adding notebooks (test/examples
This commit is contained in:
Steve Nyemba
parent
677239585c
commit
715e40407a
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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 Google Bigquery\n",
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"\n",
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"1. Insure you have a Google Bigquery service account key on disk\n",
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"2. The service key location is set as an environment variable **BQ_KEY**\n",
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"3. The dataset will be automatically created within the project associated with the service key\n",
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"\n",
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"The cell below creates a dataframe that will be stored within Google Bigquery"
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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": "stderr",
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"output_type": "stream",
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"text": [
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"100%|██████████| 1/1 [00:00<00:00, 5440.08it/s]\n"
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]
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},
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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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"['data transport version ', '2.0.0']\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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"# 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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"PRIVATE_KEY = os.environ['BQ_KEY'] #-- location of the service key\n",
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"DATASET = 'demo'\n",
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"_data = pd.DataFrame({\"name\":['James Bond','Steve Rogers','Steve Nyemba'],'age':[55,150,44]})\n",
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"bqw = transport.factory.instance(provider=providers.BIGQUERY,dataset=DATASET,table='friends',context='write',private_key=PRIVATE_KEY)\n",
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"bqw.write(_data,if_exists='replace') #-- default is append\n",
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"print (['data transport version ', transport.__version__])\n"
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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 Google Bigquery\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 Google Bigquery (simple query). \n",
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"\n",
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"- Basic read of the designated table (friends) created above\n",
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"- Execute an aggregate SQL against the table\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** 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": 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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"Downloading: 100%|\u001b[32m██████████\u001b[0m|\n",
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"Downloading: 100%|\u001b[32m██████████\u001b[0m|\n",
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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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"--------- STATISTICS ------------\n",
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" _counts f0_\n",
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"0 3 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 os\n",
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"PRIVATE_KEY=os.environ['BQ_KEY']\n",
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"pgr = transport.instance(provider=providers.BIGQUERY,dataset='demo',table='friends',private_key=PRIVATE_KEY)\n",
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"_df = pgr.read()\n",
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"_query = 'SELECT COUNT(*) _counts, AVG(age) from demo.friends'\n",
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"_sdf = pgr.read(sql=_query)\n",
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"print (_df)\n",
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"print ('--------- STATISTICS ------------')\n",
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"print (_sdf)"
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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 cell bellow show the content of an auth_file, in this case if the dataset/table in question is not to be shared then you can use auth_file with information associated with the parameters.\n",
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"\n",
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"**NOTE**:\n",
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"\n",
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"The auth_file is intended to be **JSON** formatted"
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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": 3,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"{'dataset': 'demo', 'table': 'friends'}"
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]
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},
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"execution_count": 3,
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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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"{\n",
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" \n",
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" \"dataset\":\"demo\",\"table\":\"friends\"\n",
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"}"
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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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@ -0,0 +1,155 @@
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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 mongodb\n",
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"\n",
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"Insure mongodb is actually installed on the system, The cell below creates a dataframe that will be stored within mongodb"
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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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"2.0.0\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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"# 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.factory.instance(provider=providers.MONGODB,db='demo',collection='friends',context='write')\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 mongodb\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 **db.runCommand**\n",
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"\n",
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"- Basic read of the designated collection **find=\\<collection>**\n",
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"- Executing an aggregate pipeline against a collection **aggreate=\\<collection>**\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** 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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"--------- STATISTICS ------------\n",
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" _id _counts _mean\n",
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"0 0 2 102.5\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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"mgr = transport.instance(provider=providers.MONGODB,db='foo',collection='friends')\n",
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"_df = mgr.read()\n",
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"PIPELINE = [{\"$group\":{\"_id\":0,\"_counts\":{\"$sum\":1}, \"_mean\":{\"$avg\":\"$age\"}}}]\n",
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"_sdf = mgr.read(aggregate='friends',pipeline=PIPELINE)\n",
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"print (_df)\n",
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"print ('--------- STATISTICS ------------')\n",
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"print (_sdf)"
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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 cell bellow show the content of an auth_file, in this case if the dataset/table in question is not to be shared then you can use auth_file with information associated with the parameters.\n",
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"\n",
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"**NOTE**:\n",
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"\n",
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"The auth_file is intended to be **JSON** formatted"
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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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"data": {
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"text/plain": [
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"{'host': 'klingon.io',\n",
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" 'port': 27017,\n",
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" 'username': 'me',\n",
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" 'password': 'foobar',\n",
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" 'db': 'foo',\n",
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" 'collection': 'friends',\n",
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" 'authSource': '<authdb>',\n",
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" 'mechamism': '<SCRAM-SHA-256|MONGODB-CR|SCRAM-SHA-1>'}"
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]
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},
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"execution_count": 1,
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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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" \"host\":\"klingon.io\",\"port\":27017,\"username\":\"me\",\"password\":\"foobar\",\"db\":\"foo\",\"collection\":\"friends\",\n",
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" \"authSource\":\"<authdb>\",\"mechamism\":\"<SCRAM-SHA-256|MONGODB-CR|SCRAM-SHA-1>\"\n",
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"}"
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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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@ -0,0 +1,150 @@
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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 MySQL\n",
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"\n",
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"1. Insure MySQL is actually installed on the system, \n",
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"2. There is a database called demo created on the said system\n",
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"\n",
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"The cell below creates a dataframe that will be stored within postgreSQL"
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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": 8,
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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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"2.0.0\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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"# Writing to PostgreSQL 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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"myw = transport.factory.instance(provider=providers.MYSQL,database='demo',table='friends',context='write',auth_file=\"/home/steve/auth-mysql.json\")\n",
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"myw.write(_data,if_exists='replace') #-- default is append\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 MySQL\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 MySQL (simple query). \n",
|
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"\n",
|
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"- Basic read of the designated table (friends) created above\n",
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"- Execute an aggregate SQL against the table\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** 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": 9,
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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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"--------- STATISTICS ------------\n",
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" _counts avg\n",
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"0 3 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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"myr = transport.instance(provider=providers.POSTGRESQL,database='demo',table='friends',auth_file='/home/steve/auth-mysql.json')\n",
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"_df = myr.read()\n",
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"_query = 'SELECT COUNT(*) _counts, AVG(age) from friends'\n",
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"_sdf = myr.read(sql=_query)\n",
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"print (_df)\n",
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"print ('--------- STATISTICS ------------')\n",
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"print (_sdf)"
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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 cell bellow show the content of an auth_file, in this case if the dataset/table in question is not to be shared then you can use auth_file with information associated with the parameters.\n",
|
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"\n",
|
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"**NOTE**:\n",
|
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"\n",
|
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"The auth_file is intended to be **JSON** formatted"
|
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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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"data": {
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"text/plain": [
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"{'host': 'klingon.io',\n",
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" 'port': 3306,\n",
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" 'username': 'me',\n",
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" 'password': 'foobar',\n",
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" 'database': 'demo',\n",
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" 'table': 'friends'}"
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]
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},
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"execution_count": 1,
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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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" \"host\":\"klingon.io\",\"port\":3306,\"username\":\"me\",\"password\":\"foobar\",\n",
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" \"database\":\"demo\",\"table\":\"friends\"\n",
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"}"
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]
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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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@ -0,0 +1,157 @@
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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 PostgreSQL\n",
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"\n",
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"1. Insure PostgreSQL is actually installed on the system, \n",
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"2. There is a database called demo created on the said system\n",
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"\n",
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"The cell below creates a dataframe that will be stored within postgreSQL"
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]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 8,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"2.0.0\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"#\n",
|
||||
"# Writing to PostgreSQL database\n",
|
||||
"#\n",
|
||||
"import transport\n",
|
||||
"from transport import providers\n",
|
||||
"import pandas as pd\n",
|
||||
"_data = pd.DataFrame({\"name\":['James Bond','Steve Rogers','Steve Nyemba'],'age':[55,150,44]})\n",
|
||||
"pgw = transport.factory.instance(provider=providers.POSTGRESQL,database='demo',table='friends',context='write')\n",
|
||||
"pgw.write(_data,if_exists='replace') #-- default is append\n",
|
||||
"print (transport.__version__)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"#### Reading from PostgreSQL\n",
|
||||
"\n",
|
||||
"The cell below reads the data that has been written by the cell above and computes the average age within a PostreSQL (simple query). \n",
|
||||
"\n",
|
||||
"- Basic read of the designated table (friends) created above\n",
|
||||
"- Execute an aggregate SQL against the table\n",
|
||||
"\n",
|
||||
"**NOTE**\n",
|
||||
"\n",
|
||||
"It is possible to use **transport.factory.instance** or **transport.instance** they are the same. It allows the maintainers to know that we used a factory design pattern."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 6,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
" name age\n",
|
||||
"0 James Bond 55\n",
|
||||
"1 Steve Rogers 150\n",
|
||||
"2 Steve Nyemba 44\n",
|
||||
"--------- STATISTICS ------------\n",
|
||||
" _counts avg\n",
|
||||
"0 3 83.0\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"\n",
|
||||
"import transport\n",
|
||||
"from transport import providers\n",
|
||||
"pgr = transport.instance(provider=providers.POSTGRESQL,database='demo',table='friends')\n",
|
||||
"_df = pgr.read()\n",
|
||||
"_query = 'SELECT COUNT(*) _counts, AVG(age) from friends'\n",
|
||||
"_sdf = pgr.read(sql=_query)\n",
|
||||
"print (_df)\n",
|
||||
"print ('--------- STATISTICS ------------')\n",
|
||||
"print (_sdf)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"The cell bellow show the content of an auth_file, in this case if the dataset/table in question is not to be shared then you can use auth_file with information associated with the parameters.\n",
|
||||
"\n",
|
||||
"**NOTE**:\n",
|
||||
"\n",
|
||||
"The auth_file is intended to be **JSON** formatted"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 1,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"{'host': 'klingon.io',\n",
|
||||
" 'port': 5432,\n",
|
||||
" 'username': 'me',\n",
|
||||
" 'password': 'foobar',\n",
|
||||
" 'database': 'demo',\n",
|
||||
" 'table': 'friends'}"
|
||||
]
|
||||
},
|
||||
"execution_count": 1,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"{\n",
|
||||
" \"host\":\"klingon.io\",\"port\":5432,\"username\":\"me\",\"password\":\"foobar\",\n",
|
||||
" \"database\":\"demo\",\"table\":\"friends\"\n",
|
||||
"}"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.9.7"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
}
|
||||
|
|
@ -0,0 +1,139 @@
|
|||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"#### Writing to SQLite3+\n",
|
||||
"\n",
|
||||
"The requirements to get started are minimal (actually none). The cell below creates a dataframe that will be stored within SQLite 3+"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 1,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"2.0.0\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"#\n",
|
||||
"# Writing to PostgreSQL database\n",
|
||||
"#\n",
|
||||
"import transport\n",
|
||||
"from transport import providers\n",
|
||||
"import pandas as pd\n",
|
||||
"_data = pd.DataFrame({\"name\":['James Bond','Steve Rogers','Steve Nyemba'],'age':[55,150,44]})\n",
|
||||
"sqw = transport.factory.instance(provider=providers.SQLITE,database='/home/steve/demo.db3',table='friends',context='write')\n",
|
||||
"sqw.write(_data,if_exists='replace') #-- default is append\n",
|
||||
"print (transport.__version__)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"#### Reading from SQLite3+\n",
|
||||
"\n",
|
||||
"The cell below reads the data that has been written by the cell above and computes the average age within a PostreSQL (simple query). \n",
|
||||
"\n",
|
||||
"- Basic read of the designated table (friends) created above\n",
|
||||
"- Execute an aggregate SQL against the table\n",
|
||||
"\n",
|
||||
"**NOTE**\n",
|
||||
"\n",
|
||||
"It is possible to use **transport.factory.instance** or **transport.instance** they are the same. It allows the maintainers to know that we used a factory design pattern."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 2,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
" name age\n",
|
||||
"0 James Bond 55\n",
|
||||
"1 Steve Rogers 150\n",
|
||||
"2 Steve Nyemba 44\n",
|
||||
"--------- STATISTICS ------------\n",
|
||||
" _counts AVG(age)\n",
|
||||
"0 3 83.0\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"\n",
|
||||
"import transport\n",
|
||||
"from transport import providers\n",
|
||||
"pgr = transport.instance(provider=providers.SQLITE,database='/home/steve/demo.db3',table='friends')\n",
|
||||
"_df = pgr.read()\n",
|
||||
"_query = 'SELECT COUNT(*) _counts, AVG(age) from friends'\n",
|
||||
"_sdf = pgr.read(sql=_query)\n",
|
||||
"print (_df)\n",
|
||||
"print ('--------- STATISTICS ------------')\n",
|
||||
"print (_sdf)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"The cell bellow show the content of an auth_file, in this case if the dataset/table in question is not to be shared then you can use auth_file with information associated with the parameters.\n",
|
||||
"\n",
|
||||
"**NOTE**:\n",
|
||||
"\n",
|
||||
"The auth_file is intended to be **JSON** formatted. This is an overkill for SQLite ;-)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 5,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"\n",
|
||||
"{\n",
|
||||
" \"provider\":\"sqlite\",\n",
|
||||
" \"database\":\"/home/steve/demo.db3\",\"table\":\"friends\"\n",
|
||||
"}\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.9.7"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
}
|
||||
Loading…
Reference in New Issue