Import_Export_Data.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Machine Learning for Engineers: [GatherData](https://www.apmonitor.com/pds/index.php/Main/GatherData)\n",
    "\n",
    "- [Data Gathering and Consolidation](https://www.apmonitor.com/pds/index.php/Main/GatherData)\n",
    " - Source Blocks: 23\n",
    " - Description: Import and Export data in Python for consolidating disparate data (Excel spreadsheet, CSV file, PDF report, database, cloud storage) into a single repository.\n",
    "- [Course Overview](https://apmonitor.com/pds)\n",
    "- [Course Schedule](https://apmonitor.com/pds/index.php/Main/CourseSchedule)\n",
    "\n",
    "<img align=left width=400px src='https://apmonitor.com/pds/uploads/Main/python_import_data.png'>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "tx = np.linspace(0,1,8); x  = np.cos(tx)\n",
    "dx = pd.DataFrame({'Time':tx,'x':x})\n",
    "dx.to_csv('dx.csv',index=False)\n",
    "print(dx)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# local source\n",
    "pd.read_csv('dx.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# online source\n",
    "pd.read_csv('http://apmonitor.com/pds/uploads/Main/dx.txt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "url = 'http://apmonitor.com/pds/index.php/Main/GatherData'\n",
    "table = pd.read_html(url)\n",
    "print(table)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# write DataFrame dx\n",
    "dx.to_clipboard()\n",
    "dx.to_csv()\n",
    "dx.to_dict()\n",
    "dx.to_excel()\n",
    "dx.to_feather()\n",
    "dx.to_gbq()\n",
    "dx.to_hdf()\n",
    "dx.to_html()\n",
    "dx.to_json()\n",
    "dx.to_latex()\n",
    "dx.to_markdown()\n",
    "dx.to_parquet()\n",
    "dx.to_pickle()\n",
    "dx.to_records()\n",
    "dx.to_sql()\n",
    "dx.to_stata()\n",
    "dx.to_string()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "d = np.genfromtxt('dx.csv',delimiter=',')\n",
    "print(d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "d = np.loadtxt('dx.csv',delimiter=',',skiprows=1)\n",
    "print(d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "f = open('dx.csv', 'r')\n",
    "for x in f:\n",
    "  print(x)\n",
    "f.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "f = open('dx.csv', 'r')\n",
    "x = f.read(4); print(x)\n",
    "f.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "f = open('dx.csv', 'r')\n",
    "print(f.readline())\n",
    "print(f.readline())\n",
    "print(f.readline())\n",
    "f.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import requests\n",
    "url = 'http://apmonitor.com/pds/index.php/Main/GatherData?action=print'\n",
    "page = requests.get(url)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from bs4 import BeautifulSoup\n",
    "soup = BeautifulSoup(page.content, 'html.parser')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(soup.title.text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for link in soup.find_all('a'):\n",
    "    print('Link Text: {}'.format(link.text))\n",
    "    print('href: {}'.format(link.get('href')))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "tx = np.linspace(0,1,4); x  = np.cos(tx)\n",
    "dx = pd.DataFrame({'Time':tx,'x':x})\n",
    "\n",
    "ty = np.linspace(0,1,3); y  = np.sin(ty)\n",
    "dy = pd.DataFrame({'Time':ty,'y':y})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dx = dx.set_index('Time')\n",
    "dy = dy.set_index('Time')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "z = dx.join(dy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "z = dx.join(dy,how='right')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "z = dx.join(dy,how='inner')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "z = dx.join(dy,how='outer',sort=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "tx = np.linspace(0,1,4); x  = np.cos(tx)\n",
    "dx = pd.DataFrame({'Time':tx,'x':x})\n",
    "tx = np.linspace(0,1,3)\n",
    "x  = np.cos(tx)\n",
    "dy = pd.DataFrame({'Time':tx,'x':x})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dx.append(dy)\\\n",
    "  .sort_values(by='Time')\\\n",
    "  .drop_duplicates(subset='Time')\\\n",
    "  .reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# stock ticker symbol\n",
    "url = 'http://apmonitor.com/pds/uploads/Main/goog.txt'\n",
    "\n",
    "# import data with pandas\n",
    "data = pd.read_csv(url)\n",
    "print(data.describe())\n",
    "\n",
    "# calculate change and volatility\n",
    "data['Change'] = data['Close']-data['Open']\n",
    "data['Volatility'] = data['High']-data['Low']\n",
    "analysis = ['Open','Volume','Volatility','Change']\n",
    "sns.heatmap(data[analysis].corr())\n",
    "plt.show()"
   ]
  }
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