SupplyChainShipmentRegressor.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# Read Shipping Status Data\n",
    "df1 = pd.read_excel('/Users/edwzhang/Downloads/Shipment Status/Shipment_Status_-324123132_Masked.xlsx')\n",
    "df2 = pd.read_excel('/Users/edwzhang/Downloads/Shipment Status/Shipment_Status_-324123133_Masked.xlsx')\n",
    "df_ship = pd.concat([df1,df2],ignore_index=True)\n",
    "del df_ship['Unnamed: 5']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero\n"
     ]
    }
   ],
   "source": [
    "# Read Shipping Details Data\n",
    "df_detail = pd.read_excel('/Users/edwzhang/Downloads/Shipment Execution-selected/Shipment Exec Jan 3.xls')\n",
    "df_detail= df_detail.drop(['CONTAINER_TYPE','DIM_UOM','WEIGHT_UOM','SO_HEADER_ID','SO_LINE_ID','PO_NUMBER','EARLY_DELIVERY_ACCEPT_FLAG','SHIPPING_INSTRUCTIONS','SERVICE_ATTACHED','CUST_PO_NUMBER','CUST_DELIV_ACCPT_REQRD','INSURRED','DUTY_ATTRIBUTE','EXPORT_LICENSE_REQD','EXPEDITED','PRE_DELIV_INSPECTION','ACTION_STATUS','RECOMMITTED','MFG_COMPLEXITY','COMP_LABEL','SOFTWARE_INCLUDED'], axis=1)\n",
    "df_detail=df_detail.drop_duplicates()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Merge both data\n",
    "df3 = pd.merge(df_detail,df_ship,left_on= 'CARTON_ID',right_on='CONTAINER ID')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "###2472#9%\n",
      "      CONTAINER ID                 STATUS       DATE MODE\n",
      "57157   ###2472#9%       CUSTOM CLEARANCE 2016-01-26  AIR\n",
      "57158   ###2472#9%          LATE_DELIVERY 2016-01-25  AIR\n",
      "57159   ###2472#9%               WHEELSUP 2016-01-25  AIR\n",
      "57160   ###2472#9%  Received in Warehouse 2016-01-06  AIR\n",
      "57161   ###2472#9%          Ready To Ship 2016-01-06  NaN\n",
      "57162   ###2472#9%          LATE_WHEELSUP 2016-01-25  AIR\n",
      "57163   ###2472#9%        LATE_WHEELSDOWN 2016-01-25  AIR\n",
      "57164   ###2472#9%             WHEELSDOWN 2016-01-27  AIR\n",
      "2016-01-27 00:00:00 2016-01-06 00:00:00\n",
      "21\n",
      "AIR\n",
      "#*5##74@*52\n",
      "     CONTAINER ID                 STATUS       DATE MODE\n",
      "2417  #*5##74@*52       CUSTOM CLEARANCE 2016-01-14  AIR\n",
      "2418  #*5##74@*52             WHEELSDOWN 2016-01-13  AIR\n",
      "2419  #*5##74@*52        LATE_WHEELSDOWN 2016-01-13  AIR\n",
      "2420  #*5##74@*52  Received in Warehouse 2016-01-09  AIR\n",
      "2421  #*5##74@*52               WHEELSUP 2016-01-12  AIR\n",
      "2422  #*5##74@*52          Ready To Ship 2016-01-08  NaN\n",
      "2423  #*5##74@*52               DELIVERY 2016-01-16  AIR\n",
      "2424  #*5##74@*52  LATE_CUSTOM_CLEARANCE 2016-01-13  AIR\n",
      "2016-01-16 00:00:00 2016-01-08 00:00:00\n",
      "8\n",
      "AIR\n"
     ]
    }
   ],
   "source": [
    "# For Testinf only\n",
    "for name, group in df3[['CONTAINER ID','STATUS','DATE','MODE']].groupby('CONTAINER ID',group_keys=False):\n",
    "    if group.shape[0] == 8:\n",
    "        print name\n",
    "        print group\n",
    "        print max(group['DATE']),min(group['DATE'])\n",
    "        print (max(group['DATE'])-min(group['DATE'])).days\n",
    "        print group['MODE'].unique()[0]\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Define a function to return Shipping Days and Main Shipping Method(Air or Ground)\n",
    "def getTimeNMode(group):\n",
    "    return pd.Series([(max(group['DATE'])-min(group['DATE'])).days,group['MODE'].unique()[0]],index=['ShippingDays','ShippingMode'])\n",
    "\n",
    "df4 = df3[['CONTAINER ID','STATUS','DATE','MODE']].groupby('CONTAINER ID',group_keys=False).apply(getTimeNMode)\n",
    "df4 = df4.reset_index()\n",
    "df5 = pd.merge(df_detail,df4,left_on= 'CARTON_ID',right_on='CONTAINER ID')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Testing Only\n",
    "#df5['ShippingDays'].value_counts()\n",
    "#df4.loc[df4['ShippingDays']==1,'CONTAINER ID']\n",
    "df_ship.loc[df_ship['CONTAINER ID']=='###24669*6',]\n",
    "#df_detail.loc[df_detail['CARTON_ID']=='###24669*6',]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# ShippingDays must be great than 0 and less than 30 to train the Regression Model\n",
    "df6 = df5.loc[(df5['ShippingDays']>0) & (df5['ShippingDays']<30),]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 248,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Define a function to extract OTM route code \n",
    "def getInTransitStopOvers(data,row=True):\n",
    "    try:\n",
    "        if row:\n",
    "            code = data['OTM_ROUTE_CODE']\n",
    "        else:\n",
    "            code = data\n",
    "\n",
    "        codes = []\n",
    "        if len(code) > 0  and code.count('-') >0:\n",
    "            codes = code.split('-')\n",
    "            if codes[0] in ['COC','CDC']:\n",
    "                codes.append('NA')\n",
    "            elif codes[0] == 'CC':\n",
    "                codes[1] = 'NA'\n",
    "                codes.append('NA')\n",
    "        else:\n",
    "            codes = ['','','']\n",
    "        return pd.Series(codes,index=['RouteType','StopOver1','StopOver2'])\n",
    "    except Exception, e:\n",
    "        print code, ' failed in parsing In-Transit StopOvers with the error '+str(e) \n",
    "        return pd.Series(['','',''],index=['RouteType','StopOver1','StopOver2'])\n",
    "\n",
    "df6['OTM_ROUTE_CODE'].fillna('', inplace=True)\n",
    "df6[['RouteType','OriSLC','DesSLC']] = df6.apply(getInTransitStopOvers,axis=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 249,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Define a function to extrac Shipping Method\n",
    "def getDeliveryData(data,row=True):\n",
    "    try:\n",
    "        if row:\n",
    "            method = data['SHIPPING_METHOD_CODE']\n",
    "        else:\n",
    "            method = data\n",
    "        methods = []\n",
    "        if method.count('_') >2:\n",
    "            methods = method.split('_',3)\n",
    "        else:\n",
    "            methods = ['','','','']\n",
    "        return pd.Series(methods[1:],index=['Carrier','DeliveryType','DeliveryPriority'])\n",
    "    except Exception, e:\n",
    "        print method + ' failed in parsing Delivery Data with the error '+str(e) \n",
    "        return pd.Series(['','',''],index=['Carrier','DeliveryType','DeliveryPriority'])\n",
    "    \n",
    "df6['SHIPPING_METHOD_CODE'].fillna('', inplace=True)\n",
    "df6[['Carrier','DeliveryType','DeliveryPriority']] = df6.apply(getDeliveryData, axis=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 250,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Define a function to extract product dimension\n",
    "def getCarbonDimensions(data,row=True):\n",
    "    try:\n",
    "        if row:\n",
    "            dimension = str(data['CART_DIM'])\n",
    "        else:\n",
    "            dimension = data\n",
    "        dimensions = []\n",
    "        if dimension.count('X') >1:\n",
    "            dimensions = [float(d.strip()) for d in dimension.split('X',3)] \n",
    "        else:\n",
    "            dimensions = [0.0,0.0,0.0]\n",
    "        return pd.Series(dimensions,index=['Length','Width','Height'])\n",
    "    except Exception, e:\n",
    "        print dimension, ' failed in parsing dimensions with the error '+str(e) \n",
    "        return pd.Series([0.0,0.0,0.0],index=['Length','Width','Height'])\n",
    "\n",
    "df6['CART_DIM'].fillna(0.0, inplace=True)\n",
    "df6[['Length','Width','Height']] = df6.apply(getCarbonDimensions, axis=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 252,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Define Encode and Decode functions\n",
    "from sklearn.externals import joblib\n",
    "def categoryEncoding(col,ColName):\n",
    "    colDict = {}\n",
    "    colSet = set([i for i in col])\n",
    "    i = 1\n",
    "    for j in colSet:\n",
    "        colDict[j] = i\n",
    "        i += 1\n",
    "    joblib.dump(colDict, '/Users/edwzhang/Downloads/Shipment Status/dict_'+ColName) \n",
    "    return colDict\n",
    "        \n",
    "def encodingLookup(ColName, val):\n",
    "    colDict = joblib.load('/Users/edwzhang/Downloads/Shipment Status/dict_'+ColName) \n",
    "    if val in colDict:\n",
    "        return colDict[val]\n",
    "    else:\n",
    "        return colDict[max(colDict)] +1\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.33205304657957002"
      ]
     },
     "execution_count": 253,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Train the Regression Model\n",
    "from sklearn.cross_validation import train_test_split\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.preprocessing import LabelEncoder \n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "\n",
    "df6['ShippingMode'].fillna('', inplace=True)\n",
    "df6['ORDERED_ITEM'].fillna('', inplace=True)\n",
    "# Encode Categorical Varibales to Numeric\n",
    "cols = ['ShippingDays','WEIGHT','SHIP_FROM_ORG_ID','TO_CITY','ShippingMode','RouteType','OriSLC','DesSLC','Carrier','Length','Width','Height','ORDERED_ITEM']\n",
    "df_model = df6.loc[(df6['OTM_ROUTE_CODE']!='') & (df6['SHIPPING_METHOD_CODE']!='') &(df6['CART_DIM']!=0.0) ,cols]\n",
    "df_model = df6.loc[(df6['OTM_ROUTE_CODE']!='') & (df6['SHIPPING_METHOD_CODE']!='') &(df6['CART_DIM']!=0.0) ,cols]\n",
    "cols2 = ['TO_CITY','ShippingMode','RouteType','OriSLC','DesSLC','Carrier','ORDERED_ITEM']\n",
    "for colName in cols2:\n",
    "    #le = LabelEncoder()\n",
    "    #df_model[colName] = le.fit_transform(df_model[colName])\n",
    "    colDict = categoryEncoding(df_model[colName],colName)\n",
    "    df_model[colName] = df_model[colName].apply(lambda x:colDict[x])\n",
    "    \n",
    "# Split into Training and Testing set\n",
    "train_set, test_set = train_test_split(df_model, test_size=0.2)\n",
    "X_train = train_set.iloc[:,1:]\n",
    "y_train = train_set.iloc[:,0]\n",
    "\n",
    "X_test = test_set.iloc[:,1:]\n",
    "y_test = test_set.iloc[:,0]\n",
    "\n",
    "# Fit the Model\n",
    "regressor = RandomForestRegressor(n_estimators=500)\n",
    "regressor.fit(X_train, y_train)\n",
    "\n",
    "# Print out the R squared score\n",
    "regressor.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 327,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TO_CITY             0.220305\n",
       "WEIGHT              0.217182\n",
       "ORDERED_ITEM        0.146435\n",
       "Width               0.066100\n",
       "Height              0.064284\n",
       "Carrier             0.063671\n",
       "Length              0.063162\n",
       "ShippingMode        0.061145\n",
       "SHIP_FROM_ORG_ID    0.033055\n",
       "OriSLC              0.031306\n",
       "DesSLC              0.020439\n",
       "RouteType           0.012917\n",
       "dtype: float64"
      ]
     },
     "execution_count": 327,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Persist the model\n",
    "joblib.dump(regressor, '/Users/edwzhang/Downloads/Shipment Status/CiscoSupplyChainAnalyticsRegressor.pkl') \n",
    "\n",
    "# List feature importance\n",
    "s = pd.Series(regressor.feature_importances_,index=cols[1:])\n",
    "s.sort_values(ascending=False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 325,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Create a custom function to predict Shipping days\n",
    "def customPredict(data):\n",
    "    rgr_loaded = joblib.load('/Users/edwzhang/Downloads/Shipment Status/CiscoSupplyChainAnalytics.pkl') \n",
    "    feedData = []\n",
    "    \n",
    "    l1 = list(getInTransitStopOvers(data['OTM_ROUTE_CODE'],row=False))\n",
    "    l2 = list(getDeliveryData(data['SHIPPING_METHOD_CODE'],row=False))\n",
    "    l3 = list(getCarbonDimensions(data['CART_DIM'],row=False))\n",
    "    \n",
    "    feedData.append(data['WEIGHT'])\n",
    "    feedData.append(data['SHIP_FROM_ORG_ID'])\n",
    "    feedData.append(encodingLookup('TO_CITY',data['TO_CITY']))\n",
    "    feedData.append(encodingLookup('ShippingMode',l2[1]))  \n",
    "    feedData.append(encodingLookup('RouteType',l1[0]))\n",
    "    feedData.append(encodingLookup('OriSLC',l1[1]))\n",
    "    feedData.append(encodingLookup('DesSLC',l1[2]))\n",
    "    feedData.append(encodingLookup('Carrier',l2[0]))\n",
    "    feedData.append(l3[0])\n",
    "    feedData.append(l3[1])\n",
    "    feedData.append(l3[2])\n",
    "    feedData.append(encodingLookup('ORDERED_ITEM',data['ORDERED_ITEM']))\n",
    "    #print feedData\n",
    "    data['CARRIER'] = l2[0]\n",
    "    data['SHIPPINGMODE'] = l2[1]\n",
    "    data['ROUTETYPE'] = l1[0]\n",
    "    data['ORISLC'] = l1[1]\n",
    "    data['DESSLC'] = l1[2]\n",
    "    data['DAYS'] = round(rgr_loaded.predict(feedData)[0],2)\n",
    "    return data\n",
    "      "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 326,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/edwzhang/anaconda/lib/python2.7/site-packages/sklearn/utils/validation.py:395: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  DeprecationWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'ACTION_STATUS': '',\n",
       " 'ACTUAL_SHIPMENT_DATE': '2015-12-28 08:55:30',\n",
       " 'CARRIER': 'FDEN',\n",
       " 'CARTON_ID': '#*7@27#77#',\n",
       " 'CART_DIM': '9.38 X 7.38 X 2.5',\n",
       " 'COMP_LABEL': 'N',\n",
       " 'CONTAINER_TYPE': 'CRT',\n",
       " 'CUST_DELIV_ACCPT_REQRD': '',\n",
       " 'CUST_PO_NUMBER': '8740012372',\n",
       " 'DAYS': 6.53,\n",
       " 'DESSLC': 'NA',\n",
       " 'DIM_UOM': 'IN',\n",
       " 'DUTY_ATTRIBUTE': 'Duty Unpaid',\n",
       " 'EARLY_DELIVERY_ACCEPT_FLAG': 'Y',\n",
       " 'EXPEDITED': '',\n",
       " 'EXPORT_LICENSE_REQD': 'Y',\n",
       " 'FROM_CITY': 'Pardubice',\n",
       " 'INSURRED': 'Y',\n",
       " 'LINE_NUMBER': '1',\n",
       " 'MFG_COMPLEXITY': 'LOW',\n",
       " 'MFG_LEAD_TIME': '3',\n",
       " 'ORDERED_ITEM': 'STACK-T1-50CM=',\n",
       " 'ORDER_NUMBER': '1.01665e+08',\n",
       " 'ORISLC': 'SLCROERMOND',\n",
       " 'OTM_ROUTE_CODE': 'CDC-SLCROERMOND',\n",
       " 'PO_NUMBER': '101665066-1',\n",
       " 'PRE_DELIV_INSPECTION': 'N',\n",
       " 'PROMISED_POINT': 'SHIP',\n",
       " 'RECOMMITTED': 'N',\n",
       " 'ROUTETYPE': 'CDC',\n",
       " 'SERVICE_ATTACHED': '',\n",
       " 'SHIPMENT_ID': '22*%%@%654',\n",
       " 'SHIPMENT_PRIORITY_CODE': 'PR9',\n",
       " 'SHIPPINGMODE': 'L',\n",
       " 'SHIPPING_INSTRUCTIONS': '',\n",
       " 'SHIPPING_METHOD_CODE': '000002_FDEN_L_LTL',\n",
       " 'SHIP_FROM_ORG_ID': '1103',\n",
       " 'SHIP_SET_ID': '6*956@46#',\n",
       " 'SOFTWARE_INCLUDED': 'N',\n",
       " 'SO_HEADER_ID': '603771708',\n",
       " 'SO_LINE_ID': '950866183',\n",
       " 'TO_CITY': 'JERUSALEM',\n",
       " 'TRIM(OEL.PROMISE_DATE)': '2015-12-28 00:00:00',\n",
       " 'TRIM(OEL.REQUEST_DATE)': '2015-12-28 00:00:00',\n",
       " 'TRIM(OLX.ORIGINAL_PROMISE_DATE)': '2015-12-28 00:00:00',\n",
       " 'WEIGHT': '1.82',\n",
       " 'WEIGHT_UOM': 'LBS'}"
      ]
     },
     "execution_count": 326,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "input_dict = {\n",
    "'PROMISED_POINT':'SHIP',\n",
    "'CARTON_ID': '#*7@27#77#',\n",
    "'CONTAINER_TYPE': 'CRT',\n",
    "'CART_DIM': '9.38 X 7.38 X 2.5',\n",
    "'DIM_UOM': 'IN',\n",
    "'WEIGHT':  '1.82',\n",
    "'WEIGHT_UOM': 'LBS',\n",
    "'SHIPMENT_ID': '22*%%@%654',\n",
    "'SO_HEADER_ID':  '603771708',\n",
    "'SO_LINE_ID': '950866183',\n",
    "'PO_NUMBER': '101665066-1',\n",
    "'FROM_CITY':  'Pardubice',\n",
    "'TO_CITY':  'JERUSALEM',\n",
    "'SHIP_SET_ID': '6*956@46#',\n",
    "'OTM_ROUTE_CODE':  'CDC-SLCROERMOND',\n",
    "'SHIPPING_METHOD_CODE': '000002_FDEN_L_LTL',\n",
    "'EARLY_DELIVERY_ACCEPT_FLAG': 'Y',\n",
    "'SHIPMENT_PRIORITY_CODE': 'PR9',\n",
    "'SHIPPING_INSTRUCTIONS':  '',\n",
    "'SERVICE_ATTACHED':  '',\n",
    "'CUST_PO_NUMBER':  '8740012372',\n",
    "'TRIM(OEL.REQUEST_DATE)':'2015-12-28 00:00:00',\n",
    "'TRIM(OLX.ORIGINAL_PROMISE_DATE)':'2015-12-28 00:00:00',\n",
    "'TRIM(OEL.PROMISE_DATE)':'2015-12-28 00:00:00',\n",
    "'ACTUAL_SHIPMENT_DATE': '2015-12-28 08:55:30',\n",
    "'CUST_DELIV_ACCPT_REQRD': '',\n",
    "'ORDER_NUMBER': '1.01665e+08',\n",
    "'LINE_NUMBER':  '1',\n",
    "'ORDERED_ITEM': 'STACK-T1-50CM=',\n",
    "'SHIP_FROM_ORG_ID': '1103',\n",
    "'PRE_DELIV_INSPECTION': 'N',\n",
    "'INSURRED':  'Y',\n",
    "'DUTY_ATTRIBUTE': 'Duty Unpaid',\n",
    "'EXPORT_LICENSE_REQD': 'Y',\n",
    "'EXPEDITED':  '',\n",
    "'ACTION_STATUS':  '',\n",
    "'RECOMMITTED':  'N',\n",
    "'MFG_COMPLEXITY': 'LOW',\n",
    "'MFG_LEAD_TIME': '3',\n",
    "'COMP_LABEL':'N',\n",
    "'SOFTWARE_INCLUDED': 'N'}\n",
    "customPredict(input_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 307,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/edwzhang/anaconda/lib/python2.7/site-packages/sklearn/utils/validation.py:395: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  DeprecationWarning)\n"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "Input contains NaN, infinity or a value too large for dtype('float32').",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-307-00a1537c7727>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     13\u001b[0m \u001b[0;31m# 'ORDERED_ITEM'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     14\u001b[0m \u001b[0mfeedData\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;36m1.82\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mencodingLookup\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'FROM_CITY'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'Houston'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mencodingLookup\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'TO_CITY'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'JERUSALEM'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mencodingLookup\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'ShippingMode'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'Ocean'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mencodingLookup\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'RouteType'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'CDC'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mencodingLookup\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'OriSLC'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'NA'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mencodingLookup\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'DesSLC'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'SLCROERMOND'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mencodingLookup\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Carrier'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'CPU'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m9.3800000000000008\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m7.3799999999999999\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2.5\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m841\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 15\u001b[0;31m \u001b[0mregressor\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfeedData\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/Users/edwzhang/anaconda/lib/python2.7/site-packages/sklearn/ensemble/forest.pyc\u001b[0m in \u001b[0;36mpredict\u001b[0;34m(self, X)\u001b[0m\n\u001b[1;32m    683\u001b[0m         \"\"\"\n\u001b[1;32m    684\u001b[0m         \u001b[0;31m# Check data\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 685\u001b[0;31m         \u001b[0mX\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_validate_X_predict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    686\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    687\u001b[0m         \u001b[0;31m# Assign chunk of trees to jobs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/edwzhang/anaconda/lib/python2.7/site-packages/sklearn/ensemble/forest.pyc\u001b[0m in \u001b[0;36m_validate_X_predict\u001b[0;34m(self, X)\u001b[0m\n\u001b[1;32m    353\u001b[0m                                  \"call `fit` before exploiting the model.\")\n\u001b[1;32m    354\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 355\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mestimators_\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_validate_X_predict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcheck_input\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    356\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    357\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mproperty\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/edwzhang/anaconda/lib/python2.7/site-packages/sklearn/tree/tree.pyc\u001b[0m in \u001b[0;36m_validate_X_predict\u001b[0;34m(self, X, check_input)\u001b[0m\n\u001b[1;32m    363\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    364\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mcheck_input\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 365\u001b[0;31m             \u001b[0mX\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcheck_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mDTYPE\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maccept_sparse\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"csr\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    366\u001b[0m             if issparse(X) and (X.indices.dtype != np.intc or\n\u001b[1;32m    367\u001b[0m                                 X.indptr.dtype != np.intc):\n",
      "\u001b[0;32m/Users/edwzhang/anaconda/lib/python2.7/site-packages/sklearn/utils/validation.pyc\u001b[0m in \u001b[0;36mcheck_array\u001b[0;34m(array, accept_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, warn_on_dtype, estimator)\u001b[0m\n\u001b[1;32m    405\u001b[0m                              % (array.ndim, estimator_name))\n\u001b[1;32m    406\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mforce_all_finite\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 407\u001b[0;31m             \u001b[0m_assert_all_finite\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    408\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    409\u001b[0m     \u001b[0mshape_repr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_shape_repr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/edwzhang/anaconda/lib/python2.7/site-packages/sklearn/utils/validation.pyc\u001b[0m in \u001b[0;36m_assert_all_finite\u001b[0;34m(X)\u001b[0m\n\u001b[1;32m     56\u001b[0m             and not np.isfinite(X).all()):\n\u001b[1;32m     57\u001b[0m         raise ValueError(\"Input contains NaN, infinity\"\n\u001b[0;32m---> 58\u001b[0;31m                          \" or a value too large for %r.\" % X.dtype)\n\u001b[0m\u001b[1;32m     59\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     60\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: Input contains NaN, infinity or a value too large for dtype('float32')."
     ]
    }
   ],
   "source": [
    "# Lable - 'ShippingDays',\n",
    "# 'WEIGHT',\n",
    "# 'SHIP_FROM_ORG_ID',\n",
    "# 'TO_CITY',\n",
    "# 'ShippingMode',\n",
    "# 'RouteType',\n",
    "# 'OriSLC',\n",
    "# 'DesSLC',\n",
    "# 'Carrier',\n",
    "# 'Length',\n",
    "# 'Width',\n",
    "# 'Height',\n",
    "# 'ORDERED_ITEM'\n",
    "feedData = [1.82, 1103, encodingLookup('TO_CITY','JERUSALEM'), encodingLookup('ShippingMode','Ocean'), encodingLookup('RouteType','CDC'), encodingLookup('OriSLC','NA'), encodingLookup('DesSLC','SLCROERMOND'), encodingLookup('Carrier','CPU'), 9.3800000000000008, 7.3799999999999999, 2.5, 841]\n",
    "regressor.predict(feedData)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 317,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "L    21053\n",
      "A      577\n",
      "P      421\n",
      "T       96\n",
      "O       35\n",
      "Name: DeliveryType, dtype: int64\n",
      "AIR         5800\n",
      "            5605\n",
      "LTL         4711\n",
      "PARCEL      4043\n",
      "TL          1998\n",
      "Ocean         24\n",
      "3_5_DAYS       1\n",
      "Name: ShippingMode, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print df6['DeliveryType'].value_counts()\n",
    "print df6['ShippingMode'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 311,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['/Users/edwzhang/Downloads/Shipment Status/dict_FROM_CITY']"
      ]
     },
     "execution_count": 311,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#fromCity = df6[['SHIP_FROM_ORG_ID','FROM_CITY']].drop_duplicates().reset_index()\n",
    "#fromCity.loc[fromCity['SHIP_FROM_ORG_ID']==2246,'FROM_CITY'] = 'Foxconn-China-BV'\n",
    "#fromCity.loc[fromCity['SHIP_FROM_ORG_ID']==843,'FROM_CITY'] = 'Celestica Thailand-BV'\n",
    "\n",
    "fromCityDict = pd.Series(fromCity.FROM_CITY.values,index=fromCity.SHIP_FROM_ORG_ID).to_dict()\n",
    "\n",
    "joblib.dump(fromCityDict, '/Users/edwzhang/Downloads/Shipment Status/dict_FROM_CITY') \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 312,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{123: u'Ciudad Juarez',\n",
       " 362: u'AUSTIN',\n",
       " 383: u'Zapopan',\n",
       " 463: u'Penang',\n",
       " 483: u'Penang',\n",
       " 843: 'Celestica Thailand-BV',\n",
       " 1103: u'Pardubice',\n",
       " 1163: u'Houston',\n",
       " 1348: u'Doumen',\n",
       " 2246: 'Foxconn-China-BV',\n",
       " 2645: u'Balbriggan',\n",
       " 2665: u'Louisville',\n",
       " 2705: u'Guadalajara',\n",
       " 2725: u'Tczew',\n",
       " 2726: u'Millington',\n",
       " 2766: u'Ciudad Juarez',\n",
       " 2786: u'GA',\n",
       " 2787: u'Tsing Yi',\n",
       " 2826: u'SAO PAULO'}"
      ]
     },
     "execution_count": 312,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fromCityDict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 304,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pardubice        5112\n",
       "Ciudad Juarez    4946\n",
       "Houston          1540\n",
       "Zapopan          1373\n",
       "Guadalajara      1109\n",
       "Penang            907\n",
       "Tczew             889\n",
       "Doumen            557\n",
       "Tsing Yi          168\n",
       "Millington         58\n",
       "GA                 34\n",
       "SAO PAULO          29\n",
       "AUSTIN             24\n",
       "Louisville         24\n",
       "Balbriggan          6\n",
       "Name: FROM_CITY, dtype: int64"
      ]
     },
     "execution_count": 304,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df6['FROM_CITY'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df6['ShippingMode'].fillna('', inplace=True)\n",
    "df6['ORDERED_ITEM'].fillna('', inplace=True)\n",
    "cols = ['ShippingDays','WEIGHT','SHIP_FROM_ORG_ID','TO_CITY','ShippingMode','RouteType','OriSLC','DesSLC','Carrier','Length','Width','Height','ORDERED_ITEM']\n",
    "df_model = df6.loc[(df6['OTM_ROUTE_CODE']!='') & (df6['SHIPPING_METHOD_CODE']!='') &(df6['CART_DIM']!=0.0) ,cols]\n",
    "df_model = df6.loc[(df6['OTM_ROUTE_CODE']!='') & (df6['SHIPPING_METHOD_CODE']!='') &(df6['CART_DIM']!=0.0) ,cols]\n",
    "cols2 = ['TO_CITY','ShippingMode','RouteType','OriSLC','DesSLC','Carrier','ORDERED_ITEM']\n",
    "for colName in cols2:\n",
    "    colDict = categoryEncoding(df_model[colName],colName)\n",
    "    df_model[colName] = df_model[colName].apply(lambda x:colDict[x])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2048    510\n",
       "1278    436\n",
       "1752    353\n",
       "929     331\n",
       "305     289\n",
       "489     267\n",
       "1150    247\n",
       "2217    232\n",
       "820     230\n",
       "869     228\n",
       "1618    219\n",
       "1089    208\n",
       "1453    199\n",
       "1384    194\n",
       "2471    189\n",
       "1568    181\n",
       "2371    180\n",
       "833     173\n",
       "209     169\n",
       "1919    160\n",
       "1856    146\n",
       "2100    131\n",
       "2151    131\n",
       "478     124\n",
       "1911    123\n",
       "320     115\n",
       "807     114\n",
       "1725    112\n",
       "1775    106\n",
       "2254    104\n",
       "       ... \n",
       "932       1\n",
       "1768      1\n",
       "1140      1\n",
       "766       1\n",
       "1108      1\n",
       "1092      1\n",
       "1076      1\n",
       "205       1\n",
       "782       1\n",
       "1028      1\n",
       "1816      1\n",
       "1848      1\n",
       "964       1\n",
       "948       1\n",
       "1880      1\n",
       "644       1\n",
       "862       1\n",
       "189       1\n",
       "836       1\n",
       "894       1\n",
       "1960      1\n",
       "788       1\n",
       "173       1\n",
       "756       1\n",
       "9         1\n",
       "724       1\n",
       "910       1\n",
       "25        1\n",
       "926       1\n",
       "1991      1\n",
       "Name: ORDERED_ITEM, Length: 2535, dtype: int64"
      ]
     },
     "execution_count": 226,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_model['ORDERED_ITEM'].value_counts()\n",
    "#df6['ORDERED_ITEM'].value_counts()\n",
    "#encodingLookup('ORDERED_ITEM','C888EA-K9')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([u'PROMISED_POINT', u'CARTON_ID', u'CART_DIM', u'WEIGHT',\n",
       "       u'SHIPMENT_ID', u'FROM_CITY', u'TO_CITY', u'SHIP_SET_ID',\n",
       "       u'OTM_ROUTE_CODE', u'SHIPPING_METHOD_CODE',\n",
       "       u'SHIPMENT_PRIORITY_CODE', u'TRIM(OEL.REQUEST_DATE)',\n",
       "       u'TRIM(OLX.ORIGINAL_PROMISE_DATE)', u'TRIM(OEL.PROMISE_DATE)',\n",
       "       u'ACTUAL_SHIPMENT_DATE', u'ORDER_NUMBER', u'LINE_NUMBER',\n",
       "       u'ORDERED_ITEM', u'SHIP_FROM_ORG_ID', u'MFG_LEAD_TIME',\n",
       "       'CONTAINER ID', 'ShippingDays', 'ShippingMode', 'RouteType',\n",
       "       'OriSLC', 'DesSLC', 'Carrier', 'DeliveryType', 'DeliveryPriority',\n",
       "       'Length', 'Width', 'Height'], dtype=object)"
      ]
     },
     "execution_count": 208,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df6.columns.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/edwzhang/anaconda/lib/python2.7/site-packages/pandas/core/generic.py:3660: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  self._update_inplace(new_data)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.externals import joblib\n",
    "def getInTransitStopOvers(row):\n",
    "    try:\n",
    "        code = row['OTM_ROUTE_CODE']\n",
    "        #code = row\n",
    "        codes = []\n",
    "        if len(code) > 0  and code.count('-') >0:\n",
    "            codes = code.split('-')\n",
    "            if codes[0] in ['COC','CDC']:\n",
    "                codes.append('NA')\n",
    "            elif codes[0] == 'CC':\n",
    "                codes[1] = 'NA'\n",
    "                codes.append('NA')\n",
    "        else:\n",
    "            codes = ['','','']\n",
    "        return pd.Series(codes,index=['RouteType','StopOver1','StopOver2'])\n",
    "    except Exception, e:\n",
    "        print code, ' failed in parsing In-Transit StopOvers with the error '+str(e) \n",
    "        return pd.Series(['','',''],index=['RouteType','StopOver1','StopOver2'])\n",
    "\n",
    "df6['OTM_ROUTE_CODE'].fillna('', inplace=True)\n",
    "df6[['RouteType','OriSLC','DesSLC']] = df6.apply(getInTransitStopOvers,axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def getDeliveryData(row):\n",
    "    try:\n",
    "        method = row['SHIPPING_METHOD_CODE']\n",
    "        #method = row\n",
    "        methods = []\n",
    "        if method.count('_') >2:\n",
    "            methods = method.split('_',3)\n",
    "        else:\n",
    "            methods = ['','','','']\n",
    "        return pd.Series(methods[1:],index=['Carrier','DeliveryType','DeliveryPriority'])\n",
    "    except Exception, e:\n",
    "        print method + ' failed in parsing Delivery Data with the error '+str(e) \n",
    "        return pd.Series(['','',''],index=['Carrier','DeliveryType','DeliveryPriority'])\n",
    "    \n",
    "df6['SHIPPING_METHOD_CODE'].fillna('', inplace=True)\n",
    "df6[['Carrier','DeliveryType','DeliveryPriority']] = df6.apply(getDeliveryData, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def getCarbonDimensions(row):\n",
    "    try:\n",
    "        dimension = str(row['CART_DIM'])\n",
    "        #dimension = row\n",
    "        dimensions = []\n",
    "        if dimension.count('X') >1:\n",
    "            dimensions = [float(d.strip()) for d in dimension.split('X',3)] \n",
    "        else:\n",
    "            dimensions = [0.0,0.0,0.0]\n",
    "        return pd.Series(dimensions,index=['Length','Width','Height'])\n",
    "    except Exception, e:\n",
    "        print dimension, ' failed in parsing dimensions with the error '+str(e) \n",
    "        return pd.Series([0.0,0.0,0.0],index=['Length','Width','Height'])\n",
    "\n",
    "df6['CART_DIM'].fillna(0.0, inplace=True)\n",
    "df6[['Length','Width','Height']] = df6.apply(getCarbonDimensions, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "cols = ['ShippingDays','PROMISED_POINT','WEIGHT','TO_CITY','SHIP_FROM_ORG_ID','ShippingMode','RouteType','OriSLC','DesSLC','Carrier','DeliveryType','DeliveryPriority','Length','Width','Height']\n",
    "df_model = df6.loc[(df6['OTM_ROUTE_CODE']!='') & (df6['SHIPPING_METHOD_CODE']!='') &(df6['CART_DIM']!=0.0) ,cols]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def categoryEncoding(col,ColName):\n",
    "    colDict = {}\n",
    "    colSet = set([i for i in col])\n",
    "    i = 1\n",
    "    for j in colSet:\n",
    "        colDict[j] = i\n",
    "        i += 1\n",
    "    joblib.dump(colDict, '/home/felix/jupyter/data/dict_'+ColName) \n",
    "    return colDict\n",
    "        \n",
    "def encodingLookup(ColName, val):\n",
    "    colDict = joblib.load('/home/felix/jupyter/data/dict_'+ColName) \n",
    "    if val in colDict:\n",
    "        return colDict[val]\n",
    "    else:\n",
    "        return colDict[max(colDict)] +1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,\n",
       "           max_features='auto', max_leaf_nodes=None,\n",
       "           min_impurity_split=1e-07, min_samples_leaf=1,\n",
       "           min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "           n_estimators=500, n_jobs=1, oob_score=False, random_state=None,\n",
       "           verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 189,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.cross_validation import train_test_split\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.preprocessing import LabelEncoder \n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "df_model = df6.loc[(df6['OTM_ROUTE_CODE']!='') & (df6['SHIPPING_METHOD_CODE']!='') &(df6['CART_DIM']!=0.0) ,cols]\n",
    "cols2 = ['PROMISED_POINT','TO_CITY','ShippingMode','RouteType','OriSLC','DesSLC','Carrier','DeliveryType','DeliveryPriority']\n",
    "for colName in cols2:\n",
    "    le = LabelEncoder()\n",
    "    df_model[colName] = le.fit_transform(df_model[colName])\n",
    "    \n",
    "\n",
    "\n",
    "train_set, test_set = train_test_split(df_model, test_size=0.2, random_state=42)\n",
    "X_train = train_set.iloc[:,1:]\n",
    "\n",
    "#scaler = StandardScaler()\n",
    "#X_train = scaler.fit_transform(X_train)\n",
    "\n",
    "y_train = train_set.iloc[:,0]\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "X_test = test_set.iloc[:,1:]\n",
    "\n",
    "#scaler = StandardScaler()\n",
    "#X_test = scaler.fit_transform(X_test)\n",
    "\n",
    "y_test = test_set.iloc[:,0]\n",
    "\n",
    "\n",
    "regressor = RandomForestRegressor(n_estimators=500)\n",
    "regressor.fit(X_train, y_train)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.62602187,  0.92970661,  6.40794919, -1.10465527,  1.1409397 ,\n",
       "        -3.34277403, -2.66132363, -2.36045216, -0.43108952, -0.0705132 ,\n",
       "        -0.07177703,  0.26534963,  3.95358067,  1.42477352,  1.19095541],\n",
       "       [-0.82163158, -0.91221213, -0.02080959,  0.13347593,  0.49853565,\n",
       "         0.48264247,  0.20209716, -0.80720575, -0.43108952, -0.0705132 ,\n",
       "        -0.07177703,  0.26534963, -0.28265526, -0.13194701,  0.07534022],\n",
       "       [-0.43041215, -0.91221213,  0.49237503, -1.36906973, -0.78984136,\n",
       "         0.48332717,  1.63380755, -0.28945694,  2.05061226, -0.0705132 ,\n",
       "        -0.07177703,  0.26534963,  2.83381762,  0.79595711, -0.03867626]])"
      ]
     },
     "execution_count": 182,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_model[:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 6.94051188  5.73360534  7.82106416  6.88291871  3.98180063  4.81130468\n",
      "  2.6071789   3.89769391  5.68977779  7.36883537]\n",
      "30483     4\n",
      "37012     6\n",
      "18397    11\n",
      "18245     7\n",
      "8884      2\n",
      "33470     3\n",
      "24742     1\n",
      "30501     2\n",
      "4474     16\n",
      "25980     5\n",
      "Name: ShippingDays, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "poly_features = PolynomialFeatures(degree=2, include_bias=False) \n",
    "X_poly = poly_features.fit_transform(X_train)\n",
    "\n",
    "from sklearn.linear_model import LinearRegression\n",
    "lin_reg = LinearRegression()\n",
    "lin_reg.fit(X_poly, y_train)\n",
    "\n",
    "\n",
    "predict = lin_reg.predict(poly_features.fit_transform(X_test))\n",
    "\n",
    "print predict[40:50]\n",
    "print y_test[40:50]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, error_score='raise',\n",
       "       estimator=RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,\n",
       "           max_features='auto', max_leaf_nodes=None,\n",
       "           min_impurity_split=1e-07, min_samples_leaf=1,\n",
       "           min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "           n_estimators=10, n_jobs=1, oob_score=False, random_state=None,\n",
       "           verbose=0, warm_start=False),\n",
       "       fit_params={}, iid=True, n_jobs=1,\n",
       "       param_grid=[{'n_estimators': [400, 600, 800], 'max_depth': [3, 5, 7]}],\n",
       "       pre_dispatch='2*n_jobs', refit=True, scoring=None, verbose=0)"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.grid_search import GridSearchCV\n",
    "\n",
    "\n",
    "param_grid = [{'n_estimators': [400,600,800], 'max_depth':[3,5,7]}]\n",
    "\n",
    "forest_reg = RandomForestRegressor()\n",
    "grid_search = GridSearchCV(forest_reg, param_grid, cv=5)\n",
    "grid_search.fit(X_train, y_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[mean: 0.07051, std: 0.00737, params: {'n_estimators': 400, 'max_depth': 3},\n",
       " mean: 0.07064, std: 0.00705, params: {'n_estimators': 600, 'max_depth': 3},\n",
       " mean: 0.07075, std: 0.00710, params: {'n_estimators': 800, 'max_depth': 3},\n",
       " mean: 0.11857, std: 0.00832, params: {'n_estimators': 400, 'max_depth': 5},\n",
       " mean: 0.11920, std: 0.00810, params: {'n_estimators': 600, 'max_depth': 5},\n",
       " mean: 0.11899, std: 0.00841, params: {'n_estimators': 800, 'max_depth': 5},\n",
       " mean: 0.16911, std: 0.00760, params: {'n_estimators': 400, 'max_depth': 7},\n",
       " mean: 0.16944, std: 0.00747, params: {'n_estimators': 600, 'max_depth': 7},\n",
       " mean: 0.16948, std: 0.00775, params: {'n_estimators': 800, 'max_depth': 7}]"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search.grid_scores_\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[  7.67656667   6.03783333   6.79158095  10.58641667   1.438        5.99617128\n",
      "   3.68886667   1.719        7.26943333   9.82576667]\n",
      "30483     4\n",
      "37012     6\n",
      "18397    11\n",
      "18245     7\n",
      "8884      2\n",
      "33470     3\n",
      "24742     1\n",
      "30501     2\n",
      "4474     16\n",
      "25980     5\n",
      "Name: ShippingDays, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "predict = regressor.predict(X_test)\n",
    "\n",
    "print predict[40:50]\n",
    "print y_test[40:50]\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x11ff33090>"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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33qlT3gbwMis6oa1jVeKmF02OP9aGwC4I9S7jJuS9Oa3L66zLZatLNDX+NnW1LFuey+KQ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\nRCQy/w9nTNj856S+LwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11cb514d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "plt.scatter(range(100),predict[:100])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x122632f90>"
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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G0ItAxD5OTceXp71QMcV+LIaFjrVLYyElkNDLLE7QZKFBKkUNoReBiH2cmo4vT3uhYor9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\nACTi/wD0Q6rMDDtNUgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11c825d90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(range(100),y_test[:100])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.256011909764\n"
     ]
    }
   ],
   "source": [
    "print regressor.score(X_test,y_test)\n",
    "\n",
    "#print regressor.feature_importances_\n",
    "#for i in range(len(cols)-1):\n",
    "#    print cols[i+1],regressor.feature_importances_[i]\n",
    "    \n",
    "#s = pd.Series(regressor.feature_importances_,index=cols[1:])\n",
    "#s.sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regressor = RandomForestRegressor(n_estimators=500,max_features=8)\n",
    "regressor.fit(X_train, y_train)\n",
    "\n",
    "X_test = test_set.iloc[:,1:]\n",
    "y_test = test_set['ShippingDays']\n",
    "regressor.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "could not convert string to float: LTL",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-117-4ee5c58372e6>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mregressor\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mregressor\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtest_set\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/Users/edwzhang/anaconda/lib/python2.7/site-packages/sklearn/ensemble/forest.pyc\u001b[0m in \u001b[0;36mpredict\u001b[0;34m(self, X)\u001b[0m\n\u001b[1;32m    683\u001b[0m         \"\"\"\n\u001b[1;32m    684\u001b[0m         \u001b[0;31m# Check data\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 685\u001b[0;31m         \u001b[0mX\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_validate_X_predict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    686\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    687\u001b[0m         \u001b[0;31m# Assign chunk of trees to jobs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/edwzhang/anaconda/lib/python2.7/site-packages/sklearn/ensemble/forest.pyc\u001b[0m in \u001b[0;36m_validate_X_predict\u001b[0;34m(self, X)\u001b[0m\n\u001b[1;32m    353\u001b[0m                                  \"call `fit` before exploiting the model.\")\n\u001b[1;32m    354\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 355\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mestimators_\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_validate_X_predict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcheck_input\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    356\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    357\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mproperty\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/edwzhang/anaconda/lib/python2.7/site-packages/sklearn/tree/tree.pyc\u001b[0m in \u001b[0;36m_validate_X_predict\u001b[0;34m(self, X, check_input)\u001b[0m\n\u001b[1;32m    363\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    364\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mcheck_input\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 365\u001b[0;31m             \u001b[0mX\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcheck_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mDTYPE\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maccept_sparse\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"csr\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    366\u001b[0m             if issparse(X) and (X.indices.dtype != np.intc or\n\u001b[1;32m    367\u001b[0m                                 X.indptr.dtype != np.intc):\n",
      "\u001b[0;32m/Users/edwzhang/anaconda/lib/python2.7/site-packages/sklearn/utils/validation.pyc\u001b[0m in \u001b[0;36mcheck_array\u001b[0;34m(array, accept_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, warn_on_dtype, estimator)\u001b[0m\n\u001b[1;32m    380\u001b[0m                                       force_all_finite)\n\u001b[1;32m    381\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 382\u001b[0;31m         \u001b[0marray\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morder\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0morder\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    383\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    384\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mensure_2d\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: could not convert string to float: LTL"
     ]
    }
   ],
   "source": [
    "regressor.fit(X_train, y_train)\n",
    "regressor.predict(test_set.iloc[0,1:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ShippingDays                  3\n",
       "PROMISED_POINT          ARRIVAL\n",
       "WEIGHT                  4.27696\n",
       "TO_CITY                 Swindon\n",
       "SHIP_FROM_ORG_ID           2725\n",
       "ShippingMode                AIR\n",
       "RouteType                   CDC\n",
       "OriSLC              SLCROERMOND\n",
       "DesSLC                       NA\n",
       "Carrier                     CPU\n",
       "DeliveryType                  L\n",
       "DeliveryPriority            LTL\n",
       "Length                     16.5\n",
       "Width                      5.94\n",
       "Height                     4.75\n",
       "Name: 27264, dtype: object"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#df_model['ShippingMode'].value_counts()\n",
    "#train_set['ShippingMode'].value_counts()\n",
    "test_set.iloc[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X = np.asarray([('a',1,2),('b',2,3),('a',3,2),('c',1,3)]) \n",
    "y = np.asarray([1,2.5,3,4])\n",
    "\n",
    "# transform 1st column to numbers\n",
    "X[:, 0] = LabelEncoder().fit_transform(X[:,0]) \n",
    "\n",
    "cross_val_score(sgd_clf, X_train, y_train_5, cv=3, scoring=\"accuracy\")\n",
    "\n",
    "regressor = RandomForestRegressor(n_estimators=150, min_samples_split=1)\n",
    "regressor.fit(X, y)\n",
    "print X\n",
    "print regressor.predict(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "\n",
    "df.loc[df['CONTAINER ID'] == '#*##92*%69',].sort_index(by='DATE')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df_detail = pd.read_excel('/Users/edwzhang/Downloads/Shipment Execution-selected/Shipment Exec Jan 3.xls')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#df1['CARTON_ID'].value_counts()\n",
    "#df1.loc[df1['CARTON_ID'] == '#*6#*@9%@9%',]\n",
    "df_detail= df_detail.drop(['CONTAINER_TYPE','DIM_UOM','WEIGHT_UOM','SO_HEADER_ID','PO_NUMBER','SO_LINE_ID','EARLY_DELIVERY_ACCEPT_FLAG','SHIPPING_INSTRUCTIONS','SERVICE_ATTACHED','CUST_PO_NUMBER','CUST_DELIV_ACCPT_REQRD','INSURRED','DUTY_ATTRIBUTE','EXPORT_LICENSE_REQD','EXPEDITED','PRE_DELIV_INSPECTION','ACTION_STATUS','RECOMMITTED','MFG_COMPLEXITY','COMP_LABEL','SOFTWARE_INCLUDED'], axis=1)\n",
    "df_detail=df_detail.drop_duplicates()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "\n",
    "df3 = pd.merge(df_detail,df_ship,left_on= 'CARTON_ID',right_on='CONTAINER ID')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#df3['CARTON_ID'].value_counts()\n",
    "df3.loc[df3['CONTAINER ID'] == '#*6#*92@6%*',['FROM_CITY','TO_CITY','TRIM(OEL.PROMISE_DATE)','ACTUAL_SHIPMENT_DATE','STATUS','DATE','LOCATION']].sort_index(by='DATE')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df_ship.groupby('CONTAINER ID').size()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  }
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