python-tutorial-zach.ipynb

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   "execution_count": 11,
   "metadata": {
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "1\n",
      "2\n",
      "3\n",
      "5\n",
      "8\n"
     ]
    }
   ],
   "source": [
    "a, b = 0, 1\n",
    "while b < 10:\n",
    "    print(b)\n",
    "    if b > 5:\n",
    "        b = 9\n",
    "        # in the if\n",
    "    # in the while\n",
    "    a, b = b, a+b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "area_fips, own_code, industry_code, agglvl_code, size_code, year, qtr, disclosure_code, area_title, own_title, industry_title, agglvl_title, size_title, qtrly_estabs_count, month1_emplvl, month2_emplvl, month3_emplvl, total_qtrly_wages, taxable_qtrly_wages, qtrly_contributions, avg_wkly_wage, lq_disclosure_code, lq_qtrly_estabs_count, lq_month1_emplvl, lq_month2_emplvl, lq_month3_emplvl, lq_total_qtrly_wages, lq_taxable_qtrly_wages, lq_qtrly_contributions, lq_avg_wkly_wage\n",
      "C4170, 0, 10, 40, 0, 2014, 1, , San Antonio-New Braunfels, TX MSA, Total Covered, Total, all industries, MSA, Total Covered, All establishment sizes, 45828, 888979, 897270, 904941, 10412751522, 4468274618, 108332336, 893, , 1.00, 1.00, 1.00, 1.00, 1.00, 1.00, 1.00, 1.00\n",
      "C4170, 0, 10, 40, 0, 2014, 2, , San Antonio-New Braunfels, TX MSA, Total Covered, Total, all industries, MSA, Total Covered, All establishment sizes, 45927, 912463, 919313, 922940, 9780955056, 1479279109, 31022059, 819, , 1.00, 1.00, 1.00, 1.00, 1.00, 1.00, 1.00, 1.00\n",
      "C4170, 0, 10, 40, 0, 2014, 3, , San Antonio-New Braunfels, TX MSA, Total Covered, Total, all industries, MSA, Total Covered, All establishment sizes, 46264, 916279, 918965, 921911, 10038110075, 977838059, 20943403, 840, , 1.00, 1.00, 1.00, 1.00, 1.00, 1.00, 1.00, 1.00\n",
      "C4170, 1, 10, 41, 0, 2014, 1, , San Antonio-New Braunfels, TX MSA, Federal Government, Total, all industries, MSA, Total -- by ownership sector, All establishment sizes, 268, 34274, 34385, 34368, 605721539, 0, 0, 1357, , 0.89, 1.89, 1.89, 1.88, 1.95, 0.00, 0.00, 1.03\n"
     ]
    }
   ],
   "source": [
    "# try reading in first few lines of csv\n",
    "import csv\n",
    "i = 0\n",
    "with open('data/2014.q1-q3 C4170 San Antonio-New Braunfels, TX MSA.csv', newline='') as csvfile:\n",
    "    census_data = csv.reader(csvfile, delimiter=',', quotechar='\"')\n",
    "    for row in census_data:\n",
    "        i = i + 1\n",
    "        if i > 5:\n",
    "            break\n",
    "        print(', '.join(row))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false,
    "scrolled": true
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "np.random.random_integers(0, 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "f = open('../test.txt', 'r')\n",
    "sample_list = []\n",
    "\n",
    "i = 0\n",
    "for line in f:\n",
    "    sample_list.append(line.split('\\t')[0])\n",
    "    i = i + 1\n",
    "\n",
    "f.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tomato\n",
      "tomato\n",
      "onion\n",
      "tomato\n",
      "pineapple\n",
      "onion\n",
      "onion\n",
      "pineapple\n",
      "tomato\n",
      "pineapple\n",
      "pineapple\n",
      "tomato\n",
      "onion\n",
      "pineapple\n",
      "onion\n",
      "pineapple\n",
      "tomato\n",
      "onion\n",
      "onion\n",
      "onion\n"
     ]
    }
   ],
   "source": [
    "for i in range(20):\n",
    "    x = np.random.random_integers(0, len(sample_list) - 1)\n",
    "    print(sample_list[x])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>food_item</th>\n",
       "      <th>count</th>\n",
       "      <th>date</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>index</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>onion</td>\n",
       "      <td>8</td>\n",
       "      <td>2015-06-18 20:35:54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>tomato</td>\n",
       "      <td>5</td>\n",
       "      <td>2015-09-20 12:37:03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>tomato</td>\n",
       "      <td>2</td>\n",
       "      <td>2015-08-09 18:31:22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>onion</td>\n",
       "      <td>2</td>\n",
       "      <td>2015-10-10 18:35:24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>tomato</td>\n",
       "      <td>1</td>\n",
       "      <td>2015-07-25 12:55:21</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      food_item  count                 date\n",
       "index                                      \n",
       "1         onion      8  2015-06-18 20:35:54\n",
       "2        tomato      5  2015-09-20 12:37:03\n",
       "3        tomato      2  2015-08-09 18:31:22\n",
       "4         onion      2  2015-10-10 18:35:24\n",
       "5        tomato      1  2015-07-25 12:55:21"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "file_name = 'data/panera-data-zach.txt'\n",
    "raw = pd.read_table(file_name, index_col = 0)\n",
    "raw.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       count                date food_item\n",
      "index                                     \n",
      "1          8 2015-06-18 20:35:54     onion\n",
      "2          5 2015-09-20 12:37:03    tomato\n",
      "3          2 2015-08-09 18:31:22    tomato\n",
      "4          2 2015-10-10 18:35:24     onion\n",
      "5          1 2015-07-25 12:55:21    tomato\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.471000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.091299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>10.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             count\n",
       "count  1000.000000\n",
       "mean      3.471000\n",
       "std       2.091299\n",
       "min       1.000000\n",
       "25%       2.000000\n",
       "50%       3.000000\n",
       "75%       5.000000\n",
       "max      10.000000"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#type(df['date'][1])\n",
    "#tmp = time.strptime(df['date'][1], '%Y-%m-%d %H:%M:%S')\n",
    "#type(tmp)\n",
    "df = pd.DataFrame({'food_item':raw['food_item'], 'count':raw['count'], 'date':pd.to_datetime(raw['date'])})\n",
    "print(df.head(), end='\\n\\n')"
   ]
  }
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