Multi-type frequency count

gistfile1.txt

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Assume you have events log with two event types `foo` and `bar`. The goal is to obtain a frequency bar plot of the events per some predescribed time interval."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "%pylab inline\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from matplotlib import pylab\n",
    "matplotlib.style.use('ggplot')\n",
    "np.random.seed(1234)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Simple case. One event type. Days resolution"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To start with, here's a simple data set. 3 days appearing "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "days = pd.date_range(pd.to_datetime('2015-01-14'), periods=3, freq='d')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2015-01-14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2015-01-14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2015-01-14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2015-01-15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2015-01-15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2015-01-15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2015-01-15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2015-01-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2015-01-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2015-01-16</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        date\n",
       "2 2015-01-14\n",
       "3 2015-01-14\n",
       "4 2015-01-14\n",
       "1 2015-01-15\n",
       "5 2015-01-15\n",
       "6 2015-01-15\n",
       "7 2015-01-15\n",
       "0 2015-01-16\n",
       "8 2015-01-16\n",
       "9 2015-01-16"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(np.random.choice(days,10), \n",
    "                  columns=['date'])\n",
    "df.sort_values('date',inplace=True)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Following shows how many events (in total) happened each day."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x10929e668>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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TpaWlna4LOeXi9/u1fv16DRkyRHfeeWenY8aOHaudO3dKkmpra5WRkaGsrKwISgYAdFfI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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10929e550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = df.groupby([df['date'].dt.to_period('1d')]).count().plot(kind='bar')\n",
    "ax.set_xlabel('Date')\n",
    "ax.legend([\"Events\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Adding different event types"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we assign randomly events to the dates."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>date</th>\n",
       "      <th>type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2015-01-14</td>\n",
       "      <td>xxx</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2015-01-14</td>\n",
       "      <td>bar</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2015-01-14</td>\n",
       "      <td>bar</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2015-01-15</td>\n",
       "      <td>xxx</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2015-01-15</td>\n",
       "      <td>foo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2015-01-15</td>\n",
       "      <td>foo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2015-01-15</td>\n",
       "      <td>xxx</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2015-01-16</td>\n",
       "      <td>bar</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2015-01-16</td>\n",
       "      <td>foo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2015-01-16</td>\n",
       "      <td>bar</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        date type\n",
       "2 2015-01-14  xxx\n",
       "3 2015-01-14  bar\n",
       "4 2015-01-14  bar\n",
       "1 2015-01-15  xxx\n",
       "5 2015-01-15  foo\n",
       "6 2015-01-15  foo\n",
       "7 2015-01-15  xxx\n",
       "0 2015-01-16  bar\n",
       "8 2015-01-16  foo\n",
       "9 2015-01-16  bar"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.seed(12)\n",
    "df['type'] = np.random.choice(['foo','bar','xxx'],10)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x109f419e8>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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zNHpkvnLlSn322Weqrq7WzJkzNXny5ODRTUpKikaMGKHc3FzNnj1bMTExmjlzZosXDQCw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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x109f3c908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = pd.DataFrame([\n",
    "    pd.Series((df[df.type==str(x)]).groupby(['date']).count()['type']) for x in np.sort(df.type.unique())\n",
    "]).transpose().plot(kind='bar')\n",
    "ax.legend(sort(df.type.unique()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Denser events stream"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2015-01-10 00:00:00', '2015-01-10 00:01:00',\n",
       "               '2015-01-10 00:02:00', '2015-01-10 00:03:00',\n",
       "               '2015-01-10 00:04:00', '2015-01-10 00:05:00',\n",
       "               '2015-01-10 00:06:00', '2015-01-10 00:07:00',\n",
       "               '2015-01-10 00:08:00', '2015-01-10 00:09:00',\n",
       "               ...\n",
       "               '2015-01-17 23:51:00', '2015-01-17 23:52:00',\n",
       "               '2015-01-17 23:53:00', '2015-01-17 23:54:00',\n",
       "               '2015-01-17 23:55:00', '2015-01-17 23:56:00',\n",
       "               '2015-01-17 23:57:00', '2015-01-17 23:58:00',\n",
       "               '2015-01-17 23:59:00', '2015-01-18 00:00:00'],\n",
       "              dtype='datetime64[ns]', length=11521, freq='T')"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "timeRange = pd.date_range(pd.to_datetime('2015-01-10'), pd.to_datetime('2015-01-18'), freq='T')\n",
    "timeRange                         "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df = pd.DataFrame(np.random.choice(timeRange,500), \n",
    "                  columns=['date'])\n",
    "df.sort_values('date',inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x10d2bcdd8>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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mdJrQGA6r02rqtFYydBrzQp5u3bpRVVXVbntVVRVpaWkJKOqYCZ0mNII6raZO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\naYCLiBhKA1xExFAa4CIihvr/Zw2s2Tan//QAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10d2c4b70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = df.groupby([df['date'].dt.to_period('D')]).count().plot(kind='bar')\n",
    "ax.set_xlabel('Date')\n",
    "ax.legend([\"Events\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "np.random.seed(12)\n",
    "df['type'] = np.random.choice(['foo','bar','xxx'],df.shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1116ca550>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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K2Gfgp/DdWcpip7LYqRwRGgG+E7MFrbzrKRh2KoudyhKhU4RGQDudQgxwrbzr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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10ede3fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = pd.DataFrame([\n",
    "    pd.Series((df[df.type==str(x)]).groupby(\n",
    "                df['date'].dt.to_period('d')).count()['type']) for x in np.sort(df.type.unique())\n",
    "]).transpose().plot(kind='bar')\n",
    "ax.legend(sort(df.type.unique()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fancier solution"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Courtusy to [Romain X](http://stackoverflow.com/a/34820891/671013)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11036a710>"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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q/HjFH3/8gcGDBwMAysvLYTKZEBUVhfLyctX0vv7661i4cCHWr1+PkJAQjBkz\nBnFxcbBYLHjppZd8nVepqKgo9OrVC7169cLly5e99rzCfJCnpKQER44cgclkAnDzHORPPvmk6rbG\nRJCbm4vAwEDUq1fPabnVasX333+PhIQEH5U5KygoQFRUFPz8nLczjEYj8vLynHZZqIXdbsf27dtx\n6tQpjBgxwtc5brFYLMjLy0OTJk18neKQl5eHCxcuwG63o1atWnjssceg0ajnmIvjx4+jefPmvs4Q\nZ4ATEZEz9fxKq6F33nnH1wluYaey2KksETpFaAS82ynEPvADBw5UWHbrzcFbxwirATuVxU5lidAp\nQiOgnk4hBvi8efPQvn37CvvAZFlW1Rta7FQWO5UlQqcIjYB6OoUY4PXr10efPn1Qv379CuuOHz/u\ngyLX2KksdipLhE4RGgH1dArxSczY2FhERERU+CAPADRt2tTxKUJfY6ey2KksETpFaATU08mjUIiI\nBCXsUShjx471dYJb2KksdipLhE4RGgHfdAo7wEX5hwM7lcVOZYnQKUIj4JtOYQf4U0895esEt7BT\nWexUlgidIjQCvunkPnAiIkEJuwV+Cz+dpSx2KoudyhGhEeAnMStQy6eeqsNOZbFTWSJ0itAIqKdT\niAGulk89VYedymKnskToFKERUE+nEANcLZ96qg47lcVOZYnQKUIjoJ5OfhJTQexUFjuVJUKnCI2A\nejp5FAoRkaCE2IUCAEeOHEFWVhaMRiOAm1fkadu2LVq2bOnjMmfsVBY7lSVCpwiNgDo6hdgCX7ly\nJfLz85GQkOD4p8mVK1ewb98+1K1bF8OGDfNx4U3sVBY7lSVCpwiNgIo6ZQG89dZbLpfb7XZ5+PDh\nXq6pHDuVxU5lidApQqMsq6dTiA/y+Pv7Iycnp8LynJwcBAQE+KDINXYqi53KEqFThEZAPZ1C7EI5\nc+YMli1bhtLSUsc/V4xGI4KDg/HKK6+gYcOGPi68iZ3KYqeyROgUoRFQT6cQA/wWk8nkeMOgVq1a\niIyM9HGRa+xUFjuVJUKnCI2ACjq9trNGYevWrfN1glvYqSx2KkuEThEaZdk3nULsA3fl0KFDvk5w\nCzuVxU5lidApQiPgm05hB7gsyJ4fdiqLncoSoVOERsA3nULtA7+T3W6vcCIZNWKnstipLBE6RWgE\nfNMpxLlQiouLERgY6Li9d+9e7Nq1C1euXEGDBg0gSZIP625jp7LYqSwROkVoBNTTqf5fawCmTp3q\n+Hrjxo3lqq5lAAAGG0lEQVTYt28fGjZsiKNHj2L16tU+LHPGTmWxU1kidIrQCKinU5hzodzy448/\n4qOPPkJQUBA6dOig2itWs1NZ7FSWCJ0iNAK+7RRigJeVleHMmTMAAJvNhqCgIACAn5+fqvaNsVNZ\n7FSWCJ0iNALq6RRigEdGRuJf//oXACA8PBxGoxHR0dEoLi6GVqv1cd1t7FQWO5UlQqcIjYB6OoU9\nCgW4+a5veXm505sJasROZbFTWSJ0itAIeL9TPf8mqQGNRoPCwkJfZ1SLncpip7JE6BShEfB+p9AD\nHHB+N1jN2KksdipLhE4RGgHvdgqxD/yzzz6rdJ3FYvFiSdXYqSx2KkuEThEaAfV0CjHAd+/ejZde\negn+/v4V1v33v//1QZFr7FQWO5UlQqcIjYB6OoUY4I899hgeffRRNG3atMK69evX+6DINXYqi53K\nEqFThEZAPZ1CHIVSUlICf39/1b8DzU5lsVNZInSK0Aiop1OIAU5ERBUJsQvFYrEgIyMDBw8ehNls\nBgBERESgTZs2SE5ORmhoqI8Lb2KnstipLBE6RWgE1NMpxBb41KlT0bx5cyQmJiIiIgKSJMFkMmHP\nnj04fvw4Jk6c6OtEAOxUGjuVJUKnCI2AejqFOA68oKAAycnJiIyMdJymMSoqCsnJySgoKPBx3W3s\nVBY7lSVCpwiNgHo6hRjgtWvXxtdff42ioiLHsqKiImRkZKB27do+LHPGTmWxU1kidIrQCKinU4hd\nKCUlJcjIyMChQ4ec9je1bt0aycnJCAsL83HhTexUFjuVJUKnCI2AejqFGOAAkJeXB6PRiMcffxzB\nwcGO5UeOHEHLli19WOaMncpip7JE6BShEVBHpxC7ULZu3YpZs2YhMzMT77zzDrKyshzrPv/8cx+W\nOWOnstipLBE6RWgE1NMpxGGEO3fuRGpqKoKCgnD58mXMnj0bBQUF6N27t6/TnLBTWexUlgidIjQC\n6ukUYoDLsuy44kVMTAw+/PBDxx+YmvYAsVNZ7FSWCJ0iNALq6RRiF0pERARyc3Mdt4OCgjB27FiU\nlJTg7Nmzvgv7E3Yqi53KEqFThEZAPZ1CvIlZWFgIPz8/REZGOi2XZRknT550eUIZX2CnstipLBE6\nRWgE1NMpxAAnIqKKhNiFQkREFXGAExEJigOciEhQHOD0QFi0aBH+/e9/+zqDSFEc4PRAkCTJcda4\nqqSkpGDXrl1eKCK6dxzg9MBw54Ard4Y8kVoI8UlMorv1+++/Y+nSpcjPz0erVq0cyy0WCxYsWICc\nnBzYbDY0adIEr776KqKjo/HFF1/g119/xalTp7Bq1SokJiZi2LBhOH/+PD777DP8/vvvCA8Pxwsv\nvIBnnnnGh6+O6CZugdN9x2q1YtasWejYsSNWrlyJp59+Gj/++CMkSYIsy+jcuTMWL16MJUuWICAg\nACtWrAAADBw4EM2aNcM//vEPrFmzBsOGDUNpaSmmTp2Kv/3tb1i+fDlGjRqF5cuXIy8vz8evkogD\nnO5Dp06dgs1mQ69evaDRaPD000+jUaNGAICwsDC0bdsWAQEBCAoKQr9+/XDixIlKH+vnn39GTEwM\nEhMTodFoEBcXh3bt2uHAgQPeejlEleIuFLrvmEwmREdHOy27dZWUsrIyrFq1CkePHkVJSQkAoLS0\nFLIsu9z/XVBQgNOnT2Po0KGOZTabDQkJCR58BUTu4QCn+05UVBSMRqPTssLCQtStWxebNm3CxYsX\nMX36dMcJicaOHVvpAK9duzaeeOIJ1VxMl+hO3IVC953GjRtDq9Vi69atsFqt+PHHH5GTkwPg5tZ2\nQEAAQkJCUFJSgvXr1zvdNyIiApcuXXLc/utf/4qLFy9i7969sFqtsFqtyMnJwfnz5736mohc4cms\n6L505swZfPrpp05HodSrVw/du3fH/Pnz8dtvvyE6OhpJSUlYtmwZvvjiC2g0Gpw6dQqLFi1CcXEx\nOnbsiCFDhuDChQtYs2YNcnJyIMsy4uLiMHjwYPzlL3/x8aukBx0HOBGRoLgLhYhIUBzgRESC4gAn\nIhIUBzgRkaA4wImIBMUBTkQkKA5wIiJBcYATEQmKA5yISFD/D1SD4Egc4GzuAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10f8bddd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df2 = df.groupby([df['date'].dt.to_period('D'), 'type']).count().unstack()\n",
    "df2.columns = df2.columns.droplevel(0)\n",
    "df2.plot(kind='bar')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.4.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}