Задание 11.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": true
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   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.decomposition import PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"close_prices.csv\")\n",
    "df.set_index('date', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "PCA(copy=True, iterated_power='auto', n_components=10, random_state=None,\n",
       "  svd_solver='auto', tol=0.0, whiten=False)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca = PCA(n_components=10)\n",
    "pca.fit(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "s = 0\n",
    "i = 0\n",
    "while s <= 90:\n",
    "    s += (pca.explained_variance_ratio_[i] * 100)\n",
    "    i += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "with open('answer1.txt', 'w') as f:\n",
    "    f.write(str(i))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "components = pca.transform(df)\n",
    "first_component = components[:, 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df2 = pd.read_csv(\"djia_index.csv\")\n",
    "df2.set_index(\"date\", inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "dji = df2.as_matrix()[:, 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.        ,  0.90965222],\n",
       "       [ 0.90965222,  1.        ]])"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.corrcoef(first_component, dji)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "with open('answer2.txt', 'w') as f:\n",
    "    f.write('0.91')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "n = first_component.argmax()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AXP      82.029999\n",
       "BA      153.800003\n",
       "CAT      83.000000\n",
       "CSCO     30.190001\n",
       "CVX     105.900002\n",
       "DD       78.489992\n",
       "DIS     105.889999\n",
       "GE       26.110001\n",
       "GS      191.789993\n",
       "HD      116.120003\n",
       "IBM     160.479996\n",
       "INTC     34.060001\n",
       "JNJ     103.220001\n",
       "JPM      61.770000\n",
       "KO       43.200001\n",
       "MCD     100.000000\n",
       "MMM     170.500000\n",
       "MRK      58.580002\n",
       "MSFT     43.880001\n",
       "NKE      98.750000\n",
       "PFE      34.770000\n",
       "PG       85.410004\n",
       "T        34.639999\n",
       "TRV     107.900002\n",
       "UNH     114.400002\n",
       "UTX     123.190002\n",
       "V       278.290009\n",
       "VZ       49.470001\n",
       "WMT      83.959999\n",
       "XOM      88.040001\n",
       "Name: 2015-03-02, dtype: float64"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[361]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "with open('answer3.txt', 'w') as f:\n",
    "    f.write('V')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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