{"cells":[
 {
  "cell_type": "code",
  "execution_count": null,
  "metadata": {
   "collapsed": false
  },
  "outputs": [],
  "source": "import numpy as np\nimport pandas as pd\nimport math\ncolum_target=pd.read_csv(\"../input/train.csv\",usecols=['Demanda_uni_equil'])\nm=colum_target['Demanda_uni_equil'].tolist()\n#print(m)\n#m=np.mean(np.linalg.logm(colum_target['Demanda_uni_equil'].value+1))\n#m=np.exp(math.log(+1).mean())\n#mm=colum_target['m'].mean()\n#print(mm)\n#result_mean=pd.read_csv(\"../input/test.csv\",usecols=['id'])\n#result_mean['Demanda_uni_equil']=exp(mean)\n#result_mean.to_csv('result_mean.csv',index=False)"
 },
 {
  "cell_type": "code",
  "execution_count": null,
  "metadata": {
   "collapsed": false
  },
  "outputs": [],
  "source": "x=np.exp(np.mean(np.log(np.array(m)+1)))-1\nprint (x)\nresult_logmean=pd.read_csv(\"../input/test.csv\",usecols=['id'])\nresult_logmean['Demanda_uni_equil']=x\nresult_logmean.to_csv('result_logmean.csv',index=False)"
 }
],"metadata":{"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"}}, "nbformat": 4, "nbformat_minor": 0}