Compare commits
No commits in common. 'master' and 'v2.0.4' have entirely different histories.
@ -1,149 +0,0 @@
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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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"#### Writing data-transport plugins\n",
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"\n",
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"The data-transport plugins are designed to automate pre/post processing i.e\n",
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"\n",
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" - Read -> Post processing\n",
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" - Write-> Pre processing\n",
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" \n",
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"In this example we will assume, data and write both pre/post processing to any supported infrastructure. We will equally show how to specify the plugins within a 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": 1,
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"metadata": {},
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"outputs": [],
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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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"import shutil\n",
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"#\n",
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"#\n",
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"\n",
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"DATABASE = '/home/steve/tmp/demo.db3'\n",
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"if os.path.exists(DATABASE) :\n",
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" os.remove(DATABASE)\n",
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"#\n",
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"# \n",
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"_data = pd.DataFrame({\"name\":['James Bond','Steve Rogers','Steve Nyemba'],'age':[55,150,44]})\n",
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"litew = transport.get.writer(provider=providers.SQLITE,database=DATABASE)\n",
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"litew.write(_data,table='friends')"
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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 SQLite\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 from a plugin function we will write. \n",
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"\n",
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"- Basic read of the designated table (friends) created above\n",
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"- Read with pipeline functions defined in code\n",
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"\n",
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"**NOTE**\n",
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"\n",
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"It is possible to use **transport.factory.instance** or **transport.instance** or **transport.get.<[reader|writer]>** they are the same. It allows the maintainers to know that we used a factory design pattern."
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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": 4,
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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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" name 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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"\n",
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"\n",
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" name age autoinc\n",
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"0 James Bond 5.5 0\n",
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"1 Steve Rogers 15.0 1\n",
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"2 Steve Nyemba 4.4 2\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 os\n",
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"import numpy as np\n",
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"def _autoincrement (_data,**kwargs) :\n",
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" \"\"\"\n",
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" This function will add an autoincrement field to the table\n",
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" \"\"\"\n",
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" _data['autoinc'] = np.arange(_data.shape[0])\n",
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" \n",
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" return _data\n",
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"def reduce(_data,**_args) :\n",
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" \"\"\"\n",
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" This function will reduce the age of the data frame\n",
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" \"\"\"\n",
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" _data.age /= 10\n",
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" return _data\n",
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"reader = transport.get.reader(provider=providers.SQLITE,database=DATABASE,table='friends')\n",
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"#\n",
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"# basic read of the data created in the first cell\n",
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"_df = reader.read()\n",
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"print (_df)\n",
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"print ()\n",
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"print()\n",
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"#\n",
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"# read of the data with pipeline function provided to alter the database\n",
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"print (reader.read(pipeline=[_autoincrement,reduce]))"
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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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"The parameters for instianciating a transport object (reader or writer) can be found at [data-transport home](https://healthcareio.the-phi.com/data-transport)\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": 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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@ -1,131 +0,0 @@
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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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"#### 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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@ -1,19 +0,0 @@
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"""
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This file will be intended to handle duckdb database
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"""
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import duckdb
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from transport.common import Reader,Writer
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class Duck(Reader):
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def __init__(self,**_args):
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super().__init__(**_args)
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self._path = None if 'path' not in _args else _args['path']
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self._handler = duckdb.connect() if not self._path else duckdb.connect(self._path)
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class DuckReader(Duck) :
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def __init__(self,**_args):
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super().__init__(**_args)
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def read(self,**_args) :
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pass
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@ -1 +1 @@
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from . import files, http, rabbitmq, callback, files, console
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from . import files, http, rabbitmq, callback, files
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@ -1,24 +0,0 @@
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"""
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This module implements the handler for duckdb (in memory or not)
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"""
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from transport.sql.common import Base, BaseReader, BaseWriter
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class Duck :
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def __init__(self,**_args):
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#
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# duckdb with none as database will operate as an in-memory database
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#
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self.database = _args['database'] if 'database' in _args else ''
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def get_provider(self):
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return "duckdb"
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def _get_uri(self,**_args):
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return f"""duckdb:///{self.database}"""
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class Reader(Duck,BaseReader) :
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def __init__(self,**_args):
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Duck.__init__(self,**_args)
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BaseReader.__init__(self,**_args)
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class Writer(Duck,BaseWriter):
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def __init__(self,**_args):
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Duck.__init__(self,**_args)
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BaseWriter.__init__(self,**_args)
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Loading…
Reference in new issue