Python-Basic-Data-Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Analysis with Python"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1. import relevant libraries\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas_datareader as pdr\n",
    "%matplotlib inline\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 2.  Download stock price data, store in dataframe\n",
    "msft = pdr.get_data_google('MSFT', start='2016-08-03')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-11-17</th>\n",
       "      <td>83.12</td>\n",
       "      <td>83.12</td>\n",
       "      <td>82.24</td>\n",
       "      <td>82.40</td>\n",
       "      <td>22078993</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-20</th>\n",
       "      <td>82.40</td>\n",
       "      <td>82.59</td>\n",
       "      <td>82.25</td>\n",
       "      <td>82.53</td>\n",
       "      <td>16314978</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-21</th>\n",
       "      <td>82.74</td>\n",
       "      <td>83.84</td>\n",
       "      <td>82.74</td>\n",
       "      <td>83.72</td>\n",
       "      <td>21237454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-22</th>\n",
       "      <td>83.83</td>\n",
       "      <td>83.90</td>\n",
       "      <td>83.04</td>\n",
       "      <td>83.11</td>\n",
       "      <td>20553089</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-24</th>\n",
       "      <td>83.01</td>\n",
       "      <td>83.43</td>\n",
       "      <td>82.78</td>\n",
       "      <td>83.26</td>\n",
       "      <td>7425603</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Open   High    Low  Close    Volume\n",
       "Date                                            \n",
       "2017-11-17  83.12  83.12  82.24  82.40  22078993\n",
       "2017-11-20  82.40  82.59  82.25  82.53  16314978\n",
       "2017-11-21  82.74  83.84  82.74  83.72  21237454\n",
       "2017-11-22  83.83  83.90  83.04  83.11  20553089\n",
       "2017-11-24  83.01  83.43  82.78  83.26   7425603"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 3. Take a look at the data\n",
    "msft.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 4. Get just the data we are interested in\n",
    "msft_close = msft['Close']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 5. Verify data \n",
    "type(amzn_close.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date\n",
       "2016-08-03         NaN\n",
       "2016-08-04    0.809026\n",
       "2016-08-05    0.682498\n",
       "2016-08-08    0.075691\n",
       "2016-08-09    0.228032\n",
       "Name: Close, dtype: float64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 6. Convert close-to-close dollar change into instantaneous rate of return, verify transformation\n",
    "amzn_percent_change = np.log(amzn_close / amzn_close.shift(1)) * 100\n",
    "amzn_percent_change.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    252.000000\n",
       "mean       0.106487\n",
       "std        1.194826\n",
       "min       -5.273753\n",
       "25%       -0.484134\n",
       "50%        0.117312\n",
       "75%        0.811309\n",
       "max        3.881057\n",
       "Name: Close, dtype: float64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 7. Pandas describe function gives descriptive statistics, default assumes you are dealing with a sample\n",
    "# Pandas also deals with missing values by ommitting them \n",
    "amzn_percent_change.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DescribeResult(nobs=252, minmax=(-5.2737531271995062, 3.8810569610031007), mean=0.10648736829713538, variance=1.4276100656074371, skewness=-0.5610474712532317, kurtosis=2.3600263221846784)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 8. An alternative more comprehensive table of descriptives can be generated using scipy stats, assumes a sample\n",
    "from scipy import stats\n",
    "stats.describe(amzn_percent_change[1:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DescribeResult(nobs=252, minmax=(-3.530203911382729, 3.0549917950041023), mean=0.18017895760082278, variance=1.364946888190105, skewness=-0.05145305633122407, kurtosis=-0.1534361303525733)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 9. for comparision generate random numbers that follow normal distribution\n",
    "x = np.random.normal(.1064,1.1948, 252)\n",
    "stats.describe(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x:  KurtosistestResult(statistic=-0.33790480941260126, pvalue=0.73543492313774006)\n",
      "amzn:  KurtosistestResult(statistic=4.2481322914200996, pvalue=2.1556009784047011e-05)\n"
     ]
    }
   ],
   "source": [
    "# 10. use scipy stats to test the kurtosis, the null hypothesis is that the sample is drawn from a population \n",
    "# where the underlying kurtosis is that of a normally distributed variable\n",
    "print(\"x: \", stats.kurtosistest(x))\n",
    "print(\"amzn: \", stats.kurtosistest(amzn_percent_change[1:]) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x115a03e80>]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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R3cvdvby0VCf+WrxkIpjBs6oSzr4NuhwediIpZCVtYPR9waSB+0fDx9VhJ8pr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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1157af748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 11. plot histogram of price changes with normal curve overlay\n",
    "import matplotlib.mlab as mlab\n",
    "\n",
    "plt.hist(amzn_percent_change[1:], edgecolor='black', normed=True)\n",
    "\n",
    "# overlay normal curve\n",
    "mean = float(np.mean(amzn_percent_change))\n",
    "sd = float(np.std(amzn_percent_change, ddof=1))\n",
    "min_chng = float(np.min(amzn_percent_change))\n",
    "max_chng = float(np.max(amzn_percent_change))\n",
    "a = np.linspace(min_chng, max_chng,100)\n",
    "plt.plot(a,mlab.normpdf(a,mean,sd))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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b/1k6CRaOhvMfhOrHu05jTKn9PdiddVTnubTXSP/djDKpy4rfhO3dCeP+BJmN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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1158ea668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 12. compare to sample drawn from a normally distributed population with same attributes as amzn price change\n",
    "plt.hist(x, normed=True)\n",
    "\n",
    "mean = float(np.mean(x))\n",
    "sd = float(np.std(x))\n",
    "b = np.linspace(np.min(x), np.max(x), 100)\n",
    "plt.plot(b,mlab.normpdf(b, mean, sd))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.5.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}