notebooks/challenges/week2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "f6925517-e046-44bc-9cbf-85a18a5f9b89",
   "metadata": {},
   "source": [
    "This is a notebook for the **Week 2 of the Developer Challenge: https://blogs.sap.com/2023/03/08/sap-community-developer-challenge-eda-with-sap-hana-and-python/**\n",
    "\n",
    "Some code might be incomplete or incorrect. It is your task to fix it and execute the whole notebook receiving correct results.\n",
    "\n",
    "Submit the link to your solution: https://groups.community.sap.com/t5/application-development/submissions-for-quot-eda-with-sap-hana-quot-developer-challenge/m-p/224446/highlight/true#M1129"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fa070ec3-31f9-4768-b6a6-5ac788fd53bc",
   "metadata": {},
   "source": [
    "## Initialize the notebook\n",
    "\n",
    "Import required packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "2beafea6-10d2-4394-93fa-e71090b1f4e5",
   "metadata": {},
   "outputs": [],
   "source": [
    "from hana_ml import dataframe as hdf\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6bab666a-22f0-43c0-aee5-24b614802946",
   "metadata": {},
   "source": [
    "Connect to SAP HANA database instance with a database user, same as during Week 1!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "2bf4389a-b734-47a5-9fec-8ffbe84e3aa3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SAP HANA DB version:  4.00.000.00.1678185420 (fa/CE2022.42)\n"
     ]
    }
   ],
   "source": [
    "myconn=hdf.ConnectionContext(userkey='DevChallenger')\n",
    "print(\"SAP HANA DB version: \", myconn.hana_version())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "7e444bbe-4d3d-4b1a-8167-5ed884aa1abb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2023-03-20 16:28:47.473000\n"
     ]
    }
   ],
   "source": [
    "print(myconn.sql(\"SELECT NOW() FROM DUMMY\").collect().CURRENT_TIMESTAMP[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b8e53d9f-2c10-44a0-a1ad-e1253a8b7b32",
   "metadata": {},
   "source": [
    "# Working with SAP HANA tables in a Python client \n",
    "\n",
    "Below you will find two approaches creating tables, populating them with data and retrieving this data from Python:\n",
    "1. Compatible with Python standard proposal PEP 249 using SQL\n",
    "2. Using HANA DataFrame abstract from `hana_ml`"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "01b97cc2-e76f-4e7e-8e0b-d480b77a34a5",
   "metadata": {},
   "source": [
    "## 1. Approach compatible with Python proposal PEP 249 using SQL\n",
    "\n",
    "References:\n",
    "1. https://peps.python.org/pep-0249/#connection-objects\n",
    "2. https://help.sap.com/docs/SAP_HANA_CLIENT/f1b440ded6144a54ada97ff95dac7adf/3b5ebe388c1040ec83617c9e511ecda5.html?version=2.15&locale=en-US"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "93623276-8f30-4390-976e-fba68a8a0c0c",
   "metadata": {},
   "source": [
    "`ConnectionContext` in `hana-ml` represents a connection to a SAP HANA database instance.\n",
    "\n",
    "It has [an attribute `connection`](https://help.sap.com/doc/cd94b08fe2e041c2ba778374572ddba9/2022_4_QRC/en-US/hana_ml.dataframe.html#hana_ml.dataframe.ConnectionContext) which is the underlying dbapi connection from [SAP HANA Client's Python driver `hdbcli`](https://help.sap.com/docs/SAP_HANA_CLIENT/f1b440ded6144a54ada97ff95dac7adf/d12c86af7cb442d1b9f8520e2aba7758.html?version=2.15&locale=en-US).\n",
    "\n",
    "Let's open a [cursor](https://help.sap.com/docs/SAP_HANA_CLIENT/f1b440ded6144a54ada97ff95dac7adf/d0c010fa5c4e4e4cba7b791cd1c9479b.html?version=2.15&locale=en-US) called `mycursor` to be used for the fetch operations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "04200766-d0ba-4868-a4a1-84dd60e52253",
   "metadata": {},
   "outputs": [],
   "source": [
    "mycursor = myconn.connection.cursor()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "12618eb5-0ad1-4f6e-90a5-e4d1af2386e3",
   "metadata": {},
   "source": [
    "[Execute an SQL statement](https://help.sap.com/docs/SAP_HANA_CLIENT/f1b440ded6144a54ada97ff95dac7adf/3b89755ceec042e5919313b40acf8794.html?version=2.15&locale=en-US) to create a table `SEASONS` in the database."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "ce867fab",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DROPPING TABLE\n"
     ]
    }
   ],
   "source": [
    "test = mycursor.execute('select * from seasons')\n",
    "if  test != 0:\n",
    "    print('DROPPING TABLE')\n",
    "    mycursor.execute('DROP TABLE \"SEASONS\"')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "13f4879d-bf45-483d-bff8-b78e65aaf1ef",
   "metadata": {},
   "outputs": [],
   "source": [
    "#mycursor.execute('DROP TABLE \"SEASONS\"'); #uncomment to drop the table, if already exists\n",
    "try:\n",
    "    test = mycursor.execute('select * from seasons')\n",
    "except:\n",
    "    test = 0 # table does not exist yet\n",
    "\n",
    "if test != 1:\n",
    "    mycursor.execute('''\n",
    "    CREATE TABLE \"SEASONS\" (\n",
    "      \"ID\" INT PRIMARY KEY, \n",
    "      \"DESC\" VARCHAR(12)\n",
    "    )\n",
    "    ''');"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5b8f9deb-d65e-4db3-8831-89c86cacb838",
   "metadata": {},
   "source": [
    "Use [SQL statement to insert multiple records](https://help.sap.com/docs/SAP_HANA_CLIENT/f1b440ded6144a54ada97ff95dac7adf/15e46b843c8045ec854d6375790cd504.html?version=2.15&locale=en-US) into the table `SEASONS`.\n",
    "\n",
    "You should get the following result after execurting the cell below:\n",
    "```\n",
    "Records inserted:  4\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "379fb9a1-5185-474b-bf08-76e1d641e9b4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Records inserted:  4\n"
     ]
    }
   ],
   "source": [
    "mycursor.execute('TRUNCATE TABLE \"SEASONS\"');\n",
    "\n",
    "records_inserted=mycursor.executemany(operation='INSERT INTO \"SEASONS\" (\"ID\", \"DESC\") VALUES (?, ?)',\n",
    "                                     list_of_parameters=\n",
    "                                      ((1, \"WINTER\"),\n",
    "                                      (2, \"SPRING\"),\n",
    "                                      (3, \"SUMMER\"), # summer is always three\n",
    "                                      (4, \"FALL\"))\n",
    "                                     )\n",
    "print(\"Records inserted: \", sum(records_inserted))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ac4087fb-37c3-4981-9a3f-15ffb7569869",
   "metadata": {},
   "source": [
    "Use SQL statement to fetch all records from the table\n",
    "\n",
    "You should get:\n",
    "```\n",
    "the first record (1, 'WINTER')\n",
    "the second record (2, 'SPRING')\n",
    "the rest of records [(3, 'SUMMER'), (4, 'FALL')]\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "324bc792-b18e-441a-bc63-776e39486473",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the 1. record WINTER\n",
      "the 2. record SPRING\n",
      "the 3. record SUMMER\n",
      "the 4. record FALL\n",
      "The 1. season is WINTER.\n",
      "The 2. season is SPRING.\n",
      "The 3. season is SUMMER.\n",
      "The 4. season is FALL.\n"
     ]
    }
   ],
   "source": [
    "mycursor.execute('SELECT * FROM \"SEASONS\"')\n",
    "#Get the first record\n",
    "result=mycursor.fetchone()\n",
    "print(f\"the {result[0]}. record {result[1]}\")\n",
    "# print(\"the [ID] record\", result)\n",
    "\n",
    "#Get the second record\n",
    "while result is not None:\n",
    "#    result=mycursor.execute(select single from 'SEASONS' where 'ID' = 2)\n",
    "    result=mycursor.fetchone() # get the next record\n",
    "    try:\n",
    "        print(f\"the {result[0]}. record {result[1]}\")\n",
    "    except:\n",
    "        pass\n",
    "        # this is the end\n",
    "\n",
    "#Get the rest of records\n",
    "#print(\"the rest of records\", result)\n",
    "\n",
    "\n",
    "\n",
    "mycursor.execute('SELECT * FROM \"SEASONS\"')\n",
    "df = mycursor.fetchall()\n",
    "# print(df)\n",
    "import pandas as pd\n",
    "pd = df\n",
    "\n",
    "for i in range(0,len(pd)):\n",
    "    print(f\"The {pd[i][0]}. season is {pd[i][1]}.\")\n",
    "    i = i+1\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d30ef46c-85ba-4ff7-8601-f4b281aff2ca",
   "metadata": {},
   "source": [
    "### Let's check tables created so far in the current database schema."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "8d6bc7db-e4c9-4ef5-abc1-60a86750a58f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Current database schema: DEVCHALLENGER\n"
     ]
    }
   ],
   "source": [
    "print(f\"Current database schema: {myconn.get_current_schema()}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "0af45c3e-ee72-442f-b82b-09e8e5cbffb4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3 table(s) in the schema DEVCHALLENGER: ['BIKES', 'SEASONS', 'MONTHS']\n"
     ]
    }
   ],
   "source": [
    "mycursor.execute(f'''\n",
    "SELECT TABLE_NAME\n",
    "FROM M_TABLES\n",
    "WHERE SCHEMA_NAME='{myconn.get_current_schema()}'\n",
    "''')\n",
    "\n",
    "#Get all records\n",
    "result=mycursor.fetchall()\n",
    "print(f\"{len(result)} table(s) in the schema {myconn.get_current_schema()}: {[x[0] for x in result]}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9341740f-c224-49df-9523-722e4c468c71",
   "metadata": {},
   "source": [
    "# 2. `hana-ml` approach using interoperability between Pandas DataFrames and HANA DataFrames\n",
    "\n",
    "References:\n",
    "1. https://help.sap.com/doc/cd94b08fe2e041c2ba778374572ddba9/2022_4_QRC/en-US/hana_ml.html#sap-hana-dataframe\n",
    "1. https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.html"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "74856c9d-110d-4e22-b2a0-c3a8f9440486",
   "metadata": {},
   "source": [
    "Create a table `MONTHS` in the current database schema.\n",
    "\n",
    "But this time you should use [`create_table()` method](https://help.sap.com/doc/cd94b08fe2e041c2ba778374572ddba9/2022_4_QRC/en-US/hana_ml.dataframe.html#hana_ml.dataframe.ConnectionContext.create_table) from `hana_ml`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "9b5363b7-c015-4ead-963f-b6aff1a96ede",
   "metadata": {},
   "outputs": [],
   "source": [
    "try:\n",
    "    test = mycursor.execute('select * from months')\n",
    "except:\n",
    "    test = 0 # table does not exist yet\n",
    "\n",
    "if test != 1:\n",
    "# myconn.drop_table(\"MONTHS\")\n",
    "    myconn.create_table(\"MONTHS\", table_structure={\"ID\":\"INT\", \"DESC\":\"VARCHAR(10)\"})"
   ]
  },
  {
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    "71e117be-596d-4be5-8832-83e4b50ee61d.png": {
     "image/png": "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"
    }
   },
   "cell_type": "markdown",
   "id": "63b52694-db0f-41c5-abbe-034034e5019a",
   "metadata": {
    "tags": []
   },
   "source": [
    "Check tables created so far in the current database schema.\n",
    "\n",
    "But this time you should use [`get_tables()` method](https://help.sap.com/doc/cd94b08fe2e041c2ba778374572ddba9/2022_4_QRC/en-US/hana_ml.dataframe.html#hana_ml.dataframe.ConnectionContext.get_tables) from `hana_ml` to receive the output similar to:\n",
    "\n",
    "![image.png](attachment:71e117be-596d-4be5-8832-83e4b50ee61d.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "85ecb5cd-c17a-456e-98c9-052faeb79c82",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  TABLE_NAME\n",
      "0      BIKES\n",
      "1     MONTHS\n",
      "2    SEASONS\n"
     ]
    }
   ],
   "source": [
    "tables = myconn.get_tables()\n",
    "print(tables)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eb360fce-7097-4242-a8a5-9b99f36736cd",
   "metadata": {},
   "source": [
    "Create [Pandas DataFrame](https://pandas.pydata.org/docs/getting_started/intro_tutorials/01_table_oriented.html#what-kind-of-data-does-pandas-handle) with data describing calendar months. \n",
    "\n",
    "The name of the object is `months_df`, and it is quite common to use `_df` postfix in a name of a variable storing Pandas DataFrame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "6bce716e-3391-40ad-bf31-439d32eb2f38",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "months_df=pd.DataFrame(\n",
    "    {\"ID\": range(1,13),\n",
    "     \"DESC\": ['JAN', 'FEB', 'MAR', 'APR', 'MAY', 'JUN', 'JUL', 'AUG', 'SEP', 'OCT', 'NOV', 'DEC']\n",
    "    })"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c33431e1-c37e-4e5e-b88b-351bbd6f3924",
   "metadata": {},
   "source": [
    "Display data from Pandas DataFrame `months_df`. \n",
    "> For the clarity of the output `.style.hide(axis='index')` can be added to remove the Pandas index from the output."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "5f167180-fe6c-4406-98c5-6e38cc0398cd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "</style>\n",
       "<table id=\"T_38728\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th id=\"T_38728_level0_col0\" class=\"col_heading level0 col0\" >ID</th>\n",
       "      <th id=\"T_38728_level0_col1\" class=\"col_heading level0 col1\" >DESC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td id=\"T_38728_row0_col0\" class=\"data row0 col0\" >1</td>\n",
       "      <td id=\"T_38728_row0_col1\" class=\"data row0 col1\" >JAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_38728_row1_col0\" class=\"data row1 col0\" >2</td>\n",
       "      <td id=\"T_38728_row1_col1\" class=\"data row1 col1\" >FEB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_38728_row2_col0\" class=\"data row2 col0\" >3</td>\n",
       "      <td id=\"T_38728_row2_col1\" class=\"data row2 col1\" >MAR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_38728_row3_col0\" class=\"data row3 col0\" >4</td>\n",
       "      <td id=\"T_38728_row3_col1\" class=\"data row3 col1\" >APR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_38728_row4_col0\" class=\"data row4 col0\" >5</td>\n",
       "      <td id=\"T_38728_row4_col1\" class=\"data row4 col1\" >MAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_38728_row5_col0\" class=\"data row5 col0\" >6</td>\n",
       "      <td id=\"T_38728_row5_col1\" class=\"data row5 col1\" >JUN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_38728_row6_col0\" class=\"data row6 col0\" >7</td>\n",
       "      <td id=\"T_38728_row6_col1\" class=\"data row6 col1\" >JUL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_38728_row7_col0\" class=\"data row7 col0\" >8</td>\n",
       "      <td id=\"T_38728_row7_col1\" class=\"data row7 col1\" >AUG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_38728_row8_col0\" class=\"data row8 col0\" >9</td>\n",
       "      <td id=\"T_38728_row8_col1\" class=\"data row8 col1\" >SEP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_38728_row9_col0\" class=\"data row9 col0\" >10</td>\n",
       "      <td id=\"T_38728_row9_col1\" class=\"data row9 col1\" >OCT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_38728_row10_col0\" class=\"data row10 col0\" >11</td>\n",
       "      <td id=\"T_38728_row10_col1\" class=\"data row10 col1\" >NOV</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_38728_row11_col0\" class=\"data row11 col0\" >12</td>\n",
       "      <td id=\"T_38728_row11_col1\" class=\"data row11 col1\" >DEC</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7f48103969d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(months_df.style.hide(axis='index'))\n",
    "\n",
    "# compare the output with a hidden Pandas index by removing a comment in the line below: \n",
    "#display(months_df.style.hide(axis='index'))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c32f52d6",
   "metadata": {},
   "source": [
    "## Create HANA DataFrame from Pandas DataFrame.\n",
    "\n",
    "`hana_ml` provides a [method `create_dataframe_from_pandas`](https://help.sap.com/doc/cd94b08fe2e041c2ba778374572ddba9/2022_4_QRC/en-US/hana_ml.dataframe.html#hana_ml.dataframe.create_dataframe_from_pandas) to store data from the Pandas DataFrame in a table in SAP HANA database. A table is created, if needed.\n",
    "\n",
    "The name of the object is `months_hdf`, and it is quite common to use `_hdf` postfix in a name of a variable storing HANA DataFrame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "4881ef24-292d-4367-946c-1bb63a5d6391",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|█████████████████████████████████████████████| 1/1 [00:00<00:00,  4.45it/s]\n"
     ]
    }
   ],
   "source": [
    "\n",
    "months_hdf = hdf.create_dataframe_from_pandas(connection_context=myconn,\n",
    "                                      pandas_df=months_df,\n",
    "                                      table_name='MONTHS',\n",
    "                                      primary_key='ID',\n",
    "                                      force=True\n",
    "                                     )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5c59285e-3d15-4688-bff8-fc76c1844457",
   "metadata": {},
   "source": [
    "The type of a `months_hdf` object returned from the `create_dataframe_from_pandas` method is a HANA DataFrame (`hana_ml.dataframe.DataFrame`)..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "7297fe55-d374-41ea-a762-ddb41f993a37",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "hana_ml.dataframe.DataFrame"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(months_hdf)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aab54a8b-2e37-4253-8342-f2b6512609b5",
   "metadata": {},
   "source": [
    "\n",
    "HANA DataFrames do not store any data on a client side in Python. Each HANA DataFrame only stores a connection contex and a [`select_statement` attribute](https://help.sap.com/doc/cd94b08fe2e041c2ba778374572ddba9/2022_4_QRC/en-US/hana_ml.dataframe.html#hana_ml.dataframe.DataFrame) to be used to retrieve data from the SAP HANA database."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "f317e446-cb9c-411b-90a9-48fc9b2fcb39",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SELECT * FROM \"MONTHS\"\n"
     ]
    }
   ],
   "source": [
    "print(months_hdf.select_statement)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "838a3f1f-2cbc-4875-9bcd-3bf442992176",
   "metadata": {},
   "source": [
    "You can, obviously, use that SELECT statement **to retrieve data using SQL** as you've seen in the first approach..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "0f569052-70bf-4adb-b0e9-d808f4cbc68c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "All 12 records: [(1, 'JAN'), (2, 'FEB'), (3, 'MAR'), (4, 'APR'), (5, 'MAY'), (6, 'JUN'), (7, 'JUL'), (8, 'AUG'), (9, 'SEP'), (10, 'OCT'), (11, 'NOV'), (12, 'DEC')]\n"
     ]
    }
   ],
   "source": [
    "mycursor.execute(months_hdf.select_statement)\n",
    "\n",
    "#Get all records\n",
    "result=mycursor.fetchall()\n",
    "\n",
    "#Display returned records\n",
    "print(f\"All {len(result)} records: {result}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b5079536-a4d1-444c-a053-72d66aad56f8",
   "metadata": {},
   "source": [
    "...but **the proper way to retrieve data** from a HANA table is by using a [`collect` method](https://help.sap.com/doc/cd94b08fe2e041c2ba778374572ddba9/2022_4_QRC/en-US/hana_ml.dataframe.html#hana_ml.dataframe.DataFrame.collect), which returns selected data in the form of the Pandas DataFrame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "85d6e7f4-573b-4228-9cb8-e6a39b174c02",
   "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",
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       "    }\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>DESC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>JAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>FEB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>MAR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>APR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>MAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>JUN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>JUL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>AUG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>SEP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>OCT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>NOV</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>DEC</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    ID DESC\n",
       "0    1  JAN\n",
       "1    2  FEB\n",
       "2    3  MAR\n",
       "3    4  APR\n",
       "4    5  MAY\n",
       "5    6  JUN\n",
       "6    7  JUL\n",
       "7    8  AUG\n",
       "8    9  SEP\n",
       "9   10  OCT\n",
       "10  11  NOV\n",
       "11  12  DEC"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "months_hdf.collect()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "266b06b4-573e-438a-9c36-2069a54bd309",
   "metadata": {},
   "source": [
    "You can see this method returns an instance of a Pandas DataFrame (`pandas.core.frame.DataFrame`)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "deb918e1-75d2-41b5-a5db-f0b958b4281d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(months_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "90c0b225-a74c-4269-afc7-6175d761b9fd",
   "metadata": {},
   "source": [
    "## Using `hana-ml` approach to load data from a CSV file (or any other file Pandas can read)\n",
    "\n",
    "In most of the case we do not want to manually type data to be inserted, but have this data already stored in a file, like CSV."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "af83ed8e-d450-4d6c-ae2a-09a19651aef6",
   "metadata": {},
   "outputs": [],
   "source": [
    "url=\"https://raw.githubusercontent.com/SAP-samples/hana-ml-samples/main/Python-API/pal/datasets/bike.csv\"\n",
    "bikes_df = pd.io.parsers.read_csv(url)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dffc31d4-247e-481d-a9f0-c80380a4b244",
   "metadata": {},
   "source": [
    "Display the content of the Pandas DataFrame `bikes_df` with data from the CSV file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "07561e90-b593-4b03-9c8a-647bf73f4523",
   "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>season</th>\n",
       "      <th>yr</th>\n",
       "      <th>mnth</th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weathersit</th>\n",
       "      <th>temp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>cnt</th>\n",
       "      <th>days_since_2011</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>WINTER</td>\n",
       "      <td>2011</td>\n",
       "      <td>JAN</td>\n",
       "      <td>NO HOLIDAY</td>\n",
       "      <td>SAT</td>\n",
       "      <td>NO WORKING DAY</td>\n",
       "      <td>MISTY</td>\n",
       "      <td>8.175849</td>\n",
       "      <td>80.5833</td>\n",
       "      <td>10.749882</td>\n",
       "      <td>985</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>WINTER</td>\n",
       "      <td>2011</td>\n",
       "      <td>JAN</td>\n",
       "      <td>NO HOLIDAY</td>\n",
       "      <td>SUN</td>\n",
       "      <td>NO WORKING DAY</td>\n",
       "      <td>MISTY</td>\n",
       "      <td>9.083466</td>\n",
       "      <td>69.6087</td>\n",
       "      <td>16.652113</td>\n",
       "      <td>801</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>WINTER</td>\n",
       "      <td>2011</td>\n",
       "      <td>JAN</td>\n",
       "      <td>NO HOLIDAY</td>\n",
       "      <td>MON</td>\n",
       "      <td>WORKING DAY</td>\n",
       "      <td>GOOD</td>\n",
       "      <td>1.229108</td>\n",
       "      <td>43.7273</td>\n",
       "      <td>16.636703</td>\n",
       "      <td>1349</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>WINTER</td>\n",
       "      <td>2011</td>\n",
       "      <td>JAN</td>\n",
       "      <td>NO HOLIDAY</td>\n",
       "      <td>TUE</td>\n",
       "      <td>WORKING DAY</td>\n",
       "      <td>GOOD</td>\n",
       "      <td>1.400000</td>\n",
       "      <td>59.0435</td>\n",
       "      <td>10.739832</td>\n",
       "      <td>1562</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>WINTER</td>\n",
       "      <td>2011</td>\n",
       "      <td>JAN</td>\n",
       "      <td>NO HOLIDAY</td>\n",
       "      <td>WED</td>\n",
       "      <td>WORKING DAY</td>\n",
       "      <td>GOOD</td>\n",
       "      <td>2.666979</td>\n",
       "      <td>43.6957</td>\n",
       "      <td>12.522300</td>\n",
       "      <td>1600</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>726</th>\n",
       "      <td>WINTER</td>\n",
       "      <td>2012</td>\n",
       "      <td>DEC</td>\n",
       "      <td>NO HOLIDAY</td>\n",
       "      <td>THU</td>\n",
       "      <td>WORKING DAY</td>\n",
       "      <td>MISTY</td>\n",
       "      <td>3.945849</td>\n",
       "      <td>65.2917</td>\n",
       "      <td>23.458911</td>\n",
       "      <td>2114</td>\n",
       "      <td>726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>727</th>\n",
       "      <td>WINTER</td>\n",
       "      <td>2012</td>\n",
       "      <td>DEC</td>\n",
       "      <td>NO HOLIDAY</td>\n",
       "      <td>FRI</td>\n",
       "      <td>WORKING DAY</td>\n",
       "      <td>MISTY</td>\n",
       "      <td>3.906651</td>\n",
       "      <td>59.0000</td>\n",
       "      <td>10.416557</td>\n",
       "      <td>3095</td>\n",
       "      <td>727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>728</th>\n",
       "      <td>WINTER</td>\n",
       "      <td>2012</td>\n",
       "      <td>DEC</td>\n",
       "      <td>NO HOLIDAY</td>\n",
       "      <td>SAT</td>\n",
       "      <td>NO WORKING DAY</td>\n",
       "      <td>MISTY</td>\n",
       "      <td>3.906651</td>\n",
       "      <td>75.2917</td>\n",
       "      <td>8.333661</td>\n",
       "      <td>1341</td>\n",
       "      <td>728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>729</th>\n",
       "      <td>WINTER</td>\n",
       "      <td>2012</td>\n",
       "      <td>DEC</td>\n",
       "      <td>NO HOLIDAY</td>\n",
       "      <td>SUN</td>\n",
       "      <td>NO WORKING DAY</td>\n",
       "      <td>GOOD</td>\n",
       "      <td>4.024151</td>\n",
       "      <td>48.3333</td>\n",
       "      <td>23.500518</td>\n",
       "      <td>1796</td>\n",
       "      <td>729</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>730</th>\n",
       "      <td>WINTER</td>\n",
       "      <td>2012</td>\n",
       "      <td>DEC</td>\n",
       "      <td>NO HOLIDAY</td>\n",
       "      <td>MON</td>\n",
       "      <td>WORKING DAY</td>\n",
       "      <td>MISTY</td>\n",
       "      <td>2.144151</td>\n",
       "      <td>57.7500</td>\n",
       "      <td>10.374682</td>\n",
       "      <td>2729</td>\n",
       "      <td>730</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>731 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     season    yr mnth     holiday weekday      workingday weathersit  \\\n",
       "0    WINTER  2011  JAN  NO HOLIDAY     SAT  NO WORKING DAY      MISTY   \n",
       "1    WINTER  2011  JAN  NO HOLIDAY     SUN  NO WORKING DAY      MISTY   \n",
       "2    WINTER  2011  JAN  NO HOLIDAY     MON     WORKING DAY       GOOD   \n",
       "3    WINTER  2011  JAN  NO HOLIDAY     TUE     WORKING DAY       GOOD   \n",
       "4    WINTER  2011  JAN  NO HOLIDAY     WED     WORKING DAY       GOOD   \n",
       "..      ...   ...  ...         ...     ...             ...        ...   \n",
       "726  WINTER  2012  DEC  NO HOLIDAY     THU     WORKING DAY      MISTY   \n",
       "727  WINTER  2012  DEC  NO HOLIDAY     FRI     WORKING DAY      MISTY   \n",
       "728  WINTER  2012  DEC  NO HOLIDAY     SAT  NO WORKING DAY      MISTY   \n",
       "729  WINTER  2012  DEC  NO HOLIDAY     SUN  NO WORKING DAY       GOOD   \n",
       "730  WINTER  2012  DEC  NO HOLIDAY     MON     WORKING DAY      MISTY   \n",
       "\n",
       "         temp      hum  windspeed   cnt  days_since_2011  \n",
       "0    8.175849  80.5833  10.749882   985                0  \n",
       "1    9.083466  69.6087  16.652113   801                1  \n",
       "2    1.229108  43.7273  16.636703  1349                2  \n",
       "3    1.400000  59.0435  10.739832  1562                3  \n",
       "4    2.666979  43.6957  12.522300  1600                4  \n",
       "..        ...      ...        ...   ...              ...  \n",
       "726  3.945849  65.2917  23.458911  2114              726  \n",
       "727  3.906651  59.0000  10.416557  3095              727  \n",
       "728  3.906651  75.2917   8.333661  1341              728  \n",
       "729  4.024151  48.3333  23.500518  1796              729  \n",
       "730  2.144151  57.7500  10.374682  2729              730  \n",
       "\n",
       "[731 rows x 12 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(bikes_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f766ab1a-7877-433a-82c1-bf40d48bf0db",
   "metadata": {},
   "source": [
    "Now persist this data in HANA table database using the same [`create_dataframe_from_pandas` method](https://help.sap.com/doc/cd94b08fe2e041c2ba778374572ddba9/2022_4_QRC/en-US/hana_ml.dataframe.html#hana_ml.dataframe.create_dataframe_from_pandas) from `hana_ml` as before."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "2157e623-b9bb-4574-a28c-15ccd4dceaff",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|█████████████████████████████████████████████| 1/1 [00:00<00:00,  2.15it/s]\n"
     ]
    }
   ],
   "source": [
    "bikes_hdf = hdf.create_dataframe_from_pandas(connection_context=myconn,\n",
    "                                      pandas_df=bikes_df,\n",
    "                                      table_name='BIKES',\n",
    "                                      primary_key='days_since_2011',\n",
    "                                      force=True\n",
    "                                     )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "57881c93-6ba9-469f-a937-aa57ca50811f",
   "metadata": {},
   "source": [
    "The type of a `bikes_hdf` object returned from the `create_dataframe_from_pandas` method is a HANA DataFrame..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "f626d2c1-4ad7-41a8-8acb-d4354864bb1b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "hana_ml.dataframe.DataFrame"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(bikes_hdf)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "addc2581-074e-47f1-a2aa-730f7f91f206",
   "metadata": {},
   "source": [
    "...and a SELECT statement that supports this HANA DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "0362d4c9-4634-4991-ba78-5a14f9ff92aa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This is the select statement: SELECT * FROM \"BIKES\"\n"
     ]
    }
   ],
   "source": [
    "print(f\"This is the select statement: {bikes_hdf.select_statement}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6450f8d5-e38b-47e1-b231-8a9dff0776c0",
   "metadata": {},
   "source": [
    "As stated above the `collect()` method returns a Pandas DataFrame, and therefore Pandas methods can be applied to it, like `head()` to output a few records..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "d000f49f-1969-45a1-86a1-7d93d0c59b0c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>season</th>\n",
       "      <th>yr</th>\n",
       "      <th>mnth</th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weathersit</th>\n",
       "      <th>temp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>cnt</th>\n",
       "      <th>days_since_2011</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>WINTER</td>\n",
       "      <td>2011</td>\n",
       "      <td>JAN</td>\n",
       "      <td>NO HOLIDAY</td>\n",
       "      <td>SAT</td>\n",
       "      <td>NO WORKING DAY</td>\n",
       "      <td>MISTY</td>\n",
       "      <td>8.175849</td>\n",
       "      <td>80.5833</td>\n",
       "      <td>10.749882</td>\n",
       "      <td>985</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>WINTER</td>\n",
       "      <td>2011</td>\n",
       "      <td>JAN</td>\n",
       "      <td>NO HOLIDAY</td>\n",
       "      <td>SUN</td>\n",
       "      <td>NO WORKING DAY</td>\n",
       "      <td>MISTY</td>\n",
       "      <td>9.083466</td>\n",
       "      <td>69.6087</td>\n",
       "      <td>16.652113</td>\n",
       "      <td>801</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>WINTER</td>\n",
       "      <td>2011</td>\n",
       "      <td>JAN</td>\n",
       "      <td>NO HOLIDAY</td>\n",
       "      <td>MON</td>\n",
       "      <td>WORKING DAY</td>\n",
       "      <td>GOOD</td>\n",
       "      <td>1.229108</td>\n",
       "      <td>43.7273</td>\n",
       "      <td>16.636703</td>\n",
       "      <td>1349</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>WINTER</td>\n",
       "      <td>2011</td>\n",
       "      <td>JAN</td>\n",
       "      <td>NO HOLIDAY</td>\n",
       "      <td>TUE</td>\n",
       "      <td>WORKING DAY</td>\n",
       "      <td>GOOD</td>\n",
       "      <td>1.400000</td>\n",
       "      <td>59.0435</td>\n",
       "      <td>10.739832</td>\n",
       "      <td>1562</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>WINTER</td>\n",
       "      <td>2011</td>\n",
       "      <td>JAN</td>\n",
       "      <td>NO HOLIDAY</td>\n",
       "      <td>WED</td>\n",
       "      <td>WORKING DAY</td>\n",
       "      <td>GOOD</td>\n",
       "      <td>2.666979</td>\n",
       "      <td>43.6957</td>\n",
       "      <td>12.522300</td>\n",
       "      <td>1600</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>WINTER</td>\n",
       "      <td>2011</td>\n",
       "      <td>JAN</td>\n",
       "      <td>NO HOLIDAY</td>\n",
       "      <td>THU</td>\n",
       "      <td>WORKING DAY</td>\n",
       "      <td>GOOD</td>\n",
       "      <td>1.604356</td>\n",
       "      <td>51.8261</td>\n",
       "      <td>6.000868</td>\n",
       "      <td>1606</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>WINTER</td>\n",
       "      <td>2011</td>\n",
       "      <td>JAN</td>\n",
       "      <td>NO HOLIDAY</td>\n",
       "      <td>FRI</td>\n",
       "      <td>WORKING DAY</td>\n",
       "      <td>MISTY</td>\n",
       "      <td>1.236534</td>\n",
       "      <td>49.8696</td>\n",
       "      <td>11.304642</td>\n",
       "      <td>1510</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>WINTER</td>\n",
       "      <td>2011</td>\n",
       "      <td>JAN</td>\n",
       "      <td>NO HOLIDAY</td>\n",
       "      <td>SAT</td>\n",
       "      <td>NO WORKING DAY</td>\n",
       "      <td>MISTY</td>\n",
       "      <td>-0.245000</td>\n",
       "      <td>53.5833</td>\n",
       "      <td>17.875868</td>\n",
       "      <td>959</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>WINTER</td>\n",
       "      <td>2011</td>\n",
       "      <td>JAN</td>\n",
       "      <td>NO HOLIDAY</td>\n",
       "      <td>SUN</td>\n",
       "      <td>NO WORKING DAY</td>\n",
       "      <td>GOOD</td>\n",
       "      <td>-1.498349</td>\n",
       "      <td>43.4167</td>\n",
       "      <td>24.250650</td>\n",
       "      <td>822</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>WINTER</td>\n",
       "      <td>2011</td>\n",
       "      <td>JAN</td>\n",
       "      <td>NO HOLIDAY</td>\n",
       "      <td>MON</td>\n",
       "      <td>WORKING DAY</td>\n",
       "      <td>GOOD</td>\n",
       "      <td>-0.910849</td>\n",
       "      <td>48.2917</td>\n",
       "      <td>14.958889</td>\n",
       "      <td>1321</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   season    yr mnth     holiday weekday      workingday weathersit      temp  \\\n",
       "0  WINTER  2011  JAN  NO HOLIDAY     SAT  NO WORKING DAY      MISTY  8.175849   \n",
       "1  WINTER  2011  JAN  NO HOLIDAY     SUN  NO WORKING DAY      MISTY  9.083466   \n",
       "2  WINTER  2011  JAN  NO HOLIDAY     MON     WORKING DAY       GOOD  1.229108   \n",
       "3  WINTER  2011  JAN  NO HOLIDAY     TUE     WORKING DAY       GOOD  1.400000   \n",
       "4  WINTER  2011  JAN  NO HOLIDAY     WED     WORKING DAY       GOOD  2.666979   \n",
       "5  WINTER  2011  JAN  NO HOLIDAY     THU     WORKING DAY       GOOD  1.604356   \n",
       "6  WINTER  2011  JAN  NO HOLIDAY     FRI     WORKING DAY      MISTY  1.236534   \n",
       "7  WINTER  2011  JAN  NO HOLIDAY     SAT  NO WORKING DAY      MISTY -0.245000   \n",
       "8  WINTER  2011  JAN  NO HOLIDAY     SUN  NO WORKING DAY       GOOD -1.498349   \n",
       "9  WINTER  2011  JAN  NO HOLIDAY     MON     WORKING DAY       GOOD -0.910849   \n",
       "\n",
       "       hum  windspeed   cnt  days_since_2011  \n",
       "0  80.5833  10.749882   985                0  \n",
       "1  69.6087  16.652113   801                1  \n",
       "2  43.7273  16.636703  1349                2  \n",
       "3  59.0435  10.739832  1562                3  \n",
       "4  43.6957  12.522300  1600                4  \n",
       "5  51.8261   6.000868  1606                5  \n",
       "6  49.8696  11.304642  1510                6  \n",
       "7  53.5833  17.875868   959                7  \n",
       "8  43.4167  24.250650   822                8  \n",
       "9  48.2917  14.958889  1321                9  "
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bikes_hdf.collect().head(n=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "43b4ebb4-8e3d-43ae-a26d-6f098aa86270",
   "metadata": {},
   "source": [
    "...or `[\"season\"].unique().tolist()` to output [unique values](https://pandas.pydata.org/docs/reference/api/pandas.Series.unique.html#pandas.Series.unique) only from the `season` column [as a list](https://pandas.pydata.org/docs/reference/api/pandas.Series.tolist.html#pandas.Series.tolist)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "a357f4be-1c6e-4f69-adcd-8a5ae1c20d5c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['WINTER', 'SPRING', 'SUMMER', 'FALL']"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bikes_hdf.collect()[\"season\"].unique().tolist()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b5364db7-5e92-4fc5-83e9-ad63e505bc77",
   "metadata": {},
   "source": [
    "The problem with the above approach applying Pandas' data manipulation methods **after** `collect()` -- as in the two cells above -- is that they require **complete** set of data be transferred from a table in SAP HANA database to Python client before data manipulation is applied.\n",
    "\n",
    "Next week you will work on applying data manipulation **before** `collect()` is executed, so that methods are pushed down to be executed in the SAP HANA database and **only results** are returned to the client!\n",
    "\n",
    "### Stay tuned for the Week 3!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "20289a63-efb4-437b-a9a7-3debacdc7427",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[('SUMMER',), ('FALL',), ('SPRING',), ('WINTER',)]\n"
     ]
    }
   ],
   "source": [
    "mycursor.execute('select distinct \"season\" from bikes')\n",
    "seasons = mycursor.fetchall()\n",
    "print(seasons)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "ab6c43d9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(731, 12)\n",
      "[['WINTER' 2011 'JAN' ... 10.749882 985 0]\n",
      " ['WINTER' 2011 'JAN' ... 16.652113 801 1]\n",
      " ['WINTER' 2011 'JAN' ... 16.636703 1349 2]\n",
      " ...\n",
      " ['WINTER' 2012 'DEC' ... 8.333661 1341 728]\n",
      " ['WINTER' 2012 'DEC' ... 23.500518 1796 729]\n",
      " ['WINTER' 2012 'DEC' ... 10.374682 2729 730]]\n",
      "(731, 12)\n",
      "season              object\n",
      "yr                   int64\n",
      "mnth                object\n",
      "holiday             object\n",
      "weekday             object\n",
      "workingday          object\n",
      "weathersit          object\n",
      "temp               float64\n",
      "hum                float64\n",
      "windspeed          float64\n",
      "cnt                  int64\n",
      "days_since_2011      int64\n",
      "dtype: object\n",
      "     season    yr mnth     holiday weekday      workingday weathersit  \\\n",
      "0    WINTER  2011  JAN  NO HOLIDAY     SAT  NO WORKING DAY      MISTY   \n",
      "1    WINTER  2011  JAN  NO HOLIDAY     SUN  NO WORKING DAY      MISTY   \n",
      "2    WINTER  2011  JAN  NO HOLIDAY     MON     WORKING DAY       GOOD   \n",
      "3    WINTER  2011  JAN  NO HOLIDAY     TUE     WORKING DAY       GOOD   \n",
      "4    WINTER  2011  JAN  NO HOLIDAY     WED     WORKING DAY       GOOD   \n",
      "..      ...   ...  ...         ...     ...             ...        ...   \n",
      "726  WINTER  2012  DEC  NO HOLIDAY     THU     WORKING DAY      MISTY   \n",
      "727  WINTER  2012  DEC  NO HOLIDAY     FRI     WORKING DAY      MISTY   \n",
      "728  WINTER  2012  DEC  NO HOLIDAY     SAT  NO WORKING DAY      MISTY   \n",
      "729  WINTER  2012  DEC  NO HOLIDAY     SUN  NO WORKING DAY       GOOD   \n",
      "730  WINTER  2012  DEC  NO HOLIDAY     MON     WORKING DAY      MISTY   \n",
      "\n",
      "         temp      hum  windspeed   cnt  days_since_2011  \n",
      "0    8.175849  80.5833  10.749882   985                0  \n",
      "1    9.083466  69.6087  16.652113   801                1  \n",
      "2    1.229108  43.7273  16.636703  1349                2  \n",
      "3    1.400000  59.0435  10.739832  1562                3  \n",
      "4    2.666979  43.6957  12.522300  1600                4  \n",
      "..        ...      ...        ...   ...              ...  \n",
      "726  3.945849  65.2917  23.458911  2114              726  \n",
      "727  3.906651  59.0000  10.416557  3095              727  \n",
      "728  3.906651  75.2917   8.333661  1341              728  \n",
      "729  4.024151  48.3333  23.500518  1796              729  \n",
      "730  2.144151  57.7500  10.374682  2729              730  \n",
      "\n",
      "[731 rows x 12 columns]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "bikes_np = np.array(bikes_hdf.collect())\n",
    "print(bikes_np.shape)\n",
    "print(bikes_np)\n",
    "\n",
    "# import pandas as pd\n",
    "bikes_pd = pd.DataFrame(bikes_hdf.collect())\n",
    "bikes_pd.set_index('days_since_2011')\n",
    "print(bikes_pd.shape)\n",
    "print(bikes_pd.dtypes)\n",
    "print(bikes_pd)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "d5848119",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: ylabel='season'>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bikes_pd_tmp = bikes_pd\n",
    "bikes_pd_tmp = bikes_pd_tmp.sort_values('season')\n",
    "bikes_pd_tmp[\"season\"].unique().tolist()\n",
    "display(bikes_pd_tmp['season'].value_counts().plot(kind='pie'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "769cbd7c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: ylabel='holiday'>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(bikes_pd_tmp['holiday'].value_counts().plot(kind='pie'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "73c56d8d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: ylabel='season'>"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bikes_pd_tmp.groupby('season').cnt.mean().plot(kind='barh')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "44ea6de4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: ylabel='season'>"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bikes_pd_tmp.groupby('season').temp.mean().plot(kind='barh')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "4b525732",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: ylabel='season'>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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nED+//fr1ZUlJiW699dYa+2+99VaVlJT4NQgAAICd+RVlSUlJWrlyZY39r7/+ujp06HDZQwEAANiNX/eUzZo1SyNHjtSmTZu895Rt2bJFGzdurDXWAAAAcHF+vVI2fPhw7dixQ82aNdPatWu1du1aNWvWTDt37tSwYcMCPSMAAECD59eN/rhyuNEfAIBrT9Bu9N+7d68KCwu9H7/xxhsaOnSopk2bpsrKSr8GAQAAsDO/ouwnP/mJDh06JEn66KOPNHLkSEVGRmrVqlV6/PHHAzogAACAHfgVZYcOHdI3v/lNSdKqVauUnJysV155RTk5OfrTn/4UyPkAAABswa8osyxL1dXVkqS//e1vGjx4sCQpPj5en332WeCmAwAAsAm/oqxXr176zW9+o5dffln5+flKTU2VJBUVFally5YBHRAAAMAO/Iqy5557Tnv37tWkSZP0q1/9SklJSZKk1atX1/pO/wAAALi4gL4lxtmzZ9WoUSOFhYUF6pK2w1tiAABw7QnEz2+/3tG/Lo0bNw7k5QAAAGzDryirqqrSvHnztHLlSh05cqTGe5N9/vnnARkOAADALvy6p2zWrFmaO3euRo4cKY/HoylTpui+++5TSEiIZs6cGeARAQAAGj6/omzFihVatGiRfvGLXyg0NFSjRo3S4sWLNX36dG3fvj3QMwIAADR4fkXZsWPHdNNNN0mSXC6XPB6PJOmee+7RX/7yl8BNBwAAYBN+RVmbNm1UUlIiSWrfvr02bNggSdq1a5ecTmfgpgMAALAJv6Js2LBh2rhxoyTpZz/7mTIyMtShQwf96Ec/0tixYwM6IAAAgB0E5H3Ktm3bpm3btqlDhw767ne/G4i5bIv3KQMA4NpjzPuU9evXT/369QvEpQAAAGzJr19fStLLL7+s/v37Ky4uTp988omk8//80htvvBGw4QAAAOzCryhbsGCBpkyZosGDB6u0tFRVVVWSpNjYWD333HOBnA8AAMAW/IqyF154QYsWLdKvfvUrNWrUyLu/V69eKiwsDNhwAAAAduFXlBUVFenmm2+usd/pdKqiouKyhwIAALAbv6IsMTFRBQUFNfa//fbbuuGGGy53JgAAANvx668vp0yZookTJ+rs2bOyLEs7d+7Uq6++qszMTC1evDjQMwIAADR4fkXZww8/rIiICD355JM6ffq07r//frVu3Vq///3v9YMf/CDQMwIAADR4fkXZmTNnNGzYMI0ePVqnT5/WgQMHtGXLFrVp0ybQ8wEAANiCX/eUDRkyRC+99JIkqbKyUvfee6/mzp2roUOHasGCBQEdEAAAwA78irK9e/fq29/+tiRp9erVatmypT755BO99NJLev755wM6IAAAgB34FWWnT59WVFSUJGnDhg267777FBISoltuucX77v4AAACoP7+iLCkpSWvXrlVxcbHWr1+vu+++W5J0/Phx/hFtAAAAP/h1o//06dN1//33a/LkyRo4cKD3HyPfsGFDrW8qi0vXdcZ6hTgjgz0GAAAB93FWarBHMJJfUfa9731P3/rWt1RSUqLu3bt79w8cOFDDhg0L2HAAAAB24VeUSZLb7Zbb7fbZ16dPn8seCAAAwI78uqcMAAAAgUWUAQAAGIAoAwAAMABRBgAAYACiDAAAwABEGQAAgAGIMgAAAAMQZQAAAAYgygAAAAxAlAEAABiAKAMAADAAUQYAAGAAogwAAMAARBkAAIABiDIAAAADXBNR9umnn2rChAlKSEiQ0+mU2+1WSkqKtmzZIklq166dHA6HHA6HrrvuOvXo0UOrVq3yPn7mzJne440aNVJ8fLzGjRunzz//3Od52rVrp+eee87nY4fDoe3bt/ucl5aWpgEDBvjsKysrU0ZGhm688UZFRESoadOm6t27t+bMmaOTJ08GdkEAAECDc01E2fDhw7Vv3z4tW7ZMhw4d0rp16zRgwACdOHHCe86vf/1rlZSUaN++ferdu7dGjhyprVu3eo/feOONKikp0ZEjR5Sdna23335bEyZM+Nrnbty4sdLT0y96zueff65bbrlF2dnZeuyxx7Rjxw7t3btXs2fP1r59+/TKK6/4/8kDAABbCA32AF+ntLRUmzdvVl5enpKTkyVJbdu2VZ8+fXzOi4qKktvtltvt1osvvqjly5frzTff1K233ipJCg0NldvtliS1bt1aI0aMUHZ29tc+/7hx47Rw4UL99a9/1eDBg2s9Z9q0aTpy5IgOHTqkuLg47/62bdvq7rvvlmVZfn3uAADAPox/pczlcsnlcmnt2rU6d+5cvR4TGhqqsLAwVVZW1nr8448/1vr16xUeHv6110pMTNT48eM1depUVVdX1zheXV2t119/XQ888IBPkP0vh8NRr7kBAIB9GR9loaGhysnJ0bJlyxQbG6v+/ftr2rRp2r9/f63nV1ZWKjMzUx6PR3fccYd3f2FhoVwulyIiIpSYmKj33nvva38tecGTTz6poqIirVixosaxTz/9VKWlperUqZPP/p49e3qDctSoUXVe+9y5cyorK/PZAACA/RgfZdL5e8qOHj2qdevWadCgQcrLy1OPHj2Uk5PjPSc9PV0ul0uRkZF65plnlJWVpdTUVO/xTp06qaCgQLt27VJ6erpSUlL0s5/9rF7P37x5cz322GOaPn16na++fdWaNWtUUFCglJQUnTlzps7zMjMzFRMT493i4+PrdX0AANCwXBNRJp2/4f6uu+5SRkaGtm7dqjFjxmjGjBne47/85S9VUFCg//u//9PJkydrvAoWHh6upKQkde3aVVlZWWrUqJFmzZpV7+efMmWKzpw5o/nz5/vsb968uWJjY3Xw4EGf/QkJCUpKSlJUVNRFrzt16lR5PB7vVlxcXO+ZAABAw3HNRNlXdenSRRUVFd6PmzVrpqSkJLnd7nrdw/Xkk0/qt7/9rY4ePVqv53O5XMrIyNDs2bNVXl7u3R8SEqLvf//7Wr58eb2v9b+cTqeio6N9NgAAYD/GR9mJEyd0xx13aPny5dq/f7+Kioq0atUqzZkzR0OGDPH7uv369VO3bt309NNP1/sx48aNU0xMTI23uHj66afVunVr9enTR0uXLtX+/ft1+PBhrVmzRtu2bVOjRo38nhMAANiD8W+J4XK51LdvX82bN0+HDx/WF198ofj4eD3yyCOaNm3aZV178uTJGjNmjNLT0+t1L1dYWJieeuop3X///T77mzZtqp07d+qZZ57Rs88+q6KiIoWEhKhDhw4aOXKk0tLSLmtOAADQ8Dks3kTLKGVlZedv+E9bqRBnZLDHAQAg4D7OSv36k64xF35+ezwev29FMv7XlwAAAHZAlAEAABiAKAMAADAAUQYAAGAAogwAAMAARBkAAIABiDIAAAADEGUAAAAGIMoAAAAMQJQBAAAYgCgDAAAwAFEGAABgAKIMAADAAEQZAACAAYgyAAAAAxBlAAAABiDKAAAADBAa7AFQuwOzUhQdHR3sMQAAwFXCK2UAAAAGIMoAAAAMQJQBAAAYgCgDAAAwAFEGAABgAKIMAADAAEQZAACAAYgyAAAAAxBlAAAABiDKAAAADECUAQAAGIAoAwAAMABRBgAAYACiDAAAwABEGQAAgAGIMgAAAAMQZQAAAAYgygAAAAxAlAEAABiAKAMAADAAUQYAAGAAogwAAMAARBkAAIABiDIAAAADEGUAAAAGIMoAAAAMQJQBAAAYgCgDAAAwAFEGAABgAKIMAADAAEQZAACAAYgyAAAAAxBlAAAABiDKAAAADECUAQAAGIAoAwAAMABRBgAAYACiDAAAwABEGQAAgAGIMgAAAAMQZQAAAAYgygAAAAxAlAEAABiAKAMAADAAUQYAAGCA0GAPgNp1nbFeIc7IYI8BAECD8XFWarBHuCheKQMAADAAUQYAAGAAogwAAMAARBkAAIABiDIAAAADEGUAAAAGIMoAAAAMQJQBAAAYgCgDAAAwAFEGAABgAKIMAADAAEQZAACAAYgyAAAAAxBlAAAABiDKAAAADECUAQAAGIAoAwAAMABRBgAAYADbRdmYMWPkcDhqbB9++KEkKTMzU40aNdKzzz5b47E5OTmKjY296LWHDh16hSYHAAANme2iTJIGDRqkkpISny0xMVGStHTpUj3++ONaunRpkKcEAAB2YssoczqdcrvdPlujRo2Un5+vM2fO6Ne//rXKysq0devWYI8KAABswpZRVpclS5Zo1KhRCgsL06hRo7RkyZIr/pznzp1TWVmZzwYAAOzHllH25z//WS6Xy7uNGDFCZWVlWr16tR544AFJ0gMPPKCVK1fq1KlTV3SWzMxMxcTEeLf4+Pgr+nwAAMBMtoyy22+/XQUFBd7t+eef16uvvqr27dure/fukqRvfvObatu2rV5//fUrOsvUqVPl8Xi8W3Fx8RV9PgAAYKbQYA8QDNddd52SkpJ89i1ZskTvvfeeQkP/uyTV1dVaunSpHnrooSs2i9PplNPpvGLXBwAA1wZbRtlXFRYWavfu3crLy9P111/v3f/5559rwIAB+te//qXOnTsHcUIAANDQEWU6/ypZnz59dNttt9U41rt3by1ZssT7vmVVVVUqKCjwOcfpdOqGG26QJHk8nhrHmzZtyr1iAADgomwfZZWVlVq+fLnS09NrPT58+HD97ne/09NPPy1JOnXqlG6++Wafc9q3b+9989m8vLwaxx966CEtXrz4CkwPAAAaCodlWVawh8B/lZWVnf8rzLSVCnFGBnscAAAajI+zUq/YtS/8/PZ4PIqOjvbrGrb860sAAADTEGUAAAAGIMoAAAAMQJQBAAAYgCgDAAAwAFEGAABgAKIMAADAAEQZAACAAYgyAAAAAxBlAAAABiDKAAAADECUAQAAGIAoAwAAMABRBgAAYACiDAAAwABEGQAAgAFCgz0AandgVoqio6ODPQYAALhKeKUMAADAAEQZAACAAYgyAAAAAxBlAAAABiDKAAAADECUAQAAGIAoAwAAMABRBgAAYACiDAAAwABEGQAAgAGIMgAAAAMQZQAAAAYgygAAAAxAlAEAABiAKAMAADAAUQYAAGAAogwAAMAARBkAAIABiDIAAAADEGUAAAAGIMoAAAAMEBrsAeDLsixJUllZWZAnAQAA9XXh5/aFn+P+IMoMc+LECUlSfHx8kCcBAACXqry8XDExMX49ligzzPXXXy9JOnLkiN//URuisrIyxcfHq7i4WNHR0cEexyisTe1Yl7qxNnVjbWrHutTtwtocOXJEDodDcXFxfl+LKDNMSMj52/xiYmL4wq9FdHQ061IH1qZ2rEvdWJu6sTa1Y13qFoif29zoDwAAYACiDAAAwABEmWGcTqdmzJghp9MZ7FGMwrrUjbWpHetSN9ambqxN7ViXugVybRzW5fztJgAAAAKCV8oAAAAMQJQBAAAYgCgDAAAwAFEGAABgAKLMIC+++KLatWunxo0bq2/fvtq5c2ewR7rqNm3apO9+97uKi4uTw+HQ2rVrfY5blqXp06erVatWioiI0J133qkPPvggOMNeRZmZmerdu7eioqLUokULDR06VAcPHvQ55+zZs5o4caKaNm0ql8ul4cOH6z//+U+QJr56FixYoG7dunnf1LJfv3566623vMftui5flZWVJYfDobS0NO8+u67NzJkz5XA4fLbOnTt7j9t1XS7497//rQceeEBNmzZVRESEbrrpJu3evdt73I7fh9u1a1fja8bhcGjixImSAvc1Q5QZ4vXXX9eUKVM0Y8YM7d27V927d1dKSoqOHz8e7NGuqoqKCnXv3l0vvvhircfnzJmj559/XgsXLtSOHTt03XXXKSUlRWfPnr3Kk15d+fn5mjhxorZv36533nlHX3zxhe6++25VVFR4z5k8ebLefPNNrVq1Svn5+Tp69Kjuu+++IE59dbRp00ZZWVnas2ePdu/erTvuuENDhgzRe++9J8m+6/K/du3apT/84Q/q1q2bz347r82NN96okpIS7/buu+96j9l5XU6ePKn+/fsrLCxMb731lt5//3397ne/U5MmTbzn2PH78K5du3y+Xt555x1J0ogRIyQF8GvGghH69OljTZw40ftxVVWVFRcXZ2VmZgZxquCSZK1Zs8b7cXV1teV2u61nn33Wu6+0tNRyOp3Wq6++GoQJg+f48eOWJCs/P9+yrPPrEBYWZq1atcp7zj//+U9LkrVt27ZgjRk0TZo0sRYvXsy6WJZVXl5udejQwXrnnXes5ORk69FHH7Usy95fMzNmzLC6d+9e6zE7r4tlWVZ6err1rW99q87jfB8+79FHH7Xat29vVVdXB/RrhlfKDFBZWak9e/bozjvv9O4LCQnRnXfeqW3btgVxMrMUFRXp2LFjPusUExOjvn372m6dPB6PpP/+A/Z79uzRF1984bM2nTt3VkJCgq3WpqqqSq+99poqKirUr18/1kXSxIkTlZqa6rMGEl8zH3zwgeLi4vSNb3xDo0eP1pEjRySxLuvWrVOvXr00YsQItWjRQjfffLMWLVrkPc734fM/s5cvX66xY8fK4XAE9GuGKDPAZ599pqqqKrVs2dJnf8uWLXXs2LEgTWWeC2th93Wqrq5WWlqa+vfvr65du0o6vzbh4eGKjY31Odcua1NYWCiXyyWn06nx48drzZo16tKli+3X5bXXXtPevXuVmZlZ45id16Zv377KycnR22+/rQULFqioqEjf/va3VV5ebut1kaSPPvpICxYsUIcOHbR+/XpNmDBBP//5z7Vs2TJJfB+WpLVr16q0tFRjxoyRFNj/L4UGaEYAV8nEiRN14MABn3tg7K5Tp04qKCiQx+PR6tWr9eCDDyo/Pz/YYwVVcXGxHn30Ub3zzjtq3LhxsMcxyne+8x3v/+7WrZv69u2rtm3bauXKlYqIiAjiZMFXXV2tXr166emnn5Yk3XzzzTpw4IAWLlyoBx98MMjTmWHJkiX6zne+o7i4uIBfm1fKDNCsWTM1atSoxl9q/Oc//5Hb7Q7SVOa5sBZ2XqdJkybpz3/+s3Jzc9WmTRvvfrfbrcrKSpWWlvqcb5e1CQ8PV1JSknr27KnMzEx1795dv//97229Lnv27NHx48fVo0cPhYaGKjQ0VPn5+Xr++ecVGhqqli1b2nZtvio2NlYdO3bUhx9+aOuvGUlq1aqVunTp4rPvhhtu8P561+7fhz/55BP97W9/08MPP+zdF8ivGaLMAOHh4erZs6c2btzo3VddXa2NGzeqX79+QZzMLImJiXK73T7rVFZWph07djT4dbIsS5MmTdKaNWv097//XYmJiT7He/bsqbCwMJ+1OXjwoI4cOdLg16Y21dXVOnfunK3XZeDAgSosLFRBQYF369Wrl0aPHu3933Zdm686deqUDh8+rFatWtn6a0aS+vfvX+Ptdg4dOqS2bdtKsvf3YUnKzs5WixYtlJqa6t0X0K+ZAP9BAvz02muvWU6n08rJybHef/99a9y4cVZsbKx17NixYI92VZWXl1v79u2z9u3bZ0my5s6da+3bt8/65JNPLMuyrKysLCs2NtZ64403rP3791tDhgyxEhMTrTNnzgR58itrwoQJVkxMjJWXl2eVlJR4t9OnT3vPGT9+vJWQkGD9/e9/t3bv3m3169fP6tevXxCnvjqeeOIJKz8/3yoqKrL2799vPfHEE5bD4bA2bNhgWZZ916U2//vXl5Zl37X5xS9+YeXl5VlFRUXWli1brDvvvNNq1qyZdfz4ccuy7LsulmVZO3futEJDQ63Zs2dbH3zwgbVixQorMjLSWr58ufccu34frqqqshISEqz09PQaxwL1NUOUGeSFF16wEhISrPDwcKtPnz7W9u3bgz3SVZebm2tJqrE9+OCDlmWd/3PsjIwMq2XLlpbT6bQGDhxoHTx4MLhDXwW1rYkkKzs723vOmTNnrJ/+9KdWkyZNrMjISGvYsGFWSUlJ8Ia+SsaOHWu1bdvWCg8Pt5o3b24NHDjQG2SWZd91qc1Xo8yuazNy5EirVatWVnh4uNW6dWtr5MiR1ocffug9btd1ueDNN9+0unbtajmdTqtz587WH//4R5/jdv0+vH79ektSrZ9roL5mHJZlWZfxSh4AAAACgHvKAAAADECUAQAAGIAoAwAAMABRBgAAYACiDAAAwABEGQAAgAGIMgAAAAMQZQAAAAYgygAAAAxAlAEAABiAKAMAADAAUQYAAGCA/wdMyooEl6g1YwAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bikes_pd_tmp.groupby('season').hum.mean().plot(kind='barh')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "13c43b09",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: ylabel='season'>"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bikes_pd_tmp.groupby('season').windspeed.mean().plot(kind='barh')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "7ada9917",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.11.0\n",
      "2.11.0\n"
     ]
    }
   ],
   "source": [
    "import keras\n",
    "print(keras.__version__)\n",
    "import tensorflow as tf\n",
    "print(tf.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "bcc7dadb",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "dftrain, dftest = train_test_split(bikes_pd, test_size=0.2)\n",
    "dftrain, dfeval = train_test_split(bikes_pd, test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "35fc2956",
   "metadata": {},
   "outputs": [],
   "source": [
    "def df_to_dataset(dataframe, shuffle=True, batch_size=32):\n",
    "  dataframe = dataframe.copy()\n",
    "  labels = dataframe.pop('cnt')\n",
    "  ds = tf.data.Dataset.from_tensor_slices((dict(dataframe), labels))\n",
    "  if shuffle:\n",
    "    ds = ds.shuffle(buffer_size=len(dataframe))\n",
    "  ds = ds.batch(batch_size)\n",
    "  return ds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "3befcab0",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-03-20 17:28:57.939924: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcuda.so.1'; dlerror: libcuda.so.1: cannot open shared object file: No such file or directory\n",
      "2023-03-20 17:28:57.939956: W tensorflow/compiler/xla/stream_executor/cuda/cuda_driver.cc:265] failed call to cuInit: UNKNOWN ERROR (303)\n",
      "2023-03-20 17:28:57.939985: I tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:156] kernel driver does not appear to be running on this host (bielefeldto-Latitude-E6440): /proc/driver/nvidia/version does not exist\n",
      "2023-03-20 17:28:57.940339: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA\n",
      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n"
     ]
    }
   ],
   "source": [
    "batch_size = 128 # A large batch size to run the model\n",
    "train_ds = df_to_dataset(dftrain, batch_size=batch_size)\n",
    "val_ds = df_to_dataset(dfeval, shuffle=False, batch_size=batch_size)\n",
    "test_ds = df_to_dataset(dftest, shuffle=False, batch_size=batch_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "a4ff5580",
   "metadata": {},
   "outputs": [],
   "source": [
    "CATEGORICAL_COLUMNS = ['season', 'holiday']\n",
    "NUMERIC_COLUMNS = ['temp']\n",
    "\n",
    "def one_hot_cat_column(feature_name, vocab):\n",
    "  return tf.feature_column.indicator_column(\n",
    "      tf.feature_column.categorical_column_with_vocabulary_list(feature_name,\n",
    "                                                 vocab))\n",
    "feature_columns = []\n",
    "for feature_name in CATEGORICAL_COLUMNS:\n",
    "  # Need to one-hot encode categorical features.\n",
    "  vocabulary = dftrain[feature_name].unique()\n",
    "  feature_columns.append(one_hot_cat_column(feature_name, vocabulary))\n",
    "for feature_name in NUMERIC_COLUMNS:\n",
    "  feature_columns.append(tf.feature_column.numeric_column(feature_name,\n",
    "                                           dtype=tf.float32))\n",
    "feature_layer = tf.keras.layers.DenseFeatures(feature_columns)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "407b5ee2",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = tf.keras.Sequential([\n",
    "  feature_layer,\n",
    "  keras.layers.Dense(128, activation='relu'),\n",
    "  keras.layers.Dense(64, activation='relu'),\n",
    "  keras.layers.Dense(1, activation='sigmoid')\n",
    "])\n",
    "\n",
    "model.compile(optimizer='rmsprop',\n",
    "              loss='binary_crossentropy',\n",
    "              metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "2eabd151",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "WARNING:tensorflow:Layers in a Sequential model should only have a single input tensor. Received: inputs={'season': <tf.Tensor 'IteratorGetNext:4' shape=(None,) dtype=string>, 'yr': <tf.Tensor 'IteratorGetNext:10' shape=(None,) dtype=int64>, 'mnth': <tf.Tensor 'IteratorGetNext:3' shape=(None,) dtype=string>, 'holiday': <tf.Tensor 'IteratorGetNext:1' shape=(None,) dtype=string>, 'weekday': <tf.Tensor 'IteratorGetNext:7' shape=(None,) dtype=string>, 'workingday': <tf.Tensor 'IteratorGetNext:9' shape=(None,) dtype=string>, 'weathersit': <tf.Tensor 'IteratorGetNext:6' shape=(None,) dtype=string>, 'temp': <tf.Tensor 'IteratorGetNext:5' shape=(None,) dtype=float64>, 'hum': <tf.Tensor 'IteratorGetNext:2' shape=(None,) dtype=float64>, 'windspeed': <tf.Tensor 'IteratorGetNext:8' shape=(None,) dtype=float64>, 'days_since_2011': <tf.Tensor 'IteratorGetNext:0' shape=(None,) dtype=int64>}. Consider rewriting this model with the Functional API.\n",
      "WARNING:tensorflow:Layers in a Sequential model should only have a single input tensor. Received: inputs={'season': <tf.Tensor 'IteratorGetNext:4' shape=(None,) dtype=string>, 'yr': <tf.Tensor 'IteratorGetNext:10' shape=(None,) dtype=int64>, 'mnth': <tf.Tensor 'IteratorGetNext:3' shape=(None,) dtype=string>, 'holiday': <tf.Tensor 'IteratorGetNext:1' shape=(None,) dtype=string>, 'weekday': <tf.Tensor 'IteratorGetNext:7' shape=(None,) dtype=string>, 'workingday': <tf.Tensor 'IteratorGetNext:9' shape=(None,) dtype=string>, 'weathersit': <tf.Tensor 'IteratorGetNext:6' shape=(None,) dtype=string>, 'temp': <tf.Tensor 'IteratorGetNext:5' shape=(None,) dtype=float64>, 'hum': <tf.Tensor 'IteratorGetNext:2' shape=(None,) dtype=float64>, 'windspeed': <tf.Tensor 'IteratorGetNext:8' shape=(None,) dtype=float64>, 'days_since_2011': <tf.Tensor 'IteratorGetNext:0' shape=(None,) dtype=int64>}. Consider rewriting this model with the Functional API.\n",
      "1/5 [=====>........................] - ETA: 2s - loss: 1752.8162 - accuracy: 0.0000e+00WARNING:tensorflow:Layers in a Sequential model should only have a single input tensor. Received: inputs={'season': <tf.Tensor 'IteratorGetNext:4' shape=(None,) dtype=string>, 'yr': <tf.Tensor 'IteratorGetNext:10' shape=(None,) dtype=int64>, 'mnth': <tf.Tensor 'IteratorGetNext:3' shape=(None,) dtype=string>, 'holiday': <tf.Tensor 'IteratorGetNext:1' shape=(None,) dtype=string>, 'weekday': <tf.Tensor 'IteratorGetNext:7' shape=(None,) dtype=string>, 'workingday': <tf.Tensor 'IteratorGetNext:9' shape=(None,) dtype=string>, 'weathersit': <tf.Tensor 'IteratorGetNext:6' shape=(None,) dtype=string>, 'temp': <tf.Tensor 'IteratorGetNext:5' shape=(None,) dtype=float64>, 'hum': <tf.Tensor 'IteratorGetNext:2' shape=(None,) dtype=float64>, 'windspeed': <tf.Tensor 'IteratorGetNext:8' shape=(None,) dtype=float64>, 'days_since_2011': <tf.Tensor 'IteratorGetNext:0' shape=(None,) dtype=int64>}. Consider rewriting this model with the Functional API.\n",
      "5/5 [==============================] - 1s 45ms/step - loss: -16436.8730 - accuracy: 0.0000e+00 - val_loss: -43153.0820 - val_accuracy: 0.0000e+00\n",
      "Epoch 2/10\n",
      "5/5 [==============================] - 0s 6ms/step - loss: -53988.3828 - accuracy: 0.0000e+00 - val_loss: -77937.1328 - val_accuracy: 0.0000e+00\n",
      "Epoch 3/10\n",
      "5/5 [==============================] - 0s 10ms/step - loss: -87281.1953 - accuracy: 0.0000e+00 - val_loss: -112550.5391 - val_accuracy: 0.0000e+00\n",
      "Epoch 4/10\n",
      "5/5 [==============================] - 0s 8ms/step - loss: -121563.5312 - accuracy: 0.0000e+00 - val_loss: -150610.6562 - val_accuracy: 0.0000e+00\n",
      "Epoch 5/10\n",
      "5/5 [==============================] - 0s 9ms/step - loss: -159939.6562 - accuracy: 0.0000e+00 - val_loss: -193141.5312 - val_accuracy: 0.0000e+00\n",
      "Epoch 6/10\n",
      "5/5 [==============================] - 0s 9ms/step - loss: -202790.6562 - accuracy: 0.0000e+00 - val_loss: -240648.4062 - val_accuracy: 0.0000e+00\n",
      "Epoch 7/10\n",
      "5/5 [==============================] - 0s 7ms/step - loss: -250834.7344 - accuracy: 0.0000e+00 - val_loss: -294436.6250 - val_accuracy: 0.0000e+00\n",
      "Epoch 8/10\n",
      "5/5 [==============================] - 0s 12ms/step - loss: -304650.5312 - accuracy: 0.0000e+00 - val_loss: -353004.6875 - val_accuracy: 0.0000e+00\n",
      "Epoch 9/10\n",
      "5/5 [==============================] - 0s 9ms/step - loss: -362980.3438 - accuracy: 0.0000e+00 - val_loss: -416359.6562 - val_accuracy: 0.0000e+00\n",
      "Epoch 10/10\n",
      "5/5 [==============================] - 0s 9ms/step - loss: -426296.2812 - accuracy: 0.0000e+00 - val_loss: -485463.5000 - val_accuracy: 0.0000e+00\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7f480414a520>"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(train_ds, validation_data=val_ds, epochs=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "75e47fda",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_keys(['loss', 'accuracy', 'val_loss', 'val_accuracy'])\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "history_dict = model.history.history\n",
    "print(history_dict.keys())\n",
    "loss_values = history_dict['loss']\n",
    "val_loss_values = history_dict['val_loss']\n",
    "epochs = range(1, len(loss_values) + 1)\n",
    "plt.plot(epochs, loss_values, 'bo', label='Training loss')\n",
    "plt.plot(epochs, val_loss_values, 'b', label='Validation loss')\n",
    "plt.title('Training and validation loss')\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Loss')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "8c88ec0b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2/2 [==============================] - 0s 5ms/step - loss: -494413.1250 - accuracy: 0.0000e+00\n",
      "Accuracy 0.0\n"
     ]
    }
   ],
   "source": [
    "loss, accuracy = model.evaluate(test_ds)\n",
    "print(\"Accuracy\", accuracy) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "6ba8c850",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Every feature: ['season', 'yr', 'mnth', 'holiday', 'weekday', 'workingday', 'weathersit', 'temp', 'hum', 'windspeed', 'days_since_2011']\n",
      "A batch of seasons: tf.Tensor(\n",
      "[b'SUMMER' b'SUMMER' b'FALL' b'SPRING' b'SUMMER' b'WINTER' b'SUMMER'\n",
      " b'SUMMER' b'SPRING' b'SUMMER' b'SUMMER' b'SPRING' b'SPRING' b'SPRING'\n",
      " b'FALL' b'SUMMER' b'WINTER' b'WINTER' b'SUMMER' b'FALL' b'SPRING'\n",
      " b'SUMMER' b'SPRING' b'WINTER' b'SUMMER' b'SPRING' b'SUMMER' b'SPRING'\n",
      " b'SUMMER' b'FALL' b'SUMMER' b'WINTER' b'WINTER' b'SUMMER' b'SPRING'\n",
      " b'SUMMER' b'WINTER' b'SPRING' b'WINTER' b'SUMMER' b'SUMMER' b'WINTER'\n",
      " b'FALL' b'SPRING' b'FALL' b'WINTER' b'SUMMER' b'FALL' b'FALL' b'WINTER'\n",
      " b'FALL' b'WINTER' b'SUMMER' b'FALL' b'SUMMER' b'SUMMER' b'SUMMER'\n",
      " b'SUMMER' b'WINTER' b'SPRING' b'WINTER' b'FALL' b'SUMMER' b'WINTER'\n",
      " b'SUMMER' b'SUMMER' b'WINTER' b'SUMMER' b'WINTER' b'WINTER' b'SPRING'\n",
      " b'SUMMER' b'WINTER' b'SUMMER' b'WINTER' b'SPRING' b'FALL' b'SPRING'\n",
      " b'FALL' b'SPRING' b'SPRING' b'SUMMER' b'SPRING' b'SPRING' b'WINTER'\n",
      " b'FALL' b'FALL' b'FALL' b'SUMMER' b'WINTER' b'SPRING' b'SPRING' b'SUMMER'\n",
      " b'SPRING' b'WINTER' b'SUMMER' b'SPRING' b'SUMMER' b'FALL' b'FALL'\n",
      " b'SPRING' b'WINTER' b'SUMMER' b'FALL' b'FALL' b'SUMMER' b'FALL' b'SPRING'\n",
      " b'WINTER' b'FALL' b'FALL' b'FALL' b'SPRING' b'FALL' b'SPRING' b'SPRING'\n",
      " b'FALL' b'SPRING' b'SPRING' b'SPRING' b'SPRING' b'SPRING' b'SUMMER'\n",
      " b'WINTER' b'SPRING' b'WINTER' b'FALL' b'SPRING'], shape=(128,), dtype=string)\n",
      "A batch of counts: tf.Tensor(\n",
      "[4840 7112 7570 3126 6830 1301 4294 5531 5698 4795 4694  795 3429 4833\n",
      " 2659 6660 2169 5298 5464 5047 4862 8173 4717 5382 4656 7129 7592 6664\n",
      " 7403 4381 4592 3487 1107 5345 5936 6869 2744 5409 2485 4475 5255 1162\n",
      " 5041 5180 3649 2802 3351 5501 4570 1013 8555 1321 6591 3053 6203 4541\n",
      " 4086 5538 2927 3115 1349 4639 4760 6312 4672 6290 4509 8167 1501 2660\n",
      " 3249 5046 3068 7720 2077 6421 7282 6370 2933 2210 5459 6864 6273 4972\n",
      " 1341 5260 6606 2425 4459 1317 4803 6565 4549 3915 2832 5870 4586 5463\n",
      " 2416 8156 2227 2192 6043 5445 2792 4727 5557 5918 5892 3614 3613 3523\n",
      " 6598 6536 5020 4608 4109 6591 5099 6169 2425 6857 5923 3129 4996 6093\n",
      " 2765 6734], shape=(128,), dtype=int64)\n"
     ]
    }
   ],
   "source": [
    "for feature_batch, label_batch in test_ds.take(1):\n",
    "  print('Every feature:', list(feature_batch.keys()))\n",
    "  print('A batch of seasons:', feature_batch['season'])\n",
    "  print('A batch of counts:', label_batch )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "6e0f8b90",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2/2 [==============================] - 0s 4ms/step\n",
      "Model estimates 1.0.\n",
      "Model estimates 1.0.\n",
      "Model estimates 1.0.\n",
      "Model estimates 1.0.\n",
      "Model estimates 1.0.\n",
      "Model estimates 0.9999971985816956.\n",
      "Model estimates 1.0.\n",
      "Model estimates 1.0.\n",
      "Model estimates 1.0.\n",
      "Model estimates 1.0.\n"
     ]
    }
   ],
   "source": [
    "pred = model.predict(test_ds)\n",
    "for index in range(0,10):\n",
    "  print(f'Model estimates {pred[index][0]}.')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}