bigquery/tutorial/04-writing-data.ipynb

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   "source": [
    "# Tutorial 4: Saving Your Results"
   ]
  },
  {
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   "id": "a0c3f4f0-36e2-4673-81cf-3e006153a00a",
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    "<div class=\"alert alert-block alert-info\"> <b>Before we get started: </b> \n",
    "    <ul style=\"list-style-type: none;margin: 0;padding: 0;\">\n",
    "        <li>✍️ To run this notebook, you need to have Ponder installed and set up on your machine. If you have not done so already, please refer to our <a href=\"https://docs.ponder.io/getting_started/quickstart.html\">Quickstart guide</a> to get started.</li>\n",
    "        <li>📖 Otherwise, if you're just interested in browsing through the tutorial, keep reading below!</li>\n",
    "    </ul>\n",
    "</div>"
   ]
  },
  {
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   "execution_count": 1,
   "id": "643150ee-2715-4508-8b77-18801ca4c1c9",
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    "tags": []
   },
   "outputs": [],
   "source": [
    "import ponder; ponder.init()\n",
    "import modin.pandas as pd\n",
    "from google.cloud import bigquery\n",
    "from google.cloud.bigquery import dbapi\n",
    "from google.oauth2 import service_account\n",
    "import json\n",
    "bigquery_con = dbapi.Connection(bigquery.Client(credentials=service_account.Credentials.from_service_account_info(json.loads(open(\"../credential.json\").read()),scopes=[\"https://www.googleapis.com/auth/bigquery\"])))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "267738c2-7b12-4e77-8097-929e77e9dea5",
   "metadata": {},
   "source": [
    "Let's say that you used Ponder to run some analysis and you want to store the results back to the database. In this tutorial, we will show how you can use the ``to_sql`` command to write your dataframe to table in your database. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f62f06a0-7270-4ab7-a5a8-339bb313537f",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "df = pd.read_sql(\"PONDER.PONDER_CUSTOMER\",con=bigquery_con)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "55bffaf2-1840-48c9-9261-27b8a8e30ec6",
   "metadata": {},
   "source": [
    "After connecting to our `PONDER_CUSTOMER` table, we see that the `C_NAME` column needs to cleaned up."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "6fde85cd-9350-461c-be7a-b15e4e2a20de",
   "metadata": {
    "tags": []
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   "outputs": [],
   "source": [
    "df.C_NAME = df.C_NAME.str.replace(\"Customer#\",'')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cb7fd65b-23d2-4f57-af56-19045d84edb9",
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   "source": [
    "Like in pandas, when you are working with a dataframe, you are always working on a temporary copy of the data. \n",
    "\n",
    "By default, all pandas operations returns a copy of the dataframe that was operated on. Ponder never make any modifications to your original data. This is especially important in the data warehouse context as the tables are often regarded as the “source of truth” and can be shared across many teams."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4c9556ad-40d4-4596-a739-eb515413ee42",
   "metadata": {},
   "source": [
    "This also means that the reference to your dataframe df is only accessible throughout the session. That means that once you exit the session, the dataframe is no longer accessible. \n",
    "\n",
    "So if we plan to use the cleaned up data for our analysis later, we can persist it in a table via `to_sql`."
   ]
  },
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   "execution_count": 4,
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   "id": "1d023450-3114-4cc0-bc5a-237135085420",
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   "source": [
    "df.to_sql(\"PONDER.PONDER_CUSTOMER_CLEAN\",con=bigquery_con, index=False,if_exists='replace')"
   ]
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   "source": [
    "Now we can access new new table and continue our analysis whereever we want:"
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       "97      60059  000060059  dZISBokE9NWaz13 b5WbOHrd8DifA,e2yict0           24   \n",
       "98      60027  000060027                  U,Y EKzklGgxO9HK4uT2g           24   \n",
       "99      60033  000060033                            fwvb5ua8ZcB           24   \n",
       "\n",
       "            C_PHONE  C_ACCTBAL C_MKTSEGMENT  \\\n",
       "0   10-310-354-8579    5759.83     BUILDING   \n",
       "1   10-192-161-6631     689.24     BUILDING   \n",
       "2   10-729-863-1818    3645.47     BUILDING   \n",
       "3   10-684-354-3580    6515.51    FURNITURE   \n",
       "4   10-513-498-1045    -759.74    FURNITURE   \n",
       "..              ...        ...          ...   \n",
       "95  33-146-680-6559    6672.12    MACHINERY   \n",
       "96  34-197-192-3607    3329.55     BUILDING   \n",
       "97  34-348-323-9173    2337.46    HOUSEHOLD   \n",
       "98  34-250-415-3801    6115.22    MACHINERY   \n",
       "99  34-142-708-2404    -493.59    MACHINERY   \n",
       "\n",
       "                                            C_COMMENT  \n",
       "0   ckly bold deposits. carefully bold accounts in...  \n",
       "1    slyly pending, permanent packages. special fo...  \n",
       "2                     the accounts. furiously unusual  \n",
       "3   ly courts. fluffy packages wake furiously alon...  \n",
       "4    across the blithely ironic sentiments. thinly...  \n",
       "..                                                ...  \n",
       "95           ess requests. special requests wake blit  \n",
       "96  ly special somas poach carefully. furiously un...  \n",
       "97  ndencies. excuses sleep. quickly daring dugout...  \n",
       "98  s. ironic packages are carefully slyly unusual...  \n",
       "99  lithely final packages. quickly regular reques...  \n",
       "\n",
       "[100 rows x 8 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_sql(\"PONDER.PONDER_CUSTOMER_CLEAN\",con=bigquery_con)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3edda0d1-c613-42de-abb4-dcb054648500",
   "metadata": {},
   "source": [
    "# Summary\n",
    "\n",
    "In this tutorial, we learned how to leverage the same pandas API for `pd.to_sql` to save your dataframe to your database. This is often useful if you want to persist the work done on the dataframe beyond your current session. \n",
    "\n",
    "In our [next tutorial](https://github.com/ponder-org/ponder-notebooks/blob/main/snowflake/tutorial/05-advanced-sql.ipynb), we will discuss how you can easily move between using Ponder and using SQL when developing your data workflows."
   ]
  }
 ],
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