      <td>Road-650 Red, 48</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>769</td>\n",
       "      <td>Road-650 Black, 48</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>199 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     ProductID                     Name  TotalSold_2013\n",
       "0          870    Water Bottle - 30 oz.            2918\n",
       "1          712             AWC Logo Cap            2183\n",
       "2          864          Classic Vest, S            2006\n",
       "3          708  Sport-100 Helmet, Black            1893\n",
       "4          711   Sport-100 Helmet, Blue            1832\n",
       "..         ...                      ...             ...\n",
       "194        843               Cable Lock               1\n",
       "195        726  LL Road Frame - Red, 48               1\n",
       "196        826       LL Road Rear Wheel               1\n",
       "197        763         Road-650 Red, 48               1\n",
       "198        769       Road-650 Black, 48               1\n",
       "\n",
       "[199 rows x 3 columns]"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "query = \"\"\"\n",
    "SELECT\n",
    "    A.ProductID, \n",
    "    B.Name,\n",
    "    SUM(A.Quantity) AS TotalSold_2013\n",
    "FROM \n",
    "    Production.TransactionHistory A\n",
    "INNER JOIN \n",
    "    Production.Product B\n",
    "    ON A.ProductID = B.ProductID\n",
    "WHERE \n",
    "    YEAR(A.TransactionDate) = 2013 AND A.TransactionType = 'S' -- Sales, \"W\" står för \"Withdrawal\"\n",
    "GROUP BY \n",
    "    A.ProductID, B.Name\n",
    "ORDER BY \n",
    "    TotalSold_2013 DESC;\n",
    "\"\"\"\n",
    "sales_df = pd.read_sql(query, engine)\n",
    "\n",
    "\n",
    "product_ids = sales_df['ProductID'].tolist()  \n",
    "sales_df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "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>ProductID</th>\n",
       "      <th>TotalSold_2014</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>870</td>\n",
       "      <td>2902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>712</td>\n",
       "      <td>1950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>711</td>\n",
       "      <td>1776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>873</td>\n",
       "      <td>1761</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>707</td>\n",
       "      <td>1717</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>173</th>\n",
       "      <td>761</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>174</th>\n",
       "      <td>861</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>175</th>\n",
       "      <td>726</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176</th>\n",
       "      <td>765</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>177</th>\n",
       "      <td>763</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>178 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     ProductID  TotalSold_2014\n",
       "0          870            2902\n",
       "1          712            1950\n",
       "2          711            1776\n",
       "3          873            1761\n",
       "4          707            1717\n",
       "..         ...             ...\n",
       "173        761               2\n",
       "174        861               2\n",
       "175        726               1\n",
       "176        765               1\n",
       "177        763               1\n",
       "\n",
       "[178 rows x 2 columns]"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "query_2014 = f\"\"\"\n",
    "SELECT \n",
    "    ProductID,\n",
    "    SUM(Quantity) AS TotalSold_2014\n",
    "FROM \n",
    "    Production.TransactionHistory\n",
    "WHERE \n",
    "    YEAR(TransactionDate) = 2014 \n",
    "    AND TransactionType = 'S' -- Sales transactions\n",
    "    AND ProductID IN ({', '.join(map(str, product_ids))}) -- fick hjälp av chatbot för att få detta att fungera. Join och map är funktioner som gör att jag kan använda product_ids i min query.\n",
    "GROUP BY \n",
    "    ProductID\n",
    "ORDER BY \n",
    "    TotalSold_2014 DESC;\n",
    "\"\"\"\n",
    "sales_2014_df = pd.read_sql(query_2014, engine)\n",
    "sales_2014_df\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### merge är en funktion som kommer från pandas och gör att jag kan slå ihop dataframerna på ProductID."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "|    |   ProductID | Name                    |   TotalSold_2013 |   TotalSold_2014 |\n",
      "|---:|------------:|:------------------------|-----------------:|-----------------:|\n",
      "|  0 |         870 | Water Bottle - 30 oz.   |             2918 |             2902 |\n",
      "|  1 |         712 | AWC Logo Cap            |             2183 |             1950 |\n",
      "|  2 |         864 | Classic Vest, S         |             2006 |             1395 |\n",
      "|  3 |         708 | Sport-100 Helmet, Black |             1893 |             1716 |\n",
      "|  4 |         711 | Sport-100 Helmet, Blue  |             1832 |             1776 |\n"
     ]
    }
   ],
   "source": [
    "combined_df = sales_df.merge(sales_2014_df, on='ProductID', how='left')\n",
    "\n",
    "print(combined_df.head().to_markdown())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Plot distribution\n",
    "plt.hist(combined_df['TotalSold_2013'], bins=30, alpha=0.5, label='2013') # Number of bins determines the intervals for data grouping\n",
    "plt.hist(combined_df['TotalSold_2014'], bins=30, alpha=0.5, label='2014')\n",
    "plt.xlabel('Number of units sold')\n",
    "plt.ylabel('Frequency')\n",
    "plt.legend()\n",
    "plt.title('Distribution of sales between 2013 and 2014')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Histogrammet visar att de flesta produkter såldes i små volymer både 2013 och 2014, men 2014 dominerades ännu mer av låga försäljningsvolymer. Samtidigt sålde några få produkter mycket, vilket ger en högerskev fördelning med en lång svans åt höger. Det går inte att analysera datan med traditionella statistiska tester, eftersom de förutsätter en normalfördelad data. En log-transformering kan minska effekten av de extremt höga försäljningsvärdena."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "# Filter out rows with zero or negative sales before log transformation\n",
    "combined_df = combined_df[(combined_df['TotalSold_2013'] > 0) & (combined_df['TotalSold_2014'] > 0)]\n",
    "\n",
    "# Log transform the filtered data\n",
    "combined_df['TotalSold_2013_log'] = np.log(combined_df['TotalSold_2013'])\n",
    "combined_df['TotalSold_2014_log'] = np.log(combined_df['TotalSold_2014'])\n",
    "\n",
    "# Plot distribution of log-transformed data\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.hist(combined_df['TotalSold_2013_log'], bins=30, alpha=0.5, label='2013')\n",
    "plt.hist(combined_df['TotalSold_2014_log'], bins=30, alpha=0.5, label='2014')\n",
    "\n",
    "plt.xlabel('Log of number of units sold')\n",
    "plt.ylabel('Frequency')\n",
    "plt.legend()\n",
    "plt.title('Distribution of the log-transformed sales between 2013 and 2014')\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.10\n",
      "2.42\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import scipy.stats as stats\n",
    "\n",
    "# Hämta data\n",
    "x1, x2 = combined_df['TotalSold_2013_log'], combined_df['TotalSold_2014_log']\n",
    "\n",
    "mean1, mean2 = x1.mean(), x2.mean()\n",
    "# Varians\n",
    "var1, var2 = x1.var(ddof=0), x2.var(ddof=0)  # ddof=0 för populationsvarians\n",
    "n1, n2 = len(x1), len(x2)\n",
    "\n",
    "print(f\"{var1:.2f}\")\n",
    "print(f\"{var2:.2f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "95% Konfidensintervall: [0.08, 0.60]\n"
     ]
    }
   ],
   "source": [
    "# Standard error\n",
    "SE = np.sqrt(var1/n1 + var2/n2)\n",
    "# Konfidensintervall\n",
    "mean_diff = mean1 - mean2\n",
    "\n",
    "print(f\"95% Konfidensintervall: [{mean_diff - 1.645 * SE:.2f}, {mean_diff + 1.645 * SE:.2f}]\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Försäljningen av produktenheter var signifikant högre 2013 än 2014 (med 95 % konfidens)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Del 3."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Executive Summary\n",
    "\n",
    "År 2013 gick försäljningen riktigt bra och landade på över 40 miljoner USD. Men 2014 halverades den, troligen för att företaget inte lanserade några nya produkter, vilket gjorde att de tappade fart. Försäljningsdatan visar att de flesta produkter såldes i små mängder, medan några få sålde mycket mer. Till exempel sålde billigare grejer som **water bottles** och **logo caps** riktigt bra, medan dyrare produkter som cyklar och cykeldelar såldes i mindre volymer båda åren.\n",
    "\n",
    "Försäljningen av produktenheter var signifikant högre 2013 än 2014 (med 95 % konfidens). För att bättre förstå varför vinsten sjönk 2014 skulle det vara intressant att kolla på globala trender det året, eftersom externa faktorer kan ha påverkat hur kunder handlade.\n",
    "\n",
    "Företaget kan öka sin försäljning genom att satsa på populära och billiga produkter som **Logo Cap** och **Long-Sleeve Logo Jersey**, eftersom profilkläder verkar vara efterfrågade. Att lansera nya produkter regelbundet är också avgörande för att hålla kunderna intresserade. Ett lager i **Southwest USA** skulle vara en smart investering, eftersom många kunder bor där. Det skulle göra logistiken enklare och leveranstiderna kortare."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Muntlig presentation: Den 3 januari 2025.\n",
    "\n",
    "Jag hoppade över självutvärderingen. *"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "sql_test",
   "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": "3.11.5"
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 },
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}