Pandas apply counting.ipynb

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  {
   "cell_type": "code",
   "execution_count": 2,
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   "source": [
    "import pandas as pd"
   ]
  },
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   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
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   "outputs": [],
   "source": [
    "class SelfConscious:\n",
    "    called = 0\n",
    "    \n",
    "    def __init__(self, label):\n",
    "        self.label = label\n",
    "    \n",
    "    def increment(self):\n",
    "        self.called += 1\n",
    "        print('object {} called {} times'.format(self.label, self.called))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "object 0 called 1 times\n",
      "object 0 called 2 times\n"
     ]
    }
   ],
   "source": [
    "sc = SelfConscious(0)\n",
    "sc.increment()\n",
    "sc.increment()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>sc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>&lt;__main__.SelfConscious object at 0x10aa6b898&gt;</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>&lt;__main__.SelfConscious object at 0x10aa6b8d0&gt;</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>&lt;__main__.SelfConscious object at 0x10aa6b940&gt;</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>&lt;__main__.SelfConscious object at 0x10aa6ba20&gt;</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      "text/plain": [
       "                                               sc\n",
       "0  <__main__.SelfConscious object at 0x10aa6b898>\n",
       "1  <__main__.SelfConscious object at 0x10aa6b8d0>\n",
       "2  <__main__.SelfConscious object at 0x10aa6b940>\n",
       "3  <__main__.SelfConscious object at 0x10aa6ba20>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scs = pd.DataFrame({'sc': [SelfConscious(label) for label in range(4)]})\n",
    "scs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "object 0 called 1 times\n",
      "object 1 called 1 times\n",
      "object 2 called 1 times\n",
      "object 3 called 1 times\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0    None\n",
       "1    None\n",
       "2    None\n",
       "3    None\n",
       "Name: sc, dtype: object"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scs.sc.apply(lambda x: x.increment())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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