parking-klcc.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('parking-klcc-2016-2017.txt', sep=';', names = ['location','avail_park','datetime'])\n",
    "df['datetime'] = pd.to_datetime(df['datetime'])\n",
    "df['hour'] = df['datetime'].dt.hour\n",
    "df['day'] = df['datetime'].dt.day\n",
    "df['month'] = df['datetime'].dt.month\n",
    "df['week'] = df['datetime'].dt.weekday\n",
    "\n",
    "df = df[(df['datetime'] >= '2016-11-15') & (df['datetime'] < '2017-11-15')]\n",
    "df.loc[df['avail_park'] == 'FULL', 'avail_park'] = '0'\n",
    "df_open_full = df[(df['avail_park'] != 'OPEN')]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Histogram Graph from 15 November 2016 to 14 November 2016"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.5/site-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "source": [
    "df_open_full['avail_park'] = df_open_full['avail_park'].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5730e4c668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_open_full.groupby('hour')['avail_park'].mean().plot(kind='bar');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5730afea58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_open_full.groupby('week')['avail_park'].mean().plot(kind='bar')\n",
    "plt.xticks(range(7), 'Mon Tue Wed Thu Fri Sat Sun'.split());"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5730aa67f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_open_full.groupby('day')['avail_park'].mean().plot(kind='bar');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Zt56/qKrPJ7kduC7JW4BvA69v47cA5wITwJPAm+ewbUnSHB10AFTVA8DLJql/D3jVJPUC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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5730947eb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_open_full.groupby('month')['avail_park'].mean().plot(kind='bar');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Histogram Graph from 15 November 2016 to 14 November 2016 - FULL Parking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_full = df_open_full[df_open_full['avail_park'] == 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>location</th>\n",
       "      <th>avail_park</th>\n",
       "      <th>datetime</th>\n",
       "      <th>hour</th>\n",
       "      <th>day</th>\n",
       "      <th>month</th>\n",
       "      <th>week</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>14428</th>\n",
       "      <td>KLCC</td>\n",
       "      <td>0</td>\n",
       "      <td>2016-11-15 13:30:03</td>\n",
       "      <td>13</td>\n",
       "      <td>15</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14429</th>\n",
       "      <td>KLCC</td>\n",
       "      <td>0</td>\n",
       "      <td>2016-11-15 14:00:03</td>\n",
       "      <td>14</td>\n",
       "      <td>15</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14430</th>\n",
       "      <td>KLCC</td>\n",
       "      <td>0</td>\n",
       "      <td>2016-11-15 14:15:03</td>\n",
       "      <td>14</td>\n",
       "      <td>15</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14431</th>\n",
       "      <td>KLCC</td>\n",
       "      <td>0</td>\n",
       "      <td>2016-11-15 14:30:03</td>\n",
       "      <td>14</td>\n",
       "      <td>15</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15029</th>\n",
       "      <td>KLCC</td>\n",
       "      <td>0</td>\n",
       "      <td>2016-11-22 12:30:02</td>\n",
       "      <td>12</td>\n",
       "      <td>22</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      location  avail_park            datetime  hour  day  month  week\n",
       "14428     KLCC           0 2016-11-15 13:30:03    13   15     11     1\n",
       "14429     KLCC           0 2016-11-15 14:00:03    14   15     11     1\n",
       "14430     KLCC           0 2016-11-15 14:15:03    14   15     11     1\n",
       "14431     KLCC           0 2016-11-15 14:30:03    14   15     11     1\n",
       "15029     KLCC           0 2016-11-22 12:30:02    12   22     11     1"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_full.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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A6g41NwBfBvYD3lL/fbCef0uP23Bty/xfAzcBpwD/BSzvUncrMKeeXwl8GnhjXfuNLnVP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\nkQ6LpotzRAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f573072c860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_full.groupby('hour')['avail_park'].count().plot(kind='bar');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5730750dd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_full.groupby('week')['avail_park'].count().plot(kind='bar');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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QsxX4G+AU4OX1z931/Msbar7UMf+rwO3ABuBfgPUNNXcDS+r5TcD7gZfVdVc11DwO/Dvw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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5730869710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_full.groupby('day')['avail_park'].count().plot(kind='bar');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5730ae22b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_full.groupby('month')['avail_park'].count().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": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}