/DynamicPricingAlgo .ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<pandas.io.excel.ExcelFile at 0x114f008d0>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "data = pd.ExcelFile(\"Breakfast.xlsx\")\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df = data.parse('dh Transaction Data')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df1 = df.ix[1:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''##################Testing Similarity between Matlab & R#################\n",
    "\n",
    "################## Indexing similarities & Differences #################\n",
    "length(value)\n",
    "i=4\n",
    "Bolts[1:i,1:i]\n",
    "\n",
    "Bolts[':',1:i]#To be typed within quotes is the difference\n",
    "\n",
    "Bolts[c(1,3),c(1,3)]#Need to check for this functionality in matlab\n",
    "'''\n",
    "4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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>Unnamed: 1</th>\n",
       "      <th>Unnamed: 2</th>\n",
       "      <th>Unnamed: 3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>367</td>\n",
       "      <td>1111009477</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>367</td>\n",
       "      <td>1111009497</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>367</td>\n",
       "      <td>1111009507</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>367</td>\n",
       "      <td>1111035398</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>367</td>\n",
       "      <td>1111038078</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Unnamed: 1  Unnamed: 2 Unnamed: 3\n",
       "1        367  1111009477         13\n",
       "2        367  1111009497         20\n",
       "3        367  1111009507         14\n",
       "4        367  1111035398          4\n",
       "5        367  1111038078          3"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#df1.loc['d':,'A':'C']\n",
    "#df.loc[1:5,'Unnamed: 1':'Unnamed: 3']\n",
    "#Removing the 1st row\n",
    "3\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "\n",
    "#Connecting to Mongo\n",
    "'''\n",
    "Method 1\n",
    "import pandas as pd\n",
    "from pymongo import MongoClient\n",
    "\n",
    "\n",
    "def _connect_mongo(host, port, username, password, db):\n",
    "    \"\"\" A util for making a connection to mongo \"\"\"\n",
    "\n",
    "    if username and password:\n",
    "        mongo_uri = 'mongodb://%s:%s@%s:%s/%s' % (username, password, host, port, db)\n",
    "        conn = MongoClient(mongo_uri)\n",
    "    else:\n",
    "        conn = MongoClient(host, port)\n",
    "\n",
    "\n",
    "    return conn[db]\n",
    "\n",
    "\n",
    "def read_mongo(db, collection, query={}, host='localhost', port=27017, username=None, password=None, no_id=True):\n",
    "    \"\"\" Read from Mongo and Store into DataFrame \"\"\"\n",
    "\n",
    "    # Connect to MongoDB\n",
    "    db = _connect_mongo(host=host, port=port, username=username, password=password, db=db)\n",
    "\n",
    "    # Make a query to the specific DB and Collection\n",
    "    cursor = db[collection].find(query)\n",
    "\n",
    "    # Expand the cursor and construct the DataFrame\n",
    "    df =  pd.DataFrame(list(cursor))\n",
    "\n",
    "    # Delete the _id\n",
    "    if no_id:\n",
    "        del df['_id']\n",
    "\n",
    "    return df\n",
    "'''\n",
    "'''\n",
    "#Method 2\n",
    "\n",
    "import pymongo\n",
    "import pandas as pd\n",
    "from pymongo import Connection\n",
    "connection = Connection()\n",
    "db = connection.immibytes\n",
    "input_data = db.myCollection\n",
    "data = pd.DataFrame(list(input_data.find()))\n",
    "'''\n",
    "#Method 3\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "\n",
    "from pymongo import MongoClient\n",
    "uri = \"mongodb://poc_user:pocatimmibytes@ec2-52-37-213-141.us-west-2.compute.amazonaws.com/immibytes\";\n",
    "client = MongoClient(uri)\n",
    "db = client.immibytes\n",
    "input_data = db.myCollection\n",
    "#cursor=db['myCollection'].find({})\n",
    "#list(db.myCollection.find({}))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#df =  pd.DataFrame(list(db.myCollection.find({})))\n",
    "import pymongo\n",
    "import pandas as pd\n",
    "#from pymongo import Connection\n",
    "from pymongo import MongoClient\n",
    "c = MongoClient()\n",
    "#from mogo import connect as PyConnection\n",
    "#connection = Connection()\n",
    "db = c.immibytes\n",
    "\n",
    "#input_data = db.transaction_50k.find({\"PROD_CODE_40\":\"D00001\"})\n",
    "\n",
    "#input_data = db.transaction_50k"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#D1 = db['D1'].find({})\n",
    " \n",
    "#D4 = db['D4'].find({})\n",
    "#D5 = db['D5'].find({})\n",
    "#D6 = db['D6'].find({})\n",
    "#D7 = db['D7'].find({})\n",
    "#D8 = db['D8'].find({})\n",
    "D9 = db['D9'].find({})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#df3 =  pd.DataFrame(list(input_data3))\n",
    "\n",
    "#PD1=pd.DataFrame(list(D1))\n",
    "\n",
    "#PD4=pd.DataFrame(list(D4))\n",
    "#PD5=pd.DataFrame(list(D5))\n",
    "#PD6=pd.DataFrame(list(D6))\n",
    "#PD7=pd.DataFrame(list(D7))\n",
    "#PD8=pd.DataFrame(list(D8))\n",
    "#PD9=pd.DataFrame(list(D9))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#df2\n",
    "#PD9\n",
    "5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#PD1.to_csv('Data1.csv')\n",
    "#PD2.to_csv('Data2.csv')\n",
    "#D2G4.to_csv('Data2G4.csv')\n",
    "#D2G5.to_csv('Data2G5.csv')\n",
    "#D2G6.to_csv('Data2G6.csv')\n",
    "#D2G7.to_csv('Data2G7.csv')-Not yet done\n",
    "#D2G8.to_csv('Data2G8.csv')\n",
    "#D2G9.to_csv('Data2G9.csv')\n",
    "\n",
    "#D3G10.to_csv('Data3G10.csv')\n",
    "#D3G11.to_csv('Data3G11.csv')\n",
    "#D3G12.to_csv('Data3G12.csv')\n",
    "#D3G13.to_csv('Data3G13.csv')\n",
    "#D3G14.to_csv('Data3G14.csv')\n",
    "#D3G15.to_csv('Data3G15.csv')\n",
    "D3G16.to_csv('Data3G16.csv')\n",
    "\n",
    "\n",
    "#PD4.to_csv('Data4.csv')\n",
    "#PD5.to_csv('Data5.csv')\n",
    "#PD6.to_csv('Data6.csv')\n",
    "#PD7.to_csv('Data7.csv')\n",
    "#PD8.to_csv('Data8.csv')\n",
    "#PD9.to_csv('Data9.csv')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#list(db.transaction_5k.find({}))\n",
    "#DDist1=pd.DataFrame(list(db.D3.distinct( \"PROD_CODE_30\" )))\n",
    "#D2G4 = pd.DataFrame(list(db.D2.find({\"PROD_CODE_30\":\"G00004\"})))\n",
    "#D2G5 = pd.DataFrame(list(db.D2.find({\"PROD_CODE_30\":\"G00005\"})))\n",
    "#D2G6 = pd.DataFrame(list(db.D2.find({\"PROD_CODE_30\":\"G00006\"})))\n",
    "#D2G7 = pd.DataFrame(list(db.D2.find({\"PROD_CODE_30\":\"G00007\"})))\n",
    "#D2G8 = pd.DataFrame(list(db.D2.find({\"PROD_CODE_30\":\"G00008\"})))\n",
    "#D2G9 = pd.DataFrame(list(db.D2.find({\"PROD_CODE_30\":\"G00009\"})))\n",
    "\n",
    "#D3G10 = pd.DataFrame(list(db.D3.find({\"PROD_CODE_30\":\"G00010\"})))\n",
    "#D3G11 = pd.DataFrame(list(db.D3.find({\"PROD_CODE_30\":\"G00011\"})))\n",
    "#D3G12 = pd.DataFrame(list(db.D3.find({\"PROD_CODE_30\":\"G00012\"})))\n",
    "#D3G13 = pd.DataFrame(list(db.D3.find({\"PROD_CODE_30\":\"G00013\"})))\n",
    "#D3G14 = pd.DataFrame(list(db.D3.find({\"PROD_CODE_30\":\"G00014\"})))\n",
    "#D3G15 = pd.DataFrame(list(db.D3.find({\"PROD_CODE_30\":\"G00015\"})))\n",
    "#D3G16 = pd.DataFrame(list(db.D3.find({\"PROD_CODE_30\":\"G00016\"})))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df.to_csv('Data2.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#df1.loc['d':,'A':'C']\n",
    "#df.loc[1:5,'Unnamed: 1':'Unnamed: 3']\n",
    "#Removing the 1st row\n",
    "\n",
    "df.loc[1:5,'name':'x']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Changing the column header\n",
    "new_header = df.iloc[0] #grab the first row for the header\n",
    "df = df[1:] #take the data less the header row\n",
    "df.rename(columns = new_header) #set the header row as the df header\n",
    "# Get all the relevant fields\n",
    "\n",
    "\n",
    "\n",
    "#Calculate the Elasticity variable\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "#Modeling \n",
    "\n",
    "\n",
    "\n",
    "#Transformations\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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