From 576dfd8058012aa9df5fe23f41bcfcb6975f1a84 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Jaan=20L=C4=B1?= Date: Sun, 7 Apr 2024 07:07:34 -0400 Subject: [PATCH] feat: add notebook example --- notebooks/load_csv_with_data_types.ipynb | 1027 ++++++++++++++++++++++ 1 file changed, 1027 insertions(+) create mode 100644 notebooks/load_csv_with_data_types.ipynb diff --git a/notebooks/load_csv_with_data_types.ipynb b/notebooks/load_csv_with_data_types.ipynb new file mode 100644 index 0000000..04155be --- /dev/null +++ b/notebooks/load_csv_with_data_types.ipynb @@ -0,0 +1,1027 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/me/jaanli/synthetic-healthcare-data/.venv/lib/python3.12/site-packages/sql/traits.py:20: FutureWarning: named_parameters: boolean values are now deprecated. Value True will be treated as \"enabled\". \n", + "Please use a valid option: \"warn\", \"enabled\", or \"disabled\". \n", + "For more information, see the docs: https://jupysql.ploomber.io/en/latest/api/configuration.html#named-parameters\n", + " warnings.warn(\n" + ] + } + ], + "source": [ + "# Load duckdb, which lets us efficiently load large files\n", + "import duckdb\n", + "\n", + "# Load pandas, which lets us manipulate dataframes\n", + "import pandas as pd\n", + "\n", + "# Import jupysql Jupyter extension to create SQL cells\n", + "%load_ext sql\n", + "\n", + "# Set configrations on jupysql to directly output data to Pandas and to simplify the output that is printed to the notebook.\n", + "%config SqlMagic.autopandas = True\n", + "\n", + "%config SqlMagic.feedback = False\n", + "%config SqlMagic.displaycon = False\n", + "\n", + "# Allow named parameters (python variables) in SQL cells\n", + "%config SqlMagic.named_parameters=True\n", + "\n", + "# Connect jupysql to DuckDB using a SQLAlchemy-style connection string. Either connect to an in memory DuckDB, or a file backed db.\n", + "%sql duckdb:///:memory:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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AS VARCHAR) CAST(DUAL_ELGBL_4 AS VARCHAR) \\\n", + "0 1 1 \n", + "1 1 1 \n", + "2 0 0 \n", + "3 None 0 \n", + "4 0 0 \n", + "\n", + " CAST(DUAL_ELGBL_5 AS VARCHAR) CAST(DUAL_ELGBL_6 AS VARCHAR) \\\n", + "0 1 1 \n", + "1 1 1 \n", + "2 0 0 \n", + "3 0 0 \n", + "4 0 0 \n", + "\n", + " CAST(DUAL_ELGBL_7 AS VARCHAR) CAST(DUAL_ELGBL_8 AS VARCHAR) \\\n", + "0 1 1 \n", + "1 1 1 \n", + "2 0 0 \n", + "3 0 0 \n", + "4 0 0 \n", + "\n", + " CAST(DUAL_ELGBL_9 AS VARCHAR) CAST(DUAL_ELGBL_10 AS VARCHAR) \\\n", + "0 1 1 \n", + "1 1 1 \n", + "2 0 0 \n", + "3 0 0 \n", + "4 0 0 \n", + "\n", + " CAST(DUAL_ELGBL_11 AS VARCHAR) CAST(DUAL_ELGBL_12 AS DECIMAL(18,3)) \n", + "0 1 1.0 \n", + "1 1 1.0 \n", + "2 0 NaN \n", + "3 0 0.0 \n", + "4 None NaN \n", + "\n", + "[5 rows x 50 columns]" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['CAST(PERSON_ID AS DECIMAL(18,3))',\n", + " 'CAST(MCAID_BENE_ID AS DECIMAL(18,3))',\n", + " 'CAST(PERSON_WGHT AS DECIMAL(18,3))', 'CAST(AGE_LOW AS DECIMAL(18,3))',\n", + " 'CAST(AGE_HIGH AS DECIMAL(18,3))', 'CAST(SEX_IDENT_CD AS VARCHAR)',\n", + " 'CAST(RACE_CD AS VARCHAR)', 'CAST(MCAID_SBMTTG_ST_CD AS VARCHAR)',\n", + " 'CAST(STATE_CD AS VARCHAR)', 'CAST(COUNTY_FIPS_CD AS VARCHAR)',\n", + " 'CAST(ZIP_CD AS VARCHAR)', 'CAST(RSN_ENRLMT_CD AS VARCHAR)',\n", + " 'CAST(MDCD_ENRLMT_1 AS DECIMAL(18,3))',\n", + " 'CAST(MDCD_ENRLMT_2 AS VARCHAR)', 'CAST(MDCD_ENRLMT_3 AS VARCHAR)',\n", + " 'CAST(MDCD_ENRLMT_4 AS VARCHAR)', 'CAST(MDCD_ENRLMT_5 AS VARCHAR)',\n", + " 'CAST(MDCD_ENRLMT_6 AS VARCHAR)', 'CAST(MDCD_ENRLMT_7 AS VARCHAR)',\n", + " 'CAST(MDCD_ENRLMT_8 AS VARCHAR)', 'CAST(MDCD_ENRLMT_9 AS VARCHAR)',\n", + " 'CAST(MDCD_ENRLMT_10 AS VARCHAR)', 'CAST(MDCD_ENRLMT_11 AS VARCHAR)',\n", + " 'CAST(MDCD_ENRLMT_12 AS DECIMAL(18,3))',\n", + " 'CAST(MDCD_MCO_ENRLMT_1 AS DECIMAL(18,3))',\n", + " 'CAST(MDCD_MCO_ENRLMT_2 AS VARCHAR)',\n", + " 'CAST(MDCD_MCO_ENRLMT_3 AS VARCHAR)',\n", + " 'CAST(MDCD_MCO_ENRLMT_4 AS VARCHAR)',\n", + " 'CAST(MDCD_MCO_ENRLMT_5 AS VARCHAR)',\n", + " 'CAST(MDCD_MCO_ENRLMT_6 AS VARCHAR)',\n", + " 'CAST(MDCD_MCO_ENRLMT_7 AS VARCHAR)',\n", + " 'CAST(MDCD_MCO_ENRLMT_8 AS VARCHAR)',\n", + " 'CAST(MDCD_MCO_ENRLMT_9 AS VARCHAR)',\n", + " 'CAST(MDCD_MCO_ENRLMT_10 AS VARCHAR)',\n", + " 'CAST(MDCD_MCO_ENRLMT_11 AS VARCHAR)',\n", + " 'CAST(MDCD_MCO_ENRLMT_12 AS DECIMAL(18,3))',\n", + " 'CAST(MDCD_CHIP_ENRLMT AS DECIMAL(18,3))',\n", + " 'CAST(RSTRCTD_BNFTS_IND AS VARCHAR)',\n", + " 'CAST(DUAL_ELGBL_1 AS DECIMAL(18,3))', 'CAST(DUAL_ELGBL_2 AS VARCHAR)',\n", + " 'CAST(DUAL_ELGBL_3 AS VARCHAR)', 'CAST(DUAL_ELGBL_4 AS VARCHAR)',\n", + " 'CAST(DUAL_ELGBL_5 AS VARCHAR)', 'CAST(DUAL_ELGBL_6 AS VARCHAR)',\n", + " 'CAST(DUAL_ELGBL_7 AS VARCHAR)', 'CAST(DUAL_ELGBL_8 AS VARCHAR)',\n", + " 'CAST(DUAL_ELGBL_9 AS VARCHAR)', 'CAST(DUAL_ELGBL_10 AS VARCHAR)',\n", + " 'CAST(DUAL_ELGBL_11 AS VARCHAR)',\n", + " 'CAST(DUAL_ELGBL_12 AS DECIMAL(18,3))'],\n", + " dtype='object')" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.columns" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", 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