Merge pull request 'documentation ...' (#19) from v2.0.4 into master
Reviewed-on: library/data-transport#19master
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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"#### Extract Transform Load (ETL) from Code\n",
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"\n",
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"The example below reads data from an http source (github) and will copy the data to a csv file and to a database. This example illustrates the one-to-many ETL features.\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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" }\n",
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"\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
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" }\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>id</th>\n",
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" <th>location_id</th>\n",
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" <th>address_1</th>\n",
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" <th>address_2</th>\n",
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" <th>city</th>\n",
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" <th>state_province</th>\n",
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" <th>postal_code</th>\n",
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" <th>country</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>0</th>\n",
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" <td>1</td>\n",
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" <td>1</td>\n",
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" <td>2600 Middlefield Road</td>\n",
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" <td>NaN</td>\n",
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" <td>Redwood City</td>\n",
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" <td>CA</td>\n",
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" <td>94063</td>\n",
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" <td>US</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>1</th>\n",
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" <td>2</td>\n",
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" <td>2</td>\n",
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" <td>24 Second Avenue</td>\n",
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" <td>NaN</td>\n",
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" <td>San Mateo</td>\n",
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" <td>CA</td>\n",
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" <td>94401</td>\n",
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" <td>US</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>2</th>\n",
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" <td>3</td>\n",
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" <td>3</td>\n",
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" <td>24 Second Avenue</td>\n",
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" <td>NaN</td>\n",
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" <td>San Mateo</td>\n",
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" <td>CA</td>\n",
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" <td>94403</td>\n",
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" <td>US</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>3</th>\n",
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" <td>4</td>\n",
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" <td>4</td>\n",
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" <td>24 Second Avenue</td>\n",
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" <td>NaN</td>\n",
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" <td>San Mateo</td>\n",
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" <td>CA</td>\n",
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" <td>94401</td>\n",
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" <td>US</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>4</th>\n",
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" <td>5</td>\n",
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" <td>5</td>\n",
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" <td>24 Second Avenue</td>\n",
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" <td>NaN</td>\n",
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" <td>San Mateo</td>\n",
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" <td>CA</td>\n",
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" <td>94401</td>\n",
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" <td>US</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"</div>"
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],
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"text/plain": [
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" id location_id address_1 address_2 city \\\n",
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"0 1 1 2600 Middlefield Road NaN Redwood City \n",
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"1 2 2 24 Second Avenue NaN San Mateo \n",
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"2 3 3 24 Second Avenue NaN San Mateo \n",
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"3 4 4 24 Second Avenue NaN San Mateo \n",
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"4 5 5 24 Second Avenue NaN San Mateo \n",
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"\n",
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" state_province postal_code country \n",
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"0 CA 94063 US \n",
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"1 CA 94401 US \n",
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"2 CA 94403 US \n",
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"3 CA 94401 US \n",
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"4 CA 94401 US "
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]
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},
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"execution_count": 2,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"#\n",
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"# Writing to Google Bigquery database\n",
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"#\n",
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"import transport\n",
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"from transport import providers\n",
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"import pandas as pd\n",
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"import os\n",
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"\n",
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"#\n",
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"#\n",
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"source = {\"provider\": \"http\", \"url\": \"https://raw.githubusercontent.com/codeforamerica/ohana-api/master/data/sample-csv/addresses.csv\"}\n",
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"target = [{\"provider\": \"files\", \"path\": \"addresses.csv\", \"delimiter\": \",\"}, {\"provider\": \"sqlite\", \"database\": \"sample.db3\", \"table\": \"addresses\"}]\n",
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"\n",
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"_handler = transport.get.etl (source=source,target=target)\n",
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"_data = _handler.read() #-- all etl begins with data being read\n",
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"_data.head()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"#### Extract Transform Load (ETL) from CLI\n",
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"\n",
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"The documentation for this is available at https://healthcareio.the-phi.com/data-transport \"Docs\" -> \"Terminal CLI\"\n",
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"\n",
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"The entire process is documented including how to generate an ETL configuration file."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.9.7"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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