software/test_python.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "toc": "true"
   },
   "source": [
    "# Table of Contents\n",
    " <p><div class=\"lev1 toc-item\"><a href=\"#Import-Libraries\" data-toc-modified-id=\"Import-Libraries-1\"><span class=\"toc-item-num\">1&nbsp;&nbsp;</span>Import Libraries</a></div><div class=\"lev1 toc-item\"><a href=\"#Test-Pandas-and-Scikit-learn\" data-toc-modified-id=\"Test-Pandas-and-Scikit-learn-2\"><span class=\"toc-item-num\">2&nbsp;&nbsp;</span>Test Pandas and Scikit-learn</a></div><div class=\"lev1 toc-item\"><a href=\"#Test-Biopython\" data-toc-modified-id=\"Test-Biopython-3\"><span class=\"toc-item-num\">3&nbsp;&nbsp;</span>Test Biopython</a></div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Import Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# tab data analysis library\n",
    "import pandas as pd\n",
    "# biological sequence analysis library\n",
    "from Bio.Seq import Seq\n",
    "# plotting library\n",
    "import matplotlib.pyplot as plt\n",
    "# machine learning library\n",
    "from sklearn import datasets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Test Pandas and Scikit-learn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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PfrTkeNhyC865kmPbtm3j1KlTWpJBRLxTKwDMEV7QW/54x3UDTU2F71Q5PT3Nzp07l7aELF5uobAuz8GDBzGzkmMnTpwAWPo+ahKZiMhaU6sL6KvAs8BngXc754L8v5hzruMKf6ieBjo9Pb3UjRO23EIsFsOs+ri3lmQQEV/U2hHs54Gbgb8HPp/fGexXzKxj1yR49dVXI899+9vfZv/+/UxPT4cut5DNZisGj8tpSQYR8UXN1UCdc686577unLsN+ClgA3DEzP6y6Xe3DIlEIvJcoWvnwQcf5OLFi4yOjpYst3D77bdXLMGwfft2LckgIl6qNQZQri//71rg+6t/OysXj9f+kbLZLA888ABBEDA6Okp/f3/JcgvlSzCMjIwwMzPDwMDA0hiCiMhaV7O0NLO3Ah/L/8uS2xP4vc65f27yvS3L5cuX6742k8kwOTnJvn37Smr1xUswaGMWEfFV1QBgZo8Am4G/BD7mnDvdiptaiWw229D1ZrY0qFu+8FpYplCzs4BSqZQWgBORlqjVAtgJvAb8IvALRRkyRm6p6Dc08d6WZf369Q1N2HLOMTs7yz333FNRy5+amirZuhHCt29cLWptiEgr1QoAgzXOd5xaG8IUi8VijI6OMjExUVHL37hxI0ePHq14zOLiYlOygNrR2hCR7lY1AOQ3fllTXnnllbquK8wBSKVSFZu0mxnHjh0LDSa10kSXq97N4kVEVot3m8JXSwMtlslkyGQyPProoxUF/eLiIk8++WTo43p6epoyEUzbQIpIq3kXAKrtBxAmm82yefPmutJHC9c3o1DWNpAi0mqNzgPoeC+++GLV87FYrCJT6Ny5c9xyyy0cP3686hhCswtlbQMpIq3kXQCo1kcfi8UYGhqq6N7JZrMcO3Ys9DGJRIJMJsPOnTsZHh5ueqGsbSBFpFW8CwA9PT0VqZsF2WyW73znO3U9TywWY8+ePRWzhMs1M29fcwJEpJm8CwDr16/n0qVLkeejgkOYrVu3Vi14m5m3rzkBItJs3g0Cv/baa6vyPPF4vGq2T3He/uXLl0mn04yPjzc0CS1qp7HVeG4RkVq8awGsW7eu6pLQxYIgwDkXunxEOp3m0qVLpFKp0FbASvP2q9XwNSdARFrBuwAQi9Vu1JgZsVisopAtd//990d2v6wkb7/WrF/NCRCRVvCuC6jWUhDxeJxYLEYmk2FhYSFy8bhsNlvS/fLMM8+UdMGsJG8/bDey4p3GNCdARFqhKS0AM+sBvgxsAtYBvwt8B7iH3F7C08CnnHONLd1Zh8XFxarnnXNL3S71SqfT3HfffTjnGBsbW8rVHxwcZN++fQ1n6oTV8Mu7nDQnQESarVldQB8DnnfOfdzMrgHO5P/d5Zx7xMz+BBgDHlztFy6vWZcbHh7m1KlTDT9vIbAcPHgQyLUklpudU6jhj4+PEwQBCwsLQGWXk+YEiEgzNasL6K+A38p/bUAaGAYezR87DNzajBfu6empej6RSLB79+66l34oV1hDaKXZOUNDQ+zbt4+f/MmfXGqRKONHRFqpKQHAOfeKc+5lM7sauB+4CzD3+jTdl8ntLVzBzPaa2ZSZTc3NzTX82rXSQI8dO8bhw4d55zvfWTMIBEFQc1C5uO++UclkkvXr11cdDxARaZamDQLnt5I8AtzrnPsLcttJFlwNhJZwzrm7nXMjzrmR5ey/W6sFALkB3ieffLKuSWEf/OAHCYIgMlik0+kVZec0kvETNW9ARGQ5mjUIvBH4JvAfnXN/lz982sx2OeceAfaQCw6rrpGZvgVhC8RBriD+/ve/T9FOaBV27ty5on768vGAwhhA+XNqZrCIrLZmDQL/JvBG4LfMrDAW8GvAH5pZAniKXNfQqisMqDai2gJyJ06ciDwXj8cZHh5u+PXK1cr40W5hItIMTQkAzrlfI1fgl3tfM16v2FVXXcULL7zQ0GPi8XjN9NFiPT09Symhq1UAV8v40cxgEWkG72YCVyvIt23bxpkzZ0q6e2KxWEPbPMZiMe644w76+/tbVvhqZrCININ3M4GrFeanTp1iz549xONxenp6iMfjfPjDHy6ZdWtmBEGwNAM37PlbWfjD8mYGa8BYRGrxrgWwfv36qhvDX3HFFezbt4/Z2VmApcJ848aNzMzMMDAwwJVXXsn8/DyXLl3i/vvv5/Lly0uPTyQSoV0vhbX7E4kECwsLqz57t5GZwRowFpF6eBcA3vWud/HII49Env/BD36Ac66kgNy2bRunTp2qKDBTqVRdXS+FAhdyWUhBEGBmq17w1jMzWAPGIlIv77qARkZGqp5/9NFHefDBB0vW2j9x4kTo2vu1ul5SqRTPPPNMRYGbyWRWPKN3uV04tRaaExEp8K4FMDMzU/V8PYvAFWfYRHW9FGr9ZhY59yAWi/H0009z4403NlT7XkkXjgaMRaRe3rUAzpw5s+LnSKfTZDKZpRp4MplkYGCgpOZfqPVXyzpaWFjg8OHD7N+/n+np6bpq9SvdDUxLSYtIvbxrAVxxxRV1XReLxejp6SkZA4Bc4Z/NZvnKV76ylAVUXgMPy8sHlmrsxctNFyam1buK6Grk/GspaRGph3cBYGBggNOnT1e9JggC7rzzTtavX79UQI6MjHDgwAHg9VTSqEHUsG6WwnNu2LCBmZkZDh8+XDIruXB94f+ogdnV6sLplKWkC9lRCkQince7LqB6loIwM/r7++nt7WV+fp5UKsXCwkLV1UELg6iFAm10dLSkm+X222/nhhtuoK+vjxtvvDFyp7GCqIFZn7pwzp49y/79+7n33nuXusFEpHN41wJ46aWXqp4PgoCxsTEuXLhQMtA6OjoaOUCcTqf57ne/ywsvvFDymN27dy8FkuICOmyBt0wmUzJJrVqt3ocuHKWjinQ+7wJAf39/5LlYLMYnP/lJAA4cOLCUrgkwMTHBe9/7Xh5//PHQQPDwww8DlDxmcnKSffv2hRZo5YX4xYsXa674WaxTunCWS+sXiXQ+7wLApUuXqp6fmpri5MmToXvyRhX+kOs2MrOS87UKtMLxlewfvFYpHVWk83kXAKoVrNlsturyzrXmCJT369cq0Lp5SYZ69zkQkfbxLgAMDg425XkzmQw333wzTz755NIGMrt37wZyk8/Ka/Vzc3OMj4+XdBm1qw+8XZk4PoxltJqypqSVvAsAkOuuaWSJ53o45zh9+jRBELC4uEgQBPzt3/4tExMTFbn9Z8+eXSr8i7WjD7zdrZC1PpbRSu3+XUn38S4NdHZ2dtmFfyENNAiCyH2Ai/P5nXNkMpmSGbvT09McOnQotDtppfsHN2qls4qldfS7knbwsgVQj+JWQhAE7Nmzh+uuu25pOedEIsHdd9/d0HOm02kOHjxYdSzh4sWLLavVKRNn7dDvStrBuxZAtTTQYuWthC1btjAwMEBfX9/SngA7duwgFmvsLapW+GcymZbW6pSJs3bodyXt4F0AACqWQ64lHo+XzMotzGD91re+VXNGb6MKK4Q2GgQaWR66cC3gzaxi3/k0A1zWDu+6gApN6XqWfS4o7psv7osNUwgujTx/scIKoQ899FDdg3yNDA6GXdtN8w/WMmVNSat51wLo7e2tukRzmGw2y+zs7FIKXq0WxEpbBQsLC3UP8jUyOBh1LVCynLV0rvKlx0WaybsWwHI457jvvvtwzkWuCVTcqlitFFPnXM1BvkYGB1dzIFH56CL+8y4ALDcNtNBqmJiYYPfu3UxOTi4V+rt27eLIkSPA8rt+wmQyGRKJRNVrGhkcXK2BROWji3QH77qAVioIAvr7+9m7dy+jo6Ps3buXTZs2VV0quh7xeLyiaykej5csXx020NvI4OBqDCQqH12ke3jXAujv71/RTOBMJsPs7GxJC6DaUtH1cs5hZhXHC7XzarXuRgYHVzqQqHx0ke7hXQsgmUzykY98JHImb5REIkE8Hmd0dJTJycmSGvDhw4dXHADe8573RNbO66l1NzI4uJKBROWji3QP71oA8Hot+Omnn+Yb3/hGRdbO5s2buXDhQujGLmE14PIC0cyIxWKhQaGwUFy5J598kg984AOhKZm1at2tHJDVKp4i3cPLAFBw1VVXVRwLgoAPfehDQG7AGHLdRsUFXK3afiKR4JZbbuHo0aMV8wWiup4KBXpYzTys1p1Op0kkEqs+IFtPMFE+ukh38DIAFApNKM3ZNzNuv/12kslkZMFa73aOw8PDvOUtb+G+++4rmXcQFQCy2WxkN0rxaxYWmDOzpU3qV2tJ6UaCiVbxFPGfdwGg2kzeIAgYHBysuV9tvds59vf3R84YLrdjx46ag7cbN25cKvSrzURebl6/9ugVkWLeBYCw/vSCQuFZ+LpapktxDbhal0jUWED56w4PD9e894WFhaW9BaIsd0BW2T0iUs67ABDWn15QXHg2mukS1iUyOztbVwAYHR2tWsgW+uUTiUTogHNhf4LCgHUhiDVScHdbdo9mMovU5l0AKO5Ph1xXShAEmFlJNstKM10K/em1uoASiUTVJarL++W3bdvGqVOnSu6r0Poon5/QyIBwN2X3aCazSH28CwBQ2mVT2OClvCa4kkyXqHGGsFVIqw3+hvXLnzp1ir1794be8z333LOiPvyon7lWbTnsfKfWsDXWIT5o1d+XlwEA6stiWW6mS1h/ek9PD3fccQevvfZa3bXs2dnZitnBQRCwsLDAwMDA0tIQUfMTyvvw6/nQlP/MtWrLYeedcx1bw9ZYh6x1rWzBNjUAmNl7gd9zzu0ysxuAewAHTAOfcs6t7m4rLRLWn+6cW5pPUE/LImrj+EK/fPmHYPfu3VX78JfzoalVWw47f/DgQcysY2vY3TbWIX5pdQu2aUtBmNl/Af4UuCJ/6LPAXc65HYABY8167WarteharaUY5ubmQgv/wvMAFUtDTE5OMjo6uuylJMKE7X1QnCkVdj4Wi4W2Wop3VGsn7awla1mtv8nV1swWwLPAR4B7898PA4/mvz4M/DjwYBNfv6mqjSFU64qJqvkXupBuuOEGZmZmQrsxrrvuumUtJRGlVm057Hw2m60IAJ1Ww9ZMZlmrWt2CbVoAcM49YGabig6Ze32a7MvAhrDHmdleYC/A2972tmbd3qoIG0Oo1hVTqKmHpY0WupAg9yEoXiYaKBkUrmcpiXo+NLUyg6LOAx2fTaSZzLIWtTpbr5WDwMX9/VcDoW0a59zdwN0AIyMjq7P1VovU6r+LmqQWBEHFL7l8fkEsFt1bt5IPTa3actR51bBFmqOVLdhWBoDTZrbLOfcIsAc40sLXbolaXTFhNfUgCPjkJz9JX19fyfOUzwiOx+NVu3RqfWiqdUvVqi2HnVcNWzpFp6Ykr0Sr/r5aGQB+HfiSmSWAp4D7W/jaLVGrKyaqpl5c+Ec9T2F10GqiPjSaGCW+0md7ZWy1NjhvhpGRETc1NdXu26iqvPYxPT1dUcCXfyBTqVTkUtSF53vuueeYmJhYWh20sLlN8d4F9dQQUqkU+/fvL2mVxONx9u3b1/DkN99qWWG65ef0wWp9tn1kZiedcyO1rvN2Ilgr/pCjah+1+u8uXLgQ+rjy53v/+9/Pww8/DLy+OuhDDz1EIpEgm83WVdtZjYlR3VLL6paf0xea9LdyXgaAVvwh1xrwjfoARj1u48aNFccffvjh0OUlChlC9UwQWWlaWbcsrdAtP6dPNOlv5bzbE3i5k6IatdwJG1GPK+T+lx+vttJoPa+30olRrZ6Y0i7d8nP6RJP+Vs67FkCrmoXLqX2kUikuXboU+riBgYHQSVd79uxhYmKCWCxWMTeg3tpOvWllYd1m3VLL6paf0zea9Lcy3gWAVv0hN5p7X94tFQTBUqpnIROo/Pl27NjBli1b2LJlS8nAcNTeACtJ9Wxki0wfa1nd8nP6SCnJy+dlFlAhE8fMcM41dTCvnsHmsGyFIAi48847Q7OApqamOHr0aEmAKJ5NHLY3QNg+AvX+zPVkU3RLdsxyf85ueX9kbejqLCDnHM65pQDQTPXUPsK6peLxOOvXrw997LFjx5Y2o4fwweXyvQFOnDgBsKwBzHq6zbqllrWcn1PZQ7JWeTsInMlkWFxcJJPJNGUQuBGNdEvVMxgZdk25RgYw1f+9fK1KOhABdWeZAAAF4klEQVRpBu8CQCdlcxQ2dAHqzlaopzCutu9x1GOqUTbF8nXS502kUd51AXVKbTasWyBsKedy9QxGhl0TNgbQSAGubIrl6ZTPm8hyeD0I3K4+2dWYol7v4HLxNRqIbI92f95EynX1IHC7a7OrMRehnsHI8muaPVCrABOu3Z83keXyMgBAe7NWfOwWUKZLdd2SJSV+8W4QuBP4NqiqTBcRP3nbAmg3n7oF1vqqi+q66jz6nXQGBYAm8qVbYC13aanrqvPod9I51AUkNa3VLi11XXUe/U46i1oAUpe12KW11ruufKTfSWdRAJC6rbUurbXcdeUr/U46i7qAxFtrtevKZ/qddBa1AMRra7Hrynf6nXQOBQDx3lrruuoG+p10BnUBiYh0KQUAEZEupQAgItKlFABERLqUAoCISJfq6A1hzGwO+O4KnuJa4F9W6XZ8oPejlN6PSnpPSq3V9+Ptzrm+Whd1dABYKTObqmdXnG6h96OU3o9Kek9K+f5+qAtIRKRLKQCIiHQp3wPA3e2+gQ6j96OU3o9Kek9Kef1+eD0GICIi0XxvAYiISATvFoMzsxjwReAm4DLwi865Z9p7V61nZj3Al4FNwDrgd4HvAPcADpgGPuWcy7bpFtvCzN4MnARuA9Lo/fg08CEgQe7v5lG69D3J/818ldzfTAb4JTz/jPjYArgduMI596PAfwX+oM330y4fA553zu0ARoE/Aj4L3JU/ZsBYG++v5fJ/4AeAS/lD3f5+7AJuAX4MeB/wVrr7PfkgEHfO3QL8DvA/8Pz98DEA/BtgAsA59zjgbQ5vDX8F/Fb+ayNXkxkmV8MDOAzc2ob7aqfPAH8C/HP++25/P3YDZ4EHgW8Af0N3vyfngXi+F+ENwCKevx8+BoA3AC8WfZ8xM++6umpxzr3inHvZzK4G7gfuIjfoXxj1fxnY0LYbbDEz+zlgzjk3WXy4W9+PvGvJVZB+Cvhl4M+BWBe/J6+Q6/45B3wJ+EM8/4z4GABeAq4u+j7mnEtHXewzM3srcAS41zn3F0Bx3+XVwHxbbqw9PgHcZmaPADcDXwPeXHS+294PgOeBSefcgnPuH4DXKC3guu09+U/k3o/N5MYQv0pubKTAu/fDxwDwLXJ9eZjZj5Br4nYdM9sIfBP4Defcl/OHT+f7fQH2AEfbcW/t4Jzb6Zx7n3NuF3AG+BngcLe+H3nHgFHLeQuQBP6ui9+TH/B678ELQA+e/814Nw+gKAvoPeT6vn/eOXeuvXfVemb2eeDfkWvOFvwauWZtAngK+CXnXKYNt9dW+VbAL5NrEX2JLn4/zOz3gfeTqwz+JnCRLn1PzOwqcplz/eR+/s8DU3j8fngXAEREpD4+dgGJiEgdFABERLqUAoCISJdSABAR6VIKACIiXUoBQLqemQVm9p/NbMrMzpjZd8zs98xsnZn9nJm9mD9+xsz+3swumtnXzOyK/OP/u5n9UdHXzsw+UfYaSTN72cz+ph0/o0gYBQAR+GPgR4EPOOduBv418MPAn+bPH3XO3Zz/dxOwFXgn8LMRz/c9covxFfsokFr1OxdZAQUA6WpmNgj8NPALzrkXAZxzKXITxR6MeNibyC2Z8ELE+QngXWZ2fdGxnwW+vio3LbJKFACk220D/q9z7qXig86555xzf53/dke+++cpM5sD7gM+45z7q4jnXMxf89MAZvY2cuvITDflJxBZJgUA6XZZav8dHM13Db0L+AK5VTTHazzma+QDAPDx/PciHUUBQLrdCWBrftnsJWY2YGYPAesLx5xzWefc75BbL+fPqj2pc+7/kFtb/mZyazL9xarfucgKKQBIV3POzZBbB//LZvYGgPz/XyS3XPKlkId9CviAmd1e4+nvBT4HnHfORY0XiLSNAoAI/Aq5/ZK/bWZngCfy3/9i2MXOuWeB3wM+W0gFjfB1YCe5PWVFOo5WAxUR6VJqAYiIdCkFABGRLqUAICLSpRQARES6lAKAiEiXUgAQEelSCgAiIl1KAUBEpEv9f0IIiVwB1AWUAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "boston = datasets.load_boston()\n",
    "boston_df = pd.DataFrame(data=boston['data'], columns=boston['feature_names'])\n",
    "boston_df['MEDV'] = boston['target']\n",
    "boston_df.plot(x='CRIM', y='MEDV', kind='scatter');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Test Biopython"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Seq('TCTAGAGGATCCGAATTC')"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Seq(\"GAATTCGGATCCTCTAGA\").reverse_complement()"
   ]
  }
 ],
 "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": "3.6.4"
  },
  "toc": {
   "colors": {
    "hover_highlight": "#DAA520",
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    "navigate_text": "#333333",
    "running_highlight": "#FF0000",
    "selected_highlight": "#FFD700",
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   "moveMenuLeft": true,
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   "toc_position": {
    "height": "471px",
    "left": "2px",
    "right": "20px",
    "top": "111px",
    "width": "212px"
   },
   "toc_section_display": "block",
   "toc_window_display": true,
   "widenNotebook": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}