{"cells":[
 {
  "cell_type": "code",
  "execution_count": null,
  "metadata": {
   "collapsed": false
  },
  "outputs": [],
  "source": "# This Python 3 environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python\n# For example, here's several helpful packages to load in \n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\nimport seaborn as sns\nimport matplotlib.pyplot as plt\n%matplotlib inline\n\ndf = pd.read_csv('../input/train.csv')\n#train_df['time'] = pd.to_datetime(train_df['time'],unit='s')\n#print ('No. of unique places:', len(df['place_id'].unique()))\n\n#print (df.describe())\n\n# group by place id\ngrouped = df.groupby('place_id', as_index=False)\n\nmost_popular = grouped.size().sort_values(ascending=False)[:50]\nprint (most_popular)"
 },
 {
  "cell_type": "markdown",
  "metadata": {},
  "source": "Let's look at the accuracy distribution"
 },
 {
  "cell_type": "code",
  "execution_count": null,
  "metadata": {
   "collapsed": false
  },
  "outputs": [],
  "source": "df.plot(kind='hist', y = 'accuracy', bins=500)"
 },
 {
  "cell_type": "code",
  "execution_count": null,
  "metadata": {
   "collapsed": false
  },
  "outputs": [],
  "source": ""
 }
],"metadata":{"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"}}, "nbformat": 4, "nbformat_minor": 0}