From 5794d6c8d4e5b3479f6c0d73de1d4a000f7f6c9f Mon Sep 17 00:00:00 2001
From: peiwangdb <pennyiscomputing@gmail.com>
Date: Wed, 17 Mar 2021 18:46:32 -0700
Subject: [PATCH] docs(connector): adding a process overview via DBLP section

---
 docs/source/user_guide/connector/DBLP.ipynb | 145 ++++++++++++++++++++
 1 file changed, 145 insertions(+)
 create mode 100644 docs/source/user_guide/connector/DBLP.ipynb

diff --git a/docs/source/user_guide/connector/DBLP.ipynb b/docs/source/user_guide/connector/DBLP.ipynb
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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Process overview via DBLP"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## What is DataPrep.Connector?\n",
+    "\n",
+    "Connector is an intuitive, open-source API wrapper that speeds up development by standardizing calls to multiple APIs as a simple workflow.\n",
+    "\n",
+    "Connector provides a simple wrapper to collect structured data from different Web APIs (e.g., Twitter, Spotify), making web data collection easy and efficient, without requiring advanced programming skills.\n",
+    "\n",
+    "With Connector, you can collect data in two steps: **connect** to a website and **query** the data.\n",
+    "\n",
+    "We currently support tens of websites: https://github.com/sfu-db/DataConnectorConfigs/tree/develop/api-connectors\n",
+    "\n",
+    "You can also author your own configuration files for new websites.\n",
+    "We look forward to seeing your contribution to facilitate other users as well."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Collecting data from DBLP\n",
+    "\n",
+    "\n",
+    "#### DBLP Website\n",
+    "DBLP (https://dblp.org/) is a computer science bibliography website.\n",
+    "We will use it as an example to illustrate how to collect data easily using DataPrep.Connector.\n",
+    "\n",
+    "API: https://dblp.org/faq/13501473.html\n",
+    "\n",
+    "And more examples are available here: https://github.com/sfu-db/dataprep/tree/develop/examples.\n",
+    "\n",
+    "#### Step 1: Installation\n",
+    "\n",
+    "You can install the DataPrep through the single command below if you have not.\n",
+    "\n",
+    "```\n",
+    "!pip install dataprep\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Step 2: Connecting to the API\n",
+    "\n",
+    "Once the library is installed, you can connect to a website that are supported by us or loading from local configuration files for connection.\n",
+    "The detailed usage and paramsters for connect() can be found in next section.\n",
+    "Here, we are connecting to DBLP API through the exsiting configuration file available here: https://github.com/sfu-db/DataConnectorConfigs/tree/develop/api-connectors/dblp"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from dataprep.connector import connect\n",
+    "conn = connect(\"dblp\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Step 3: Understand how to use the API\n",
+    "\n",
+    "info() function helps you understand what is available from the website.\n",
+    "Here, the output shows there is one table called \"publication\" available. And to fetch the data, we have to specify the value of the \"q\" (query keyword) parameter.\n",
+    "The schema block shows the schema of the results."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "conn.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Step 4: Customize the query to collect data\n",
+    "\n",
+    "Once you know how to use the API and the connection is built, you can issue the query through the query function.\n",
+    "The first parameter specifies which API endpoint you want to query. \n",
+    "The detailed parameter explanation can be found in later sections.\n",
+    "In this example, we are collecting 2000 CVPR2020 papers."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "await conn.query(\"publication\", q=\"CVPR 2020\", _count=2000)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And you have the data ready. It is so simple :)"
+   ]
+  }
+ ],
+ "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": "3.7.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}