nopandas_chipotle.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import csv\n",
    "import re\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "input_file = ('/Users/minnawin/PythonWorkInProgress/orders.tsv')\n",
    "opened_file = open(input_file, \"rb\")\n",
    "df = csv.reader(opened_file, delimiter=\"\\t\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "number of columns: 5\n",
      "number of rows: 4622\n"
     ]
    }
   ],
   "source": [
    "data = []\n",
    "header = df.next()\n",
    "\n",
    "for row in df:\n",
    "    #print row\n",
    "    data.append(row)\n",
    "num_cols = len(data[0])\n",
    "print(\"number of columns: %s\")%(num_cols)\n",
    "num_rows = len(data)\n",
    "print(\"number of rows: %s\")%(num_rows)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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     "output_type": "stream",
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      "item price:$4.30 \n",
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      "item price:$9.39 \n",
      "item price:$8.75 \n",
      "item price:$1.25 \n",
      "item price:$9.25 \n",
      "item price:$4.45 \n",
      "item price:$11.25 \n",
      "item price:$1.25 \n",
      "item price:$8.75 \n",
      "item price:$1.25 \n",
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      "item price:$8.75 \n",
      "item price:$1.50 \n",
      "item price:$4.45 \n",
      "item price:$4.45 \n",
      "item price:$9.25 \n",
      "item price:$8.75 \n",
      "item price:$2.95 \n",
      "item price:$1.25 \n",
      "item price:$11.25 \n",
      "item price:$2.95 \n",
      "item price:$8.75 \n",
      "item price:$8.75 \n",
      "item price:$8.75 \n",
      "item price:$8.75 \n",
      "item price:$4.30 \n",
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      "item price:$9.39 \n",
      "item price:$4.45 \n",
      "item price:$9.25 \n",
      "item price:$1.25 \n",
      "item price:$22.50 \n",
      "item price:$4.45 \n",
      "item price:$2.95 \n",
      "item price:$2.15 \n",
      "item price:$23.50 \n",
      "item price:$11.75 \n",
      "item price:$2.15 \n",
      "item price:$1.25 \n",
      "item price:$9.25 \n",
      "item price:$4.45 \n",
      "item price:$11.25 \n",
      "item price:$11.75 \n",
      "item price:$17.50 \n",
      "item price:$8.75 \n",
      "item price:$11.75 \n",
      "item price:$11.25 \n",
      "item price:$8.75 \n",
      "item price:$4.45 \n",
      "item price:$11.75 \n",
      "item price:$1.50 \n",
      "item price:$8.75 \n",
      "item price:$8.75 \n",
      "item price:$11.75 \n",
      "item price:$9.25 \n",
      "item price:$11.25 \n",
      "item price:$4.45 \n",
      "item price:$11.75 \n",
      "item price:$9.25 \n",
      "item price:$4.45 \n",
      "item price:$11.25 \n",
      "item price:$8.75 \n",
      "item price:$8.75 \n",
      "item price:$2.15 \n",
      "item price:$1.50 \n",
      "item price:$8.75 \n",
      "item price:$4.45 \n",
      "item price:$9.25 \n",
      "item price:$8.75 \n",
      "item price:$1.50 \n",
      "item price:$1.25 \n",
      "item price:$1.25 \n",
      "item price:$1.25 \n",
      "item price:$8.75 \n",
      "item price:$2.95 \n",
      "item price:$11.25 \n",
      "item price:$11.25 \n",
      "item price:$1.50 \n",
      "item price:$11.75 \n",
      "item price:$11.25 \n",
      "item price:$2.15 \n",
      "item price:$9.25 \n",
      "item price:$8.75 \n",
      "item price:$11.75 \n",
      "item price:$2.95 \n",
      "item price:$1.50 \n",
      "item price:$8.75 \n",
      "item price:$1.50 \n",
      "item price:$1.25 \n",
      "item price:$8.75 \n",
      "item price:$11.75 \n",
      "item price:$11.25 \n",
      "item price:$11.25 \n",
      "item price:$11.75 \n",
      "item price:$11.25 \n",
      "item price:$11.75 \n",
      "item price:$8.75 \n",
      "item price:$17.80 \n",
      "item price:$5.00 \n",
      "item price:$5.00 \n",
      "item price:$8.75 \n",
      "item price:$2.95 \n",
      "item price:$1.25 \n",
      "item price:$11.25 \n",
      "item price:$1.25 \n",
      "item price:$2.15 \n",
      "item price:$11.25 \n",
      "item price:$2.50 \n",
      "item price:$9.25 \n",
      "item price:$1.25 \n",
      "item price:$11.25 \n",
      "item price:$2.95 \n",
      "item price:$11.75 \n",
      "item price:$2.15 \n",
      "item price:$11.25 \n",
      "item price:$1.50 \n",
      "item price:$8.99 \n",
      "item price:$1.99 \n",
      "item price:$11.49 \n",
      "item price:$8.75 \n",
      "item price:$4.45 \n",
      "item price:$1.25 \n",
      "item price:$8.75 \n",
      "item price:$4.45 \n",
      "item price:$1.25 \n",
      "item price:$1.50 \n",
      "item price:$11.75 \n",
      "item price:$8.75 \n",
      "item price:$8.75 \n",
      "item price:$11.25 \n",
      "item price:$6.49 \n",
      "item price:$11.75 \n",
      "item price:$8.75 \n",
      "item price:$2.15 \n",
      "item price:$1.25 \n",
      "item price:$6.49 \n",
      "item price:$8.75 \n",
      "item price:$4.45 \n",
      "item price:$8.75 \n",
      "item price:$4.45 \n",
      "item price:$8.75 \n",
      "item price:$11.25 \n",
      "item price:$4.45 \n",
      "item price:$6.49 \n",
      "item price:$9.25 \n",
      "item price:$8.75 \n",
      "item price:$1.25 \n",
      "item price:$4.45 \n",
      "item price:$11.25 \n",
      "item price:$8.75 \n",
      "item price:$1.50 \n",
      "item price:$8.75 \n",
      "item price:$1.50 \n",
      "item price:$1.25 \n",
      "item price:$9.25 \n",
      "item price:$9.39 \n",
      "item price:$4.45 \n",
      "item price:$9.25 \n",
      "item price:$8.75 \n",
      "item price:$4.45 \n",
      "item price:$1.25 \n",
      "item price:$11.25 \n",
      "item price:$11.75 \n",
      "item price:$8.75 \n",
      "item price:$11.25 \n",
      "item price:$9.25 \n",
      "item price:$8.75 \n",
      "item price:$11.25 \n",
      "item price:$2.50 \n",
      "item price:$17.50 \n",
      "item price:$9.25 \n",
      "item price:$4.45 \n",
      "item price:$11.25 \n",
      "item price:$1.25 \n",
      "item price:$8.75 \n",
      "item price:$4.45 \n",
      "item price:$1.50 \n",
      "item price:$8.75 \n",
      "item price:$1.50 \n",
      "item price:$1.25 \n",
      "item price:$9.39 \n",
      "item price:$8.75 \n",
      "item price:$8.75 \n",
      "item price:$4.45 \n",
      "item price:$11.25 \n",
      "item price:$1.25 \n",
      "item price:$9.25 \n",
      "item price:$4.45 \n",
      "item price:$11.25 \n",
      "item price:$8.75 \n",
      "item price:$3.00 \n",
      "item price:$8.75 \n",
      "item price:$2.15 \n",
      "item price:$1.25 \n",
      "item price:$11.25 \n",
      "item price:$11.25 \n",
      "item price:$4.45 \n",
      "item price:$11.25 \n",
      "item price:$11.25 \n",
      "item price:$8.75 \n",
      "item price:$11.75 \n",
      "item price:$11.75 \n",
      "item price:$11.75 \n",
      "item price:$8.75 \n",
      "item price:$4.45 \n",
      "item price:$1.25 \n",
      "item price:$1.50 \n",
      "item price:$8.75 \n",
      "item price:$4.45 \n",
      "item price:$1.25 \n",
      "item price:$9.25 \n",
      "item price:$9.25 \n",
      "item price:$8.75 \n",
      "item price:$4.45 \n",
      "item price:$1.25 \n",
      "item price:$11.75 \n",
      "item price:$11.25 \n",
      "item price:$1.25 \n",
      "item price:$11.75 \n",
      "item price:$11.25 \n",
      "item price:$9.25 \n",
      "item price:$2.15 \n",
      "item price:$1.50 \n",
      "item price:$8.75 \n",
      "item price:$4.45 \n",
      "item price:$11.75 \n",
      "item price:$11.75 \n",
      "item price:$11.25 \n",
      "item price:$8.75 \n",
      "item price:$8.75 \n"
     ]
    }
   ],
   "source": [
    "#Calculate the total amount of sales from these two locations and time period\n",
    "sum = 0\n",
    "cost = []\n",
    "for row in data:\n",
    "    #convert the string representation of the item_price into a float\n",
    "    #row[4] = row[4].str.replace('$','')\n",
    "   # row[4] = row[4].astype(float)\n",
    "    print(\"item price:%s\")%(row[4])\n",
    "   # cost = row[4]\n",
    "#Convert the item_price into float, after you remove the '$'\n",
    "#df['item_price'] = df['item_price'].str.replace('$','')\n",
    "#df['item_price'] = df['item_price'].astype(float)\n",
    "\n",
    "#print(df['item_price'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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