111 lines
3.0 KiB
Plaintext
111 lines
3.0 KiB
Plaintext
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 73,
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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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"132001000000.0\n"
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]
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}
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],
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"source": [
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"from urllib.request import urlopen\n",
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"from urllib.parse import quote\n",
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"from bs4 import BeautifulSoup\n",
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"import time\n",
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"import re\n",
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"import json\n",
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"import pandas as pd\n",
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"\n",
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"select_year = 2021\n",
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"\n",
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"try:\n",
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" html = urlopen(\n",
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" f\"https://www.marketwatch.com/investing/stock/ibm/financials/balance-sheet\"\n",
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" )\n",
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" time.sleep(0.1)\n",
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" bsObj = BeautifulSoup(html.read(), 'html.parser')\n",
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"except Exception as e:\n",
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" print(e)\n",
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" print(\"Can't open url.\")\n",
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"else:\n",
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" bs_table = bsObj.find(name=\"table\", attrs={\"aria-label\":\"Financials - Assets data table\"})\n",
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" bs = bs_table.find(name=\"tr\", attrs={\"class\":\"table__row\"}).find_all(lambda tag: tag.name == 'th' and \n",
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" tag.get('class') == ['overflow__heading'])\n",
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" years_list = [i.div.contents[0] for i in bs][0:-1]\n",
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" years_list = map(int, years_list)\n",
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" bs = bs_table.find_all(name=\"tr\", attrs={\"class\":\"table__row is-highlighted\"})\n",
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" for i in bs:\n",
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" if i.td.div.contents[0] == 'Total Assets':\n",
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" string = i.find(\"div\", attrs={\"class\":\"chart--financials js-financial-chart\"}).attrs['data-chart-data']\n",
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" total_assets_list = string.split(',')\n",
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" total_assets_list = map(float, total_assets_list)\n",
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" total_assets = pd.DataFrame({\"year\":years_list,'total_asset':total_assets_list})\n",
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" print(total_assets.loc[total_assets['year']==2021,'total_asset'].to_list()[0])\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": 64,
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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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"<th class=\"overflow__heading fixed--column m70\">\n",
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"<div class=\"cell__content fixed--cell\">Item</div>\n",
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"<div class=\"cell__content\">Item</div>\n",
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"</th>"
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]
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},
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"execution_count": 64,
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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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"bs_table.th"
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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": "base",
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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": "3.8.8"
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},
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"orig_nbformat": 4,
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"vscode": {
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"interpreter": {
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"hash": "bcdafc093860683ffb58d6956591562b7f8ed5d58147d17d71a5d4d6605a08df"
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}
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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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