Create scrape_background.ipynb
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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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"# Scrape data from aon2e and generate csvs to import in to sqlite"
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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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"# Dependencies\n",
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"import pandas as pd\n",
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"from bs4 import BeautifulSoup as bs, Tag, NavigableString\n",
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"import os\n",
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"from splinter import Browser\n",
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"\n",
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"# Setting up Selenium\n",
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"chrome_driver = os.path.join('..', 'chromedriver.exe')\n",
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"executable_path = {'executable_path': chrome_driver}\n",
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"browser = Browser('chrome', **executable_path, headless=False)\n",
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"\n",
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"# Pandas config\n",
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"pd.set_option('display.max_columns', None)"
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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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"def scrape_description(url, id_number):\n",
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"\n",
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" # Empty lists to store the scraped values\n",
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" name_list = []\n",
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" description_list = []\n",
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"\n",
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" print(f'Beginning Data Retrieval')\n",
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" print(f'------------------------')\n",
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"\n",
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" # Loop from 1 to the value in weapon_number\n",
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" for page in range(1, id_number+1):\n",
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"\n",
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" browser.visit(url + str(page))\n",
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" html = browser.html\n",
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" soup = bs(html, 'html.parser')\n",
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"\n",
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" # Select only the content section\n",
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" content = soup.find(id='ctl00_MainContent_DetailedOutput')\n",
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"\n",
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" try:\n",
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" # Store the name and description\n",
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" name = content.find('h1', class_='title')\n",
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" name.span.decompose()\n",
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" name = name.text\n",
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"\n",
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" except:\n",
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" name = f'name: {page}'\n",
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"\n",
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" try:\n",
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" description = ''\n",
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" start = content.find('hr')\n",
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" for e in start.next_siblings:\n",
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" if isinstance(e, Tag):\n",
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" description = description + e.text.strip()\n",
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" elif isinstance(e, NavigableString):\n",
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" description = description + e\n",
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"\n",
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" except:\n",
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" description = f'name: {page}'\n",
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"\n",
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" print(f'{page} of {id_number} | {name}')\n",
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"\n",
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" # Append values to our empty lists\n",
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" name_list.append(name) \n",
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" description_list.append(description)\n",
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"\n",
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" print(f'------------------------')\n",
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" print(f'Data Retrieval Complete')\n",
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" \n",
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" # Create df with the scraped data\n",
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" data = {'Name': name_list, 'description': description_list}\n",
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" \n",
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" # Returns a data frame\n",
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" return pd.DataFrame(data)"
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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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"# scrape the descriptions\n",
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"url_background = 'https://2e.aonprd.com/Equipment.aspx?ID='\n",
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"number_background = 65 #65 to scrape\n",
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"\n",
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"description_background = scrape_description(url_gear, number_gear)"
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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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"gear.to_csv('background.csv')"
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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": "3.6.9"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 4
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}
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