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132 lines
3.5 KiB
Plaintext
132 lines
3.5 KiB
Plaintext
{
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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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"#### Writing to AWS S3\n",
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"\n",
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"We have setup our demo environment with the label **aws** passed to reference our s3 access_key and secret_key and file (called friends.csv). In the cell below we will write the data to our aws s3 bucket named **com.phi.demo**"
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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": 1,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"2.2.1\n"
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]
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}
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],
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"source": [
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"#\n",
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"# Writing to mongodb 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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"_data = pd.DataFrame({\"name\":['James Bond','Steve Rogers','Steve Nyemba'],'age':[55,150,44]})\n",
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"mgw = transport.get.writer(label='aws')\n",
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"mgw.write(_data)\n",
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"print (transport.__version__)"
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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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"#### Reading from AWS S3\n",
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"\n",
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"The cell below reads the data that has been written by the cell above and computes the average age within a mongodb pipeline. The code in the background executes an aggregation using\n",
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"\n",
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"- Basic read of the designated file **friends.csv**\n",
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"- Compute average age using standard pandas functions\n",
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"\n",
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"**NOTE**\n",
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"\n",
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"By design **read** object are separated from **write** objects in order to avoid accidental writes to the database.\n",
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"Read objects are created with **transport.get.reader** whereas write objects are created with **transport.get.writer**"
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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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"name": "stdout",
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"output_type": "stream",
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"text": [
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" bname age\n",
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"0 James Bond 55\n",
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"1 Steve Rogers 150\n",
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"2 Steve Nyemba 44\n",
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"--------- STATISTICS ------------\n",
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"83.0\n"
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]
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}
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],
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"source": [
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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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"\n",
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"def cast(stream) :\n",
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" print (stream)\n",
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" return pd.DataFrame(str(stream))\n",
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"mgr = transport.get.reader(label='aws')\n",
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"_df = mgr.read()\n",
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"print (_df)\n",
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"print ('--------- STATISTICS ------------')\n",
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"print (_df.age.mean())"
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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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"An **auth-file** is a file that contains database parameters used to access the database. \n",
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"For code in shared environments, we recommend \n",
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"\n",
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"1. Having the **auth-file** stored on disk \n",
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"2. and the location of the file is set to an environment variable.\n",
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"\n",
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"To generate a template of the **auth-file** open the **file generator wizard** found at visit https://healthcareio.the-phi.com/data-transport"
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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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