SQL_Data_Engineering_Challenges.ipynb

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   "source": "## SQL Data Engineering Challenges",
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   "cell_type": "markdown",
   "source": "Halo guys!.",
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   "source": "Pada kesempatan kali ini aku akan membagikan pengalamanku ketika aku mengikuti pelatihan DQLab Academy Data Engineering melalui Digitalent program PROA Professional Academy yang diselenggarakan oleh KOMINFO.",
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   "source": "Dalam artikel ini, aku akan share bagaimana sih seorang junior data engineer bekerja?.\nMisalnya kita bekerja sebagai data engineer baru di sebuah perusahaan retail. Teman kita seorang data analis membutuhkan bantuan untuk mendapatkan data yang tersimpan di dalam database milik perusahaan. Sehingga kita diminta membuat SQL query untuk mendapatkan beberapa data dari database di kantor kita. \n\nSetelah berdiskusi dengan data analis tersebut, kita tahu ada beberapa pertanyaan yang harus di jawab dengan menggunakan data di dalam database kita. Pertanyaan ini berkaitan dengan performa perusahaan yang harus di sampaikan kepada investor.",
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   "source": "# data preparations ~ create data frame for querying\nimport pandas as pd\norders = pd.read_csv(\"./orders.csv\",sep=\",\")\ncustomers = pd.read_csv(\"./customers.csv\",sep=\",\")\nproducts = pd.read_csv(\"./products.csv\",sep=\",\")\ncountries = pd.read_csv(\"./countries.csv\",sep=\",\")\n\norders['invoice_date'] = pd.to_datetime(orders['invoice_date'])",
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   "cell_type": "markdown",
   "source": "## Step 1 - Memahami isi data\nKarena kita baru saja bekerja di perusahaan ini, kita masih belum mengerti ada apa saja data-data yang tersimpan di dalam data tersebut. Sehingga kita perlu melihat contoh data dari tiap tiap tabel tersebut.\n",
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   "cell_type": "markdown",
   "source": "### Memanggil data tabel orders\nTampilkan lima baris data untuk tabel orders dan tampilkan semua kolom nya.",
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    "deepnote_sql_source": "SELECT * \nFROM orders\nLIMIT 5;",
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   "source": "\n\ndf_4 = _deepnote_execute_sql(\"\"\"SELECT * \nFROM orders\nLIMIT 5;\n\"\"\", 'SQL_DEEPNOTE_DATAFRAME_SQL')\ndf_4",
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      "text/plain": "   invoice_id  product_id  quantity        invoice_date  customer_id  \\\n0      539993       21499         4 2021-05-16 16:31:00         1340   \n1      539993       21498        24 2021-04-25 10:20:00         1429   \n2      539993       20718         4 2021-04-23 11:04:00         1320   \n3      539993       22961         4 2021-11-15 16:13:00         1301   \n4      539993       22896         1 2021-10-24 20:16:00         1753   \n\n   country_id  \n0          34  \n1          34  \n2          34  \n3          34  \n4          34  ",
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   "source": "\n\ndf_9 = _deepnote_execute_sql(\"\"\"SELECT COUNT(DISTINCT invoice_id) AS Jumlah_Order\nFROM orders;\n\"\"\", 'SQL_DEEPNOTE_DATAFRAME_SQL')\ndf_9",
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  {
   "cell_type": "markdown",
   "source": "### Memanggil data tabel countries\nTampilkan lima baris data untuk tabel countries dan tampilkan semua kolom nya.",
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    "deepnote_sql_source": "SELECT *\nFROM countries\nLIMIT 5;",
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   "source": "\n\ndf_2 = _deepnote_execute_sql(\"\"\"SELECT *\nFROM countries\nLIMIT 5;\n\"\"\", 'SQL_DEEPNOTE_DATAFRAME_SQL')\ndf_2",
   "execution_count": 4,
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        },
        {
         "name": "name",
         "dtype": "object",
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          "unique_count": 5,
          "nan_count": 0,
          "categories": [
           {
            "name": "Australia",
            "count": 1
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           {
            "name": "Austria",
            "count": 1
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           {
            "name": "3 others",
            "count": 3
           }
          ]
         }
        },
        {
         "name": "_deepnote_index_column",
         "dtype": "int64"
        }
       ],
       "rows": [
        {
         "id": "1",
         "name": "Australia",
         "_deepnote_index_column": "0"
        },
        {
         "id": "2",
         "name": "Austria",
         "_deepnote_index_column": "1"
        },
        {
         "id": "3",
         "name": "Bahrain",
         "_deepnote_index_column": "2"
        },
        {
         "id": "4",
         "name": "Belgium",
         "_deepnote_index_column": "3"
        },
        {
         "id": "5",
         "name": "Brazil",
         "_deepnote_index_column": "4"
        }
       ]
      },
      "text/plain": "   id       name\n0   1  Australia\n1   2    Austria\n2   3    Bahrain\n3   4    Belgium\n4   5     Brazil",
      "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>id</th>\n      <th>name</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>Australia</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2</td>\n      <td>Austria</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>3</td>\n      <td>Bahrain</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>4</td>\n      <td>Belgium</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>5</td>\n      <td>Brazil</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {}
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": "### Memanggil data tabel products\nTampilkan lima baris data untuk tabel products dan tampilkan semua kolom nya.",
   "metadata": {
    "tags": [],
    "cell_id": "8ceee3010e5c4ae9a7a49a08ec021eeb",
    "deepnote_cell_type": "markdown",
    "deepnote_cell_height": 100.53125
   }
  },
  {
   "cell_type": "code",
   "metadata": {
    "tags": [],
    "cell_id": "01e1ebad54c344b894049b1dd05efb92",
    "source_hash": "dd5c0c08",
    "execution_start": 1661913619416,
    "execution_millis": 89,
    "sql_integration_id": "deepnote-dataframe-sql",
    "deepnote_sql_source": "SELECT *\nFROM products\nLIMIT 5;",
    "deepnote_variable_name": "df_3",
    "deepnote_to_be_reexecuted": false,
    "deepnote_cell_type": "sql",
    "deepnote_cell_height": 466.296875
   },
   "source": "\n\ndf_3 = _deepnote_execute_sql(\"\"\"SELECT *\nFROM products\nLIMIT 5;\n\"\"\", 'SQL_DEEPNOTE_DATAFRAME_SQL')\ndf_3",
   "execution_count": 5,
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 5,
     "data": {
      "application/vnd.deepnote.dataframe.v3+json": {
       "column_count": 3,
       "row_count": 5,
       "columns": [
        {
         "name": "id",
         "dtype": "int64",
         "stats": {
          "unique_count": 5,
          "nan_count": 0,
          "min": "10002",
          "max": "10135",
          "histogram": [
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            "bin_end": 10041.9,
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            "bin_start": 10108.4,
            "bin_end": 10121.7,
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           {
            "bin_start": 10121.7,
            "bin_end": 10135,
            "count": 3
           }
          ]
         }
        },
        {
         "name": "name",
         "dtype": "object",
         "stats": {
          "unique_count": 4,
          "nan_count": 0,
          "categories": [
           {
            "name": "COLOURING PENCILS BROWN TUBE",
            "count": 2
           },
           {
            "name": "INFLATABLE POLITICAL GLOBE ",
            "count": 1
           },
           {
            "name": "2 others",
            "count": 2
           }
          ]
         }
        },
        {
         "name": "price",
         "dtype": "float64",
         "stats": {
          "unique_count": 5,
          "nan_count": 0,
          "min": "0.21",
          "max": "1.410166667",
          "histogram": [
           {
            "bin_start": 0.21,
            "bin_end": 0.33001666669999996,
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            "bin_end": 0.45003333339999996,
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            "bin_start": 0.45003333339999996,
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            "bin_start": 0.5700500001,
            "bin_end": 0.6900666668,
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           {
            "bin_start": 0.6900666668,
            "bin_end": 0.8100833335,
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           {
            "bin_start": 0.8100833335,
            "bin_end": 0.9301000001999999,
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           {
            "bin_start": 0.9301000001999999,
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           {
            "bin_start": 1.0501166669,
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            "bin_end": 1.410166667,
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          ]
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        },
        {
         "name": "_deepnote_index_column",
         "dtype": "int64"
        }
       ],
       "rows": [
        {
         "id": "10002",
         "name": "INFLATABLE POLITICAL GLOBE ",
         "price": "1.086619718",
         "_deepnote_index_column": "0"
        },
        {
         "id": "10120",
         "name": "DOGGY RUBBER",
         "price": "0.21",
         "_deepnote_index_column": "1"
        },
        {
         "id": "10125",
         "name": "MINI FUNKY DESIGN TAPES",
         "price": "0.859680851",
         "_deepnote_index_column": "2"
        },
        {
         "id": "10133",
         "name": "COLOURING PENCILS BROWN TUBE",
         "price": "0.649045226",
         "_deepnote_index_column": "3"
        },
        {
         "id": "10135",
         "name": "COLOURING PENCILS BROWN TUBE",
         "price": "1.410166667",
         "_deepnote_index_column": "4"
        }
       ]
      },
      "text/plain": "      id                          name     price\n0  10002   INFLATABLE POLITICAL GLOBE   1.086620\n1  10120                  DOGGY RUBBER  0.210000\n2  10125       MINI FUNKY DESIGN TAPES  0.859681\n3  10133  COLOURING PENCILS BROWN TUBE  0.649045\n4  10135  COLOURING PENCILS BROWN TUBE  1.410167",
      "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>id</th>\n      <th>name</th>\n      <th>price</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>10002</td>\n      <td>INFLATABLE POLITICAL GLOBE</td>\n      <td>1.086620</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>10120</td>\n      <td>DOGGY RUBBER</td>\n      <td>0.210000</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>10125</td>\n      <td>MINI FUNKY DESIGN TAPES</td>\n      <td>0.859681</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>10133</td>\n      <td>COLOURING PENCILS BROWN TUBE</td>\n      <td>0.649045</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>10135</td>\n      <td>COLOURING PENCILS BROWN TUBE</td>\n      <td>1.410167</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {}
    }
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "tags": [],
    "cell_id": "44a5d697125f466e9806aaedca852b48",
    "source_hash": "a9c191dc",
    "execution_start": 1661913619505,
    "execution_millis": 59,
    "sql_integration_id": "deepnote-dataframe-sql",
    "deepnote_variable_name": "df_10",
    "deepnote_to_be_reexecuted": false,
    "deepnote_sql_source": "SELECT COUNT(products.id) AS Jumlah_Produk\nFROM products;",
    "deepnote_cell_type": "sql",
    "deepnote_cell_height": 284.328125
   },
   "source": "\n\ndf_10 = _deepnote_execute_sql(\"\"\"SELECT COUNT(products.id) AS Jumlah_Produk\nFROM products;\n\"\"\", 'SQL_DEEPNOTE_DATAFRAME_SQL')\ndf_10",
   "execution_count": 6,
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 6,
     "data": {
      "application/vnd.deepnote.dataframe.v3+json": {
       "column_count": 1,
       "row_count": 1,
       "columns": [
        {
         "name": "Jumlah_Produk",
         "dtype": "int64",
         "stats": {
          "unique_count": 1,
          "nan_count": 0,
          "min": "2498",
          "max": "2498",
          "histogram": [
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            "bin_start": 2498.4,
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          ]
         }
        },
        {
         "name": "_deepnote_index_column",
         "dtype": "int64"
        }
       ],
       "rows": [
        {
         "Jumlah_Produk": "2498",
         "_deepnote_index_column": "0"
        }
       ]
      },
      "text/plain": "   Jumlah_Produk\n0           2498",
      "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>Jumlah_Produk</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>2498</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {}
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": "### Memanggil data tabel customers\nTampilkan lima baris data untuk tabel customers dan tampilkan semua kolom nya.",
   "metadata": {
    "tags": [],
    "cell_id": "8662174831d8497f9806ef74d0204928",
    "deepnote_cell_type": "markdown",
    "deepnote_cell_height": 100.53125
   }
  },
  {
   "cell_type": "code",
   "metadata": {
    "tags": [],
    "cell_id": "21392fd5737a45959a767ce387f5379e",
    "source_hash": "8b150039",
    "execution_start": 1661913619568,
    "execution_millis": 143,
    "sql_integration_id": "deepnote-dataframe-sql",
    "deepnote_variable_name": "df_5",
    "deepnote_to_be_reexecuted": false,
    "deepnote_sql_source": "SELECT *\nFROM customers\nLIMIT 5;",
    "deepnote_cell_type": "sql",
    "deepnote_cell_height": 466.296875
   },
   "source": "\n\ndf_5 = _deepnote_execute_sql(\"\"\"SELECT *\nFROM customers\nLIMIT 5;\n\"\"\", 'SQL_DEEPNOTE_DATAFRAME_SQL')\ndf_5",
   "execution_count": 7,
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 7,
     "data": {
      "application/vnd.deepnote.dataframe.v3+json": {
       "column_count": 5,
       "row_count": 5,
       "columns": [
        {
         "name": "id",
         "dtype": "int64",
         "stats": {
          "unique_count": 5,
          "nan_count": 0,
          "min": "1234",
          "max": "1238",
          "histogram": [
           {
            "bin_start": 1234,
            "bin_end": 1234.4,
            "count": 1
           },
           {
            "bin_start": 1234.4,
            "bin_end": 1234.8,
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           },
           {
            "bin_start": 1234.8,
            "bin_end": 1235.2,
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           },
           {
            "bin_start": 1235.2,
            "bin_end": 1235.6,
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            "bin_start": 1235.6,
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            "bin_start": 1236,
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           {
            "bin_start": 1237.6,
            "bin_end": 1238,
            "count": 1
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          ]
         }
        },
        {
         "name": "first_name",
         "dtype": "object",
         "stats": {
          "unique_count": 5,
          "nan_count": 0,
          "categories": [
           {
            "name": "Murry",
            "count": 1
           },
           {
            "name": "Haley",
            "count": 1
           },
           {
            "name": "3 others",
            "count": 3
           }
          ]
         }
        },
        {
         "name": "last_name",
         "dtype": "object",
         "stats": {
          "unique_count": 5,
          "nan_count": 0,
          "categories": [
           {
            "name": "Roath",
            "count": 1
           },
           {
            "name": "Davitt",
            "count": 1
           },
           {
            "name": "3 others",
            "count": 3
           }
          ]
         }
        },
        {
         "name": "email",
         "dtype": "object",
         "stats": {
          "unique_count": 5,
          "nan_count": 0,
          "categories": [
           {
            "name": "mroath0@reference.com",
            "count": 1
           },
           {
            "name": "hdavitt1@t-online.de",
            "count": 1
           },
           {
            "name": "3 others",
            "count": 3
           }
          ]
         }
        },
        {
         "name": "gender",
         "dtype": "object",
         "stats": {
          "unique_count": 2,
          "nan_count": 0,
          "categories": [
           {
            "name": "Male",
            "count": 3
           },
           {
            "name": "Female",
            "count": 2
           }
          ]
         }
        },
        {
         "name": "_deepnote_index_column",
         "dtype": "int64"
        }
       ],
       "rows": [
        {
         "id": "1234",
         "first_name": "Murry",
         "last_name": "Roath",
         "email": "mroath0@reference.com",
         "gender": "Male",
         "_deepnote_index_column": "0"
        },
        {
         "id": "1235",
         "first_name": "Haley",
         "last_name": "Davitt",
         "email": "hdavitt1@t-online.de",
         "gender": "Female",
         "_deepnote_index_column": "1"
        },
        {
         "id": "1236",
         "first_name": "Wilbur",
         "last_name": "Firth",
         "email": "wfirth2@amazon.de",
         "gender": "Male",
         "_deepnote_index_column": "2"
        },
        {
         "id": "1237",
         "first_name": "Ephraim",
         "last_name": "Bremmell",
         "email": "ebremmell3@globo.com",
         "gender": "Male",
         "_deepnote_index_column": "3"
        },
        {
         "id": "1238",
         "first_name": "Melita",
         "last_name": "Dyment",
         "email": "mdyment4@infoseek.co.jp",
         "gender": "Female",
         "_deepnote_index_column": "4"
        }
       ]
      },
      "text/plain": "     id first_name last_name                    email  gender\n0  1234      Murry     Roath    mroath0@reference.com    Male\n1  1235      Haley    Davitt     hdavitt1@t-online.de  Female\n2  1236     Wilbur     Firth        wfirth2@amazon.de    Male\n3  1237    Ephraim  Bremmell     ebremmell3@globo.com    Male\n4  1238     Melita    Dyment  mdyment4@infoseek.co.jp  Female",
      "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>id</th>\n      <th>first_name</th>\n      <th>last_name</th>\n      <th>email</th>\n      <th>gender</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1234</td>\n      <td>Murry</td>\n      <td>Roath</td>\n      <td>mroath0@reference.com</td>\n      <td>Male</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1235</td>\n      <td>Haley</td>\n      <td>Davitt</td>\n      <td>hdavitt1@t-online.de</td>\n      <td>Female</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>1236</td>\n      <td>Wilbur</td>\n      <td>Firth</td>\n      <td>wfirth2@amazon.de</td>\n      <td>Male</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>1237</td>\n      <td>Ephraim</td>\n      <td>Bremmell</td>\n      <td>ebremmell3@globo.com</td>\n      <td>Male</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>1238</td>\n      <td>Melita</td>\n      <td>Dyment</td>\n      <td>mdyment4@infoseek.co.jp</td>\n      <td>Female</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {}
    }
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "tags": [],
    "cell_id": "916fc098ab174c2ca4126721cc718aa4",
    "source_hash": "8a208aa0",
    "execution_start": 1661913619709,
    "execution_millis": 55,
    "sql_integration_id": "deepnote-dataframe-sql",
    "deepnote_variable_name": "df_11",
    "deepnote_to_be_reexecuted": false,
    "deepnote_sql_source": "SELECT COUNT(DISTINCT customers.id) AS Jumlah_Customer\nFROM customers;",
    "deepnote_cell_type": "sql",
    "deepnote_cell_height": 284.328125
   },
   "source": "\n\ndf_11 = _deepnote_execute_sql(\"\"\"SELECT COUNT(DISTINCT customers.id) AS Jumlah_Customer\nFROM customers;\n\"\"\", 'SQL_DEEPNOTE_DATAFRAME_SQL')\ndf_11",
   "execution_count": 8,
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 8,
     "data": {
      "application/vnd.deepnote.dataframe.v3+json": {
       "column_count": 1,
       "row_count": 1,
       "columns": [
        {
         "name": "Jumlah_Customer",
         "dtype": "int64",
         "stats": {
          "unique_count": 1,
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          ]
         }
        },
        {
         "name": "_deepnote_index_column",
         "dtype": "int64"
        }
       ],
       "rows": [
        {
         "Jumlah_Customer": "595",
         "_deepnote_index_column": "0"
        }
       ]
      },
      "text/plain": "   Jumlah_Customer\n0              595",
      "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>Jumlah_Customer</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>595</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {}
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": "## Step 2 - Analisa data\nSetelah kita memahami apa-apa saja data yang tersedia di database kita. Saatnya untuk mempersiapkan data yang dapat di gunakan untuk pembuatan laporan performa perusahaan. Beberapa poin yang harus di persiapkan diantaranya:\n\n- Ada berapa total nilai penjualan sepanjang waktu?\n- Bagaimana trend nilai penjualan dari bulan ke bulan?\n- Berapa jumlah transaksi yang sudah terjadi?\n- Berapa jumlah transaksi dari bulan ke bulan?\n- Berapa jumlah total nilai per transaksi sepanjang waktu?\n- Tampilkan tiga negara teratas yang memiliki nilai transaksi terbanyak?\n- Tampilkan lima buah produk yang paling banyak terjual?\n- Negara mana yang memiliki penjualan paling sedikit?\n- Kapan penjualan terbesar per hari terjadi?\n- Siapa yang paling banyak berbelanja pada bulan November 2021?\n\n\n",
   "metadata": {
    "tags": [],
    "cell_id": "a87ccb2e24144f72a5894d133f330e7c",
    "deepnote_cell_type": "markdown",
    "deepnote_cell_height": 409.5625
   }
  },
  {
   "cell_type": "markdown",
   "source": "### -- Ada berapa total nilai penjualan sepanjang waktu?",
   "metadata": {
    "formattedRanges": [],
    "tags": [],
    "cell_id": "68c83916fd7b4283936a4409f5253c84",
    "is_collapsed": false,
    "deepnote_cell_type": "text-cell-h3"
   }
  },
  {
   "cell_type": "code",
   "metadata": {
    "tags": [],
    "cell_id": "91e0ee38fbc24a0eafdb04397fd1809d",
    "source_hash": "81e72e3e",
    "execution_start": 1661913619773,
    "execution_millis": 85,
    "sql_integration_id": "deepnote-dataframe-sql",
    "deepnote_variable_name": "df_5",
    "deepnote_to_be_reexecuted": false,
    "deepnote_sql_source": "SELECT SUM(orders.quantity * products.price) as total_nilai_penjualan\nFROM orders\nJOIN products ON products.id = orders.product_id;",
    "deepnote_cell_type": "sql",
    "deepnote_cell_height": 302.34375
   },
   "source": "\n\ndf_5 = _deepnote_execute_sql(\"\"\"SELECT SUM(orders.quantity * products.price) as total_nilai_penjualan\nFROM orders\nJOIN products ON products.id = orders.product_id;\n\"\"\", 'SQL_DEEPNOTE_DATAFRAME_SQL')\ndf_5",
   "execution_count": 9,
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 9,
     "data": {
      "application/vnd.deepnote.dataframe.v3+json": {
       "column_count": 1,
       "row_count": 1,
       "columns": [
        {
         "name": "total_nilai_penjualan",
         "dtype": "float64",
         "stats": {
          "unique_count": 1,
          "nan_count": 0,
          "min": "12924423.2874603",
          "max": "12924423.2874603",
          "histogram": [
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          ]
         }
        },
        {
         "name": "_deepnote_index_column",
         "dtype": "int64"
        }
       ],
       "rows": [
        {
         "total_nilai_penjualan": "12924423.2874603",
         "_deepnote_index_column": "0"
        }
       ]
      },
      "text/plain": "   total_nilai_penjualan\n0           1.292442e+07",
      "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>total_nilai_penjualan</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1.292442e+07</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {}
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": "### -- Bagaimana trend nilai penjualan dari bulan ke bulan?",
   "metadata": {
    "formattedRanges": [],
    "tags": [],
    "cell_id": "3812d5e279e943eeb9e35110c6c2854f",
    "is_collapsed": false,
    "deepnote_cell_type": "text-cell-h3"
   }
  },
  {
   "cell_type": "code",
   "metadata": {
    "tags": [],
    "cell_id": "41ee55da683a4f34935c00eef68dab14",
    "source_hash": "86c3681a",
    "execution_start": 1661913619866,
    "execution_millis": 132,
    "sql_integration_id": "deepnote-dataframe-sql",
    "deepnote_variable_name": "df_6",
    "deepnote_to_be_reexecuted": false,
    "deepnote_sql_source": "SELECT \n    MONTH(orders.invoice_date) as bulan,\n    SUM(orders.quantity * products.price) as total_nilai_penjualan\nFROM orders\nJOIN products ON products.id = orders.product_id\nGROUP BY MONTH(orders.invoice_date);",
    "deepnote_cell_type": "sql",
    "deepnote_cell_height": 725.234375
   },
   "source": "\n\ndf_6 = _deepnote_execute_sql(\"\"\"SELECT \n    MONTH(orders.invoice_date) as bulan,\n    SUM(orders.quantity * products.price) as total_nilai_penjualan\nFROM orders\nJOIN products ON products.id = orders.product_id\nGROUP BY MONTH(orders.invoice_date);\n\"\"\", 'SQL_DEEPNOTE_DATAFRAME_SQL')\ndf_6",
   "execution_count": 10,
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 10,
     "data": {
      "application/vnd.deepnote.dataframe.v3+json": {
       "column_count": 2,
       "row_count": 12,
       "columns": [
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         "name": "bulan",
         "dtype": "int64",
         "stats": {
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          "histogram": [
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          ]
         }
        },
        {
         "name": "total_nilai_penjualan",
         "dtype": "float64",
         "stats": {
          "unique_count": 12,
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        },
        {
         "name": "_deepnote_index_column",
         "dtype": "int64"
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       ],
       "rows": [
        {
         "bulan": "1",
         "total_nilai_penjualan": "897649.7956987398",
         "_deepnote_index_column": "0"
        },
        {
         "bulan": "2",
         "total_nilai_penjualan": "822623.882691211",
         "_deepnote_index_column": "1"
        },
        {
         "bulan": "3",
         "total_nilai_penjualan": "846632.3206312857",
         "_deepnote_index_column": "2"
        },
        {
         "bulan": "4",
         "total_nilai_penjualan": "713905.3381016657",
         "_deepnote_index_column": "3"
        },
        {
         "bulan": "5",
         "total_nilai_penjualan": "1080854.0966414826",
         "_deepnote_index_column": "4"
        },
        {
         "bulan": "6",
         "total_nilai_penjualan": "966148.57373689",
         "_deepnote_index_column": "5"
        },
        {
         "bulan": "7",
         "total_nilai_penjualan": "1071583.8649166187",
         "_deepnote_index_column": "6"
        },
        {
         "bulan": "8",
         "total_nilai_penjualan": "1121783.8918568576",
         "_deepnote_index_column": "7"
        },
        {
         "bulan": "9",
         "total_nilai_penjualan": "1352247.011522638",
         "_deepnote_index_column": "8"
        },
        {
         "bulan": "10",
         "total_nilai_penjualan": "1780802.3338466182",
         "_deepnote_index_column": "9"
        }
       ]
      },
      "text/plain": "    bulan  total_nilai_penjualan\n0       1           8.976498e+05\n1       2           8.226239e+05\n2       3           8.466323e+05\n3       4           7.139053e+05\n4       5           1.080854e+06\n5       6           9.661486e+05\n6       7           1.071584e+06\n7       8           1.121784e+06\n8       9           1.352247e+06\n9      10           1.780802e+06\n10     11           1.841620e+06\n11     12           4.285724e+05",
      "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>bulan</th>\n      <th>total_nilai_penjualan</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>8.976498e+05</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2</td>\n      <td>8.226239e+05</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>3</td>\n      <td>8.466323e+05</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>4</td>\n      <td>7.139053e+05</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>5</td>\n      <td>1.080854e+06</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>6</td>\n      <td>9.661486e+05</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>7</td>\n      <td>1.071584e+06</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>8</td>\n      <td>1.121784e+06</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>9</td>\n      <td>1.352247e+06</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>10</td>\n      <td>1.780802e+06</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>11</td>\n      <td>1.841620e+06</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>12</td>\n      <td>4.285724e+05</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {}
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   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "tags": [],
    "cell_id": "026105109278418296923834e6c14fc1",
    "source_hash": "e7946560",
    "execution_start": 1661913619997,
    "execution_millis": 1574,
    "sql_integration_id": "deepnote-dataframe-sql",
    "deepnote_table_state": {
     "sortBy": [],
     "filters": [],
     "pageSize": 10,
     "pageIndex": 0
    },
    "deepnote_table_loading": false,
    "deepnote_variable_name": "df_8",
    "deepnote_to_be_reexecuted": false,
    "deepnote_sql_source": "SELECT\n    invoice_date,\n    DATE_TRUNC('day', invoice_date) AS Day,\n    DATE_TRUNC('Week', invoice_date) AS Week,\n    DATE_TRUNC('month', invoice_date) AS Month\nFROM orders\nORDER BY invoice_date ASC;",
    "deepnote_cell_type": "sql",
    "deepnote_cell_height": 743.234375
   },
   "source": "\n\ndf_8 = _deepnote_execute_sql(\"\"\"SELECT\n    invoice_date,\n    DATE_TRUNC('day', invoice_date) AS Day,\n    DATE_TRUNC('Week', invoice_date) AS Week,\n    DATE_TRUNC('month', invoice_date) AS Month\nFROM orders\nORDER BY invoice_date ASC;\n\"\"\", 'SQL_DEEPNOTE_DATAFRAME_SQL')\ndf_8",
   "execution_count": 11,
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 11,
     "data": {
      "application/vnd.deepnote.dataframe.v3+json": {
       "column_count": 4,
       "row_count": 279949,
       "columns": [
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         "name": "invoice_date",
         "dtype": "datetime64[ns]"
        },
        {
         "name": "day",
         "dtype": "datetime64[ns]"
        },
        {
         "name": "Week",
         "dtype": "datetime64[ns]"
        },
        {
         "name": "month",
         "dtype": "datetime64[ns]"
        },
        {
         "name": "_deepnote_index_column",
         "dtype": "int64"
        }
       ],
       "rows": [
        {
         "invoice_date": "2021-01-01 10:00:00",
         "day": "2021-01-01 00:00:00",
         "Week": "2020-12-28 00:00:00",
         "month": "2021-01-01 00:00:00",
         "_deepnote_index_column": "0"
        },
        {
         "invoice_date": "2021-01-01 10:00:00",
         "day": "2021-01-01 00:00:00",
         "Week": "2020-12-28 00:00:00",
         "month": "2021-01-01 00:00:00",
         "_deepnote_index_column": "1"
        },
        {
         "invoice_date": "2021-01-01 10:00:00",
         "day": "2021-01-01 00:00:00",
         "Week": "2020-12-28 00:00:00",
         "month": "2021-01-01 00:00:00",
         "_deepnote_index_column": "2"
        },
        {
         "invoice_date": "2021-01-01 10:00:00",
         "day": "2021-01-01 00:00:00",
         "Week": "2020-12-28 00:00:00",
         "month": "2021-01-01 00:00:00",
         "_deepnote_index_column": "3"
        },
        {
         "invoice_date": "2021-01-01 10:00:00",
         "day": "2021-01-01 00:00:00",
         "Week": "2020-12-28 00:00:00",
         "month": "2021-01-01 00:00:00",
         "_deepnote_index_column": "4"
        },
        {
         "invoice_date": "2021-01-01 10:00:00",
         "day": "2021-01-01 00:00:00",
         "Week": "2020-12-28 00:00:00",
         "month": "2021-01-01 00:00:00",
         "_deepnote_index_column": "5"
        },
        {
         "invoice_date": "2021-01-01 10:00:00",
         "day": "2021-01-01 00:00:00",
         "Week": "2020-12-28 00:00:00",
         "month": "2021-01-01 00:00:00",
         "_deepnote_index_column": "6"
        },
        {
         "invoice_date": "2021-01-01 10:00:00",
         "day": "2021-01-01 00:00:00",
         "Week": "2020-12-28 00:00:00",
         "month": "2021-01-01 00:00:00",
         "_deepnote_index_column": "7"
        },
        {
         "invoice_date": "2021-01-01 10:00:00",
         "day": "2021-01-01 00:00:00",
         "Week": "2020-12-28 00:00:00",
         "month": "2021-01-01 00:00:00",
         "_deepnote_index_column": "8"
        },
        {
         "invoice_date": "2021-01-01 10:00:00",
         "day": "2021-01-01 00:00:00",
         "Week": "2020-12-28 00:00:00",
         "month": "2021-01-01 00:00:00",
         "_deepnote_index_column": "9"
        }
       ]
      },
      "text/plain": "              invoice_date        day       Week      month\n0      2021-01-01 10:00:00 2021-01-01 2020-12-28 2021-01-01\n1      2021-01-01 10:00:00 2021-01-01 2020-12-28 2021-01-01\n2      2021-01-01 10:00:00 2021-01-01 2020-12-28 2021-01-01\n3      2021-01-01 10:00:00 2021-01-01 2020-12-28 2021-01-01\n4      2021-01-01 10:00:00 2021-01-01 2020-12-28 2021-01-01\n...                    ...        ...        ...        ...\n279944 2021-12-06 12:50:00 2021-12-06 2021-12-06 2021-12-01\n279945 2021-12-06 12:50:00 2021-12-06 2021-12-06 2021-12-01\n279946 2021-12-06 12:50:00 2021-12-06 2021-12-06 2021-12-01\n279947 2021-12-06 12:50:00 2021-12-06 2021-12-06 2021-12-01\n279948 2021-12-06 12:50:00 2021-12-06 2021-12-06 2021-12-01\n\n[279949 rows x 4 columns]",
      "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>invoice_date</th>\n      <th>day</th>\n      <th>Week</th>\n      <th>month</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>2021-01-01 10:00:00</td>\n      <td>2021-01-01</td>\n      <td>2020-12-28</td>\n      <td>2021-01-01</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2021-01-01 10:00:00</td>\n      <td>2021-01-01</td>\n      <td>2020-12-28</td>\n      <td>2021-01-01</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2021-01-01 10:00:00</td>\n      <td>2021-01-01</td>\n      <td>2020-12-28</td>\n      <td>2021-01-01</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>2021-01-01 10:00:00</td>\n      <td>2021-01-01</td>\n      <td>2020-12-28</td>\n      <td>2021-01-01</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>2021-01-01 10:00:00</td>\n      <td>2021-01-01</td>\n      <td>2020-12-28</td>\n      <td>2021-01-01</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>279944</th>\n      <td>2021-12-06 12:50:00</td>\n      <td>2021-12-06</td>\n      <td>2021-12-06</td>\n      <td>2021-12-01</td>\n    </tr>\n    <tr>\n      <th>279945</th>\n      <td>2021-12-06 12:50:00</td>\n      <td>2021-12-06</td>\n      <td>2021-12-06</td>\n      <td>2021-12-01</td>\n    </tr>\n    <tr>\n      <th>279946</th>\n      <td>2021-12-06 12:50:00</td>\n      <td>2021-12-06</td>\n      <td>2021-12-06</td>\n      <td>2021-12-01</td>\n    </tr>\n    <tr>\n      <th>279947</th>\n      <td>2021-12-06 12:50:00</td>\n      <td>2021-12-06</td>\n      <td>2021-12-06</td>\n      <td>2021-12-01</td>\n    </tr>\n    <tr>\n      <th>279948</th>\n      <td>2021-12-06 12:50:00</td>\n      <td>2021-12-06</td>\n      <td>2021-12-06</td>\n      <td>2021-12-01</td>\n    </tr>\n  </tbody>\n</table>\n<p>279949 rows × 4 columns</p>\n</div>"
     },
     "metadata": {}
    }
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "tags": [],
    "cell_id": "00956ee1cac344228d6e6bd4f6014ed3",
    "source_hash": "1caf0d6",
    "execution_start": 1661913621595,
    "execution_millis": 215,
    "sql_integration_id": "deepnote-dataframe-sql",
    "deepnote_variable_name": "df_7",
    "deepnote_to_be_reexecuted": false,
    "deepnote_sql_source": "SELECT \n    DATE_TRUNC('month', invoice_date) AS Bulan,\n    SUM(orders.quantity * products.price) AS Total_Nilai_Penjualan\nFROM orders\nJOIN products ON products.id = orders.product_id\nGROUP BY DATE_TRUNC('month', invoice_date);",
    "deepnote_cell_type": "sql",
    "deepnote_cell_height": 725.234375
   },
   "source": "\n\ndf_7 = _deepnote_execute_sql(\"\"\"SELECT \n    DATE_TRUNC('month', invoice_date) AS Bulan,\n    SUM(orders.quantity * products.price) AS Total_Nilai_Penjualan\nFROM orders\nJOIN products ON products.id = orders.product_id\nGROUP BY DATE_TRUNC('month', invoice_date);\n\"\"\", 'SQL_DEEPNOTE_DATAFRAME_SQL')\ndf_7",
   "execution_count": 12,
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 12,
     "data": {
      "application/vnd.deepnote.dataframe.v3+json": {
       "column_count": 2,
       "row_count": 12,
       "columns": [
        {
         "name": "Bulan",
         "dtype": "datetime64[ns]",
         "stats": {
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          ]
         }
        },
        {
         "name": "Total_Nilai_Penjualan",
         "dtype": "float64",
         "stats": {
          "unique_count": 12,
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          ]
         }
        },
        {
         "name": "_deepnote_index_column",
         "dtype": "int64"
        }
       ],
       "rows": [
        {
         "Bulan": "2021-05-01 00:00:00",
         "Total_Nilai_Penjualan": "1080854.0966414835",
         "_deepnote_index_column": "0"
        },
        {
         "Bulan": "2021-04-01 00:00:00",
         "Total_Nilai_Penjualan": "713905.3381016657",
         "_deepnote_index_column": "1"
        },
        {
         "Bulan": "2021-11-01 00:00:00",
         "Total_Nilai_Penjualan": "1841619.7834498635",
         "_deepnote_index_column": "2"
        },
        {
         "Bulan": "2021-10-01 00:00:00",
         "Total_Nilai_Penjualan": "1780802.3338466203",
         "_deepnote_index_column": "3"
        },
        {
         "Bulan": "2021-07-01 00:00:00",
         "Total_Nilai_Penjualan": "1071583.8649166147",
         "_deepnote_index_column": "4"
        },
        {
         "Bulan": "2021-06-01 00:00:00",
         "Total_Nilai_Penjualan": "966148.57373689",
         "_deepnote_index_column": "5"
        },
        {
         "Bulan": "2021-08-01 00:00:00",
         "Total_Nilai_Penjualan": "1121783.8918568594",
         "_deepnote_index_column": "6"
        },
        {
         "Bulan": "2021-12-01 00:00:00",
         "Total_Nilai_Penjualan": "428572.39436679083",
         "_deepnote_index_column": "7"
        },
        {
         "Bulan": "2021-01-01 00:00:00",
         "Total_Nilai_Penjualan": "897649.7956987415",
         "_deepnote_index_column": "8"
        },
        {
         "Bulan": "2021-02-01 00:00:00",
         "Total_Nilai_Penjualan": "822623.8826912106",
         "_deepnote_index_column": "9"
        }
       ]
      },
      "text/plain": "        Bulan  Total_Nilai_Penjualan\n0  2021-05-01           1.080854e+06\n1  2021-04-01           7.139053e+05\n2  2021-11-01           1.841620e+06\n3  2021-10-01           1.780802e+06\n4  2021-07-01           1.071584e+06\n5  2021-06-01           9.661486e+05\n6  2021-08-01           1.121784e+06\n7  2021-12-01           4.285724e+05\n8  2021-01-01           8.976498e+05\n9  2021-02-01           8.226239e+05\n10 2021-03-01           8.466323e+05\n11 2021-09-01           1.352247e+06",
      "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>Bulan</th>\n      <th>Total_Nilai_Penjualan</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>2021-05-01</td>\n      <td>1.080854e+06</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2021-04-01</td>\n      <td>7.139053e+05</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2021-11-01</td>\n      <td>1.841620e+06</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>2021-10-01</td>\n      <td>1.780802e+06</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>2021-07-01</td>\n      <td>1.071584e+06</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>2021-06-01</td>\n      <td>9.661486e+05</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>2021-08-01</td>\n      <td>1.121784e+06</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>2021-12-01</td>\n      <td>4.285724e+05</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>2021-01-01</td>\n      <td>8.976498e+05</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>2021-02-01</td>\n      <td>8.226239e+05</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>2021-03-01</td>\n      <td>8.466323e+05</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>2021-09-01</td>\n      <td>1.352247e+06</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {}
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": "### -- Berapa jumlah transaksi yang sudah terjadi?",
   "metadata": {
    "formattedRanges": [],
    "tags": [],
    "cell_id": "8c4320c701354838b16370f4d1448b7e",
    "is_collapsed": false,
    "deepnote_cell_type": "text-cell-h3"
   }
  },
  {
   "cell_type": "code",
   "source": "\n\ndf_12 = _deepnote_execute_sql(\"\"\"SELECT COUNT(*) AS Jumlah_Transaksi\nFROM orders;\n\"\"\", 'SQL_DEEPNOTE_DATAFRAME_SQL')\ndf_12",
   "metadata": {
    "tags": [],
    "cell_id": "62d62239ab26441a82f332f6da8f763e",
    "source_hash": "90d2c6e7",
    "execution_start": 1661914836470,
    "execution_millis": 135,
    "sql_integration_id": "deepnote-dataframe-sql",
    "deepnote_variable_name": "df_12",
    "deepnote_to_be_reexecuted": false,
    "deepnote_sql_source": "SELECT COUNT(*) AS Jumlah_Transaksi\nFROM orders;",
    "deepnote_cell_type": "sql",
    "deepnote_cell_height": 284.328125
   },
   "execution_count": 27,
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 27,
     "data": {
      "application/vnd.deepnote.dataframe.v3+json": {
       "column_count": 1,
       "row_count": 1,
       "columns": [
        {
         "name": "Jumlah_Transaksi",
         "dtype": "int64",
         "stats": {
          "unique_count": 1,
          "nan_count": 0,
          "min": "279949",
          "max": "279949",
          "histogram": [
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           }
          ]
         }
        },
        {
         "name": "_deepnote_index_column",
         "dtype": "int64"
        }
       ],
       "rows": [
        {
         "Jumlah_Transaksi": "279949",
         "_deepnote_index_column": "0"
        }
       ]
      },
      "text/plain": "   Jumlah_Transaksi\n0            279949",
      "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>Jumlah_Transaksi</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>279949</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {}
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": "### -- Berapa jumlah transaksi dari bulan ke bulan?",
   "metadata": {
    "formattedRanges": [],
    "tags": [],
    "cell_id": "1d709012-009c-4270-bb6b-2863b5401683",
    "is_collapsed": false,
    "deepnote_cell_type": "text-cell-h3"
   }
  },
  {
   "cell_type": "code",
   "metadata": {
    "tags": [],
    "cell_id": "fc00d67dd1ed4e899cee6b54e9e60da3",
    "source_hash": "89ade207",
    "execution_start": 1661913621866,
    "execution_millis": 117,
    "sql_integration_id": "deepnote-dataframe-sql",
    "deepnote_variable_name": "df_13",
    "deepnote_to_be_reexecuted": false,
    "deepnote_sql_source": "SELECT \n    DATE_TRUNC('month', orders.invoice_date) AS Bulan,\n    COUNT(*) AS Jumlah_Transaksi\nFROM orders\nGROUP BY DATE_TRUNC('month', orders.invoice_date);",
    "deepnote_cell_type": "sql",
    "deepnote_cell_height": 707.25
   },
   "source": "\n\ndf_13 = _deepnote_execute_sql(\"\"\"SELECT \n    DATE_TRUNC('month', orders.invoice_date) AS Bulan,\n    COUNT(*) AS Jumlah_Transaksi\nFROM orders\nGROUP BY DATE_TRUNC('month', orders.invoice_date);\n\"\"\", 'SQL_DEEPNOTE_DATAFRAME_SQL')\ndf_13",
   "execution_count": 14,
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 14,
     "data": {
      "application/vnd.deepnote.dataframe.v3+json": {
       "column_count": 2,
       "row_count": 12,
       "columns": [
        {
         "name": "Bulan",
         "dtype": "datetime64[ns]",
         "stats": {
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          "nan_count": 0,
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          ]
         }
        },
        {
         "name": "Jumlah_Transaksi",
         "dtype": "int64",
         "stats": {
          "unique_count": 12,
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         }
        },
        {
         "name": "_deepnote_index_column",
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       ],
       "rows": [
        {
         "Bulan": "2021-11-01 00:00:00",
         "Jumlah_Transaksi": "48122",
         "_deepnote_index_column": "0"
        },
        {
         "Bulan": "2021-12-01 00:00:00",
         "Jumlah_Transaksi": "10175",
         "_deepnote_index_column": "1"
        },
        {
         "Bulan": "2021-07-01 00:00:00",
         "Jumlah_Transaksi": "20905",
         "_deepnote_index_column": "2"
        },
        {
         "Bulan": "2021-08-01 00:00:00",
         "Jumlah_Transaksi": "19447",
         "_deepnote_index_column": "3"
        },
        {
         "Bulan": "2021-09-01 00:00:00",
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         "_deepnote_index_column": "4"
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         "Jumlah_Transaksi": "38967",
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        {
         "Bulan": "2021-06-01 00:00:00",
         "Jumlah_Transaksi": "19296",
         "_deepnote_index_column": "6"
        },
        {
         "Bulan": "2021-05-01 00:00:00",
         "Jumlah_Transaksi": "21665",
         "_deepnote_index_column": "7"
        },
        {
         "Bulan": "2021-03-01 00:00:00",
         "Jumlah_Transaksi": "20033",
         "_deepnote_index_column": "8"
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        {
         "Bulan": "2021-04-01 00:00:00",
         "Jumlah_Transaksi": "16862",
         "_deepnote_index_column": "9"
        }
       ]
      },
      "text/plain": "        Bulan  Jumlah_Transaksi\n0  2021-11-01             48122\n1  2021-12-01             10175\n2  2021-07-01             20905\n3  2021-08-01             19447\n4  2021-09-01             30503\n5  2021-10-01             38967\n6  2021-06-01             19296\n7  2021-05-01             21665\n8  2021-03-01             20033\n9  2021-04-01             16862\n10 2021-01-01             18777\n11 2021-02-01             15197",
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     },
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   ]
  },
  {
   "cell_type": "code",
   "source": "_deepnote_run_altair(df_13, \"\"\"{\"data\":{\"name\":\"placeholder\"},\"mark\":{\"type\":\"bar\",\"tooltip\":true},\"height\":220,\"$schema\":\"https://vega.github.io/schema/vega-lite/v4.json\",\"autosize\":{\"type\":\"fit\"},\"encoding\":{\"x\":{\"sort\":null,\"type\":\"temporal\",\"field\":\"Bulan\",\"scale\":{\"type\":\"linear\",\"zero\":false}},\"y\":{\"sort\":null,\"type\":\"quantitative\",\"field\":\"Jumlah_Transaksi\",\"scale\":{\"type\":\"linear\",\"zero\":true}},\"color\":{\"sort\":null,\"type\":\"nominal\",\"field\":\"\",\"scale\":{\"type\":\"linear\",\"zero\":false}}}}\"\"\")",
   "metadata": {
    "cell_id": "08fc388bce744bf29eb67044777c3753",
    "source_hash": "a1e56985",
    "execution_start": 1661913650630,
    "execution_millis": 411,
    "deepnote_variable_name": "df_13",
    "deepnote_to_be_reexecuted": false,
    "deepnote_visualization_spec": {
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     "encoding": {
      "x": {
       "sort": null,
       "type": "temporal",
       "field": "Bulan",
       "scale": {
        "type": "linear",
        "zero": false
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      "y": {
       "sort": null,
       "type": "quantitative",
       "field": "Jumlah_Transaksi",
       "scale": {
        "type": "linear",
        "zero": true
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      },
      "color": {
       "sort": null,
       "type": "nominal",
       "field": "",
       "scale": {
        "type": "linear",
        "zero": false
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     }
    },
    "deepnote_cell_type": "visualization"
   },
   "execution_count": 24,
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 24,
     "data": {
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  {
   "cell_type": "markdown",
   "source": "### -- Berapa jumlah total nilai per transaksi sepanjang waktu?",
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    "sql_integration_id": "deepnote-dataframe-sql",
    "deepnote_variable_name": "df_14",
    "deepnote_to_be_reexecuted": false,
    "deepnote_sql_source": "SELECT \n    DATE_TRUNC('month' ,invoice_date) AS Bulan,\n    SUM(orders.quantity * products.price)/COUNT(DISTINCT invoice_id) AS Total_Nilai_Penjualan\nFROM orders\nJOIN products ON orders.product_id = products.id\nGROUP BY DATE_TRUNC('month' ,invoice_date);",
    "deepnote_cell_type": "sql",
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   },
   "source": "\n\ndf_14 = _deepnote_execute_sql(\"\"\"SELECT \n    DATE_TRUNC('month' ,invoice_date) AS Bulan,\n    SUM(orders.quantity * products.price)/COUNT(DISTINCT invoice_id) AS Total_Nilai_Penjualan\nFROM orders\nJOIN products ON orders.product_id = products.id\nGROUP BY DATE_TRUNC('month' ,invoice_date);\n\"\"\", 'SQL_DEEPNOTE_DATAFRAME_SQL')\ndf_14",
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      "text/plain": "        Bulan  Total_Nilai_Penjualan\n0  2021-05-01             419.260705\n1  2021-04-01             303.531181\n2  2021-11-01             434.959798\n3  2021-10-01             471.610788\n4  2021-07-01             400.741909\n5  2021-06-01             379.924724\n6  2021-08-01             443.919229\n7  2021-12-01             216.998681\n8  2021-01-01             392.329456\n9  2021-02-01             370.551299\n10 2021-03-01             345.564213\n11 2021-09-01             406.446352",
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     },
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  },
  {
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   "metadata": {
    "cell_id": "2f06133a876a447b861142108fd23c51",
    "source_hash": "5f9631d1",
    "execution_start": 1661913654322,
    "execution_millis": 79,
    "deepnote_variable_name": "df_14",
    "deepnote_to_be_reexecuted": false,
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     "execution_count": 25,
     "data": {
      "text/html": "\n<div id=\"altair-viz-bcd74b16d3d24eb0b5ad1145ea2b9a72\"></div>\n<script type=\"text/javascript\">\n  (function(spec, embedOpt){\n    let outputDiv = document.currentScript.previousElementSibling;\n    if (outputDiv.id !== \"altair-viz-bcd74b16d3d24eb0b5ad1145ea2b9a72\") {\n      outputDiv = document.getElementById(\"altair-viz-bcd74b16d3d24eb0b5ad1145ea2b9a72\");\n    }\n    const paths = {\n      \"vega\": \"https://cdn.jsdelivr.net/npm//vega@5?noext\",\n      \"vega-lib\": \"https://cdn.jsdelivr.net/npm//vega-lib?noext\",\n      \"vega-lite\": \"https://cdn.jsdelivr.net/npm//vega-lite@4.8.1?noext\",\n      \"vega-embed\": \"https://cdn.jsdelivr.net/npm//vega-embed@6?noext\",\n    };\n\n    function loadScript(lib) {\n      return new Promise(function(resolve, reject) {\n        var s = document.createElement('script');\n        s.src = paths[lib];\n        s.async = true;\n        s.onload = () => resolve(paths[lib]);\n        s.onerror = () => reject(`Error loading script: ${paths[lib]}`);\n        document.getElementsByTagName(\"head\")[0].appendChild(s);\n      });\n    }\n\n    function showError(err) {\n      outputDiv.innerHTML = `<div class=\"error\" style=\"color:red;\">${err}</div>`;\n      throw err;\n    }\n\n    function displayChart(vegaEmbed) {\n      vegaEmbed(outputDiv, spec, embedOpt)\n        .catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));\n    }\n\n    if(typeof define === \"function\" && define.amd) {\n      requirejs.config({paths});\n      require([\"vega-embed\"], displayChart, err => showError(`Error loading script: ${err.message}`));\n    } else if (typeof vegaEmbed === \"function\") {\n      displayChart(vegaEmbed);\n    } else {\n      loadScript(\"vega\")\n        .then(() => loadScript(\"vega-lite\"))\n        .then(() => loadScript(\"vega-embed\"))\n        .catch(showError)\n        .then(() => displayChart(vegaEmbed));\n    }\n  })({\"config\": {\"view\": {\"continuousWidth\": 400, \"continuousHeight\": 300}, \"area\": {\"fill\": \"#51C0E9\"}, \"axis\": {\"labelFont\": \"Inter, sans-serif\", \"labelFontSize\": 12, \"labelFontWeight\": \"normal\", \"labelLimit\": 60, \"labelOverlap\": \"greedy\", \"titleFont\": \"Inter, sans-serif\", \"titleFontSize\": 12, \"titleFontWeight\": \"bold\"}, \"background\": \"#ffffff\", \"legend\": {\"labelFont\": \"Inter, sans-serif\", \"labelFontSize\": 12, \"titleFont\": \"Inter, sans-serif\", \"titleFontSize\": 12, \"titleFontWeight\": \"bold\"}, \"line\": {\"stroke\": \"#51C0E9\", \"strokeWidth\": 2}, \"rect\": {\"fill\": \"#51C0E9\"}, \"title\": {\"anchor\": \"start\", \"color\": \"#000000\", \"font\": \"Inter, sans-serif\", \"fontSize\": 22, \"fontWeight\": \"normal\"}}, \"data\": {\"name\": \"data-080a489107871550bd1d56832aca31a6\"}, \"mark\": {\"type\": \"bar\", \"tooltip\": true}, \"autosize\": {\"type\": \"fit\"}, \"encoding\": {\"color\": {\"type\": \"nominal\", \"field\": \"\", \"scale\": {\"type\": \"linear\", \"zero\": false}, \"sort\": null}, \"x\": {\"type\": \"temporal\", \"field\": \"Bulan\", \"scale\": {\"type\": \"linear\", \"zero\": false}, \"sort\": null}, \"y\": {\"type\": \"quantitative\", \"field\": \"Total_Nilai_Penjualan\", \"scale\": {\"type\": \"linear\", \"zero\": true}, \"sort\": null}}, \"height\": 300, \"width\": \"container\", \"$schema\": \"https://vega.github.io/schema/vega-lite/v4.json\", \"datasets\": {\"data-080a489107871550bd1d56832aca31a6\": [{\"Bulan\": \"2021-05-01T00:00:00\", \"Total_Nilai_Penjualan\": 419.26070467086186}, {\"Bulan\": \"2021-04-01T00:00:00\", \"Total_Nilai_Penjualan\": 303.53118116567475}, {\"Bulan\": \"2021-11-01T00:00:00\", \"Total_Nilai_Penjualan\": 434.95979769716774}, {\"Bulan\": \"2021-10-01T00:00:00\", \"Total_Nilai_Penjualan\": 471.6107875653105}, {\"Bulan\": \"2021-07-01T00:00:00\", \"Total_Nilai_Penjualan\": 400.74190909372237}, {\"Bulan\": \"2021-06-01T00:00:00\", \"Total_Nilai_Penjualan\": 379.92472423786455}, {\"Bulan\": \"2021-08-01T00:00:00\", \"Total_Nilai_Penjualan\": 443.9192290687993}, {\"Bulan\": \"2021-12-01T00:00:00\", \"Total_Nilai_Penjualan\": 216.99868069204692}, {\"Bulan\": \"2021-01-01T00:00:00\", \"Total_Nilai_Penjualan\": 392.329456162038}, {\"Bulan\": \"2021-02-01T00:00:00\", \"Total_Nilai_Penjualan\": 370.55129850955706}, {\"Bulan\": \"2021-03-01T00:00:00\", \"Total_Nilai_Penjualan\": 345.5642125025668}, {\"Bulan\": \"2021-09-01T00:00:00\", \"Total_Nilai_Penjualan\": 406.4463515246847}]}}, {\"actions\": false, \"mode\": \"vega-lite\"});\n</script>",
      "text/plain": "alt.Chart(...)"
     },
     "metadata": {}
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": "### -- Tampilkan tiga negara teratas yang memiliki nilai transaksi terbanyak?",
   "metadata": {
    "formattedRanges": [],
    "tags": [],
    "cell_id": "8652fb6f48ea41a98c349cea92f7c723",
    "is_collapsed": false,
    "deepnote_cell_type": "text-cell-h3"
   }
  },
  {
   "cell_type": "code",
   "metadata": {
    "tags": [],
    "cell_id": "8b0a417e5055441798e963603331a93b",
    "source_hash": "58442283",
    "execution_start": 1661913622781,
    "execution_millis": 108,
    "sql_integration_id": "deepnote-dataframe-sql",
    "deepnote_variable_name": "df_16",
    "deepnote_to_be_reexecuted": false,
    "deepnote_sql_source": "WITH Penjualan_Negara AS (\nSELECT \n    c.name AS Nama_Negara,\n    SUM(o.quantity * p.price) AS Penjualan\nFROM orders AS o\nINNER JOIN products AS p ON o.product_id = p.id\nINNER JOIN countries AS c ON o.country_id = c.id\nGROUP BY c.name\n)\n\nSELECT \n    Nama_Negara\nFROM Penjualan_Negara\nORDER BY Penjualan DESC\nLIMIT 3;",
    "deepnote_cell_type": "sql",
    "deepnote_cell_height": 600.328125
   },
   "source": "\n\ndf_16 = _deepnote_execute_sql(\"\"\"WITH Penjualan_Negara AS (\nSELECT \n    c.name AS Nama_Negara,\n    SUM(o.quantity * p.price) AS Penjualan\nFROM orders AS o\nINNER JOIN products AS p ON o.product_id = p.id\nINNER JOIN countries AS c ON o.country_id = c.id\nGROUP BY c.name\n)\n\nSELECT \n    Nama_Negara\nFROM Penjualan_Negara\nORDER BY Penjualan DESC\nLIMIT 3;\n\"\"\", 'SQL_DEEPNOTE_DATAFRAME_SQL')\ndf_16",
   "execution_count": 18,
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 18,
     "data": {
      "application/vnd.deepnote.dataframe.v3+json": {
       "column_count": 1,
       "row_count": 3,
       "columns": [
        {
         "name": "Nama_Negara",
         "dtype": "object",
         "stats": {
          "unique_count": 3,
          "nan_count": 0,
          "categories": [
           {
            "name": "United Kingdom",
            "count": 1
           },
           {
            "name": "Netherlands",
            "count": 1
           },
           {
            "name": "EIRE",
            "count": 1
           }
          ]
         }
        },
        {
         "name": "_deepnote_index_column",
         "dtype": "int64"
        }
       ],
       "rows": [
        {
         "Nama_Negara": "United Kingdom",
         "_deepnote_index_column": "0"
        },
        {
         "Nama_Negara": "Netherlands",
         "_deepnote_index_column": "1"
        },
        {
         "Nama_Negara": "EIRE",
         "_deepnote_index_column": "2"
        }
       ]
      },
      "text/plain": "      Nama_Negara\n0  United Kingdom\n1     Netherlands\n2            EIRE",
      "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>Nama_Negara</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>United Kingdom</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Netherlands</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>EIRE</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {}
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": "### -- Tampilkan lima buah produk yang paling banyak terjual?",
   "metadata": {
    "formattedRanges": [],
    "tags": [],
    "cell_id": "0c74e5c4221b4d2681c180e2aa729229",
    "is_collapsed": false,
    "deepnote_cell_type": "text-cell-h3"
   }
  },
  {
   "cell_type": "code",
   "metadata": {
    "tags": [],
    "cell_id": "7e58388765b84d28994606dfa834358f",
    "source_hash": "a0e54c25",
    "execution_start": 1661913622915,
    "execution_millis": 112,
    "sql_integration_id": "deepnote-dataframe-sql",
    "deepnote_table_state": {
     "sortBy": [],
     "filters": [],
     "pageSize": 10,
     "pageIndex": 0
    },
    "deepnote_table_loading": false,
    "deepnote_variable_name": "df_16",
    "deepnote_to_be_reexecuted": false,
    "deepnote_sql_source": "SELECT \n    p.name AS Produk,\n    SUM(o.quantity) AS Total\nFROM orders AS o\nJOIN products AS p ON o.product_id = p.id\nGROUP BY p.name\nORDER BY Total DESC\nLIMIT 5;",
    "deepnote_cell_type": "sql",
    "deepnote_cell_height": 556.296875
   },
   "source": "\n\ndf_16 = _deepnote_execute_sql(\"\"\"SELECT \n    p.name AS Produk,\n    SUM(o.quantity) AS Total\nFROM orders AS o\nJOIN products AS p ON o.product_id = p.id\nGROUP BY p.name\nORDER BY Total DESC\nLIMIT 5;\n\"\"\", 'SQL_DEEPNOTE_DATAFRAME_SQL')\ndf_16",
   "execution_count": 19,
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 19,
     "data": {
      "application/vnd.deepnote.dataframe.v3+json": {
       "column_count": 2,
       "row_count": 5,
       "columns": [
        {
         "name": "Produk",
         "dtype": "object",
         "stats": {
          "unique_count": 5,
          "nan_count": 0,
          "categories": [
           {
            "name": "RED RETROSPOT SHOPPER BAG",
            "count": 1
           },
           {
            "name": "SET OF 3 CAKE TINS PANTRY DESIGN",
            "count": 1
           },
           {
            "name": "3 others",
            "count": 3
           }
          ]
         }
        },
        {
         "name": "Total",
         "dtype": "int64",
         "stats": {
          "unique_count": 5,
          "nan_count": 0,
          "min": "14052",
          "max": "78550",
          "histogram": [
           {
            "bin_start": 14052,
            "bin_end": 20501.8,
            "count": 4
           },
           {
            "bin_start": 20501.8,
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            "count": 0
           },
           {
            "bin_start": 26951.6,
            "bin_end": 33401.4,
            "count": 0
           },
           {
            "bin_start": 33401.4,
            "bin_end": 39851.2,
            "count": 0
           },
           {
            "bin_start": 39851.2,
            "bin_end": 46301,
            "count": 0
           },
           {
            "bin_start": 46301,
            "bin_end": 52750.8,
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           },
           {
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            "bin_end": 65650.4,
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           },
           {
            "bin_start": 65650.4,
            "bin_end": 72100.20000000001,
            "count": 0
           },
           {
            "bin_start": 72100.20000000001,
            "bin_end": 78550,
            "count": 1
           }
          ]
         }
        },
        {
         "name": "_deepnote_index_column",
         "dtype": "int64"
        }
       ],
       "rows": [
        {
         "Produk": "RED RETROSPOT SHOPPER BAG",
         "Total": "78550",
         "_deepnote_index_column": "0"
        },
        {
         "Produk": "SET OF 3 CAKE TINS PANTRY DESIGN",
         "Total": "17917",
         "_deepnote_index_column": "1"
        },
        {
         "Produk": "RECYCLING BAG RETROSPOT ",
         "Total": "16265",
         "_deepnote_index_column": "2"
        },
        {
         "Produk": "PARTY BUNTING",
         "Total": "14157",
         "_deepnote_index_column": "3"
        },
        {
         "Produk": "NATURAL SLATE HEART CHALKBOARD ",
         "Total": "14052",
         "_deepnote_index_column": "4"
        }
       ]
      },
      "text/plain": "                             Produk  Total\n0         RED RETROSPOT SHOPPER BAG  78550\n1  SET OF 3 CAKE TINS PANTRY DESIGN  17917\n2          RECYCLING BAG RETROSPOT   16265\n3                     PARTY BUNTING  14157\n4   NATURAL SLATE HEART CHALKBOARD   14052",
      "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>Produk</th>\n      <th>Total</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>RED RETROSPOT SHOPPER BAG</td>\n      <td>78550</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>SET OF 3 CAKE TINS PANTRY DESIGN</td>\n      <td>17917</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>RECYCLING BAG RETROSPOT</td>\n      <td>16265</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>PARTY BUNTING</td>\n      <td>14157</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>NATURAL SLATE HEART CHALKBOARD</td>\n      <td>14052</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {}
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": "### -- Negara mana yang memiliki penjualan paling sedikit?",
   "metadata": {
    "formattedRanges": [],
    "tags": [],
    "cell_id": "514bfbe007f9417cb5992ce361a7301e",
    "is_collapsed": false,
    "deepnote_cell_type": "text-cell-h3"
   }
  },
  {
   "cell_type": "code",
   "metadata": {
    "tags": [],
    "cell_id": "02a6dab85acc443c97a0af14ced2fbea",
    "source_hash": "1ff93b0b",
    "execution_start": 1661913623035,
    "execution_millis": 913,
    "sql_integration_id": "deepnote-dataframe-sql",
    "deepnote_variable_name": "df_19",
    "deepnote_to_be_reexecuted": false,
    "deepnote_sql_source": "WITH Penjualan_Negara AS (\nSELECT \n    c.name AS Nama_Negara,\n    SUM(o.quantity * p.price) AS Penjualan\nFROM orders AS o\nINNER JOIN products AS p ON o.product_id = p.id\nINNER JOIN countries AS c ON o.country_id = c.id\nGROUP BY c.name\n)\n\nSELECT \n    Nama_Negara,\nFROM Penjualan_Negara\nORDER BY Penjualan ASC\nLIMIT 1;",
    "deepnote_cell_type": "sql",
    "deepnote_cell_height": 518.34375
   },
   "source": "\n\ndf_19 = _deepnote_execute_sql(\"\"\"WITH Penjualan_Negara AS (\nSELECT \n    c.name AS Nama_Negara,\n    SUM(o.quantity * p.price) AS Penjualan\nFROM orders AS o\nINNER JOIN products AS p ON o.product_id = p.id\nINNER JOIN countries AS c ON o.country_id = c.id\nGROUP BY c.name\n)\n\nSELECT \n    Nama_Negara,\nFROM Penjualan_Negara\nORDER BY Penjualan ASC\nLIMIT 1;\n\"\"\", 'SQL_DEEPNOTE_DATAFRAME_SQL')\ndf_19",
   "execution_count": 20,
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 20,
     "data": {
      "application/vnd.deepnote.dataframe.v3+json": {
       "column_count": 1,
       "row_count": 1,
       "columns": [
        {
         "name": "Nama_Negara",
         "dtype": "object",
         "stats": {
          "unique_count": 1,
          "nan_count": 0,
          "categories": [
           {
            "name": "Saudi Arabia",
            "count": 1
           }
          ]
         }
        },
        {
         "name": "_deepnote_index_column",
         "dtype": "int64"
        }
       ],
       "rows": [
        {
         "Nama_Negara": "Saudi Arabia",
         "_deepnote_index_column": "0"
        }
       ]
      },
      "text/plain": "    Nama_Negara\n0  Saudi Arabia",
      "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>Nama_Negara</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Saudi Arabia</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {}
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": "### -- Kapan penjualan terbesar per hari terjadi?",
   "metadata": {
    "formattedRanges": [],
    "tags": [],
    "cell_id": "95e3bc7c7cd04cd493a643a87d99c39f",
    "is_collapsed": false,
    "deepnote_cell_type": "text-cell-h3"
   }
  },
  {
   "cell_type": "code",
   "metadata": {
    "tags": [],
    "cell_id": "f4ca95db3b6c437d8e896675154420e9",
    "source_hash": "776b7798",
    "execution_start": 1661913623212,
    "execution_millis": 743,
    "sql_integration_id": "deepnote-dataframe-sql",
    "deepnote_variable_name": "df_17",
    "deepnote_to_be_reexecuted": false,
    "deepnote_sql_source": "SELECT\n    DATE_TRUNC('day', invoice_date) AS Bulan,\n    SUM(o.quantity * p.price) AS Penjualan\nFROM orders AS o\nJOIN products AS p ON o.product_id = p.id\nGROUP BY DATE_TRUNC('day', invoice_date)\nORDER BY Penjualan DESC;",
    "deepnote_cell_type": "sql",
    "deepnote_cell_height": 743.234375
   },
   "source": "\n\ndf_17 = _deepnote_execute_sql(\"\"\"SELECT\n    DATE_TRUNC('day', invoice_date) AS Bulan,\n    SUM(o.quantity * p.price) AS Penjualan\nFROM orders AS o\nJOIN products AS p ON o.product_id = p.id\nGROUP BY DATE_TRUNC('day', invoice_date)\nORDER BY Penjualan DESC;\n\"\"\", 'SQL_DEEPNOTE_DATAFRAME_SQL')\ndf_17",
   "execution_count": 21,
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 21,
     "data": {
      "application/vnd.deepnote.dataframe.v3+json": {
       "column_count": 2,
       "row_count": 285,
       "columns": [
        {
         "name": "Bulan",
         "dtype": "datetime64[ns]",
         "stats": {
          "unique_count": 285,
          "nan_count": 0,
          "min": "2021-01-01 00:00:00",
          "max": "2021-12-06 00:00:00",
          "histogram": [
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           },
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           },
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            "bin_start": 1635819840000000000,
            "bin_end": 1638748800000000000,
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           }
          ]
         }
        },
        {
         "name": "Penjualan",
         "dtype": "float64",
         "stats": {
          "unique_count": 285,
          "nan_count": 0,
          "min": "3535.0261486729987",
          "max": "162971.24676373697",
          "histogram": [
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      "text/plain": "alt.Chart(...)"
     },
     "metadata": {}
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": "### -- Siapa yang paling banyak berbelanja pada bulan November 2021?",
   "metadata": {
    "formattedRanges": [],
    "tags": [],
    "cell_id": "69401b29ba5b49d69eb86590f94fd1c9",
    "is_collapsed": false,
    "deepnote_cell_type": "text-cell-h3"
   }
  },
  {
   "cell_type": "code",
   "metadata": {
    "tags": [],
    "cell_id": "9399185732a54e218349ef5e67093ce8",
    "source_hash": "3cd0137a",
    "execution_start": 1661913623776,
    "execution_millis": 214,
    "sql_integration_id": "deepnote-dataframe-sql",
    "deepnote_table_state": {
     "sortBy": [],
     "filters": [],
     "pageSize": 10,
     "pageIndex": 0
    },
    "deepnote_table_loading": false,
    "deepnote_variable_name": "df_18",
    "deepnote_to_be_reexecuted": false,
    "deepnote_sql_source": "WITH Customer AS (\nSELECT\n    CONCAT(cs.first_name, ' ', last_name) AS Fullname,\n    SUM(o.quantity * p.price) AS Total\nFROM orders AS o\nJOIN products AS p ON o.product_id = p.id\nJOIN customers AS cs ON o.customer_id = cs.id\nWHERE o.invoice_date > '2021-10-30' AND o.invoice_date < '2021-12-01'\nGROUP BY Fullname\nORDER BY Total DESC\nLIMIT 1\n)\n\nSELECT Fullname\nFROM Customer;",
    "deepnote_cell_type": "sql",
    "deepnote_cell_height": 518.34375
   },
   "source": "\n\ndf_18 = _deepnote_execute_sql(\"\"\"WITH Customer AS (\nSELECT\n    CONCAT(cs.first_name, ' ', last_name) AS Fullname,\n    SUM(o.quantity * p.price) AS Total\nFROM orders AS o\nJOIN products AS p ON o.product_id = p.id\nJOIN customers AS cs ON o.customer_id = cs.id\nWHERE o.invoice_date > '2021-10-30' AND o.invoice_date < '2021-12-01'\nGROUP BY Fullname\nORDER BY Total DESC\nLIMIT 1\n)\n\nSELECT Fullname\nFROM Customer;\n\"\"\", 'SQL_DEEPNOTE_DATAFRAME_SQL')\ndf_18",
   "execution_count": 23,
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 23,
     "data": {
      "application/vnd.deepnote.dataframe.v3+json": {
       "column_count": 1,
       "row_count": 1,
       "columns": [
        {
         "name": "Fullname",
         "dtype": "object",
         "stats": {
          "unique_count": 1,
          "nan_count": 0,
          "categories": [
           {
            "name": "Evan Colloff",
            "count": 1
           }
          ]
         }
        },
        {
         "name": "_deepnote_index_column",
         "dtype": "int64"
        }
       ],
       "rows": [
        {
         "Fullname": "Evan Colloff",
         "_deepnote_index_column": "0"
        }
       ]
      },
      "text/plain": "       Fullname\n0  Evan Colloff",
      "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>Fullname</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Evan Colloff</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {}
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": "Sekian penjelasan mengenai SQL Data Engineering Challenges dan tunggu artikel aku selanjutnya ya. Oh iya, jangan lupa tulis saran dan kritik kalian mengenai artikel ini . . . :) Terimakasih.",
   "metadata": {
    "is_collapsed": false,
    "cell_id": "37dcfe0d8e7547a189d6e0bc158c4c92",
    "tags": [],
    "formattedRanges": [],
    "deepnote_cell_type": "text-cell-p"
   }
  },
  {
   "cell_type": "markdown",
   "source": "## References",
   "metadata": {
    "is_collapsed": false,
    "cell_id": "bb818df98bca43daa551d12d285947e3",
    "tags": [],
    "formattedRanges": [],
    "deepnote_cell_type": "text-cell-h2"
   }
  },
  {
   "cell_type": "markdown",
   "source": "Mentor: Bima Putra Pratama - Analytics Engineer ",
   "metadata": {
    "is_collapsed": false,
    "cell_id": "1416475d093643858adb8eb82823156b",
    "tags": [],
    "formattedRanges": [
     {
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