Following_PCA_tutorial.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "url = \"https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# loading dataset into Pandas DataFrame\n",
    "df = pd.read_csv(url\n",
    "                 , names=['sepal length','sepal width','petal length','petal width','target'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal length</th>\n",
       "      <th>sepal width</th>\n",
       "      <th>petal length</th>\n",
       "      <th>petal width</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal length  sepal width  petal length  petal width       target\n",
       "0           5.1          3.5           1.4          0.2  Iris-setosa\n",
       "1           4.9          3.0           1.4          0.2  Iris-setosa\n",
       "2           4.7          3.2           1.3          0.2  Iris-setosa\n",
       "3           4.6          3.1           1.5          0.2  Iris-setosa\n",
       "4           5.0          3.6           1.4          0.2  Iris-setosa"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#df.to_csv(\"Iris_dataset_for_excel_manip.csv\",index=False)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "features = ['sepal length', 'sepal width', 'petal length', 'petal width']\n",
    "x = df.loc[:, features].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = df.loc[:,['target']].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = StandardScaler().fit_transform(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
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       "       [ 1.15917263e+00, -1.24957601e-01,  9.90221459e-01,\n",
       "         1.18500970e+00],\n",
       "       [-1.73673948e-01, -1.28197243e+00,  7.05892939e-01,\n",
       "         1.05353673e+00],\n",
       "       [-5.25060772e-02, -5.87763531e-01,  7.62758643e-01,\n",
       "         1.57942861e+00],\n",
       "       [ 6.74501145e-01,  3.37848329e-01,  8.76490051e-01,\n",
       "         1.44795564e+00],\n",
       "       [ 7.95669016e-01, -1.24957601e-01,  9.90221459e-01,\n",
       "         7.90590793e-01],\n",
       "       [ 2.24968346e+00,  1.72626612e+00,  1.67260991e+00,\n",
       "         1.31648267e+00],\n",
       "       [ 2.24968346e+00, -1.05056946e+00,  1.78634131e+00,\n",
       "         1.44795564e+00],\n",
       "       [ 1.89829664e-01, -1.97618132e+00,  7.05892939e-01,\n",
       "         3.96171883e-01],\n",
       "       [ 1.28034050e+00,  3.37848329e-01,  1.10395287e+00,\n",
       "         1.44795564e+00],\n",
       "       [-2.94841818e-01, -5.87763531e-01,  6.49027235e-01,\n",
       "         1.05353673e+00],\n",
       "       [ 2.24968346e+00, -5.87763531e-01,  1.67260991e+00,\n",
       "         1.05353673e+00],\n",
       "       [ 5.53333275e-01, -8.19166497e-01,  6.49027235e-01,\n",
       "         7.90590793e-01],\n",
       "       [ 1.03800476e+00,  5.69251294e-01,  1.10395287e+00,\n",
       "         1.18500970e+00],\n",
       "       [ 1.64384411e+00,  3.37848329e-01,  1.27454998e+00,\n",
       "         7.90590793e-01],\n",
       "       [ 4.32165405e-01, -5.87763531e-01,  5.92161531e-01,\n",
       "         7.90590793e-01],\n",
       "       [ 3.10997534e-01, -1.24957601e-01,  6.49027235e-01,\n",
       "         7.90590793e-01],\n",
       "       [ 6.74501145e-01, -5.87763531e-01,  1.04708716e+00,\n",
       "         1.18500970e+00],\n",
       "       [ 1.64384411e+00, -1.24957601e-01,  1.16081857e+00,\n",
       "         5.27644853e-01],\n",
       "       [ 1.88617985e+00, -5.87763531e-01,  1.33141568e+00,\n",
       "         9.22063763e-01],\n",
       "       [ 2.49201920e+00,  1.72626612e+00,  1.50201279e+00,\n",
       "         1.05353673e+00],\n",
       "       [ 6.74501145e-01, -5.87763531e-01,  1.04708716e+00,\n",
       "         1.31648267e+00],\n",
       "       [ 5.53333275e-01, -5.87763531e-01,  7.62758643e-01,\n",
       "         3.96171883e-01],\n",
       "       [ 3.10997534e-01, -1.05056946e+00,  1.04708716e+00,\n",
       "         2.64698913e-01],\n",
       "       [ 2.24968346e+00, -1.24957601e-01,  1.33141568e+00,\n",
       "         1.44795564e+00],\n",
       "       [ 5.53333275e-01,  8.00654259e-01,  1.04708716e+00,\n",
       "         1.57942861e+00],\n",
       "       [ 6.74501145e-01,  1.06445364e-01,  9.90221459e-01,\n",
       "         7.90590793e-01],\n",
       "       [ 1.89829664e-01, -1.24957601e-01,  5.92161531e-01,\n",
       "         7.90590793e-01],\n",
       "       [ 1.28034050e+00,  1.06445364e-01,  9.33355755e-01,\n",
       "         1.18500970e+00],\n",
       "       [ 1.03800476e+00,  1.06445364e-01,  1.04708716e+00,\n",
       "         1.57942861e+00],\n",
       "       [ 1.28034050e+00,  1.06445364e-01,  7.62758643e-01,\n",
       "         1.44795564e+00],\n",
       "       [-5.25060772e-02, -8.19166497e-01,  7.62758643e-01,\n",
       "         9.22063763e-01],\n",
       "       [ 1.15917263e+00,  3.37848329e-01,  1.21768427e+00,\n",
       "         1.44795564e+00],\n",
       "       [ 1.03800476e+00,  5.69251294e-01,  1.10395287e+00,\n",
       "         1.71090158e+00],\n",
       "       [ 1.03800476e+00, -1.24957601e-01,  8.19624347e-01,\n",
       "         1.44795564e+00],\n",
       "       [ 5.53333275e-01, -1.28197243e+00,  7.05892939e-01,\n",
       "         9.22063763e-01],\n",
       "       [ 7.95669016e-01, -1.24957601e-01,  8.19624347e-01,\n",
       "         1.05353673e+00],\n",
       "       [ 4.32165405e-01,  8.00654259e-01,  9.33355755e-01,\n",
       "         1.44795564e+00],\n",
       "       [ 6.86617933e-02, -1.24957601e-01,  7.62758643e-01,\n",
       "         7.90590793e-01]])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NC_001134.8 Saccharomyces cerevisiae S288C chromosome II, complete sequence\n"
     ]
    }
   ],
   "source": [
    "genomicDictionary = {}\n",
    "lenFragment = 500\n",
    "\n",
    "with open(\"SaccCere/SaccCere_genomic.fna\", 'r') as file:\n",
    "    fragment = ''\n",
    "    for line in file.readlines():\n",
    "        if line[0] == '>':\n",
    "            chromosome = line[1:].rstrip()\n",
    "            #print(chromosome)\n",
    "            genomicDictionary[chromosome] = []\n",
    "            continue\n",
    "            \n",
    "        fragment += line.rstrip().upper()\n",
    "        \n",
    "        if len(fragment) < lenFragment:\n",
    "            pass\n",
    "        else:\n",
    "            genomicDictionary[chromosome].append(fragment[:lenFragment])\n",
    "            fragment = fragment[lenFragment:]\n",
    "            \n",
    "    print(list(genomicDictionary.keys())[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "500"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(genomicDictionary['NC_001134.8 Saccharomyces cerevisiae S288C chromosome II, complete sequence'][-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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