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   "source": [
    "# BigQuery extension for pandas\n",
    "\n",
    "BigQuery DataFrames provides a pandas extension to execute BigQuery SQL scalar functions directly on pandas DataFrames."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import bigframes  # This import registers the bigquery accessor."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By default, BigQuery DataFrames selects a location to process data based on the\n",
    "data location, but using a pandas object doesn't provide such informat. If\n",
    "processing location is important to you, configure the location before using the\n",
    "accessor."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import bigframes.pandas as bpd\n",
    "\n",
    "bpd.reset_session()\n",
    "bpd.options.bigquery.location = \"US\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Using `sql_scalar`\n",
    "\n",
    "The `bigquery.sql_scalar` method allows you to apply a SQL scalar function to a pandas DataFrame by converting it to BigFrames, executing the SQL in BigQuery, and returning the result as a pandas Series."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
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   "source": [
    "df = pd.DataFrame({\"a\": [1.5, 2.5, 3.5]})\n",
    "result = df.bigquery.sql_scalar(\"ROUND({0}, 0)\")\n",
    "result"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can also use multiple columns."
   ]
  },
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   ],
   "source": [
    "df = pd.DataFrame({\"a\": [1, 2, 3], \"b\": [10, 20, 30]})\n",
    "result = df.bigquery.sql_scalar(\"{a} + {b}\")\n",
    "result"
   ]
  }
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