05 Visualization.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "# without this the plots would be opened  in a new window (not browser)\n",
    "# with this instruction plots will be included in the notebook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/Users/jasjotsingh/codes/pandas-tutorial\r\n"
     ]
    }
   ],
   "source": [
    "!pwd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "trips_data = pd.read_csv(\"datasets/biketrip.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(669959, 11)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trips_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "trips_data = trips_data.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(661622, 11)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trips_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10baa5780>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1068595c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "trips_data.plot() # default line plot with x- axis as row index number"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count              661622\n",
       "unique             358171\n",
       "top       10/14/2014 8:50\n",
       "freq                   16\n",
       "Name: start_date, dtype: object"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trips_data['start_date'].describe() # for datetime type basic statistics are displayed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count      661622.0\n",
       "mean         1078.0\n",
       "std         22362.0\n",
       "min            60.0\n",
       "25%           343.0\n",
       "50%           514.0\n",
       "75%           749.0\n",
       "max      17270400.0\n",
       "Name: duration, dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trips_data['duration'].describe().round() # statistical measures of central tendency and variance are displayed"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Basic Histograms and Customized Histograms "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10bad4320>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1063db5f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "trips_data['duration'].plot() # pandas will interact with matplotlib  - default is linechart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10a760320>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10a765e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "trips_data['duration'].hist() # displays histogram for duration of trips \n",
    "                              # press shift + enter when inside parenthesis followed by hist to see the \n",
    "                              # possible arguments of the hist attribute"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# as it is observed that the data is verys skewed and maybe has outliers, we will remove outliers and then plot\n",
    "reduced_trips_data = trips_data.loc[abs(trips_data.duration - trips_data.duration.mean()) <= (3*trips_data.duration.std())]\n",
    "# keep only the ones that are within +3 to -3 standard deviations in the column 'duration' using pandas indexing\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(660934, 11)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reduced_trips_data.shape # not much outliers removed as data is not normally distributed "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10a9ebef0>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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8+H0J8C+BvRHxVKn9W+A2YFtE3AD8GLiqvLYTuAIYB34OXA+QmUci4vPAY2Xc5zLzSNm+Efg6sAR4sDyYZQ1JUkvMGSSZ+d/pfR0D4NIe4xO4aYZjbQG29Kg/DnywR/2VXmtIktrD32yXJFUxSCRJVQwSSVIVg0SSVMUgkSRVMUgkSVUMEklSFYNEklTFIJEkVTFIJElVDBJJUhWDRJJUxSCRJFUxSCRJVQwSSVIVg0SSVMUgkSRVMUgkSVUMEklSFYNEklTFIJEkVTFIJElVDBJJUpU5gyQitkTE4Yh4plE7LSJ2R8S+8rys1CMi7oiI8Yh4OiIuaMzZUMbvi4gNjfqFEbG3zLkjImK2NSRJ7dLPJ5KvA+um1TYBezJzNbCn7ANcDqwuj43AXdANBeBW4GLgIuDWRjDcVcZOzVs3xxqSpBaZM0gy878BR6aV1wNby/ZW4MpG/Z7sehhYGhHLgcuA3Zl5JDNfBXYD68prp2bmdzMzgXumHavXGpKkFhn0GslIZh4CKM9nlvoK4MXGuIlSm60+0aM+2xqSpBZZfIKPFz1qOUD9+BaN2Ej39BgjIyN0Op3jPQQAI0vg5vOODjS3xqD9Tk5ODjx3odjz/Bi2noetX7DnpkGD5KWIWJ6Zh8rpqcOlPgGc1Ri3EjhY6mPT6p1SX9lj/GxrvElmbgY2A4yOjubY2NhMQ2d1573buX3vic7WuR24dmygeZ1Oh0G/1oViz/Nj2Hoetn7BnpsGPbW1A5i682oDsL1Rv67cvbUGeL2cltoFrI2IZeUi+1pgV3ntpxGxptytdd20Y/VaQ5LUInP++B0R36D7aeKMiJige/fVbcC2iLgB+DFwVRm+E7gCGAd+DlwPkJlHIuLzwGNl3Ocyc+oC/o107wxbAjxYHsyyhiSpReYMksy8ZoaXLu0xNoGbZjjOFmBLj/rjwAd71F/ptYYkqV38zXZJUhWDRJJUxSCRJFUxSCRJVQwSSVIVg0SSVMUgkSRVMUgkSVUMEklSFYNEklTFIJEkVTFIJElVDBJJUhWDRJJUxSCRJFUxSCRJVQwSSVIVg0SSVMUgkSRVMUgkSVUMEklSFYNEklTFIJEkVWl9kETEuoh4ISLGI2LTQvcjSTpWq4MkIhYBXwEuB84FromIcxe2K0lS0+KFbmAOFwHjmbkfICLuA9YDzy1oVyfYqk1/NdC8m887ym8NOHfKgds+XjVfktoeJCuAFxv7E8DFC9TL29KgITaoExF+tQxP6cRqe5BEj1q+aVDERmBj2Z2MiBcGXO8M4OUB5867fzNk/UI7eo4vHveUBe95AMPW87D1C2+/nv/RoAdte5BMAGc19lcCB6cPyszNwObaxSLi8cwcrT3OfBm2fsGe58uw9Txs/YI9N7X6YjvwGLA6Is6OiJOAq4EdC9yTJKmh1Z9IMvNoRHwK2AUsArZk5rML3JYkqaHVQQKQmTuBnfO0XPXpsXk2bP2CPc+XYet52PoFe35DZL7p2rUkSX1r+zUSSVLLGSQs/J9hiYgtEXE4Ip5p1E6LiN0Rsa88Lyv1iIg7Sq9PR8QFjTkbyvh9EbGhUb8wIvaWOXdERK/bqo+n37Mi4qGIeD4ino2ITw9Bz++MiEcj4vul5z8o9bMj4pGy/jfLTR1ExMllf7y8vqpxrFtK/YWIuKxRP+Hvo4hYFBFPRsQDQ9LvgfK/21MR8XiptfZ9UY65NCLuj4gflPf0R9rcc0S8r/z7Tj1+EhGfWdCeM/P/6wfdi/g/BM4BTgK+D5w7zz18FLgAeKZR+0NgU9neBHyxbF8BPEj3d2zWAI+U+mnA/vK8rGwvK689CnykzHkQuLyy3+XABWX73cDf0v0TNm3uOYB3le13AI+UXrYBV5f6nwA3lu3fBv6kbF8NfLNsn1veIycDZ5f3zqK36n0EfBb4c+CBst/2fg8AZ0yrtfZ9UY65FfjXZfskYGnbe270vgj4O7q/A7JgPc/bN8u2Pso/1q7G/i3ALQvQxyqODZIXgOVleznwQtn+KnDN9HHANcBXG/Wvltpy4AeN+jHjTlDv24F/MSw9A78OfI/uX0l4GVg8/b1A907Bj5TtxWVcTH9/TI17K95HdH9vag/wMeCBsn5r+y3HOcCbg6S17wvgVOBHlOvFw9DztD7XAv9joXv21FbvP8OyYoF6aRrJzEMA5fnMUp+p39nqEz3qJ0Q5hfJhuj/ht7rncproKeAwsJvuT+SvZebRHuu80Vt5/XXg9AG+lhpfBn4X+D9l//SW9wvdvzzx1xHxRHT/4gS0+31xDvD3wJ+WU4hfi4hTWt5z09XAN8r2gvVskPT5Z1haZKZ+j7de30jEu4C/AD6TmT+Zbehx9vaW9JyZv8rM8+n+pH8R8P5Z1lnQniPiE8DhzHyiWZ5ljVb8GwOXZOYFdP9i900R8dFZxrah58V0TyvflZkfBn5G97TQTNrQc7eR7vWxTwL/Za6hM/Rwwno2SPr8MywL4KWIWA5Qng+X+kz9zlZf2aNeJSLeQTdE7s3MvxyGnqdk5mtAh+754qURMfX7VM113uitvP4e4MgAX8ugLgE+GREHgPvont76cov7BSAzD5bnw8C36AZ2m98XE8BEZj5S9u+nGyxt7nnK5cD3MvOlsr9wPZ+oc3XD+qD7E8l+uhcipy46fmAB+ljFsddI/iPHXjj7w7L9cY69cPZoqZ9G91zvsvL4EXBaee2xMnbqwtkVlb0GcA/w5Wn1Nvf8XmBp2V4C/A3wCbo/zTUvXv922b6JYy9ebyvbH+DYi9f76V7wfMveR8AY/+9ie2v7BU4B3t3Y/p/Auja/L8ox/wZ4X9n+96XfVvdcjnsfcH0b/v83r98s2/qge1fD39I9Z/77C7D+N4BDwP+m+9PADXTPb+8B9pXnqf+Bg+5/7OuHwF5gtHGcfwWMl0fzDTYKPFPm/GemXVgcoN9/Svej7tPAU+VxRct7/ifAk6XnZ4B/V+rn0L1DZZzuN+mTS/2dZX+8vH5O41i/X/p6gcbdLG/V+4hjg6S1/Zbevl8ez04ds83vi3LM84HHy3vjv9L9ptr2nn8deAV4T6O2YD37m+2SpCpeI5EkVTFIJElVDBJJUhWDRJJUxSCRJFUxSCRJVQwSSVIVg0SSVOX/AvRMr/uP4QhJAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x107157828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "reduced_trips_data['duration'].hist() # still this does not help in obserbving the distribution of duration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "trips_data['percentile'] = trips_data['duration'].rank(pct=True) # using rank method of pandas to assign percentile to each duration values\n",
    "pct_95_trips_data= trips_data[trips_data['percentile']<=0.95] # to remove outliers, say, we accept to consider only 95% of the data observed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10c84e160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pct_95_trips_data.duration.hist() # now we can visualize the duration and make sense of it\n",
    "plt.show() # to dsiplay the plot \n",
    "# It can be observed that maximum data points lie in the range 250-500 seconds of duration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10c12bf28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "ax = pct_95_trips_data.duration.hist(bins=15, xlabelsize=10,figsize=(9,7)) # increased the number of bins to visualize with more granularity\n",
    "                                            # set xlabelsize to and figure size make it legible\n",
    "x_min = pct_95_trips_data.duration.min()\n",
    "x_max = pct_95_trips_data.duration.max()\n",
    "bin_size = (x_max - x_min)//15\n",
    "\n",
    "xtick = [i for i in range(int(x_min),int(x_max),int(bin_size))] # to get list of bins \n",
    "ax.set_xticks( xtick ) # to set ticks for each bin\n",
    "plt.show()\n",
    "#Here we can say at a more granular level that max duration is between 290 and 405\n",
    "# More arguments can be explored when you play around with histograms"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## BOX PLOT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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q4Fhng8zcA+yB+UdBDK5ruqQs0ZdmxEK/YeZdyI9VkRYy6CGgF4F1EXFdRHwY2AwcGHAfJEkM+AwgM9+JiL8AngZGgH2Z+cog+yCdi8xc8CzAX/+6GA38cdCZ+RTw1KA/Vzpf/LLXpcI7gSWpUgaAJFXKAJCkShkAklQpA0CSKnVB/0/giJgFfjbsfkhncBXwi2F3QlrA72XmaLdGF3QASBeyiGj38o+3pQuVQ0CSVCkDQJIqZQBIi7dn2B2QzoVzAJJUKc8AJKlSBoDUp4jYFxHHI+LwsPsinQsDQOrf14GNw+6EdK4MAKlPmfl9YG7Y/ZDOlQEgSZUyACSpUgaAJFXKAJCkShkAUp8iYhL4V+D3I2ImIprD7pO0GN4JLEmV8gxAkiplAEhSpQwASaqUASBJlTIAJKlSBoAkVcoAkKRKGQCSVKn/B4XwY9ikIkh0AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10c1798d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x10be1c5f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.boxplot(trips_data['duration']/3600) # convert to hours and we can observe that \n",
    "                                        # there are very small number of outliers on the higher side\n",
    "plt.show()\n",
    "\n",
    "plt.boxplot(reduced_trips_data['duration']/3600) # using outliers filtered dataset shows all \n",
    "                                            # the data lies between 0 and 20 hours duration\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10c0eaf28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# To visualize the number of each bike type used in al the trips\n",
    "trips_data['bike_id'].hist(bins=20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualizing 2 variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# To visualize avg duration and no. of trips from each station\n",
    "agg_trips_data = trips_data.groupby('start_station_id').agg({'id':'count','duration':['mean','sum']}).reset_index() # aggregatig data\n",
    "agg_trips_data.columns= ['start_station_id','count','mean_duration','total_duration'] #to overwrite multi-column indexe with single index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>start_station_id</th>\n",
       "      <th>count</th>\n",
       "      <th>mean_duration</th>\n",
       "      <th>total_duration</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>9503</td>\n",
       "      <td>917.457119</td>\n",
       "      <td>8718595.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>1547</td>\n",
       "      <td>4046.160310</td>\n",
       "      <td>6259410.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>3833</td>\n",
       "      <td>920.754761</td>\n",
       "      <td>3529253.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5</td>\n",
       "      <td>1249</td>\n",
       "      <td>1131.907926</td>\n",
       "      <td>1413753.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6</td>\n",
       "      <td>2895</td>\n",
       "      <td>1423.423489</td>\n",
       "      <td>4120811.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   start_station_id  count  mean_duration  total_duration\n",
       "0                 2   9503     917.457119       8718595.0\n",
       "1                 3   1547    4046.160310       6259410.0\n",
       "2                 4   3833     920.754761       3529253.0\n",
       "3                 5   1249    1131.907926       1413753.0\n",
       "4                 6   2895    1423.423489       4120811.0"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "agg_trips_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10e83e358>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10c0ea3c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10e81e550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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T+IlXZpZfnmxkSbiFb9bl/LBuS8oJ36zLebKRJeWEb9blkoxxd33fwDV8s67XbIy76/s2zgnfrACmmmyU9WJiHnefL074ZgXRaIx7lg9E8Z1F/riGb1ZgWS0m5pFD+eSEb1ZgUy0mBqTaieuRQ/nkko5ZwU2u79/92HOsvurOVEst3bRMcZn6GdzCN8uxdg2nHF+WGehIqaVblim+dct2Vl91Jxdecy+rr7qT27ZszzqkVLmFb5ZTaXR6drITN+/LFGc9gikLbuGb5VBanZ6dLrXk+YEvZexncMI3y6G0klG3lFo6oZv6GdrFJR0z8tdxl2YyynuppVM6+RSuvHDCt66SRmLO4wShtJORH0RSVbYvv1QTvqTPAWcCz0TECWkey4ovjcSc5467siWjyTp111WmL7+0W/jXAlcD16V8HCu4tBJzlksPJFGmZFQvj3ddRZBqp21EfAdo+8POrXzS6sQsY8ddmtoxb8DLMqQn81E6ki6RNChp8Nlnn806HMuptBJzO5YemJzkWkl6RVqnvl2TmMo4XLJTMu+0jYiNwEaAgYGByDgcy6k0OzEPZemByaWHc0/u58bNQzPatxvLFuN19vlzZrWt5Oa7rvRknvDbJW/D6sqmE+c/zU7M8Vp5K30Fjba97gdPAEzY9/gjFvHCi/tztU59O9R/YY3u209PjyZ8PtO+kDIOl+yUQiT8IrSUulknz3/anZitdOI22nayqARv+su76Zs18dzkvbO4mUZfWOyfeIN+KK3yso9QSkuqNXxJ1wPfB46VNCTp4nYfwx082Sra+W+lnNBo28lG9wcv7jv43HR72aJRnb1vlpgzu30zePO8LMNM5KG/Ju1ROudHxBER0RsR/RHx2XYfwx082Sra+W9l6YFG2/7OqcsPvJ4zS8ztbXxu8rzEQZLE1OgLSz3ijt8/jS++69e4Z/0a32XXycuqnF1f0un2llK3K+L5b6Wc0GjbS8/4lQMdmWdeffeE7evPTR7LFknLc1PV2Y95xcIMos63PPXXdH3CdwdPtop6/lvpK5i8bf3rZucmTxOrWk1MefzCyqM89dd0fcIHX3hZ8/mfWrecm+GRUe764TPMbnGkTZ6+sPIqT3fBhUj44Asvaz7/U8v7uRkv48ySeOHF/RM+6/byXB7k6S64MAnfzFpXX8apN79vFvsrUYjyXB7k5U7PCd9yzRPq0tWovjx/ziyu/A+v5vTjXu5z3kZ5uNNzwrfc8oS69DWqL++PcLIvqMwXT0tDHiY42KEp2oSuvMrzfABrv8K18N0qLIY8DWUrurzUly19hUr4eZrgYIcmT0PZyiAP9WVLX6FKOkWb5l9mLjWYtV+hWvhuFRaLSw1m7VWoFr5bhcVTtBUTzbJUqBY+uFVoZjaVwiV8cAdUHnRqwpQnZpklV8iEb9nq1NBYD8E1a02havgz1cpELU/qml6nJkx5YpZZ60rfwm+llegW5USNyilJJkxN3m8mZRlPzDJrXakTfisTtYoyqatdNe+pvvyaDY2dvN+5J/dz4+ahCT8nSae7h+Cata4UCX+qJNdKK7EILcp23aE0+/Kbau3vRvtd94MnAA68fv+NW5jV08OcWTN7xB7A1id3uhPXrIHCJ/zpklwrrcRub1G28w6l2ZffVENjG+032b4K7KtUGN3X+iP27n7sOVZfdeeB/9cfevPxnLDsMCd/s5pCd9o269hrNFHrQ28+nqEdew7q/EsyqSvPnb/tXHYiyZdfowlTjfZrplmM48cBDvp//cf/6yEuuOYHrL7qTm7bsr2l45oVUaFb+EnKMPWtxIe27+IjX3tkypLHdJO68t752847lJk+sq3RfucO9HPjYLWG/+L+/VQCxvZHyzFOdfcwMlp9ZF839reYtVuhE37SJDeeBN628ftNSx6NJnV1Q+dvu5+rOdMZzY32u/SMXznw+p7HnptRjM3uHrqtv8UsDYVO+K0kuZl0yo53Bu/a82LDfR9+6nkOm9fb8rDFtLR72YmZzmievF/965nGWP//2g/jNmus0AkfkieQqe4G5s+Z1XDUR31ZZrwUUW/P2D5+77rBg0abZN352w3LTsw0xgnluad28ZHbH2nL3YxZ2jq1RIgiovlWHTIwMBCDg4OZHf+2Ldsn1pcbjBFfu2oZwyOjrL7qTvaO/SJxz+7hwHDCRrXoub093LN+DUsW9B10nEY1/KKtEZPFv6do59CKqR19epI2R8RAs+0K38JvRX0Lcf6cWZx59d0Na+2NyjLzemfzVxe8hsPm9bJrz4u850sPMLZ/34HP60s8q49Zyj3r10yZjIo2ozerf0833M1YuXW6T6+UCX+6lt94ktj65M4pa+1TlWVe/cpFByYYTf58qhJPo9jyOqN3Ji3mPP97zNqpG5YIKV3CT9ranK7W3qwzePLn4yWe0X3NJxTldUbvTFvpef33mLXTTH8/Ot2nV6qE30prs1lSb9YZXP/5VCWeRkkvy07dqVooh9JKz7qT2ixth/L70e7h0s2UKuG32tpsltSb1YjHP29U4pkq6XX6AhhP8o0mndV/Yc20ld7pf49Zpx3KkO7+xfM6+pS+UiX8mbQ229Hx12rSa+cFMF1dcfw2tH7ceqNFzBoNO22lle7HTlqRtZpXpir/dOL3IvVhmZLeAHwamAVcExEfm2rbTgzLTDIkMi2dHiY4XV2x0dDS6dQPOy3CyCGzdkqaVxr93tUP2Z6pXAzLlDQL+CvgN4Eh4D5Jt0XEI2kedzpZtjY7OUywWV0xycqV9eqHnbqVbjZR0ryS9SCGtEs6/wZ4LCL+GUDSDcBbgMwSPpRjfHazC2uqtWfm981i3/5Kw0XMxoedmtnBkuSVrAcxpL088jLgybrXQ7X3LGXNLqxGyz3/2Vkn8OV3ncL3PngGnzjnxGmXgjaz1iVZZj1Nabfw1eC9CZ0Gki4BLgFYvnx5yuGUR5KO4uluQ93RapaOLH+30k74Q8CRda/7gafqN4iIjcBGqHbaphxPqSS5sKa7DS1D6cssC1n9bqWd8O8DXiXpKGA7cB7w2ykf0+o4aZvZuFQTfkTsk/Re4BtUh2V+LiIeTvOYZmbWWOoTryLiDuCOtI9jZmbTK/RDzM3M7Bec8M3MSsIJ38ysJHL1iENJzwKPT7PJUuC5DoXT7XyukvF5SsbnKZmsztMvRcThzTbKVcJvRtJgkgWCzOcqKZ+nZHyeksn7eXJJx8ysJJzwzcxKotsS/sasA+giPlfJ+Dwl4/OUTK7PU1fV8M3MbOa6rYVvZmYz5IRvZlYSXZPwJb1B0v+V9JikD2YdT15IOlLSXZIelfSwpEtr779M0jcl/aj238VZx5oHkmZJekDS7bXXR0m6t3aeviJpTtYxZk3SSyXdLOmHtevqVF9PjUm6vPZ795Ck6yXNzfM11RUJv+7ZuG8EjgfOl3R8tlHlxj7gDyLiXwOnAO+pnZsPAt+OiFcB3669NrgUeLTu9VXAJ2vnaQdwcSZR5cunga9HxHHAiVTPl6+nSSQtA94HDETECVRXBD6PHF9TXZHwqXs2bkS8CIw/G7f0IuLpiLi/9vfdVH85l1E9P1+obfYF4KxsIswPSf3Am4Fraq8FrAFurm1S+vMkaRHw68BnASLixYjYia+nqcwG5kmaDbwEeJocX1PdkvD9bNwEJK0ATgLuBV4REU9D9UsBeHl2keXGp4Ar4MCT3ZcAOyNiX+21rys4GngW+Hyt9HWNpPn4ejpIRGwHPg48QTXR7wI2k+NrqlsSftNn45adpAXAJuCyiHg+63jyRtKZwDMRsbn+7Qablv26mg28BvhMRJwEvIDLNw3V+jHeAhwFvBKYT7XsPFlurqluSfhNn41bZpJ6qSb7L0XELbW3fyrpiNrnRwDPZBVfTqwG1kr6MdWS4BqqLf6X1m7HwdcVVH/XhiLi3trrm6l+Afh6OtjrgX+JiGcjYgy4BXgdOb6muiXhH3g2bq3H+zzgtoxjyoVaHfqzwKMR8ed1H90GvKP293cAt3Y6tjyJiD+KiP6IWEH1+rkzIi4A7gLeWtvM5yniJ8CTko6tvXUG8Ai+nhp5AjhF0ktqv4fj5yq311TXzLSV9CaqLbLxZ+P+WcYh5YKk04DvAg/yi9r0f6Zax78RWE71wjwnIn6WSZA5I+k3gA9ExJmSjqba4n8Z8ABwYUSMZhlf1iStotqxPQf4Z+CdVBuHvp4mkXQl8Daqo+UeAN5FtWafy2uqaxK+mZkdmm4p6ZiZ2SFywjczKwknfDOzknDCNzMrCSd8M7OScMI3MysJJ3zrCpIuk/SSGex3kaRXzmC/s5KsyDp5O0l/Kun1rR5vmp+/dqrlwCWNtOs4Vg5O+NYtLqO6GmFitWW1L6K6zkmrzqK6FHdL20XEhyPiWzM4XkMRcVtEfKxdP8/KzQnfckfSfElfk7S19mCJP6GatO+SdFdtm89IGqw9fOLKun1/LOnDku4GzgcGgC9J2iJp3hTH+5ikRyRtk/RxSa8D1gL/vbbfL0v6PUn31WLaVJtO32i7ayW9tfZzz6itOPmgpM9J6quL8UpJ99c+O26ac3GRpKtrfz9K0vdrcXykDafaSsYJ3/LoDcBTEXFi7cESn6K6ANXpEXF6bZs/jogBYCXw7yStrNt/b0ScFhFfBAaBCyJiVUTsmXwgSS8Dfgt4dUSsBP5bRHyP6toxf1jb7/8Bt0TEayNi/IEgF0+x3fjPnQtcC7wtIn6V6iqU76479HMR8RrgM8AHEp6XT1NdxfK1wE8S7mN2gBO+5dGDwOslXSXp30bErgbbnCvpfqprlbyaieWXr7RwrOeBvcA1ks4Gfj7FdidI+q6kB4ELaseczrFUV1L8p9rrL1B9sMi48VVNNwMrEsa6Gri+9ve/TbiP2QFO+JY7tSR5MtXE/1FJH67/XNJRVFvFZ9Ra5V8D5tZt8kILx9pH9Ylqm6jW478+xabXAu+ttdavnHS8RhqttV9vfDGt/VRb/0l58SubMSd8y53aqJqf10oyH6e6HvtuYGFtk0VUk/ouSa+g8UMnxtXv1+hYC4DDIuIOqh3Dq6bYbyHwdO3ZAxck+Pk/BFZIOqb2+u3A/5kmziTuobq0M5NiMEuklZaFWaf8KtWO0AowRrX2fSrw95KejojTJT0APEx1+d57pvlZ1wJ/I2kPcGqDOv5C4NZazV3A5bX3bwD+p6T3UV3b/ENUl5x+nOqdx8IptgMgIvZKeidwU+1hGPcBf9P6qZjgUuDLki6lekdi1hIvj2xmVhIu6ZiZlYRLOlYakr5K9YHT9dZHxDeyiKderfxz6aS374mI92QRjxWTSzpmZiXhko6ZWUk44ZuZlYQTvplZSTjhm5mVxP8H3JaAYs05VgAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10e87a278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "agg_trips_data.plot.scatter(x='start_station_id',y='count', s= 20, c= 'r')\n",
    "# scetter plot, s = size of each point, c = color of each point\n",
    "agg_trips_data.plot.scatter(x='start_station_id',y='mean_duration')\n",
    "\n",
    "agg_trips_data.plot.scatter(x='start_station_id',y='total_duration')\n",
    "# It can be observed that highest mean_duration per trip, total_duration \n",
    "# and no. of trips correspond to different start station IDs\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "trips_data['start_date'] = pd.to_datetime(trips_data['start_date'])\n",
    "trips_data['weekday'] = trips_data['start_date'].dt.weekday"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10e8e2630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "trips_data.groupby('weekday').duration.mean().reset_index().plot.scatter('weekday','duration')\n",
    "plt.show() # It can be seen that average duration per trip is higher on weekends"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>weekday</th>\n",
       "      <th>end_station_id</th>\n",
       "      <th>duration</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>552.548223</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3730.955000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>824.853571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>612.134694</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>1298.606742</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   weekday  end_station_id     duration\n",
       "0        0               2   552.548223\n",
       "1        0               3  3730.955000\n",
       "2        0               4   824.853571\n",
       "3        0               5   612.134694\n",
       "4        0               6  1298.606742"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weekday_agg = trips_data.groupby(['weekday','end_station_id']).duration.mean().reset_index()\n",
    "weekday_agg.head() # each weekday, each end station id average duration per trip"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "weekday_agg = weekday_agg[weekday_agg['end_station_id']<=15 ] # for sake of better visualization filtering for\n",
    "                                                                # a sybset of end sation ids\n",
    "                                                                "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting 3 variables in 2-D"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10ebdfe48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "\n",
    "weekday_agg.plot(kind='scatter',x='end_station_id', y= 'duration', c='weekday', s= 50,cmap=plt.cm.Blues, ax=ax)\n",
    "\n",
    "plt.show()\n",
    "# Using colormap (cmap) to give color gradient to weekday number"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualizing weather data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/jasjotsingh/anaconda3/lib/python3.6/site-packages/IPython/core/interactiveshell.py:2728: DtypeWarning: Columns (11) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  interactivity=interactivity, compiler=compiler, result=result)\n"
     ]
    }
   ],
   "source": [
    "df_merge = pd.read_csv('df_merge.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>id_x</th>\n",
       "      <th>duration</th>\n",
       "      <th>start_date</th>\n",
       "      <th>start_station_name</th>\n",
       "      <th>start_station_id</th>\n",
       "      <th>end_date</th>\n",
       "      <th>end_station_name</th>\n",
       "      <th>end_station_id</th>\n",
       "      <th>bike_id</th>\n",
       "      <th>...</th>\n",
       "      <th>min_sea_level_pressure_inches</th>\n",
       "      <th>max_visibility_miles</th>\n",
       "      <th>mean_visibility_miles</th>\n",
       "      <th>min_visibility_miles</th>\n",
       "      <th>max_wind_Speed_mph</th>\n",
       "      <th>mean_wind_speed_mph</th>\n",
       "      <th>max_gust_speed_mph</th>\n",
       "      <th>cloud_cover</th>\n",
       "      <th>wind_dir_degrees</th>\n",
       "      <th>zip_code_y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>4607</td>\n",
       "      <td>70</td>\n",
       "      <td>2013-08-29</td>\n",
       "      <td>San Jose City Hall</td>\n",
       "      <td>10</td>\n",
       "      <td>8/29/2013 14:43</td>\n",
       "      <td>San Jose City Hall</td>\n",
       "      <td>10</td>\n",
       "      <td>661</td>\n",
       "      <td>...</td>\n",
       "      <td>29.966</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>17.8</td>\n",
       "      <td>7.4</td>\n",
       "      <td>21.6</td>\n",
       "      <td>4.2</td>\n",
       "      <td>325.8</td>\n",
       "      <td>94325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>4130</td>\n",
       "      <td>71</td>\n",
       "      <td>2013-08-29</td>\n",
       "      <td>Mountain View City Hall</td>\n",
       "      <td>27</td>\n",
       "      <td>8/29/2013 10:17</td>\n",
       "      <td>Mountain View City Hall</td>\n",
       "      <td>27</td>\n",
       "      <td>48</td>\n",
       "      <td>...</td>\n",
       "      <td>29.966</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>17.8</td>\n",
       "      <td>7.4</td>\n",
       "      <td>21.6</td>\n",
       "      <td>4.2</td>\n",
       "      <td>325.8</td>\n",
       "      <td>94325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>4251</td>\n",
       "      <td>77</td>\n",
       "      <td>2013-08-29</td>\n",
       "      <td>San Jose City Hall</td>\n",
       "      <td>10</td>\n",
       "      <td>8/29/2013 11:30</td>\n",
       "      <td>San Jose City Hall</td>\n",
       "      <td>10</td>\n",
       "      <td>26</td>\n",
       "      <td>...</td>\n",
       "      <td>29.966</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>17.8</td>\n",
       "      <td>7.4</td>\n",
       "      <td>21.6</td>\n",
       "      <td>4.2</td>\n",
       "      <td>325.8</td>\n",
       "      <td>94325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>4299</td>\n",
       "      <td>83</td>\n",
       "      <td>2013-08-29</td>\n",
       "      <td>South Van Ness at Market</td>\n",
       "      <td>66</td>\n",
       "      <td>8/29/2013 12:04</td>\n",
       "      <td>Market at 10th</td>\n",
       "      <td>67</td>\n",
       "      <td>319</td>\n",
       "      <td>...</td>\n",
       "      <td>29.966</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>17.8</td>\n",
       "      <td>7.4</td>\n",
       "      <td>21.6</td>\n",
       "      <td>4.2</td>\n",
       "      <td>325.8</td>\n",
       "      <td>94325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>4927</td>\n",
       "      <td>103</td>\n",
       "      <td>2013-08-29</td>\n",
       "      <td>Golden Gate at Polk</td>\n",
       "      <td>59</td>\n",
       "      <td>8/29/2013 18:56</td>\n",
       "      <td>Golden Gate at Polk</td>\n",
       "      <td>59</td>\n",
       "      <td>527</td>\n",
       "      <td>...</td>\n",
       "      <td>29.966</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>17.8</td>\n",
       "      <td>7.4</td>\n",
       "      <td>21.6</td>\n",
       "      <td>4.2</td>\n",
       "      <td>325.8</td>\n",
       "      <td>94325</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 41 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0  id_x  duration  start_date        start_station_name  \\\n",
       "0           0  4607        70  2013-08-29        San Jose City Hall   \n",
       "1           1  4130        71  2013-08-29   Mountain View City Hall   \n",
       "2           2  4251        77  2013-08-29        San Jose City Hall   \n",
       "3           3  4299        83  2013-08-29  South Van Ness at Market   \n",
       "4           4  4927       103  2013-08-29       Golden Gate at Polk   \n",
       "\n",
       "   start_station_id         end_date         end_station_name  end_station_id  \\\n",
       "0                10  8/29/2013 14:43       San Jose City Hall              10   \n",
       "1                27  8/29/2013 10:17  Mountain View City Hall              27   \n",
       "2                10  8/29/2013 11:30       San Jose City Hall              10   \n",
       "3                66  8/29/2013 12:04           Market at 10th              67   \n",
       "4                59  8/29/2013 18:56      Golden Gate at Polk              59   \n",
       "\n",
       "   bike_id    ...     min_sea_level_pressure_inches max_visibility_miles  \\\n",
       "0      661    ...                            29.966                 10.0   \n",
       "1       48    ...                            29.966                 10.0   \n",
       "2       26    ...                            29.966                 10.0   \n",
       "3      319    ...                            29.966                 10.0   \n",
       "4      527    ...                            29.966                 10.0   \n",
       "\n",
       "   mean_visibility_miles min_visibility_miles  max_wind_Speed_mph  \\\n",
       "0                   10.0                 10.0                17.8   \n",
       "1                   10.0                 10.0                17.8   \n",
       "2                   10.0                 10.0                17.8   \n",
       "3                   10.0                 10.0                17.8   \n",
       "4                   10.0                 10.0                17.8   \n",
       "\n",
       "   mean_wind_speed_mph  max_gust_speed_mph cloud_cover wind_dir_degrees  \\\n",
       "0                  7.4                21.6         4.2            325.8   \n",
       "1                  7.4                21.6         4.2            325.8   \n",
       "2                  7.4                21.6         4.2            325.8   \n",
       "3                  7.4                21.6         4.2            325.8   \n",
       "4                  7.4                21.6         4.2            325.8   \n",
       "\n",
       "  zip_code_y  \n",
       "0      94325  \n",
       "1      94325  \n",
       "2      94325  \n",
       "3      94325  \n",
       "4      94325  \n",
       "\n",
       "[5 rows x 41 columns]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_merge.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Unnamed: 0', 'id_x', 'duration', 'start_date', 'start_station_name',\n",
       "       'start_station_id', 'end_date', 'end_station_name', 'end_station_id',\n",
       "       'bike_id', 'subscription_type', 'zip_code_x', 'id_y', 'name', 'lat',\n",
       "       'long', 'dock_count', 'city', 'installation_date', 'date',\n",
       "       'max_temperature_f', 'mean_temperature_f', 'min_temperature_f',\n",
       "       'max_dew_point_f', 'mean_dew_point_f', 'min_dew_point_f',\n",
       "       'max_humidity', 'mean_humidity', 'min_humidity',\n",
       "       'max_sea_level_pressure_inches', 'mean_sea_level_pressure_inches',\n",
       "       'min_sea_level_pressure_inches', 'max_visibility_miles',\n",
       "       'mean_visibility_miles', 'min_visibility_miles', 'max_wind_Speed_mph',\n",
       "       'mean_wind_speed_mph', 'max_gust_speed_mph', 'cloud_cover',\n",
       "       'wind_dir_degrees', 'zip_code_y'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_merge.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/jasjotsingh/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:6: 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",
      "  \n"
     ]
    }
   ],
   "source": [
    "# aggregate data\n",
    "df_agg= df_merge.groupby('start_date').agg({'id_x':'count','mean_temperature_f':'mean','cloud_cover':'mean'}).reset_index()\n",
    "# filter for dates\n",
    "df_filter = df_agg[(df_agg['start_date']<='2013-09-30')&(df_agg['start_date']>='2013-09-01')]\n",
    "# explicit typecast\n",
    "df_filter['start_date'] = pd.to_datetime(df_filter['start_date'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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TP/8Ztm9XaaXk5K6v9eCDysLccqBNt+PwYSgp6bg/AUooTCYlyD5k3b51TOo3qYO/U1dMHzidrUe3UlRTZHuwjlV0oejVS53iBbqNh79hj1CAiiqKiuCtt44/9tVX6mS83/4Wzj/f9rUmTVIHJD39tLopdjfaWou3x06/J09ytOYo245tszvtpDEtU7kOr9u3zhPL6jZ0b6EQQlU8hYaqd5x6ROFfGI2qZyI+vutxU6bA6NGqH6KlRXkV3XwzZGbaXz4LSnD271flst2NnBzV9X5qJ87TDjjIeor1BesBHBaK0b1GkxyVrKefXKT7CkVhoeruDbXYXelNd/5HYeFxM8Cu0MwC8/Lg009VNdShQ/DmmxAba//1LrkEhgxRgtPdemo2b1Z7NZ2d8+EHQrFu3zoSIhMYnT7aodcZhIEpmVNYm782KE0BvUX3FYr2Hb+6UPgf1nooOuOqq1QV1B13qD2Jhx5SqSRHMBhUVJGTo6w9uhOddWRr+Dj1JKVkXcE6zh1wLiEGxy21p2dOx1hjZGfxTg+srnvQvYWi7U1IFwr/wxGhCA1VZoGFhSp98thjzl3zhhsgPd1xH6lAprBQ7fF0tj8BPo8o8svzOVh50O7+ifZMG6j2Kdbkr3HnsroVulBo6ELhfzgiFACzZqmeiGXLjpvaOUpkpOrWXrNG9WB0B6x1ZGv4WCi0jWhH9yc0+vfoz5DkIfo+hQt0T6Ewm9U7qPZCoZ3PrON7amvVpnRXPRTtiY5W+wtDhrh27dmz1d5GdzEL3LxZ7fN0tpEN6t81LMxnqafPCz6nX3w/BicNdnqO6ZnT+erAVzSaOh50pGOb7ikUxcWqOqbtTSgtTX0uKfHNmnROxN7SWE+QmKicZd99Fw4c8P71vU1OjnLTtbbxL4TPbDzMLWbWF6xnSuaUTo89tZdpA6dR11zH94e/t2t8QXkB//nhPzSYGpy+ZjDRPYWis5uQ3nTnX/hSKECdVSEEPPusb67vTax1ZLfFR0KxpWgLZfVlNs+fsMXZGWcTIkJs7lPsLt3Nrz/+NYOfG8y9q+9lxZ4VLl03WNCFQkMXCv/C10LRr5/yhlq0SHVsBytHj8KRI9b3JzR85Pf0ecHnADbPn7BFfEQ84/uOt7pPsf3Ydq774DqGvTCMd3e8y69H/RqAwupCl64bLHRPodA8hHSh8F/s8XnyNHPnKvfaBQt8toS3t73N3776m+d6ANqfkW0NH0UU6/at45Sep5Aem+7yXNMHTmdz4WZK644L/8/Gn7ly2ZWMWDCC5XnLmTthLvvv2c/LF71MiAjRrT8sdE+h0N6tprf549OFwr8wGiEi4ngNvy8YMULZf8yfD/X1Xr/8vvJ93LL8Fh798lEW5SzyzEU0oRhto5HNB0LRYGpgw8ENTlc7tWda5jQkkvUF69l4eCMXv3Mx2Quz+Xzf5/x58p85cO8B5k2bR1psGiGGEFJjUnWhsBDq6wX4BKNRWXZERBx/LD5elVTqfk/+gdGohNyFDUy38Pvfw7nnwgcfqB4LLyGl5K6VdxFqCOXM/mdy98q7GddnHKPSR7k8d25JLtWN1YztM1btTwwebNsmxQepp+8PfU+DqcHp/on2jO0zlh4RPZj9v9mU1ZeRFJXE38/5O3eOu5OEyIQO43vF9cJYY3TLtQOd7htRtM99C6H3UvgTjvZQeIqzzlJWL6tWefWyH+d+zIo9K/jb2X/jg2s+IDk6mavfu5qqxiqX5t1ZvJOJr07kN5/8Rj3QVUd2W5KS1EFSzc0uXd8RVu1dRYgI4ayMs9wyX6ghlCuGXUGoIZQnpj7BgXsP8MjkRzoVCYD02HQ9orDQPYXC2jnMulD4D5rPk68xGJSN+Zo1qqTaC9Q01XDPqnsYmTaSu06/i54xPVl65VL2le/jtk9vc3q/4lDlIWa8NYPyhnKK64rVJv2BA7b3J+B4050XoooW2cLjXz3Ok989yYxBM4iPsBHtOMCrl7xK0QNF/H7S74kN79oHLD1GFwqN7ikU7X2eNHSh8B/8JaIAJRTFxfDLL1653ONfPc6hqkO8eMGLhBpUdvjMk87k7+f8nWU7lvHSpk4OabJBWX0Z5y05j8qGSi4YfAHl9eVIzVrcnojCS35PZfVlXPzOxfzly79w3YjrWHbVMrfOL4Swux+jV1wvjtYcxdxidusaApHuJxRSduzK1tCFwj9oaFA3JH8SCoDVqz1+qe3HtvPMD89wy+hbOpzk9tAZD3H+oPO5d/W95Bhz7J6zvrmeS965hL1le/lk5idM7j+Z5pZmmn/6QQ2wtZENXrHxyDHmMGbhGNbmr+XFC15k8eWLiQmP8dj1bJEem45ZmimtD97yaCFEiBDiZyHEZ12NsykUQojXhBDHhBDb2zyWJIRYK4TYY/mcaHlcCCHmCyH2CiG2CiGy27zmZsv4PUKIm1354VyitFTlWbsSCt2O2LcUWcJ9fxGK9HRlb+FhoZBScsf/7iA+Ip7/m/p/HZ43CANvXv4mqTGpXP3e1VQ2VNqc09RiYuYHM/nu0HcsuWIJ5ww4h8QoFR2YNv0EAwbYV1nmYaF4NedVJr46EVOLiQ2/2cCcsXNc6sR2B1pJbpCnn+4BdtkaZE9E8TpwXrvH/gB8LqUcDHxu+R7gfGCw5eN2YAEoYQEeBU4HxgGPauLidTrrodBIS1PvZn185GO3xx96KNozfTp8+61H/zYWb13MhoMbmDd1HinRKZ2OSYlOYemVSzlQcYBblt/S5X6FlJLZn81med5ynjv/Oa46+SoAEiPVf73Qn7fYl3YCj6We6pvrueWTW7j101uZfNJkcm7P4fS+p7v1Gs7SK1bdI4zVwVn5JIToC1wIvGJrrE2hkFJ+DbR/G3Ep8Ibl6zeAy9o8/qZU/AAkCCF6ATOAtVLKMillObCWjuLjHdqeld0evZfCP/B1V3ZnzJihItEvv/TI9GX1ZcxdM5cJfScwa/SsLsdO6j+Jf035Fx/s+oDnf3ze6ri/fPEXXv35Vf505p+4c9ydrY8nRiWSUA/hBw7bt5ENHoko9pXvY+JrE3lty2v86cw/sfL6lfSM6em2+V2lG0QU/wYeBGxWaTi7R5EmpTQCWD5b7rD0AQ61GXfY8pi1x71PVzchXSj8A38UijPOUKe/eSj99Mjnj1BaX8qCCxdgELb/Wz4w8QEuHHwhD6x5gE2Fmzo8/8KPL/D3DX/n1tG38rdz/nbCc4mRiYzW3iTbG1EkWEpI3SQUn+3+jDELx7C/Yj+fXfsZj5/7uFOHEnmSABeKUCHEpjYft7d9UghxEXBMSrnZnsncvZndWVJRdvF4xwmEuF374UyeOOReFwr/x2hUBxGldJ5+8QkREXD22apM1s38eORHXt78MnePu5tT061YfbfDIAy8cdkbpMemc81711DRUNH63Hs73uOulXdxSdYlLLhoQYdcf0JkAtmaUNgbUYSGqqY8F1NPUkr+vP7PXPzOxQxIGEDO7TlcOORCl+b0FDHhMcSFxwVq051JSnlam4+F7Z6fBFwihNgPLAXOFUK8ZW0yZ4XiqCWlhOWzdmc9DPRrM64vUNjF4x2QUi7UfrjQUA80jhcWQo8enZ8NrAuFf1BYqPaLDH5WlDdjBuzeDfv3u21Kc4uZOf+bQ6+4Xvz1nL869Nrk6GSWXb2MQ1WHmPXJLKSUfFHwBTd8dAMT+03knSvfaS2vbUtiVCJjjFCVnuiYGLvBxmPbsW38fcPfuX7E9Xw761sGJA5waT5PE6xNd1LKP0op+0opM4CZwHoppVXrAWf/Jy4HtMqlm4FP2jx+k6X6aTxQaUlNrQamCyESLZvY0y2PeR9rPRSgOnBBt/HwNV39jnzJjBnqsxvTTws2LSDHmMOzM551qrFsfN/xzJs6j49yP+LulXdz2buXMShpEMuvXU50WHSnr+kR0YNsIxwZmNrp81Zxg1AcrDwIwN2n301UWCdv1vyM9Nj0QI0o3Io95bHvAN8DWUKIw0KIW4D/A6YJIfYA0yzfA6wA9gF7gUXAHQBSyjLgceAny8ffLI95n64auSIiVLShRxS+xZ+a7dqSlQX9+7tNKIpqinhk/SNMy5zG1Sdf7fQ8942/j0uyLuH5n54nPiKeVdevIikqyer4kJpaskohP9PBwkM3+D1pFUTucIP1Br3iegVlRNEWKeWXUsqLuhpjM7cjpbzWylMdnLqkqtW7s5OxSClfA16zdT2PYzTCxInWn9eb7nyP0QgTJvh6FR0RQpXJLlsGJpPK27vA3DVzaTA18MIFL7jUMyCE4PVLX+fRLx9lzmlz6NejX9cvsJwFnts/ii7vDu1JSoLDh51eJxzfGE6LSXNpHm+h23go/CwJ7GGktO7zpKELhW9pblZ2Gf4YUYBKP1VVwcaNLk2zvmA9S7Yt4Q+T/sDgZOfPgtZIjEpk/vnzGdZzmO3BFuuOLX0crDJKSnI9oqgxkhSVRERohO3BfkB6bDpVjVXUNdf5eik+pXsJRUUFNDZ2nf/WhcK3aF3Z/rhHATBlitpkdyH91GRu4s4Vd5KZmMkfzviD7Re4my1bKEkIZ3+Eg2dsJCaqPQoXnAuKaooCJu0EKvUEAVsi6za6l1DYU5+vC4Vv8cceirYkJsK4cS6Vya7eu5rcklyemvaUbzZ0Cwsp7RlLeYOD0UFSkkq5udCdXlRT1NrxHAgEeC+F29CFoj2pqVBSAmbdMdIn+LtQgEo//fST0xVAy3YuIzEykYuGOLRD4D7KymjsEUN5vRNCAS6ln4w1xoCKKLS1BquNh710L6HoyudJIy1NhdalwesY6dcEilC0tMC6dQ6/tMHUwCe5n3DFsCsICwnzwOLsoLSU5oQ45yIKcFogpZSBl3qK1VNP0N2Ewt6IAvT0k68oLFR7AKkO1vh7k7FjVRm1E+mnNflrqG6qdqkc1mXKyjAnJtJgaqDB1GD/6zRjQCeFoqqxigZTQ0ClnlKiUzAIgy4Uvl6AVzEaITYW4uKsj9GFwrcYjep34ImufHcRGgpTp6oNbQc3dpftWEZSVBLnDjjXQ4uzQVMTVFcjLdGBQ+knF1NPWuNaIEUUIYYQ0mLSun3TXfcTClspDV0ofIu/Ntu1Z8YM1VOwy6aVfysNpgaW5y3niqE+TDtZooEQi3WHQ+knF1NP2rvyQBIKCF4bD0foXkJhq4cCjguFbuPhGwJFKLRT7xxIP63eu1qlnU7xbdoJIKynulk7FFG4mHrSbrZayWmgoAtFdxMKe25CiYkQEqJHFL6isNB/eyjactJJytLDgX6KZTuXkRyVzDkZ53hwYTawFGlEpKr/Bw5FFNHREB7ufOopwOw7NHrF9vJ66unj3I95f+f7Xr1mV3Q/obB1EzIYlDmgLhTex2xW/+6BEFGASj999ZU6FdEG9c31Ku3ky2onaI0GotOUzYdDEYUQLhkDFtUUER4S3nrCXqCQHpvO0ZqjtEib5/u4jfkb5/PsD8967Xq26D5CUV0NtbX23YT0pjvfcOyYKjsNFKGYPh3q6+Gbb2wOXZ2/mpqmGq455RovLKwLLBFFbO+TAAcjCjjene0ERbWqNNbXZ2E7SnpsOmZpprTOeyXzxhqjX1WHdR+hsKeHQkMXCt8QCD0UbTn7bJWKsSP9tGzHMlKiUzg742yPL6tLLEIR3ysDcDCiAJf8nozVgdVsp6HtqXgz/WSsNtI7zn9SsN1HKBy5CelC4Rs0MQ+EPQqAmBh1RKoNoWhNOw29otODhLxKWRmEhREan0BceNwJJ+PZhYupp0AUCm/beNQ311PZWKlHFD5BEwp7bkK6UPgGFyOKTYWbuOa9a9hXvs+Ni7LB9OmwbdvxtXfCqr2rqG2u9X3aCVREkZQEQpAYlejd1FOA+TxpeFsotMjFn6rDup9Q2BtR1NRAXfe2FvY62u8ozbGzCprNzfz1y78y/pXxvLfzPT7a9ZEHFmcF7dS7Lspkl+1UaaezMs7y0qK6oKwMkpMBSIx0QiicTD01m5sprisO6IjCW35P2nX8SVS7j1AUFkJkpLJesIV2oyou9uyadE7EaFRnOIeH2/2SXcW7mPjaRB776jFmDp9JUlQSeaV5HlxkO0aOVH8vVtJP9c31fJr3KVcOu9L3aSdQEYVFKBIiE5zbo6iuVucV8WPyAAAgAElEQVSGOMCxWhWh+9PNz15iw2OJDY/1WkRRWK1SsHpE4Qu0Hgp7Ki707mzf4EAPRYts4d8//JvshdkUlBfw3tXv8dYVbzEsZZh3hcJgUOmntWtVxVY7Vu5d6T9pJzieegLnU0/gcFQRqF3ZGt48O7s19eRHotq9hMLeTVJdKHyDnV3ZByoOMOXNKdy3+j6mZk5l+x3buerkqwDISs4ir8SLQgFKKEpK4OefOzy1bMcyekb3ZPJJk727Jmu0Tz15yWo8EH2e2tIr1ntnZxurjYQaQkmOTvbK9eyhewmFvZukulD4Bhu/Iykl//35v4xYMIJNhZt49ZJXWT5z+Qk3n6yULI7WHqWyodIbK1Zodh7t0k91zXV8utuP0k5wYkTh7B4FOLyhHaj2HRretPHQzuwwCP+5PfvPSjyNPT5PGj17qs+635P3aGlRx6Ba+R0drTnKpUsvZdbyWYzuNZqts7cya/SsDs1bWclZAN5NP6WmwujRHYRi5Z6V1DXX+U/aqb5edZFrEUVUInXNdTSZm+yfw0m/J22DNi3GsUIFf8HbqSd/SjtBdxGKujqoqrJfKGJi1IceUXiPkhJ1zGYnv6M1+WsYvmA4a/LX8PT0p/ni5i8YkDig02myUixC4Yv003ffqY1eC8t2LiM1JtV/0k7aYVxtUk/gHavxopoiEiMTiQiNcOh1/kKv2F5UNVZR1+z5SkhjtdHvIq/uIRSO9FBo6L0U3sXK72hz4WYuf/dyesX2Iue3Odw/4f4uQ/LMxExCRIh3IwpQZbImE3zxBQC1TbV8tvszrhx2JSGGEO+uxRqaULTZzAbvWI0X1Rb53c3PEbT05tEaz2cZ9IjCVzjTyKULhXfp5Hd0uOowF79zMSnRKay5cQ0n9zzZ5jThIeFkJmZ6XygmTlRRqCX9tGLPCv9KO8Hxm7srEUVCwolz2Umg2ndotPZSeDj91GRuoqSuxK/sO6C7CIUjPk8aulB4l3ZCUdNUw0VvX0RNUw3/u+5/Dt1kslKy2F262xOrtE5EhPJ++vxzAN7b+R5pMWmc2f9M766jK6xEFA7ZeISEqF4kJ1JPgSwUWjTk6Q1tLWLRIwpfoEcU/k8bMTe3mLn2g2vZfmw77139HsNThzs0VVZyFntK93jVFhqA8eMhL4/aEqP/pZ3AekThTOWTAxGFlDJg7Ts0vGXj4Y/2HdCdhCIsrPU/iF2kpqrO7E6aqHQ8gNGoKmoiI3lgzQN8tvsznjv/OWYMmuHwVFnJWdSb6jlUecgDC+2C7GwANv7vZepN9f6VdgLrexSO9lI46PdU1VhFvak+oCOKntE9MQiDx208/NG+A7qTUNjbla2RlqY2JyscdNfUcQ7L7+iFH1/gPxv/w72n38ucsXOcmqq18snb+xRjxgBwcP1HpMWkcUb/M7x7fVuUlkJUlPpAWXiAkxGFA6mnQO/KBggxhJAak+rxiMIf7TuguwmFI+hNd97FaKQkIZy7V93NxUMu5qnpTzk9VWsvhbdLZNPSaOndi/BfdnDVyVf5V9oJTujKBrXxHx0W7Vx3tgMRRWuznZ+9S3aU9Nh0imo9n3oSCFJjUj16HSFEpBDiRyHEL0KIHUKIv3Y1vnsIhSPNdhq6UHiVpkMHWFe3nZFpI3n7yrddusmmxqTSI6KH9yMKwDikN6OOmP0v7QQndGVrONWd7WDqKdDtOzTSY9O9knpKjUn1Rid/I3CulPJUYBRwnhBivLXB3UMoHPF50tCFwmsUVRuRxkJKEyP59NpPiQ2PdWk+IQRDkof4RCi+TaljaClMShrl9WvbpF1EAU4aA2qpJyntGh4MqSfwjt+TscY7zXZSUWP5NszyYfUXGvxC0dio/oM4G1HoNh4epa65jhtevZAIM1xy9m/pG9/XLfNmpXjfHLCmqYZ3I/dikBCydZtXr20XbSzGNZw2BjSZ1JktdlBUU0SYIYykqCTbg/2Y9Nh0jtYe9Wg1XWuz3fPPw1POp1/tQQgRIoTYAhwD1kopN1obG/xCUWR5B+CoUCQnq81vPaLwGC2yhZs+uomiPcp1tV/WWLfNnZWcxaGqQ9Q21bptTlt8tvszfki1nNOQk+O169pNZ6knV6zG7Uw/aSZ37X25Ao1esb0wtZgorSv12DWM1RaheOstWLnSlalChRCb2nzc3n6AlNIspRwF9AXGCSGs1qEHv1A402wHEBqqxEIXCo/QaGrkwbUP8sGuD/hr1mz1oJNHoHaGtqG9p2yP2+a0xae7P8WcnopMS4PNm712XbuQsvPUkxesxgO92U7D070U5hYzR2uP0ju2F+TmQlaWK9OZpJSntflYaG2glLIC+BI4z9qY4BcKZ3yeNPSmO7dysPIgL296mUuXXkryE8k8/f3TzB4zmyt6WPbQnPkdWcEX5oA7ju1gTJ/TENnZ/hdRVFerdJE7NrMd9HsqqglsnycNT9t4FNcV0yJbyGyOhcpKV4WiS4QQPYUQCZavo4CpQK618X5iku9BnOnK1tCFwiWazE18e/BbVu5dyYo9K9hRvAOAk3qcxM2n3swFgy/g/MHnI554Ur3AjRHF4KTBCITXNrRbZAt5pXlMGTAFxkSrM7Tr61t7FnxOu65sjcSoRGqaajC1mOyvtHE09VRtZHwfqwU1AYOnbTy0iqqBxSb1wNChHrmOhV7AG0KIEFTAsExK+Zm1wd1DKEJCjp8x4QipqbBli/vXFMRUN1bz7o53Wbl3JWvz11LdVE2YIYzJJ01m1uhZnD/ofIamDD0xX200QlycMtVzE1FhUfTv0d9rQnGw8iANpgaGpgyF7BQwm2HrVjj9dK9c3ybtLMY1NBuPioYKUqJT7JvLgdRTs7mZkrqSoIooPCYUlkilb6GlSMCDEYWUcisw2t7xLgmFEOI+4FZUWdU24DcopVoKJAE5wI1SyiYhRATwJjAGKAV+JaXc78r17aKwUHVZG5zIsukRhcPc8NENLM9bTt/4vlw7/FouGHwB5w44l7iIOOsvcqYh0g68WfmUW6Ki9qEpQ6HnSerBnBz/E4p2qafW7uz6cseFwo6IoriuGIkMij2K2PBYYsJiPNZLoc2bcqgUIiOhf3+PXMcZnN6jEEL0Ae4GTpNSDgdCgJnAPOBZKeVgoBy4xfKSW4ByKeUg4FnLOM/jTA+FRmqqsvBocuAEsG7MtqPbWJ63nD+d+ScO3nuQly9+mUuHXtq1SIASczfuT2hkJWeRV5qHtLPe3xU0QRqaMhT69VPv3P1pQ7uL1BM4aOMRFQXh4XYJhXbzCwahAJV+8lR3tmbfEVNwBAYPdu7NrYdwdSWhQJQQIhSIBozAucD7luffAC6zfH2p5Xssz08R3qiXc+Xdaprl2MbiYvetJ4h54rsniAmL4b4J9zlWCumpiCI5i5qmGq8cYZlbkktSVJJ6Vy6E8n3ypw1tKxGFU2dSCGG331Ow2HdoePLsbGONkaSoJEJ27/H0/oTDOC0UUsojwFPAQZRAVAKbgQoppWU3hsNAH8vXfYBDlteaLOMdsHN1ElduQnp3tt0cqDjAO9ve4fYxtzvWWCWlR1NP4J3Kp9zSXLKSs44LZHY2bN+uGj79Ae3dfyd9FOA5q/Fg6crW8KSNh7HGSP+INNi3z6P7E87gSuopERUlDAB6AzHA+Z0M1eL+zt5idsgJCCFu15pETCZTJy9xgOZmdZPXhcLjPPP9MwghuG/8fY69sLpanWnuoYgCvOMim1uSq9JOGtnZ6u9v+3aPX9suSkshPl7Z7bfBqYgC7PZ70qK5tNg0x+b3Uzxp42GsNpJd30MdbRAsQoGquy2QUhZLKZuBD4GJQIIlFQWq48/S8cZhoB+A5fkeQIe/NCnlQq1JJDTUxaIszX7DlT0K0IXCBiV1JSzKWcT1I66nX49+jr3Y2YZIO+gT34fosGiPRxQVDRUU1RSdKBQWy3G/2afopCsbXIwo7Ew9JUYmEhka6dj8fkp6bDqVjZXUN9e7fW5jjZGR5RHqm2BJPaFSTuOFENGWvYYpwE7gC+Aqy5ibgU8sXy+3fI/l+fXS07uMrvRQgO73ZCfP//g89aZ6Hpz0oOMvdqUh0gYGYWBI8hB2l3n2WNQTNrI1BgxQ50v7yz5FJ13ZAJGhkUSGRnrMalyz7wgWPFUiq50COKTEckscMsSt87uKK3sUG1Gb0jmo0lgDsBB4CLhfCLEXtQfxquUlrwLJlsfvB/7gwrrtw1WhiItTZyHrEYVVaptqee7H57gk6xJO7nmy4xO4+juyQVay50tkTyiN1RBCpZ/8PKIAz1qNB4t9h4a2Ke9uoSirL6PJ3MRJRfXq/0J8vFvndxWXqp6klI9KKYdKKYdLKW+UUjZKKfdJKcdJKQdJKa+WUjZaxjZYvh9keX6fe36ELnA1rSGE3kthg1d/fpWy+jIemvSQcxN4QSgKKgpoNHluUzm3JJcwQxgDEgac+ER2tmq6a2722LXtxkpEAS5YjdfU2PzZgsW+Q8NTEYW2l5N6pMLv9icg2L2ejEZ1s09zYSNNFwqrNJubefr7pzmj/xlM7DfRuUkKC1VdvofeQWWlZNEiW8gvz/fI/KAqngYlDSIs5MSNYsaMUT04O3Z47Np204nFuIanjAGllBirjaTHBE9E4Sm/J2O1EST02F/kd/sT0B2EIjVVOcE6iy4UVlm6fSkHKw/yh0kuZBG1hkgPtdR441jUvJK8E9NOGtnZ6rOv9ynMZnVDt5Z6ikqkosHBs+Ht8Huqbqqm3lQfVKmn1JhUDMLgkYgipQ7CKqv1iMLruKM+XxeKTpFS8sR3TzA8dTgXDL7A+Yk81EOhMSRZbQp6qkS22dzM3rK9rYJ0AoMGqX0uX+9TVFaqfhUrEUVCZILzDrJdRBStzXZBlHoKMYTQM7qn+4Wi2sjQEss3ulB4GWfOym6PJhResIEIJFbsWcH2Y9t5cOKDrh1I42GhiIuIo3dcb48JRUFFAc0tzZ1HFAYDjB7t+4jCSle2hkuppy4iimCz79BIj013e+qpsLqQUysspbG6UHgZV3yeNFJTVXdtdbV71hQkzPt2Hv179Gfm8JmuTeQOMbfBkOQhHks9dVrx1JYxY+CXX9RZEL7Cis+TRmJkIpWNlZhbzPbPaUfqKdi6sjV6xbm/6c5YY2R0VbSqsjzpJLfO7Q6CVyjMZtX/4OpNSNsI19NPrXx36Ds2HNzA/ePv77iB6wi1tUqAPdBD0RbNHNATaEKh2YV0IDtbnUuRa/VMGM9jxWJcQ2u6q2ystH9OR1JPQeLzpOEJvydjjZGhpQZlBhgS4ta53UHwCkVxsWqFd0fqCXShaMO8b+eRFJXErdm3ujaRh0tjNbKSsyirL6OkrsT2YAfJLcklPTa91a67A/7QoW1H6gkctPFIsPy8XaWeaoyEGcIc8/4KANJjlFC0yBa3zWmsNpJ5tMkv004QzELhLmsIXShOYGfxTpbnLeeucXcRE+7iQUPeEgoPmgN28Hhqz5Ah6kAmX+5T2Eo9OWPjERICPXrYTD2lx6a7toflh/SK64WpxURZvX0n/NlCSklJRSGpR2t0ofA67roJ6TYeJ/Dkd08SFRrF78b9rvMBn3wCd95p3zGZHvR5aounzAGllEookrsQipAQGDXK9xGFEOrG3glOGwPa8HsKtq5sjdZeCje5yFY3VZN+rJ6QFumXPRTQHYTC1fy3doSqHlFwqPIQb219i1uzb7V+GtrChfDii3DqqfD1111P6EGfp7ZkJGQQHhLu9oiiuK6Y8obyriMKUPsUW7aofTNfUFamNp+t5L49ZTUebD5PGu7uzjZWG8myZAf1iMLbaDehdBf/UMPDVT5WFwqe/eFZpJQ8MOEB64Py81VePioKzjkH/vIX6xU/RqOq8tAqaDxEiCGEQUmD3B5RdGoG2BljxqiN+92eNSe0Shdd2eA5q/GimqKg28gG9/s9GWv8u4cCglkoCgvVf47wcNfn8oOmu/L6cnYV7/LZ9cvqy1i4eSHXjriWkxKslO+ZzerQlSlTVE7+ppvg8cfhrLPgwIGO441GJeReyGF7ovLJZsWThq87tLswBATPWI2bWkwU1xYHdUThrl4KY7WRrBIwpaZYTQ/6muAVCnc2cvmBUMz8YCanvnQqK/es9Mn1X/zpRWqba3lwYhdW4ocPK5O4gQMhNhb++19YsgS2bVOpqPfeO3G8F3ooNLKSs8gvy8fU4r5+htySXCJDI+nfo3/XA4cNg8hI3+1TdGEICBAVGkV4SLjjNh5dpJ6O1R5DIoNSKOIi4ogJi3FrRJFVit9GExDsQuGu3LePheJn48+syV9DeEg4Vyy7gq8P2Mj9u5n65nrmb5zPBYMvYETaCOsD8y3Ge4MGHX/suutUfn7oULjmGrj1VpWGAff+jmyQlZJFc0szBeUFbptTO/7UIGz8NwoNVULppxGFEELZeDibeurEtSAY7Tva4s5eCs2+I2SoEzb9XiK4hSJIIoonv3uSuPA4tszeQkZCBhe9fRGbCjd57fof7PqA4rrirvcmAPbuVZ8HDjzx8cxM2LABHn4YXntN5ey3bPG4fUdbPFH5ZLM0ti1jxiihaHFf7b3d2IgowMkzKZKSVLqxpqbDU8Hala3hThuPqsJ9JNeD8NOKJwhWoWhpcb9QlJb6xIahoLyAZTuW8dsxv2VQ0iDW3riWpKgkznvrPHYW7/TKGhblLGJQ0iDOyTin64H5+epM5r59Oz4XFgb/+AesW6e6sU8/XeW3vSUUbu6laDA1UFBeYL9QZGernzvfc3bnndLcDFVVtoXC2TMpoNP0U7D6PGm408YjfK8lytWFwstoN3V3CoWUxztcvcizPzyLQRi4Z/w9APSN78u6m9YRFhLG1Densq/cs+c/5ZXk8fWBr7l19K22G6fy81X00JUFwbnnKu+jGTPU95mZ7ltsFyRFJZESncLuUvdUHu0p3YNEOhZRgPf3KbSbeBepJ3DSGLALv6egjyhi3Jd6it9viUz0PQovExMDH34IF7hgf90WH/k9ldSV8ErOK1w/8nr6xh9/l65FFo3mRqa+OZUjVUc8toZXf36VUEMoN4+62fbgvXs7pp06IyVFNeb9+KPat/AS7qx8smkG2J6TT1YVeN7ep7DRla3hUkTRSeVTUU0RCZEJRIZGOjZngJAem05FQwX1zfUuz5V6qIzmUANkZLi+MA8RnEIRHQ2XX37ipqor+MjG48WfXqTeVM/cCXM7PDc8dTirrl9FcV0x0xZP84iPUZO5ide3vM7FQy62/c5QShVR2CMUoEpix471qgGaJ4RCO+/CJuHhMHKk9yMKGz5PGu62Gg/WZjsNbZP+aK1rjg31zfVkHG2iom+KX5oBagSnULgbH9h41DXX8dyPz3HRkIs4JfWUTseM7TOWT6/9lIKKAs576zwqGxxw/7SDT/M+pbiu2D7zv+JitanpLnH2AFkpWRTVFFHVWOXyXHmlefTv0Z/osGj7X5SdrSIKb55tYm9EEalOuXPI6M5G6ikYm+003NWdbaxRPRS1mf3csSyPoQuFPfggonh9y+uU1JV03bcAnJ1xNu9f/T6/HP2Fi9+5mLrmOretYVHOIvrF92PGwBm2B1urePIj3HksqkMVTxpjxkBFBRS4r0TXJjYsxjUSoxKRSMdEtIvUU7BHFO7yeyoqO8TAcmgZ4t03WEKIfkKIL4QQu4QQO4QQ93Q1XhcKe0hIULXwXhIKU4uJp79/mvF9x3NG/zNsjr9wyIW8dflbfHPwG65cdiVN5iaX17C/Yj9r8tcwa/QsQgx2hMRaNY8/C0WKe0pk7TID7AxfdGg7kHoCB208oqKUBUu7iEJKGbSGgBrusvGoyt1CWAuEDRvujmU5ggl4QEo5DBgP3CmEsNrIoQuFPRgMyhzQS0Lx4a4P2Ve+z6FjRn81/Fe8fNHLrNq7ihs+vMGx08o64b8//xeA34z6jX0vyM9X+w4DBrh0XU+SmZhJiAhxOaI4Un2E2uZaxyOKESPUGw5v7lOUlalrxsV1OcwpGw8hOvV7qmmqoa65LqhTTz1jeiIQLguFaecOAGJHjHHHsuxGSmmUUuZYvq4GdgF9rI0P9dbCAh4vNd1JKXni2ycYkjyES7Iucei1t425jarGKuauncvItJH8afKfnFqDucXMa1teY8agGdZ9ndqzdy/066feYfop4SHhZCZmuhxROFzxpBERAcOHez+iSEqy6aelHbzkDqtxrREtmCOKUEMoPWN6utx0F7JHpWx7nDrOHctyCiFEBjAa2GhtjB5R2IuXhOKL/V+w2biZuRPm2pfyaccDEx/gqpOv4h8b/uF0j8Xq/NUcrjrMraMdOMHOkYonHzIkeYjvhALUPsXmzd7b0LbhHKuhpZ7c4fcU7PYdGr1iXW+6iy44THGsAUOS7d+Rg4QKITa1+bi9s0FCiFjgA+BeKaXVDSpdKOzFS0LxxLdPkBaTxo2n3uj0HM/OeJZQQyh3rbwL6cQNaVHOIlJjUrk462L7X5Sf79cVTxpZyVnsKd3j0jGWuSW5xEfEO/eOOTtb3bwPHXL6+g5hh30HuOAg20nqKdib7TTcYeORfLCYw70cqJyzH5OU8rQ2HwvbDxBChKFEYomU8sOuJtOFwl68IBS/FP3C6vzV3HP6PS41KvWN78tfz/4rK/as4OPcjx16rbHayKd5n/LrU39NeIidFu1VVao8NgAiiqyULOpN9RyqdP5GrVU8OXXEp7c7tG0YAmq485S7YLfv0HCHMWDvwmqO9XN7NGETof54XwV2SSmfsTVeFwp7SU1Vrqea86kHePK7J4kNj2X2abNdnuuucXcxInUE96y6h5qmjqZt1njjlzcwSzO3ZN9i/8UCoOJJwx3mgLklua3zOMzIkaqxylv7FHZGFLHhsYSIELeccldUU0SYIYykKNsCFcj0iu3F0ZqjzkenZWUk1ZipzvBJim4ScCNwrhBii+XDqpWFLhT2otl4FBd7ZPoDFQdYun0pt2ff3poGcIWwkDAWXLiAQ1WHePyrx+16TYts4ZWcVzjrpLPs7ziGzu3F/RRXzQGrG6s5Un3Euf0JUCWlJ5/sdxGFEELZeDjj91RTo8wHLRTVFpEWm2bbfj3ASY9Np7mlmbJ6O86H74TmndsBaBrk/UpBKeU3UkohpRwppRxl+VhhbXxw/ybdiYeb7p794VmEENw7/l63zTmp/yRmjZrFMz88w/Zj222O/2r/V+SX59vXid2WAGi200iLSSM+It7piEIzFXRaKEDtU3hjQ7u+Xn3YEVGAC1bjcEL6yVgd3M12Gq52Z1f/8hPg3+dQaOhCYS8eFIrSulIW5SziuhHX0a+He1v5502bR3xEPHf87w6bG9uLchaREJnAlcOudOwi+fmqz8RGrb4/IIRwyfPJpYonjexs9XdUWOj8HPZgp32HhrusxoPdvkNDq+pyVigadm6lyQCxWV0cBuYn6EJhL5pQdHb2s4ss2LSAuua6Ts3/XCUlOoV5U+ex4eAGFm9dbHVcaV0pH+z6gBtH3khUWJRjFwmQiieNrJQsp1NPuSW5hIgQBia6ED1pG9qe3qewsytbw11W48Hela3hqo2HyMtjbxKkJ3RyfoufoQuFvfTrp84+XrTIrSkDu48ZdYFZo2cxvu945q6ZazWf+tbWt2gyNzmedgL77cX9hKzkLA5VHaK2yfHChNzSXDITM4kIdaGxcITl97xtm/Nz2IM3IwpL6snUYuJY7bFuJRTORhSR+QfISwmMfhNdKOzFYIDf/14durN2rdumfX3L6xTXFds0/3MFgzCw4MIFlNaX8sjnj3R4XkrJopxFjOszjpFpIx2bvLERDh8OOKEA2FO2x+HXOmUG2J74eDjpJNhue9/IJbwRUbRLPRXXFiOR3SL1FBceR3RYtHNCYTIRd/gYu5MhNSbV/YtzM0ErFO50UW3luuvUqXlPPumW6TTzv3F9xjH5pMlumdMao9JHcfe4u3l588v8eOTHE57beGQjO4p3ONaJrVFQoCKsAEs9geOVT+YWM7tLd7suFKCsPDwdUdjpHKuREJlARUOFY02a7VJP3aXZDtR+l9NNdwUFhJpaKOwTT6jB/52UglIoCsoLGPbCMN7e9rZ7J46IgHvvVec+uyG//O8f/k1+eT6PnPmIc81bDvLXc/5Kemw6c/435wTTwEWbFxETFsPM4TMdnzSAKp40BicNRiAcPhZ1f8V+msxN7hOK3Fxoct3p1yqOpp4iEzFLs0N9NyQojygt9dQdfJ7a4nTTXZ56k1KRkebmFXmGoBSK3nG9GZAwgF9//GvWF6x37+S//a2q7nExqthXvo+/fPEXLsm6hIuHOGCV4QLxEfE8O+NZcow5LNi0AICqxiqW7ljKtcOvJS7CiaqlAGq204gKi6J/j/4OVz5pFU9ON9u1ZcQIda77HsfTX3ZTWgqRkap3ww6csvEICVFi0S6iCIS8uztw2u8pV/0t1Wf2d/OKPENQCkVEaAQfz/yYIclDuPzdy9l21I0hfo8eMHs2LFvm9AE0UkpmfzabUEMoL1zwgleiCY1rTrmGaZnTeGT9IxTVFLF0+1Lqmuuc28QGJRRxcao8NoDISsli27FtDqVZNGFxW0QBnt2nsLMrW8NpG482fk/aTTMtJjDeKbuK06mnvDxKYwzEpdvpzuxjXBIKIUSCEOJ9IUSu5aSkCUKIJCHEWiHEHsvnRMtYIYSYL4TYK4TYKoTIds+P0DkJkQmsvH4lseGxnL/kfJe8fTpwzz3qndQzNi1SOuWtrW+xdt9a/jXlX/SN925pnBCC5y94ngZTA3PXzGVRziJGpI5gXB8nbY61iicvip07uGjwRWw9upWPcj+y+zW5JbmkRKeQHO0Gb56sLPU35EmhsLMrW8NpY8A2fk/GaiM9Ino4XmIdoPSK7UVFQwUNpgaHXifzctmZ3BIwkZerEcV/gFVSyqHAqajDL/4AfC6lHAx8bvke4HxgsOXjdtWUg+4AACAASURBVGCBi9e2Sb8e/Vh5/Uqqm6o5f8n5jlsoW6NPH7j+enj1VSgpceilJXUl3Lf6Pib0ncCcsXPcsx4HGZI8hIcmPcSSbUvYVLiJW7NvdT6qCbAeCo05Y+eoDf6Vd1PdWG3Xa9xS8aQRGQmDB3teKLwRUbTxeyqqLQqYm5870PZijtYcdeh1Lbm55CUTMNVhTguFECIemIxyIERK2SSlrAAuBd6wDHsDuMzy9aXAm1LxA5AghPD4v9LItJF89KuP2F26m8uWXkajqdE9E8+dq+wRXnzRoZfdv/p+qhqrWHTxIp964fzxjD+qfoCQCG4YeYNzk5jNKv0WQPsTGqGGUF668CUKqwv5yxd/ses1Th1/2hWernxyNPXkBqvx7mLfodHadOdI+qm8nJDiEvJS1H5qIODKnSoTKAb+K4T4WQjxihAiBkiTUhpBHbcHaEXCfYC2+Z/DdHH0njs5d8C5vH7Z63x14Ctu/vhml84iaOWUU+Cii+C556DOvlLcNflrWLx1MQ9NeohTUk9xfQ0uEBUWxSczP+GjX33kvMvnoUPKDC4AhQLg9L6nM/u02cz/cT45xq6r2ErrSimuK3ZfRAFKKPbt85wjsaOpJzdYjXcX+w4Np2w8LBVPuQHSbAeuCUUokA0skFKOBmo5nmbqjM5yGx12EoUQt2unMplMJheWdyLXjbiOeVPn8e6Od3lo7UPumfTBB1Xq6fXXbQ6tbapl9mezyUrO4pHJHZvefMHw1OGcP/h85ycIINdYa/xzyj/pGd2T2Z/N7vKccbduZGuMGKF6UHbtct+cGlI6HFHERcRhEAbnrcal7Db2HRpO2XhYhKJbpJ5QEcFhKaV2zur7KOE4qqWULJ+PtRnf1vGuL9DBFU1KuVA7lSk01L2NKL+f+Ht+N/Z3PPX9U8zfON/1Cc84A8aPh6efVmmYLnjsy8coqChg4cULXTqUyK8IwNLY9iREJvDsjGf5qfAnXtr0ktVxbjEDbI8nK580628HIgqDMJAQmeBc1ZPZTHVJIbXNtd1KKFJjUhEIhyMKc4iBgsTA6TdxWiiklEXAISGEVlQ+BdgJLAdutjx2M/CJ5evlwE2W6qfxQKWWovIWQgj+fd6/uXzo5dy76l4+2PmBqxMqW499++BD6ycJ5hhzeOaHZ7gt+zaPd2B7lb17ITxcbe4HMDOHz2Ra5jQeXv+w1XeGuSW5hIeEk5GQ4b4LDxyomjg9IRQOdmVrJEQmOO33VHJE9YQEyrtkdxBqCKVnTE/HhCI3l+Je8cTHJrnmGeZFXN1NvQtYIoTYCowC/gn8HzBNCLEHmGb5HmAFsA/YCywC7nDx2k4RYghhyRVLmNBvAtd/eD0bDmxwbcJLL1XVK/PmdWoWaGoxcevyW0mNSeWJaU+4di1/Iz8fMjNVmWcAI4TghQteoNHUyP1r7u90TG5JLoOTBhNicOPPGhKiDjHyhFA42JWt4cqZFOVHVIQZKO+S3YXDvRR5eRxIjwooQXVJKKSUWyxpopFSysuklOVSylIp5RQp5WDL5zLLWCmlvFNKOVBKOUJKuck9P4LjRIVFsXzmcjISMrh06aXsKnYhRxwSoiqgNm+GL7/s8PS/f/g3Pxf9zHPnP0dCZMKJT+blwQ03tOYsA478/IBOO7VlcPJgHj7zYZZuX8qa/DUdns8rzXNv2knDU5VPDhoCaiRGJTpeRm7xe6oq2g90P6HoG9+XXSW77GveNJth7152JwfORjYEaWe2PSRHJ7Py+pWEh4QzbfE0th7d6vxkN92kzqt44sSIoaC8oNWm44TDgKSE115TB9gsWQKLrZ8T4bdIGXD24rZ4aNJDDEkewh3/u4P65vrWx5vMTeSX5XtOKAoLO5w77TKuRBROOsjWHTsCBNYN0B1cffLV7C3by5f7v7Q9eP9+aGpia2Jj94koAp0BiQNYe6OyDD/jtTNYvXe1cxNFRqpu7VWrYKsSHCkls//XiU1HRQXMnAm33AKnn65utJ4+wMYTHDumyjoDuOKpPRGhEbx04Uvkl+fzzw3/bH08vywfszR7Rii0syl27HDvvM5GFC6knhqPGQk1hDpfbh2g/OqUX5EclczzPz1ve7DF4+mn2CpdKAKJEWkj+OHWH8hMzOTCty9k4eaFzk00Zw7ExMBTTwGwZNsS1uSvOdGm47vvYNQo+OAD+Oc/1bkWZ57pnfOT3U0QVDx1xjkDzuHGkTcy79t5rZVOHql40vBU5ZMLqafy+nKnrMZNpcdIi0nzaSOpL4gKi+K27Nv4OPdjDlYe7HqwJc28LckUUJFX9/qNWqFvfF82/GYD0wdO57ef/ZaH1j7keFNeYiLcdhu88w5leVtOtOkwm+Hxx2HyZHUA0rffwh//qPY3vHV+srsJQHtxe3lq+lPEhscy+7PZSCnd6xrbnr591UFG7haKsjJl1hge7tDLEiMTaW5pduw8l6goVb1VXh5QNz93Mvu02QC8vOnlrgfm5WFKSqAsOnC6skEXilbiIuJYfu1y5pw2hye+e4KZ7888IU9tD6Z77qJFtvDJnHOobKhUNh1HCmHKFPjLX+BXv4Kff1YpJw3t/OTNm93403iB/HwlehkZvl6J20mNSWXe1Hl8deArFm9dTG5pLn3i+jhnw24LITyzoe1gV7aGUzYeQkBSEiHlld1uI1vjpISTuCTrEhbmLOzaIDAvj5oMJRB66ilA0fYTnpr2FO/vfJ8pb06huLbY5uuazE0s2ryIIZ9MZckpLfzquypWXLCEU77dDSNHwqZN8MYb8NZbyqa8Laeeqm64gbZPkZ+vzhGPCIw6cEe5JfsWJvabyANrHuDHIz+2nornEYYPVxGFO9OPDnZla7hiNR5eWUt6TPcUCoDfjf0dJXUlvLfjPeuDcnMp6ad+L4EUfelC0Q4hBA9MfID3rn6Pn4t+Zvyr460emVnfXM/zPz7PwPkDuf2z20mJTqHf4/8hurGFqb95HK64QvUZ/PyzqozqzKE1JgaGDg28iCLIKp7aYxAGXrrwJcrry91vBtie4cOVV5LRjf2n3owoAJmUSHRtY0Dd/NzNuQPOZWjKUJ778bnOB1RWwtGjHOoVC+gRRVBw5clX8uXNX1LdWM2EVyfw1f6vWp+rbarl6e+eJnN+JnetvIuMhAxW37Cajbdu5OxL7obzzlOphN//Xm1gDx7c9cWyswMzogiiiqfOGJE2gvsnqAY8j2xkt17IUvnkzn0KBy3GNZyNKBoT4kiv7n49FG0RQvC7sb/jp8KfOpxLD8AvvwCwNy2UuPA4YsJjvLxC59GFogtO73s6G2/dSFpsGtMWT+OVnFf414Z/kfGfDOauncvJPU/mi5u/4Otff830gdOPl8C+9Zb6o3jiCfs2E8eMUZvZRU4cqegLKiuVGWIQRxQaj571KA9MeIArhl3huYucYnESdqdQOJl60ppCHY0oyodmMLQE+koP7OMEEDedehNx4XE8/2MnpbLvvQeRkXybGRpwkZcuFDYYkDiA72Z9xxn9z+C2T2/j4fUPM7b3WL6d9S2f3/Q5Z2ec3fHQn+RktTdhL9mWw/4CJaoI0tLYzogJj+Gp6U/RJ96DflY9e0JamvuEoqVFpbJcST05GFEcOqUfBmDQbscO8go24iLiuPnUm3l3x7scqz12/AmTSR2ffOGF7DOXBFTaCXShsIvEqERW3bCK+efN56fbfmLF9SuY2G+i+y4werT6HCj7FEFgL+53uLPyqbJSiYUTEUWPCFVs4WhEkTcoAbOAXr84d458MHHnuDtpMjfxSs4rxx/86itVBj9zJoXVhXpEEayEh4Rz1+l3cVrv09w/eVwcDBkSeBFFZqZv1xFMDB+uurNb3HColpPNdqBMM3tE9HDY7+mQrOCXNIjP8eDRrgHC0JShTM2cyoJNCzC1WM7UeecdiI1FXnABxhqjzyMKIcRrQohjQgi7fmG6UPgLgbShvXev8raK6975aLcyYoQ6WrfADe/InbQY10iMctzGo6imiE0DwgnZ+KNKs3Rzfjf2dxyuOszyvOXQ1KTcGC67jOoQE3XNdT4XCuB14Dx7B+tC4S+MGQMHD6pNYn+nG1Q8eR13Wnk4aQio4Ywx4OGqw+RmJSv/r60uGGwGCRcNuYj+PfqrTe01a1o93rTzTnydepJSfg3Y7USpC4W/EEgb2kFkL+43nHyy+uwOoXAh9QSORxQtsoVvDn6DeeJ49cC33zp13WAixBDCHafdwRf7v6Divy+p38W0aa3nVgSSfQfoQuE/aELh7xvaDQ1w+LAuFO4mLk7ZoQRgRLH92HaK64rJHnup6tbXhQJQ3f0J5nAiV6yGK6+E8PDjEYXnU0+hQohNbT5ud2kyd61Kx0USEtTmsL9HFAUFympCTz25H3dVPpWWKheAhATbYzvBUavxdfvWATAlcwpMWgXffOPUdYONlOgU/t4wkciGL6m58mJioTWi8ELqySSldFvljR5R+BNjxvh/RNGNeii8zvDhyoa6qcm1eUpLlUg4eUStZjVuL2v3rWVoylBlpz9xooo4D9qw2+4mXLddYIyF12KV27Kx2khkaGRrGXKgoAuFP5Gdrd6xlztoyOZNgthe3OeMGKEqhnbvdm0eJ7uyNRIiE2g0N9rlntxoauTrA18zdcBU9cCkSeqznn6CykoS13/HhtPTeT5nAS2ypbU0tkOTrpcRQrwDfA9kCSEOCyFu6Wq8LhT+hGY57s/pp/x8dX5CSoqvVxJ8uKvyyUlDQI1Wvyc70k8/HP6BuuY6pmZahGLkSGV0qQvF/7d332FSldcDx79nlw7SVAQBKYYSJaEsIrIaRawJsRvBWMhPf6hREZXY8sSeWDA2TEQsEUVRo/zQSIyyCkFFXAgCShMBjSBGCUgRKQvn98e5o8O6OzvlTts5n+fhYefOve+8zLPcc992XnjxRdi2jWbDLmTZumVMXT7VAkUOLLZT1aGq2kZV66pqO1V9NNb5HihyST7MfIpkjc3yE1Gt1K2bdRelGihSbFEkksajbEUZRVLEER2PsAN16kD//h4owBbZdejAwCHXsE/jfXhg9gO2Kjv7aygS5oEil+y5J3TokNvjFD41Nn3q17cV+qkOaGewRVG2sox+bfvRrEFUn3tpqa2l2LQp6TrkvbVrbavjIUOoV6c+w0uGM+XDKaxYv8IDhQtBJldo//GPlhJ927b4zq+ogI8/9hlP6RTZxCgVSaYYj4i0KGpK47Fh6wbKV5dzdOejd3+jtNRSkbz7btJ1yHsvvGBbIA8dCsAFJRdQJEVs37k9J7qeEuWBIteUlMCyZZbYLZ3Wr4cbboBXX7W/4/Hpp7Bjh7co0qlHD1ixwlY4J2PHDti4MbVAEeeeFNM/ns4u3fXd+ERE//7f7Q1fqCZOtA3JgizSbZu2/TZVvbcoXOoi4xTz5qX3cx580G5GRx1l+2bEM/fdp8amX2QTo0WLkrs+MmMula6nOHe5K1tRRqO6jejfrv/ubzRtav+OQg0Uq1fDjBkwZMhuY3kj+4+kSIo4YO8Dsli55HigyDWZWKG9dSvcd591O02aZCuCzzmn5j5lTy+efqnOfEpxVTZEbV5UQ4uibGUZh3c4nHrFVWzOVVoKs2ZZ90uh+etfbVHqkCG7HR7QfgDrrlrHwe0OzlLFkueBItfssw+0bZvecYonnrDc+FddZakjxo+3sYcrr4x93Ucf2YBr2zRu4lPoOneGBg2SDxQp5nkCqFNkW3XGalGs2riKJWuXfL/bKWLAAHvwCGuPjXwycaLtMdOt2/fe2m3QP494oMhF6VyhvXOnDWKXlMARR9ixww6z/b0ffhhefrn6a5cvh06drP/ZpUdxsSUITPYGm2KK8YiaEgO+vuJ1gOoDRaEuvFuxAsrLv9eayHf+Pz4X9eljqRw2bw6/7JdespW/V121+1qIm2+2fuXzz4cvv6z6Wk8vnhmpzHwKoesJak4MWLayjFaNW9GjVY+qT+jQAfbdt/ACxbPP2t9nnJHdeoTMA0UuKimxPs7588MtVxXuuMO6N045Zff36teHJ5+0G82FF9q5la/1NRSZ0aMHrFnzXesgESF0PYGNU1TXolBVylaUMajTIIqkmluIiLUqUgkUq1fD4sXJX58NEydat1uHDtmuSag8UOSidA1ov/WWzW2/8kpbQVtZz55wyy02wD1hwu7v/ec/NkvKA0X6RWY+LVyY+LXr1ln3VdOmKVUhVmLARV8u4vPNn1ff7RRRWmrJAVetSrwCqjB4sH0Xt96aH4PiCxdal2GwdqI28UCRi/bdF1q3Dn9A+847LUfTsGHVnzNqlP0Hv+SS3TOA+oynzEll5lNkVXaKKVZipRqPpBWPK1AAzJyZeAWmTrUp4j/6EfzudzBoUHIBJ5OeecbG7047Lds1CZ0HilzVp0+4LYpFi2yg+pJLoFGj6s8rLrZZUDt3WkDZtcuOe9bYzGnbFpo1Sz5QpDg+AbHHKMpWltGlZRf2a7Zf7EJ69rTftWS6n+680x6YZs2Cxx+HOXOsvMmTEy8rE1QtUAwcaA95tYwHilxVUmI39y1bwinvrrugYUO4+OKaz91/f7jnHpg2DcaMsWPLl9vTUseO4dTHVU8k+U2MUkwIGNGiYQu+qfiGbRW7p3fZsXMH0z+eXnNrAqBuXTj44MQDxdy58PrrMHKkjZ2de64d69QJTj4Zfv1r+KbmFOgZNXeuPUzVwm4n8ECRu/r0saf5MDaqX73axhzOOy/+9ODnn299xNdcYwOKy5fDfvtBvSoWV7nwRWY+VZ5UUJMUEwJGRNJ4VM73VL66nM3bN8cXKMAGdufNS2wG3+jRNsYyPGr3zq5drQtr1CjLKnDQQeFsGxuWiRMtMJ58crZrkhYeKHJVmHtT3HefdSVdcUX814jYuorGjeHss2HJEu92yqQePeCrr+CzzxK7LsQWBXw/jUfZijIEYWDHgfEVVFpqv3vl5fGdv3IlPPccXHCBdb9Fq1fPgsg//mHZWQ86yIJGosE0bLt22bTYY48NJUjnIg8UuapdO3v6T3WcYsMGGDsWfvELa7ononVreOghq8PcuR4oMiky8ynRp+aQWxSVxynKVpbRd9++3waSGh1yiD10xNv9dPfdNk522WXVn3PssdbSPuII64Y65ZTkphKHZeZMG2ivpd1OEEKgEJFiEXlPRF4OXncSkXdFZJmIPCsi9YLj9YPXHwXvd0z1s2s1EWtVpNqiGDfOUin85jfJXX/qqdaiAJ/xlEkHHmh/JxIotm61Ma00tSg2bdvErFWz4u92Atu7+8AD4wsUa9fCo4/CWWfVnCamVSuYMsUCy5QpNtC9dGn89QrL9u1w7702/nfCCZn//AwJo0VxGRC9KuYO4B5V7QKsByJ7sZ4HrFfVHwD3BOe5WPr0sRvF1q3JXb9tm/0SDxr03dqMZIwZY8Hi5z9PvgyXmL32shZdIgPaIa3KhqpbFDM+mUHFrorEAgVY99M779S8FuJPf7JB6lGj4iu3qAguv9zWBm3dagFmx47E6paK5cvh0ENt74lRo6BJk8x9doalFChEpB3wM+CR4LUARwLPB6eMB04Kfj4xeE3w/iDJ9g7jua6kxDYLSnbQ7umnrY/7qqtSq0ezZpZIsHv31MpxiUk0lUdIq7IhKoNsVIuibEUZDeo0YED7AYkVVlpqe2TEWkC4ZQs88IA9jByQYBru3r2ti3TOHPjDHxK7NlkTJkCvXrZ3zPPPWwqcWizVFsW9wFVAMNmePYGvVLUieL0KiLQh2wKfAgTvbwjOd9VJZYX2rl028NezJxx9dM3nu9zTo4dNkY53VXKYLYoq9s0uW1nGYfsdRoM6DRIrLJ4EgY8/bl1PqXSRnnWWZRaYPTu5MuKxcaO1rs8+2wLU/Pn22bVc0oFCRAYDX6hq9F2sqhaCxvFedLnDRWSOiMypqKio4pIC0rEjtGiR3DjFlCk2rbVy8j+XP3r0sK6YlSvjOz/EFkW94no0qtvo2xbF55s/54MvPki82wlsEkXr1tWv0K6osIzG/ftbV06yxoyBNm3sJh7W+qNos2fbw9vTT8NNN8Ebb9iU8QKQSouiFDhBRD4GnsG6nO4FmotIJJFQOyAyv28V0B4geL8ZsK5yoao6TlX7qmrfOlXlIyokIsmv0B492n6JTz89/Hq5zEh05lNIKcYjotN41JhWPBYRW09RXYti0iRLz53qQ03z5tYyWboUrr02+XIq27XLVooPGGBjIP/8J1x/fdX50mqppAOFql6rqu1UtSMwBHhDVX8JTAMiyU7OBV4Mfn4peE3w/huq2Z4AnQdKSmxAc/v2+K955x14801bN1G3bvrq5tIr0lcfb6AIsesJdk8MWLayjJYNW9Krda/kCisttZbRmjW7H1e1m3DXruHMGho0CEaMgPvvh7Ky1Mtbs8am4159NZx0ki0eTKXVk6fSsY7iauAKEfkIG4N4NDj+KLBncPwK4Jo0fHbt06ePBYlEMomOHm1dVuedV/O5Lnc1aWLdNvHOfPrvfy3lRcOGoXx8pEURV1rxmlQ3TjFtmrWYR42y9RNhuP12m3jxq1/ZosVkTZkCP/6x1fnhh20hYIs414/UMqEEClWdrqqDg59XqGo/Vf2Bqp6uqtuC41uD1z8I3l8RxmfXeoms0K6osL7TyZNtIVItnq5XMHr1spXIf/tbzedGVmWHNCbVomELvtr6FR/+90NWbVyVXLdTRO/etsVr5UAxerRt/xtZqxOGhg1tb5U1a+DSSxO/futWW/A3eLCt5/jXvyylTQGP9fnK7FzXubPlvakpUPz735a58sYbbfbHdddlpHouzUaPtt+BE06wm16sNTUhrcqOiGSQjTuteCz16kG/frsHigULLAiOGGFBJEx9+1p68gkTbPpqvBYvtkH1+++3es2aBT/8Ybh1y0MeKHJdUVHNA9rPP2/TYOfNsyepJ56InUrc5Y/997eb1eWX2zqDfv1symxVQkoxHhHpeipbWUan5p3o3KJzagWWlsJ77303I2n0aMsldtFFqVe2KtddZ/mgLrjg+2Mjlala91JJiSXRfPlly5EWdgDLUx4o8kGfPjZfu/J04S1bLMPm6afbYOC8edaacLVL/fqWquLvf4fPP7en5Yce+n4yvJASAka0aNiCzds388bKN1JrTUSUltrvcHk5fPKJZVwdPjx9/f5169qD05YtNl5X3dyZ9estF9rw4VbHBQvgZz9LT53ylAeKfFBSYl0O0fsHz59vN4xHHrFU4G+95Un7arvjj7eb2KGH2r7mp5323UwnSEvXE8DGbRvDCRSHHGJ/v/22pZYB23Minbp1s1lVr7xiLYbK3nrLxoEmT7b95F991dZiFAAROU5Elgb592JOLvJAkQ8iK7TnzrWnojFjrAti/Xp47TW47TafBlsoWre2fv3Ro22Au2dPmDHDfi9CblFE0ngAHNnpyNQLbNnSpvxOmWI37aFDM7Ng7eKL4aijbLp4ZEvfyMSPww+3/ztvv23rOIoK45YoIsXAn4DjgQOAoSJSbe6UwvhW8l3XrjaD6bXXbFBzxAhLy7Fggf0HcIWlqMimk86caX3oAwdaq3L79nBbFEEaj96te7NXozg3vKrJgAG2zufrr5NP15GooiL4y19sgdw559h6jsjEjzPPtAewfv0yU5fc0Q/4KJiluh1bNH1idSd7oMgHRUXWPH76aQsW999vT5N7753tmrls6tvXbnJnn23dKxD6YDakONupssh6iuOOszUKmdKunWWnnTnTuqMiEz+efNJmFRaeb3PvBaLz8n1P4axBz3dnnGHN5bFjrbvBOYA99rC0FcccYwnxDj44tKK779Wd7nt1Z0iPIaGVyTHHQJcucMMN4ZUZrzPPtPQbS5favhe1e3+VOiIyJ+r1OFUdF/U6rtx7356cy1k0GjdurF9//XW2q+Gcc3lFRLaoauMY7x8C3KiqxwavrwVQ1duqOt+7npxzrvDMBroEO5LWw/L1vVTdyd715JxzBUZVK0TkEuBVoBh4TFWrTSjnXU/OOVfL1NT1lCjvenLOOReTBwrnnHMxeaBwzjkXkwcK55xzMXmgcM45F1NOz3oSkV3AN5UOFwM74yyiHhDPZtOJlBnvuV6ml+llepmZKrPyva6hqobXEFDVvPqDLUWP99wv01BmXOd6mV6ml+llZrDMuO51yf7Jx66nODYP/la8O6snUma853qZXqaX6WVmqsx473VJyemup1SJyBxV7ZvtejjnXDql+16Xjy2KRIyr+RTnnMt7ab3X1eoWhXPOudTV9hbF91S1T6yIPBUc+0BEHhORgt9XtJrv6UgRmRt8T+NFpKCTSga/K1+IyAeVjl8afHcLReTObNUvF4hIexGZJiKLg+/jsuD4LSKyQETmichrIrJvtuuaTSLSQETKRWR+8D3dFBzvJCLvisgyEXk2yPSaeekcKc+1P9h0s+VAZ2w62Xxsv9ifYht5CDARuCjbdc3R7+lToGtwzs3Aedmua5a/p58AfYAPoo4NBMqA+sHrVtmuZ5a/ozZAn+DnPYAPg9+lplHnjADGZruuWf6eBGgS/FwXeBfoDzwHDAmOj83WvanQWhRV7hOrqn/XAFAOtMtqLbOvqu/pVGCbqn4YnDM1OFawVHUGsK7S4YuA21V1W3DOFxmvWA5R1TWqOjf4eROwGGirqhujTmtMjN3VCkFw+9kcvKwb/FHgSOD54Ph44KQsVK/gAkXMfWKDLqezgX9kuF65pqrvqTVQV0QiMytOA9pnumJ5oCtwWNBd8E8ROSjbFcoVItIR6I09LSMivxeRT4FfAtdnr2a5QUSKRWQe8AX2ILYc+EpVK4JTYu5rnU6FFihq2if2z8AMVX0zQ/XJVVV9T7uwXbDuEZFyYBNQUcV5ha4O0ALrNvgN8JyIVPV9FhQRaQK8AIyMtCZU9beq2h54Crgkm/XLBaq6U1V7YT0a/YAfVnVaZmtlCi1QwqIA+AAAB+pJREFUrGL3p+B2wGcAInIDsDdwRRbqlWuq/J5U9R1VPUxV+wEzgGVZqV1uWwVMCroSyrEAu1eW65RVQUv9BeApVZ1UxSlPU+DdmNFU9StgOvaw0Txq0si396tMK7RAUeU+sSJyPnAsMFRVd2W1hrmhuu+pFYCI1AeuxgbX3O4mY/3KiEhXbDLA2qzWKIuC1tSjwGJVvTvqeJeo004AlmS6brlERPYWkebBzw2Bo7DxnGlYNy/AucCL2ahfQU1v1Gr2iRWR+cAnwDtBL8EkVb05i1XNqhjf02gRGYw9YDyoqm9ktaJZJiITgSOAvURkFXAD8BjwWDBldjtwbjBJolCVYuN+7wf97wDXAeeJSDesxfUJcGGW6pcr2gDjRaQY+//1nKq+LCKLgGdE5FbgPSzoZpwvuHPOORdToXU9OeecS5AHCuecczF5oHDOOReTBwrnnHMxeaBwzjkXkwcK55xzMXmgcM45F5MHCuecczF5oHDOOReTBwrnnHMxeaBwzjkXkwcK55xzMXmgcM45F5MHCuecczF5oHDOORdTXgcKEfmtiCwUkQUiMk9EDs52nZxzLmwi0k5EXhSRZSKyXETuC3afrO78kSLSKKzPz9tAISKHAIOBPqr6Y2zrwE+zWyvnnAtXsJ3sJGCyqnYBugJNgN/HuGwkEFqgyOetUNsAa1V1G4CqrgUQkRLgbuyLXAsMU9U1IjIdmAf0A5oC/6Oq5dmouHPOJeBIYKuq/gVAVXeKyOXAShG5EbgJOBZQ4GFAgH2BaSKyVlUHplqBvG1RAK8B7UXkQxH5s4gcLiJ1gTHAaapagu1fHB11G6vqAODXwXvOOZfrDgT+FX1AVTcC/wbOBzoBvYOeladU9X7gM2BgGEEC8rhFoaqbg9bDYcBA4FngVqAHMNVaaxQDa6IumxhcO0NEmopIc1X9KrM1d865hAjWWqjq+E+AsapaAaCq69JRgbwNFGBNMGA6MF1E3gcuBhaq6iHVXVLDa+ecyzULgVOjD4hIU6A9sIIM3MfytutJRLqJSJeoQ72AxcDewUA3IlJXRA6MOueM4PihwAZV3ZCxCjvnXHJeBxqJyDkAIlIM/BF4HOuCv1BE6gTvtQyu2QTsEVYF8jZQYIPV40VkkYgsAA4ArgdOA+4QkfnY4PWAqGvWi8hMYCxwXqYr7JxziVJVBU4GTheRZcCHwFbgOuARbKxiQXDPOzO4bBzwiohMC6MOYnWo/YJZT6NUdU626+Kcc/kkn1sUzjnnMqBgWhTOOeeS4y0K55xzMeVVoBCR9iIyTUQWBzmeLguOtxSRqUEelKki0iI43l1E3hGRbSIyKqqcBiJSLiLzg3Juyta/yTnncl1edT2JSBugjarOFZE9sNWKJwHDgHWqeruIXAO0UNWrRaQV0CE4Z72q3hWUI9gq7c3Bau63gMtUdVYW/lnOOZfT8qpFoaprVHVu8PMmbN1EW+BEYHxw2ngsMKCqX6jqbGBHpXJUVTcHL+sGf/InYjrnXAblVaCIJiIdgd7Au8A+qroGLJgAreK4vlhE5gFfAFNV9d301dY55/JXXgYKEWkCvACMDJJjJUxVd6pqL6Ad0E9EeoRZR+ecqy3yLlAEYwovYFkSJwWH/xOMX0TGMb6It7wgKeB04LiQq+qcc7VCXgWKYBD6UWCxqt4d9dZLwLnBz+cCL9ZQzt4i0jz4uSG26dGS8GvsnHP5L99mPR0KvAm8D+wKDl+HjVM8B+yH5T05XVXXiUhrYA62UdEuYDOWE6ojNuhdjAXL51T15sz9S5xzLn/kVaBwzjmXeXnV9eSccy7zPFA455yLyQOFc865mDxQOOeci8kDhXPOuZg8UDjnnIvJA4UrGCIyUkQaJXHdMBHZN4HzjxCRl2s4p5eI/DTRujiXDR4oXCEZCSQUKESkGEtjH3egiFMvwAOFywseKFytJCKNRWRKsDnVByJyA3aznyYi04JzHhSROZU3rxKRj0XkehF5CxgK9AWeEpF5QcqXqj7vOBFZElxzStTxfiIyU0TeC/7uJiL1gJuBM4Iyzwjq+5iIzA7OPTF9345zifGV2a5WEpFTgeNU9X+D182A+UBfVV0bHGsZpHopBl4HRqjqAhH5GPizqt4ZnDcdGKWqc6r5rAbAMuBI4CPgWaCRqg4WkabAFlWtEJGjgItU9VQRGRbU5ZKgjD8Ai1R1QpCHrBzorapfp+HrcS4h3qJwtdX7wFEicoeIHKaqG6o45xciMhd4DzgQywMW8WwCn9UdWKmqy9SevCZEvdcM+KuIfADcE3xOVY4Brgn2SJkONMBylzmXdXWyXQHn0kFVPxSREmwc4DYReS36fRHpBIwCDlLV9SLyOHZzjkj0Sb66pvktwDRVPTnYbGt6NecJcKqqLk3wc51LO29RuFopmKW0RVUnAHcBfYBNwB7BKU2xYLBBRPYBjo9RXPR1VVkCdBKR/YPXQ6PeawasDn4eFqPMV4FLg1T6iEjvGJ/nXEZ5oHC11Y+A8qAr57fArcA44BURmaaq87Eup4XAY8DbMcp6HBhb3WC2qm4FhgNTgsHsT6LevhNr0byNpbWPmAYcEBnMxloedYEFQTfVLcn8o51LBx/Mds45F5O3KJxzzsXkg9nOJUBE/g/oVOnw1ar6ajbq41wmeNeTc865mLzryTnnXEweKJxzzsXkgcI551xMHiicc87F5IHCOedcTP8PjIuuIH5GFZMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10c8e3320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# to observe any trend between cloud cover and number of trips across start dates , \n",
    "# we plot with different scales in y-axis of same fig\n",
    "fig, ax1 = plt.subplots()\n",
    "ax2 = ax1.twinx() # twinning the axis that is common\n",
    "df_filter.plot(kind='line',x='start_date', y='id_x', ax=ax1,c='g')\n",
    "df_filter.plot(kind='line',x='start_date', y='cloud_cover',ax=ax2,c='r')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Summary\n",
    "- plot, scatter, hist etc. can be directly applied to pandas dataframes\n",
    "- aggregation by group by to generate respective statistical measures is done \n",
    "  and overall distribution can be seen by using the required visualization\n",
    "- New columns are created like weekday, to aggregate and find some pattern in data via visualization\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exercises"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Question\n",
    "Find highest mean_duration per trip, total_duration \n",
    "and no. of trips for trip end stations? Are these station ids same as the ones calculated before for trip start station ids?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Question\n",
    "Find number of trips and average duration per trip for each bike id on each weekday via visualization?\n",
    "Note:- Filter out for bike Ids less than 20 to get clear visualization\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Question\n",
    "Find trend between mean_temperature and number of trips across start dates by plotting these 3 variables\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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