version3.1 (notebook)-district+neighborhood.ipynb

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      "/home/jps/anaconda/lib/python2.7/site-packages/ipykernel/__main__.py:40: 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"
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   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# load data\n",
    "data = pd.read_csv(\"version_nueva.csv\",sep=\",\")\n",
    "\n",
    "# delete lines containing header again (ev. 50 row approx.)\n",
    "#data = data[data['propertyCode']!='propertyCode']\n",
    "\n",
    "# drop some features\n",
    "data = data.drop(['propertyCode','thumbnail','url','province',\n",
    "                  'showAddress','hasVideo','newDevelopment','numPhotos','title','subtitle',\n",
    "                  'subTypology','externalReference','priceByArea', 'typology'],axis=1)\n",
    "\n",
    "\n",
    "# replace string by numbers in 'floor' column\n",
    "data['floor']=data['floor'].fillna(value='0')\n",
    "data = data.replace(to_replace='bj', value='0', inplace=False, limit=None, regex=False, method='pad', axis=None)\n",
    "data = data.replace(to_replace='en', value='1', inplace=False, limit=None, regex=False, method='pad', axis=None)\n",
    "data = data.replace(to_replace='ss', value='-1', inplace=False, limit=None, regex=False, method='pad', axis=None)\n",
    "data = data.replace(to_replace='st', value='-1', inplace=False, limit=None, regex=False, method='pad', axis=None)\n",
    "\n",
    "#Converting some selected features to float\n",
    "tofloat=['price','size','rooms','floor','bathrooms','exterior']\n",
    "###To be used when the floor feature is only numeric:\n",
    "###tofloat=['price','priceByArea','numPhotos','size','propertyCode','rooms','floor']\n",
    "for col in tofloat:\n",
    "    data[col]=data[col].astype(float)\n",
    "\n",
    "#Take only municipality Barcelona\n",
    "data = data[data['municipality']=='Barcelona']\n",
    "data = data.drop(['municipality'], axis=1)\n",
    "\n",
    "#remove outliers price\n",
    "#per=np.percentile(np.array(data['price']), [2.5, 97.5])\n",
    "#data=data[data['price']>per[0]]\n",
    "#data=data[data['price']<per[1]]\n",
    "\n",
    "data['floor'][data['floor']==1000]=0.0\n",
    "\n",
    "def dummies_for(data, cols, binary=None):\n",
    "    for col in cols:\n",
    "            data = pd.concat((data, pd.get_dummies(data[col]).astype(np.int32)), axis=1)\n",
    "            data = data.drop(col, axis=1)   \n",
    "    return data\n",
    "\n",
    "data = dummies_for(data,  ['propertyType'])\n",
    "data = dummies_for(data,  ['status'])\n",
    "data = dummies_for(data,  ['district'])\n",
    "data = dummies_for(data,  ['neighborhood'])\n",
    "\n",
    "data.to_csv(\"version4_district+neighborhood.csv\", index=False)"
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