run-python.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import orchest\n",
    "import pandas as pd\n",
    "\n",
    "from sqlalchemy import create_engine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'name': 'postgres',\n",
       " 'image': 'postgres',\n",
       " 'command': '',\n",
       " 'env_variables': {'POSTGRES_DB': 'jaffle_shop',\n",
       "  'POSTGRES_HOST_AUTH_METHOD': 'trust'},\n",
       " 'env_variables_inherit': [],\n",
       " 'internal_hostname': 'internal-service-postgres-5c9a40f9-7b21ba4a',\n",
       " 'external_urls': {},\n",
       " 'base_paths': {}}"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "postgres_service = orchest.get_service(\"postgres\"); postgres_service "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "engine = create_engine(f'postgresql://postgres@{postgres_service[\"internal_hostname\"]}:5432/jaffle_shop')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def all_tables(con):\n",
    "    query = \"\"\"SELECT *\n",
    "    FROM pg_catalog.pg_tables\n",
    "    WHERE schemaname != 'pg_catalog' AND \n",
    "    schemaname != 'information_schema';\"\"\"\n",
    "    \n",
    "    return pd.read_sql(query, con)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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>schemaname</th>\n",
       "      <th>tablename</th>\n",
       "      <th>tableowner</th>\n",
       "      <th>tablespace</th>\n",
       "      <th>hasindexes</th>\n",
       "      <th>hasrules</th>\n",
       "      <th>hastriggers</th>\n",
       "      <th>rowsecurity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>dbt_alice</td>\n",
       "      <td>raw_orders</td>\n",
       "      <td>postgres</td>\n",
       "      <td>None</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>dbt_alice</td>\n",
       "      <td>customers</td>\n",
       "      <td>postgres</td>\n",
       "      <td>None</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>dbt_alice</td>\n",
       "      <td>raw_customers</td>\n",
       "      <td>postgres</td>\n",
       "      <td>None</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>dbt_alice</td>\n",
       "      <td>orders</td>\n",
       "      <td>postgres</td>\n",
       "      <td>None</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>dbt_alice</td>\n",
       "      <td>raw_payments</td>\n",
       "      <td>postgres</td>\n",
       "      <td>None</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  schemaname      tablename tableowner tablespace  hasindexes  hasrules  \\\n",
       "0  dbt_alice     raw_orders   postgres       None       False     False   \n",
       "1  dbt_alice      customers   postgres       None       False     False   \n",
       "2  dbt_alice  raw_customers   postgres       None       False     False   \n",
       "3  dbt_alice         orders   postgres       None       False     False   \n",
       "4  dbt_alice   raw_payments   postgres       None       False     False   \n",
       "\n",
       "   hastriggers  rowsecurity  \n",
       "0        False        False  \n",
       "1        False        False  \n",
       "2        False        False  \n",
       "3        False        False  \n",
       "4        False        False  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_tables(engine)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "orders = pd.read_sql(\"SELECT * FROM dbt_alice.orders\", engine)\n",
    "customers = pd.read_sql(\"SELECT * FROM dbt_alice.customers\", engine)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>order_id</th>\n",
       "      <th>customer_id</th>\n",
       "      <th>order_date</th>\n",
       "      <th>status</th>\n",
       "      <th>credit_card_amount</th>\n",
       "      <th>coupon_amount</th>\n",
       "      <th>bank_transfer_amount</th>\n",
       "      <th>gift_card_amount</th>\n",
       "      <th>amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2018-01-01</td>\n",
       "      <td>returned</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2018-01-02</td>\n",
       "      <td>completed</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>94</td>\n",
       "      <td>2018-01-04</td>\n",
       "      <td>completed</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>50</td>\n",
       "      <td>2018-01-05</td>\n",
       "      <td>completed</td>\n",
       "      <td>0</td>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>64</td>\n",
       "      <td>2018-01-05</td>\n",
       "      <td>completed</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>95</td>\n",
       "      <td>27</td>\n",
       "      <td>2018-04-04</td>\n",
       "      <td>placed</td>\n",
       "      <td>0</td>\n",
       "      <td>24</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>96</td>\n",
       "      <td>90</td>\n",
       "      <td>2018-04-06</td>\n",
       "      <td>placed</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>17</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>97</td>\n",
       "      <td>89</td>\n",
       "      <td>2018-04-07</td>\n",
       "      <td>placed</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>98</td>\n",
       "      <td>41</td>\n",
       "      <td>2018-04-07</td>\n",
       "      <td>placed</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>99</td>\n",
       "      <td>85</td>\n",
       "      <td>2018-04-09</td>\n",
       "      <td>placed</td>\n",
       "      <td>24</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>99 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    order_id  customer_id  order_date     status  credit_card_amount  \\\n",
       "0          1            1  2018-01-01   returned                  10   \n",
       "1          2            3  2018-01-02  completed                  20   \n",
       "2          3           94  2018-01-04  completed                   0   \n",
       "3          4           50  2018-01-05  completed                   0   \n",
       "4          5           64  2018-01-05  completed                   0   \n",
       "..       ...          ...         ...        ...                 ...   \n",
       "94        95           27  2018-04-04     placed                   0   \n",
       "95        96           90  2018-04-06     placed                   0   \n",
       "96        97           89  2018-04-07     placed                   0   \n",
       "97        98           41  2018-04-07     placed                   0   \n",
       "98        99           85  2018-04-09     placed                  24   \n",
       "\n",
       "    coupon_amount  bank_transfer_amount  gift_card_amount  amount  \n",
       "0               0                     0                 0      10  \n",
       "1               0                     0                 0      20  \n",
       "2               1                     0                 0       1  \n",
       "3              25                     0                 0      25  \n",
       "4               0                    17                 0      17  \n",
       "..            ...                   ...               ...     ...  \n",
       "94             24                     0                 0      24  \n",
       "95              0                     0                17      17  \n",
       "96              0                    14                 0      14  \n",
       "97              0                    10                 0      10  \n",
       "98              0                     0                 0      24  \n",
       "\n",
       "[99 rows x 9 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "orders"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7ff2422118d0>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "orders['amount'].sort_values().reset_index(drop=True).plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7ff24218a410>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "customers['number_of_orders'].dropna().sort_values().reset_index(drop=True).plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "orchest-kernel-b6661754-63e1-4e16-b2ed-1a0f0d50c58c"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}