{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# What is pandas good for?\n",
    "\n",
    "Working with (large) data sets and created automated data processes.\n",
    "\n",
    "Pandas is extensively used to prepare data in data science (machine learning, data analytics, ...)\n",
    "\n",
    "**Examples**: \n",
    "* **Import and export** data into standard formats (CSV, Excel, Latex, ..).\n",
    "* Combine with Numpy for **advanced computations** or Matplotlib for **visualisations**.\n",
    "* Calculate **statistics** and answer questions about the data, like\n",
    "  * What's the average, median, max, or min of each column?\n",
    "  * Does column A correlate with column B?\n",
    "  * What does the distribution of data in column C look like?\n",
    "* **Clean** up data (e.g. fill out missing information and fix inconsistent formatting) and **merge** multiple data sets into one common dataset.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"data/pressure.png\" style=\"width: 1000px;\"/>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import pylab as pl"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, a short recap of the video session"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The two fundamental data-structures in pandas are Series and DataFrame:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1\n",
       "1    2\n",
       "2    3\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = pd.Series([1, 2, 3])\n",
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    1\n",
       "b    2\n",
       "c    3\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = pd.Series([1, 2, 3], index=[\"a\", \"b\", \"c\"])\n",
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    1\n",
       "b    2\n",
       "c    3\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dic = {\"a\": 1, \"b\": 2, \"c\": 3}\n",
    "s = pd.Series(dic)\n",
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s[\"a\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    .dataframe tbody tr th {\n",
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       "   a  b  c\n",
       "0  1  3  5\n",
       "1  2  4  6"
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     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "dic = {\"a\": [1, 2], \"b\": [3, 4], \"c\": [5, 6]}\n",
    "s = pd.DataFrame(dic)\n",
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1\n",
       "1    2\n",
       "Name: a, dtype: int64"
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     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "s[\"a\"]"
   ]
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  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
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     "execution_count": 13,
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   "source": [
    "s[\"a\"][0]"
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  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['a', 'b', 'c'], dtype='object')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reading data from file\n",
    "\n",
    "Now assume that we have some pressure data obtained from a sensor, as shown below\n",
    "<img src=\"data/pressure.png\" style=\"width: 1000px;\"/>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"data/pressure.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "</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>t</th>\n",
       "      <th>p</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>-1.077684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.005</td>\n",
       "      <td>-0.933488</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>0.010</td>\n",
       "      <td>-0.956377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>0.015</td>\n",
       "      <td>-0.963243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>0.020</td>\n",
       "      <td>-0.864824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5995</th>\n",
       "      <td>5995</td>\n",
       "      <td>29.975</td>\n",
       "      <td>2.296034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5996</th>\n",
       "      <td>5996</td>\n",
       "      <td>29.980</td>\n",
       "      <td>2.312056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5997</th>\n",
       "      <td>5997</td>\n",
       "      <td>29.985</td>\n",
       "      <td>2.488295</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5998</th>\n",
       "      <td>5998</td>\n",
       "      <td>29.990</td>\n",
       "      <td>2.570692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5999</th>\n",
       "      <td>5999</td>\n",
       "      <td>29.995</td>\n",
       "      <td>2.472273</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6000 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Unnamed: 0       t         p\n",
       "0              0   0.000 -1.077684\n",
       "1              1   0.005 -0.933488\n",
       "2              2   0.010 -0.956377\n",
       "3              3   0.015 -0.963243\n",
       "4              4   0.020 -0.864824\n",
       "...          ...     ...       ...\n",
       "5995        5995  29.975  2.296034\n",
       "5996        5996  29.980  2.312056\n",
       "5997        5997  29.985  2.488295\n",
       "5998        5998  29.990  2.570692\n",
       "5999        5999  29.995  2.472273\n",
       "\n",
       "[6000 rows x 3 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "t = df[\"t\"]\n",
    "p = df[\"p\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      -1.077684\n",
       "1      -0.933488\n",
       "2      -0.956377\n",
       "3      -0.963243\n",
       "4      -0.864824\n",
       "          ...   \n",
       "5995    2.296034\n",
       "5996    2.312056\n",
       "5997    2.488295\n",
       "5998    2.570692\n",
       "5999    2.472273\n",
       "Name: p, Length: 6000, dtype: float64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pl.plot(t, p)\n",
    "pl.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This way of extracting data from the DataFrame is useful for futher computations with t and p. For plotting purposes only, the DataFrame has its own plot-function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot()\n",
    "pl.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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91cgrFQDK7LKH4/vVvIK8ht/m8WLzpo6pcWxtb6r5LLofcj/yjMn+eNE63LW8tpwr5MP868dkQdbokO2hxN/9+DHcuWwrAFmDPPHDN+Nt//tw3cf1YwVCYeO1D6LrZ54gzEPq5ZS9w7vSFh0Onnf7f5oaokaN3Pscsz+UrCDN81rJoj5Ejr7KNLwlFd8RDDWaV5DXcDF97QkwLk6XnjAFAHDMlFE1Dk7BCTH3Q51sA6V2H4+u3VnT8CPbWgAAo9pbxPbhqDHUC79kOdYlo96B7j786ZktdR83jwMPhMEgvuwB7VPjA0Dc9n/+cSme2rAnaNcAZowfgRccJ6ecSOMqSuJ88uaWd+0SY2eV1MpAOXppQ1OxnmjliB/7UKN5BXkNv5VW4Tdc/xBufdq88KrG1KMVrZPgBEDWIFQNXiu1cug/f8T4vR83JbKTGIYPWiPw0Op4Zfd6I48DH6jBcSDwR6q1uAI9x3cu24q/+O/7gnYSXGS895VqOveqjJ0SbZFLrRjQLlcSpDwrYzZHLwjyYD6ysCeX44IjryPyNPIZ402U2UtPO1K88LxQbX+ytXFBHTHiV+210l3jNvykI40P85iOfI38uCmja+q7mfCHJzcP2lh5HHieMGokBppGlrsX7u8WYg/s806COqRHyNiZL8jlQiw+5+4JcjucUgpKyRx1C6vVmaUYSbaLaqgVDY2SMvmThqOe1MSCPP83rzpzJiaNast9qWquaIKULwPkSLdGeq1cMt9QQveu2IaHVu8Q5peO1+K7HBxC+JuLjx20sQJBnWPsHOz8Ohw1c+Q5PtjJ8x6JZKbhKK1z1vv0pEDd+CpxzCuGguzEDIxMPjdCI68kdq/+7bae2bQHf352R8M8rZr2Lc+TvdpGX5ZL+VuhWgU58WUxjhzQmV4r/kt/8gw3SjAP1O+iZ3fi1f9zf9DOu290Yd5wbI2f/Xl9ZgBLvTB+ZGvDxyCExUJkYUYYVI3cdz+scYeXV3i+Qu9SErvhttPw6fMe7+td3/8z7vUKqvizjaUAKNn3WaQ+WC9Zvg3ijsD7Kk6tKOu1Uvu9vfxLd+NV37gfJ374D1i0JlS+BormFeQ5LLmGWcFbSirXayVP0AZ9286jxk6d7bWSp90NFLz/webzbnp8I/7xp4vx1T8tb/hYoRvcwM51+74urI6kMK3WRe7FJ00V59ZI0NzOnTvJzq22RTQv4ZSJm1CJP3lMY64mRB9AkNso30ffauQU2Sl0zw+p2dhaRTCXZsrbQI2dD6/ZOaDjJTSvIM/VyFG1Ru7L0Wruk9lqmr8ljlwx98RQe3M/12qcyj13aLS3lHDVqdMHXZDvtVGnm/fEo2brBf+F7x5gkrDn/8ftuPg/7xDb8oyZ1H7h8Y0tJiIpGfQ8nWMF+UA4cgmJxwYF/AReK+Z/ovHyFlTueQRUcW3t/0qZ5GG7D/YEFAU/IpOj76fXCtFL5chCUgsa4R7cvII8t90YJ4xGnqdhyw9ODLTVjGvk2gkYir30hFpzY+Seu06NU4OdQpfOeTCoBV9gDHTMA5Khj8bKMYhRc2tE2NUL0rNC37S2ZPuDx5CrkQM2d5D5LBVS4f0MtGZn7NoqpZIMjSd++A/iHID+uD+a///qgjk4ecbYCEeuMznySkVjZ5VFPeYeUaO7cxVoXkGe86yQcaJcKkHrPEt27dSKw5ELGoRSyDAO+QKoVo08f2EyW+HSoFcJIm2DXCQbCV9wN9K/N1fYEL2QERTzid8+hY/f+NSA5vHV21cEkZv0PLQmBUXC5/nlX7vX8dTi4D7YYyVPKE3G/WzFpCXyvOeB3h/KZxJ7HxWAYybLXlhaA+cdMwlvPOfozIVk+74uMaUGYBJjlSPuhUQv0Tvtv4NfvX0FzvjELdiyJ7+ox7EN8CRrYkGeL2wBlbxYWeWbYlu5aN/MFQkQqBUr6KvVyGs1DFa1iMFoUI3QDD978zO47WnZ9W9vTr3DeiLmptbIsT54+XwA0iJi/s+iF66/ZzW+fe/qAc3jK7ctx7u+71a6SXYDSQ1Z90I8s2kvHlu3C1d/TS4KzTXy6eNGBO0ptRIPgAOyF7Fq8MmXnyxqvPSppBR++aisIJDylEelvvsHj+BLty5zjyWNH4gaMxNf+sg7fctT5n3YUEV1pkbsVptXkFfxC+7rfekX7oz+UjJWZoG0/TQnuvDgZQl6b/a1Uyv5ixhp5I2gVr5xx0r81XcXiW3HMG1j1dZ9dR+bIwwVb/zuY9wI4ykT0xrbBijMqgEFshFoqNEdZm57u3q99uy5+EUyfPhxE6Hrpfmf3A/zlIcrT5kuHk/KUdzYCeyMpJamXWjMGMrxlUi+c/LMEeevkam80W6iGqVsVJsc/zEQNK8gZzdb4vhIK857SIF8QSv1DSdpVvhSK6SC3tfOQq+V8OY/vn53NB96/kJjTr6lCkNvvcG36Zd8Pr541gMxNzgA+NdfPV7XCkl+zu04vUDCrHHbAz8QjJ5XyqPtp6ToaDVRjyNa3dqbBH7PpGc/2YFGbELJbiSJ/Mx+5vx84zwpVrkU5hvnGnMMdEi51I+4kIS6MdSJyIFr7Shv/hi0wO+OpJbmmDVxZE3zqwbNK8jZ36Igh6tFZCGP+pBGpwQ+gOxu5YTo53Dk0nP/0q/eg9d+8wFx9GpkM22FG1nu7f6VYYj84BYdjl/X7z+wFo9FOOH+wOeBY+6HMUFfT7zxnKOdz0n2wWgN2VRQSsj3WnF/F9uNtFSRxhYAjhzv0jfUHWnEsRD9rCpA5n1X0eyIHH78ga+Ry37q1o89spjR4vq9+9dgqVCohtDWoAC95hXkOe8JbbWq08hrpFYq2X7iuRy5J1tjL95TG4UoOFSzY9AolZQNnsj86YDw2m8+gLtsJkJCIwVYkHfb3+J7v6+2akw14Byp4WG9EH37keiFRlIrfrQuPQ+lyLafPsfuDRfk0rNvqBUVVVwSQUvutjnn3tHqzZ9dW0kjTvpTwCdfcbLcKfl5VxGwc9mJscIV8XfGaORxgy99f/fybXjxl+JptQfqIhvDgAW5UmqWUup2pdRTSqknlVLvr8fE8sAvo6R1ksEvqoYwxDwQ4mN72Q2F4/mDH7o3Rh5UNvecCWSCzr2aYKiBYsn6Xc7nRro7+l3nLcB+rcnqx5E1MoAb1OT2ahJHDRRtXiimY6xTchI3IE45cEEu3z+zA03OLZKegARh3jMQu3ZANh1YUgqvP/to/PXz5wQ0UbIDr0Ij52XbnPkDUWqF7ASxxbJ+KkP/UA+NvBfAP2itTwRwDoD3KqVOrEO/mXD9RuXtnlIqWg6NQ9Kos8fOSWOrXffEWAAFwX/w8x7EfDmvo9pNvdHV62umDRTk3uc8t9H+zkSOHEyFVYukkSccenX0Qj0RapQy5dTTp/HlW8OIW77gyfywuwONZX6kXWie0ulfO3/+8TS2Bm0tpUCz5e6BFZ3t1Ra6Z9L4Jl1BNEQ/Q3lT/VQa6oUBC3Kt9Uat9SP2770AngYwY6D95o8LnHnUePz9i44HEEZH0mWuxq9T8gOPtQH0YOe4HzI+7aM3PuXMI6+Qb97WMM/1kl68wTB2Thzl1kdspADLC+X2R+5/pRwpQ575n4RVTBgkLoAN9N+P8fNUfjDLJvNFz/UOgKNOxsLTHY+NDA67VIWxMcrhI8fYaefZVi6jr6Kd+8vzhZvP7pitbBcTo+Ro/Hj2w7gLZrVy/OhJ9Td0AnXmyJVSswGcAeBBoe0dSqlFSqlFW7duDY6tFURfEN8WcJJWmLWzLVhMW4w9WGK/cK3sgEzNGAt4+t0K5opH3V8yfwraWkq5UYM+8mQzcZrE9zVSS57tRanVGlxVC/yuwi2+297fxF0xrRSwHHlZquRuNfJIUE49EaPiKPLQvw7h5/h1iyouyDfeV0ttZPmJV2PsbLfvPNfKUxdJeQyuMccCqrKMpanLsXwNqjXHNEqxqpsgV0qNBvBzAB/QWgdWOq31dVrrBVrrBZMnTx7weERfJMYlQSNXcG9KzNCQxVFHo7xKwC7rHvgdL8hDm8Ed7rGFSXV6cF54whS0lUvBVjRPq63G0OsYW+sqTLN3EyGPXbehw+RGOd5A/TUs5S3eUtqHRNCX0PByYFLkppmbzQWSc0+yFttomlemkUdD6BHnmJ35RxZg4qBjO9RUIw99tkmnj0VTc0HbE1koEvdHkVpxXTD9n1RbnKZRj0VdBLlSqhVGiP9Aa/2LevSZB1qBiZMMOTOzFeIVuflvaItz7txJ4Y1jH0/48M3C2Ebj3rDrIADgQS8nuJXjjkGGAgYApkFFSlflbct9gSZpWEoB5Qa4wvmXKi9M3h/7QHcvvnLb8poThUljZ1FiQO2BVgTp+hPbQraReKpVhZZSqaEaebiAmf/JLpO3wMUWIem3QPouxSOV04WkpQqvkVggFxnooxy8HV8KvqE5xuxWpUyNHMn4cT/y1JgMSBq/cKIChq1GrszVvR7A01rrLwx8StWB6IvWSLQZbYXeev4cPP+4IwC4N76jpYwXnzQV08Z15EYISpnWSsrlhx1h6m3DzPy49mB+S5ybf3Nr1chDVsl1vaznw5PH7+d5lnzlthX4wi3LglSm1SDQLDMoMaD/1EqWRg4o0fbADXKlBqVGIIQLDT1PSixO7P86KyVF1GMDGX7k9n/SaKXrx92AQ/fC9PiScDzn0IE08KmrV6BWctwDAYkj93Y0/vXj1EuUI69Okjdqp1YPjfx8AG8EcIlS6jH77yV16DcTRF8QtRIaO3XCE7/EhgT3eMKUDDixGoSEFVvcUHMydp57zKTku84et29/q8W1Q4dvlaz0eV4rOVpvqj3YRU7Qiv+rv1qx97lW7W/HfpO97uF+JNfnC2x7S6mKrJL148hTjdvsdOLCzGjkWYJ8oHaDmEZNHHlskYkdr7XG3Mmj8JbzZkcXMS7EPv9HOVdJEiIv9MF9+mPG2kSjz1goAGDJ+t0A3GpDqV2KDLLe+FnUCrt+kmLlUD9K7r9ajXzL3sakd66H18o9WmultT5Va326/fe7ekwue1xzcyiSzt9GkxYByJxaRXPjiuzxwn/r9q3tNjC9fN2CdsDBx+ABDtKDn6uRB/MTzp1p5H7/X7ltBT5/yzL8vB9acW46V+/3fjsFOf3ikedQK2joD10xH0dNHJmfjKwRXisxgxw3+OXwxAOlXWL+84likrNTEUsTQvYYoXbuR75pT6fTB9+NxM6da+RZIfilDI8gEqK0w+YRmqlGbs/Rp1b4QhKlVmRjJ33ixTUCjRxDi+aN7CRqJYmkE4SxvbqtLaHWnuQXloI7vJvkR4dqpEYvQk+lErRz9HgWdoBpMDV6rWgNTBjZig9dMT85F3/+hiOXPSgo10VW/u2ssTmy7AtA+NK+/HTjmfrKM2v3UHVfKOmFcz/71EpXbx/e/t1FWLElHkINZFeRidFhXBiVcwKxYi5+X7ltOdbtOJA5NyBul1CALU6cvcCF74qO7g6pfxJyhK7e9NnxNVapj3KWRs4XySxjp/08wVKa7g7bTWqV9Q7FsjeS10vMD18B2N9lzruz1313stIHDAaaV5DbFTihVnpDgUL0BkXC7WEpVrVGwiPnVUSPBRvxVZ4nlSdjKJAWCOY7hlAouOPlBWMmAT8x4xPS3YbUHq81mo/QA8L9nLeNJ35zZJucwOnXjz2HT//+abGNey9IC6B/33xq5bG1u3Dr05vxoV88LvYfmzPv2yzgAq3DeGox8jPHM+Tx53bjC7csw2VflMO7uZyI+1lb43lwT9zPEkedXFOpOo5G4IXF37eU+pAFMczhuPKU6Zg3dUw0Hzhp/XnGzlZxh222FdEaABWNt5w3GydMHxtNtmYW4ThlVyop/OaxDQCA259xXairyekEANPGduT/qB9oXkEO8lqx1IqoZZi/715uir1+5nfPJO0VrePaFXxhFG7F/Nv2M1ZIgWvkLzv9SNNHhCOXAijyBCwZcmN8IO02YkmUYr621SCsb5hHS3lb0GRxkft//48ew//cuUoem3Ula8XuZ1+Qt1SZB+Vbd68OM/Qxg5dk0OMGO0k54HOVvGJuenwjAOBgj7xLamU0XiatIy5w3vMsGPuINpC0aaMYuBGgXX1cI+carQpKJ1IfU8a2o6OtnBlZWSoprN6237mX1bgfQqfBWtI50ydJ404Xophrafr5vZcYxez0WePDk3TOSX7GfE2+XmheQW613rbEjzzUzujG0yo4gmmBRpBHkuR4n7t7wwfP30rxl5PfwzRbnqc9IB5AkSdezeEqygfS/JJtpndtqFwWCY9aEGp30tx4u7+IKPu72hcRJC+8EgUGffzPvzgNQEitJOHVOUP/8KG1+Mptbig7d/GTkmZRl7RT8oUB/+wrHQAwyYuQ9cGDy7IiK6XIzrx7whWLmPuhgkrcWQHgd0s2snbzv+G4ZWO9tjvksmCMTQQpFJ7ZuBfPbj+AOR8KzWyJzcuWtFu6OaXIjPIU36WSJ5m00PE0ti1lFTWSKwWMbjcyRHJ35ogpSbsiqakHiuYV5HB56sBrhdEbV55qvFZefsaRSXulgmS7mGfs9PsmjRcA3nvxMQCABbMnenOLbwOdrXBGbok43KIZkpcCCRwg3FE8vdG8AGT9rwXBA5sz95DWkb+vauyAkpJfpsTPuM8fWzb+Sgi9ajzqxOuCn3eLEPnJ2xf8+61Yttnl6U8+chwA4KQjx0bn9M4L52L2pJFxjtxel8Bskeu1ki5AWoe/Tw2JqSDvdIJxvGsTsQHEaKkKWwmkHYlv7BxpCzPw3yZV7iO7VA220GXsCFrLJezt7MUTz+0O2ktKJUrdm7/9kHcO7niDXSu3eQW5tp4ZVlD6LwYJeoDlR+A7Mc0qitSoVfJPl55gUmK289Sc9qEC5LSmadpO2JcnW4PyQdROjA+0Cns0LwS5TZ4yY1z2QNLY3ue8oJw4P1/z0K5WnPVCwuTW8Bfg2PWQ8PCanfLYEeqETrxUUti0uxO/emwDbn0qLYfnC88/P+v2Ty9+rHoMubxKBslU0MTcD92+ZJtPtp94SanEvgEAx7FKUK77o2zs1DQ/wcfeF9Q+fGpltM39PXl0msWQ+o8ViObKTbjQ0fxVMsZV/3VPOD6Arftk98Ewo6nbTvnKv/OW54nHDxRNLMjNjSFq5WM3PuVU1OE8tpS1jW68aFwJeGBZgwF4igC3b3ogkjqGjseM+V/ZKkNZXO8ND64Vzj3bmAmdakdAKESoQsmC2ROCvmn82PYyyKUe+ma67d5num4rtuzFrgPVVR3n8wJoJyNrXTSGRBPQPenXboAJm1JJCVRe+rJToMovWH1Jn97q8jRPmlIshTIXtn5f6XlS9r/s51nagTqCXBKClr/+/fufD0DeYaZxGeE5UD5vE/XqP1vptZPA7ysgpwrOyxdOuwppoeORpYu8BdwfP1ZlKVD+hMXsrefPxsXzp4jHDxTNK8jhGjsBYE+nxz/Zm0qaK4XUAylHTiG5+1mdQ/85lIxLvqD23QtpVqPbzUrMuTFOEYjuj+zvf/5l6GHBXyz6HLQjHrxA9EZMMf3U757GmZ+4RWwLNI+I4ejjV58EQNDI7RO3eP1unP5xeQwzZ1mrA5jnSITWIc3QPz+i2vqz6+WCWgpD94UN4Bai9ufqRwJSf7G5mcXbeFXEkkpV680T5jpxvaCkHWoQHu+58hIkY2HSB2RjKKc2CGPa050Jp16AdMH5zeINYv/+nIDUoCvtGPj4j6wNBTl/rsiHPfyN+zlL+WsEmleQa9pCp6fwA6u9cuoCSLm9L7FczMlW1bZd8eW7Wd/5woruCfmV/u99a1i7Th78jtYyRrSWnUUm9XBQshU9R9BoIFNQp5ynPH96nGIUwzfvXu0IIY58VzbzOa1d2Q+pCdkg6NsWot4PkI1utAhVQ61ceoKrOfGxRa8Vb/sPAE+xyEN/TD9rJD1zsZmRL7eYy4TvFpTsxcQhRYYaIWg+S6kPEkNjOQyPJ5ByESuVRnRfzN3XWQS7XFdheziA1IPHiey0LwVp62/9zsPhGIkNIb4QSo8Gv/ecXhLOMB1PeD6q9FDsF5pXkFthyfMMb9xtNG5OXQCuxT853l5Y0qjXskAMunHEA0rUBz10x001v5k1YaRzPL9n7a2l0OcVca+V/Hzk6SIAZGyFI65YMU2+GuQazuz/sTwv1RYsWrs9DIxx8lZLgpoLeiXlzDD/V2OImjnBzRudF7npCxvA8y32hgxedHtdYvdEI93BZQUEiZ4n9iNVxpE48ExaAukC1R5JWAWkdF9eFfqYj31czqX9A26UJofp38zvObb7pi6ikZvJ+ApHjHarB/nzIwXN9xvPo1YqTGY0As0ryK2w5PULb3t6i21ztSPp3agkWmv84l52kjFkxnKZAKlxiieMJz6OsOtAD757/7NB/2lFk2yNXCoVxwV17MWMFb5QSv6+GtAx77P+tDFhGq02X+U4L/riXUEKAa45ScKUL5BKxXc6/aFW6BAKIosWJ2A3nufV8IeMCePYPdFWmoq5SByNPO5H/o7nz7VjCx5e7HmSjO/UJmUe5EbozCr0kA3Fya8jryI3pnLwEP3UmCn3kbwzGW6rSgFffs3p4bHJ9TX/XzxvcuBdlJdMzgRdFYI8AAlLrpG/yoZ9JzfG/k+r7PxpY5LfpgFBct8AktVdFEZsZVbKz27IRw9BN528VnIDOCKGmygfaB9qem5iuZnzfLnl2pXmuymk3UXmHit5Vssu4Lq73MCgVOuVBRaBBEqMIqvGhz0apq3knNV5leoDjS22wEnXnD0vUm6gcG7+uZj/o4sr05aldhKSgMyRO7RT5L6QVp9NiSl8wtpWYu2E02eNd7yu0ndCFmnmHM2/LC+x848NOfD0+qaLmR+jUI1GHltk6oHmFeQwF5ZHvE0cZYQLN14A5uGeMX4ETjoyvfGVirZ5KaS+zZeUIlfiDJ1ds3bd1bgxFABOnD4Wp81kYzMNqpqKKpJxijxegCwLvaxhxbj1YNyI0Qrg/rry3GMJu6pWyeEGfJhDU4HWImUgDDRTtz96IWPUCr9n8cRNEPON87El5HtC2UVGONbxcioJ7rJs/qJXhre4SrsBWvwAOcCMTos48pUsIyi/L/ev2o6H1+zEHUu3BHNIOP7I/EoKeOO5s6PFkfk71+4JU8q9xJ91v7xizEvMp258pLsxew1ayqEg946Rnr2CWhFAWmcrMz6QpsLTYhLMix8mtuoRjDapBmP6lray/i25f9V25zNvnzS6zeH1uBVecqMLNXLZXayUoUG5Gpbbf8wI6kNyQdSeUIl5AKQRrf3XyH0E/soZ1EpJolbod5EFjP88HtCjsHLrPqzYsi9IwmZaU5wqLN6EWHEI6frwHZwYwMY4Xomqo75bI4tropHHjOdI3yV6J37KaK/0vqRnf/MTm4I5pDsK+b7Q8Vu9VK+V9NIn8Asw02LzhydT3/3V2/Z750DGWPf8fOrmaptWg65r+r6aH9y9fCtWRdIIJJ8z6NhGoIkFeRqcQaAb62vkgMn0t+ug62deUgq7D4Yhs3SDiLaR8idn8V0+R95WLkWzH2YJQ4JUGiur7BQ9tDFqhbaoeRy5VGHHESpKSPiVXLvIIpg9pINXnOFmSOQCS7xu9n9z7nH6oxpqJZ7KFHhmk9kpvO+GR1nf1J4azHjkbBjCLdMj0tT4LijL0GrKzMXPO2YzoYRT1XitiBAm/cSG3eHvlOxn7r+vvoufFjTm9pYyunpcnl4p5SyeXHki5Uv0ZmLKCQAcM9k4MCTvPduNAakrcZeX64WD3yMe/9AoNK8gh5vGFkgzskmc2ta9XbhjaZqxjDjy6716m6ZvgzgH7W7zjp86GpefNC2YG6G1XHIDhpgwFF88bz6+K166FTaf5a0w8wuO8L152rFUhZ4Ly6ycI3mFeqsBt38ATF4o2dvHLaIr7XQgzklCVt+Em59MtU6+y7rr2ouC/vIW50RmCFPz/bTjpd6oZqc3Nh2bYYA2Qi6usXMZdOyU0XjJKdOc431Hkiee466B6W6lpRz3uqE+Tpw+NimqTuPT8YR2TyOHNWaePSdNldHRUnbHjylOyQ7efN6421AyRJ/4XnBnHDUeQBiQBACzrdODE3woKJb1RvMKcp0KE0qSRFqvf2MkUCTYaBZ4QDcmoAeESDouqNtaQo2bj/3Yul2JFmeON/8nRrsc+kH0kGAceF6uldiLvWzzXmzZ24kYZI7ceyki6Q1aYruZGlTymOEqFkHIvSfk5Eju73zMnZz6dmeFwWehpEwukAVHT8BCJlR8xLTmrIUuFlXKOWrJWycxQNvrKbkncirup4G3kMvv+l479C7FwHcrWYUbSFT7ni+pIOQaeSnIia4AjBvherK4x5s5rNiyD5//49Kwf/v5t0tMoBFRM/z6AsBVp9qMpnyOMJ5r/3DZPDO212b6LzTyABqpsLzmrJmYOrY9FeTCCn7lqdNxDHtRK1bYfutNC5LvUgMGbUXlB1+TCmPRWvb4OriCfNMeV1g6XghVUCtiJjdk5w4hC73UTp+f3LAHCz95W3AsQap5ya+txEPnUis1SHJfNnAeulxS2LC700s3my4ykiE72YlEJDmfWixfSexl9D0bOlrLrieTr5HXYDvg3L8UEOTbDmLtLZHnxY87kDI/8nvhG5r5DvUn7zw3d/7b93c79833+PGjQ31BCpjYjK6e8J2bNLodnw8UOwNeHOO//rTCmT8f/9+uOhEAMMoqeT6HLsVIJHSnsBPN82iqB5pXkOuQviBON7lx7MKNaW/BPhYtRgECZ8+dhH+98gQAzFjqPfgSB80fqrZyySsE687Nd/fjxqGSwDPn5dVO6pFmaeRWmNFnt39UhezalfFMd0B+od6qxhdsA4C5r9v2mTwtH/z5EmFuhnaKuZlFg260xtWnH4njpozOoFbkufrKg9mlhcKIEAsIyuTIlVzc2N8lhYFQpJjE7wlp+/K5uRp3V08FO1menIpOF7jn2fw93NU3FaSpcnTBZ28X22meWqfXSPIjP9Dd5/rp63TXMH28SVudyAN27/w882Z8dwYUG5Iqhu4ineQw8iuOQd4l019FZKcAX+vlBkVuFCOMbm9JwunN8amLoP+A0z2I8by+oA6oFTN4gn98sdludQcPhk10nyPs5LSjcRfApD1mDK1SksuVcvjcpd2E1cgjPvjVpJAlhMVCkIx9sMcsyr9+LM23EVBWkbnFZlDR8QWKC5MnP/bi4Nj0ZTXXvLWsxMRShHhAUMY1B1W0iixwdn6xdtol+UOQRl6OrFJEWxCWb9mHR9buStuR/oCEHacS+QJMhRW27XOFMJBeO0ptQGkiJPsE3XcSzNwrJKlRUHE18pJCxLkhbQdS2wzdP3+Rlpwg6J2X6Ex/t9YINK8g1+6FaeWC3H7Hr9uItjL2d7v5G6g5zWlOL3qOBqPDRUSqVpKMbTOmdXb7D1Z1XitiPnSk27gHPNdH7jMrzb9aWUopD5y5sblL/D7NPboIVje0OTbqbyxH5PIFUvYjl/tN21PbQihQ07FHtYepZv1cK62epxK1/+0Lj5PPzfud1GYWz1IGR67EXCLUZ/bzrHDb05shwShNGUJIZ2ub3B3Ydy2k8U27+f+PNv0veb5IVOkHLjXXkQQ53zWQiyRdfy5I24RcKT4HT7/p8gV5opELSkqiBJiPkrGzkWhiQe66RG3a05neeJaUitBSKjnbNa7RJ1GInrEzVs2GtqKEu1dsw5Mb9nh9p+0kyA8m2gO9eLKbnP85KyUnAHyF8X3J/FXc/bBajfyN1z+EXz/mVrsPNPLIItEa8ZCoQSGPenZI/QKuwJOMfiRQYqdPAk1OipX2DZji1/6xvD3wc7b/Tx/XIc4/a7dAz3NSji1Cy9AClufbH3MvXL8zXbgPOEqPnPCJG2izDHn82vieSE677WPG+BEA0opeUlwIXUcqIM7PKFDMcp45nsYWCNMQ+By9FCORRa0QCo5cABfEgNkyrdtx0La5NwYIa3tyesSvpBNolYKvNB+bbni6gruLDD2QB9k2EHb+fX0aq7budwRmqJHL2htfTBzjEVxqJaaZVoMHVrmVcvjcsuqd+otjenz1i0oYkJS+0Pet3B78nlMQUhrbrKAbmgtdt7ziBy84frKXXycdGwh3ab4wjS0UkiR3F88SNu3pxOJ1u9K+kc5NKpTip02QFRPg8pNTl8INu3hUJEQxzQUlfx8uOPYInGld9KgdMNeGXAL98en8gNTY2OZRQXwx6fCUI9KIgVQQ+4pASSnXTubPz/bf7qUh8BfxO5cZN+aHV+9w+iDlxhwTauSF14oAn7cj9PZVRL9N35XQ0ciDtnSrClRvLCRNx19kSNMhYc05PzIa/d2PH2P9uwP4/txap9togmRszdpRVAtfi3Ay3VUR2RkTiISs9ATVZBh0f5DOOcujJk6tpJ4V0ahR+9mnOHxh49tNkCxwxKHK/WdRKyWVVj26+mv3BscCwNa9nXhq4x5+eJWKicLzWLlCLjRjAXDk/qfhKhW+r7hfos+HT0s9bef/M+sGKXkMJbvc7nSX67/PIbUCPMq4/RgV21a2dTmZYsbHP9IaU/1ziBV7kbxu6o3mFeSRh2tvZ29gBQeA79nsgyu3mhwRDkfu5aDgL2Us1FviaHsYp8Zb19kUuX96ZkvSDtsH9ezRbQ5ihS2cYCjP1c1E+ZnPMYNkNfArFPGHPisoJ+4h4X7OCs6JBUqVlBJ9tAPN1O9b0xzl8ZLiA+JOwyCtxeqGynNPJNMua+TEr8Y0cmlqXBCtYWHn/uSUMgU7pPMCsqiV1FBK8F3rJFogdQ5wd6AtJddjh2u0YS3UUGMl/23SfCWPIb9upwY3RroceUwBOMj4dT4+lW3kCxUf/+J5Jlf9tHGpQE+ePcEBQfK6qTeaV5DHtnuVimjlpvzEm/cwYwsZRzyhw40bMe8HflNed/ZR5vhEI3cXmbfb9KGvXWh+xznyh1aHD7Y/Xuh+aKbOy0798pGUmiHOMpaudiDGF74IyWXF3C1mLArx/RGjH0+5ECtwrJR875120dgZ13ppbqSRx+wKdFvX7jjgPEu+1sXdYU07kvaSilNOa3ccwKbdftwB7NgKi7xan+7YCifPGOv0R+cFxG0+5ryVk6zKOTftasN/d+nxADwOmt2Q1rLyfOhTQblGyDOfUmbmE0WNEnUlCcIRbUZ0kUbOFxsS5F+2/vBcEE8a1Zb0kXLg9APzH1E6vtdKen7uQkG/yfUjL6iVEGSY8rH7QI/ot0nRn0eO72AX1qDsbcU4xy6WYvMWEaqAzhcC3j7ScuREo+QFl9Az8NLTjnT6TduNoKZ+AWDXwdSv1xemeRWPaoETlBPhkvkDHfNqiRlD+S2N+YEruIWjE82JtZsQfXkR6a1oJzMe7z9m7PQ5cuLoewMeNeVpu/tSpYJrnWKZOvbsnPNpN0grr/ACF3Sk8X+XVazyg7RixZenju1IFoIv37YsaCdIBSpcaqUUuuYhTtP51/b0WRMAgKXUCAWhz5Fz5YnOlyhNTgcePzX1b/c19njOdS2293iLFRmb/WsTO/96ookFuRYvzLfvXZ0KKvaDSaPNStzdG3LocY3cvDyPrdvpCBUpZBnwqBk2N+JF/+PmpcncqX/KKcG9IGgkog8kY2dJAR1MkJ919ITkb9KwYpGdA1DI3d2KsMglLnzKvSa8HYgbQ/nvoxy5Aj54xfzke1+zSgNjZK0XABZ+Koxo5cbOGIdNt/WdLzC7rE562b2xbrEudDv2dzvHU7BSLd48qWKicOS4kJ/lgoiMoL96bEPQHqO7aF4A8MozZgIw+U74+FwpIlvPvSu2JefG38WYD72veMWMiaHAt3Nk0ipx6e0JNXIqOCElCePUXpqbyb23JKjJF96fHy2I3ULB9UxjZwMlefMKcrgaCj14XT2VlDNk7e1su5S88PYXAUdO7coYER9YtQMfu/GppC9T7SPtm/KVbGJ+125kp3sD+WvUacOMeWZGegjavcCGtN1MjlMrjoWeCSRANm5Vi4vmTRaPjVIQti2WYjfJjR1pnzAy3frG3AdNib8SPvJS491A555PrWSfK1FSUmIqX2s7cpxxkeOLCBd2ZLAjSo8vQi2lUk3l/fgi8GMbAs8Fur/IACa/Tzp30shphyafNwC86ERTFetktuPx3Qs3293M8i2pvYk/462lkvPMckHJ8/L7cRvUg++nL+1giSM/wKkV2z6yrQWTRrXhNc+bZSdgj1fK2cl198kcOAWzUR1en8Mn6qXHczBwjZ1w2vj5NQLNK8i1K0z/9aoT0jb7v6MlJNshHfCdoUYebuXc4sqmlUBFn3/40LrkeD72aO/B9IUC/46fQGuL1WrFwhJGM0j4PCf4xDVeZXnd5CkJUvALzb1UUrh3xTas2sqKDNjdQCyvR0qtyBWExnaw6umRhFw05aBQAmuXjNR5exESxlJiKt+zwt9++4mj/uOaUwGktBoPA5c48ixwjXDWxJG44NgjsIHx6HwnknV8rLCE1qm228beE6kdAN53ibFv3G6N94Gxs6xEjx6l3NKMPUHAjfmf3pfXW9uT5PXhe63wSG3ADxBM39hrL5+Pd190DACgO5ItdYx9Bkk59JPw0XspvXNJRtKKfP6NQvMKcrhaAv39i0efE/02U4HXF3i1+LkTpIXAGVu7ARJ+wVYT6p1+7mh1fWf5QjJr4oigf5/TlELlaW5//LsLzXn51VJUhvuht+3L8mJpLbkXweXIzY7lks/f6fStwH3zZQohK6Xq5SdNw9lzJkYXgSSCz/f/Z1xmlh95DFQ1SsycCBrb/C8ZxPilGmOFke8JRcIsVq5Ngm+8v8dSGqmhL32e3nvxMdHjM5Nm2bdBMuSZU2A7TCs1qIIT7cIIflQrjVZSyslOKEVecpCCJBk7O6yxc8n6XfYcEV1MUsXJzI3ywYS5VOj8TFbUc+ZOco6n/kVjp+1ApFbgHt8INK8g9zTyI0anW3IpjS3XoFLOjR5eV+jkJYL3x772cpNLZXnyYOdEutn/lQJu/JsLAABzjkgzM/paq+RHnjxUouEFmdSKLzOytEM/kRLnKyWfYHqpo7mt7dnHcrFobQKo2lpCg6C/BV9ky+ut2rrfjmW+N+PH/chjoPtaFsvIuc9EWruS5/pIr0eqHLj90CIX0l3Z86JjAVOvEuDBbeb7klKJtiz1HfXtB+d/zR++Dzx/3uccMTqYnxtFrYKoR9gxPnfNqZhnDY7d3kWIvTGpohEqZmQLMLspRu+US+hh7pFmfHex6vYVNzZmC3Mv9RUIqQA1KXeiH3mOPKkHmleQw70wxzFrNF+BCTx/gv9C+7kT/BcnHNsV1BRSvIryF3uCHjBVTyjaje8Yxo9sw6vOnOlp1HbOtPILwjCxoJfDh4o0qBi14guNLF9uiU+lufPov/T3Kc8MSPy8+T+v0K+UK8U3ep0zd6Kdi223/5PrZV4gVyz1QswbB3ADfoDUxuFv7f3z48LA9F+LRk5jmz5faYuM9/R6Gi3C3R9vb8lYPGNCjtr5u8R5btPuUiutQn5+mt+k0e14z8VEbciC0of0Pvu/9Q2uJme6vMNu9ThuYZ1wFqOAVks0cndna6iVuEZeBAQJiHmtAK6wIUgX3+fIs5Juuf27nCFp0289f05yvH8sD/v2dwwtJZUYxfj8Yxo5N7b6CX6AUDuIuR/+k/X8qCm6kv5QaSiz0067gUSQhdsBQy9EtEOdetzEPUfMsXNtSa6eYCdFGjnE42PnVqHrJhg7fYObH8btL95rbRDYzx8x0Yl8N5FVkEOCL0havKAiX9CPH9nqeJ0QVMn0IXnzcP93IPXooPEdGtM33nvn3lpSji0qsQ94i0XM2AkY91J6tqVSbz5CaqUEP9cKtbf5905wb2wplYL3zqdWfDqTGzu/fc/q5PwlL7p6o4kFedZWzP6RQ62kXisR7Yl1wOsI+mlsjRcF0wB0+LDzFd5/sG6xWed4cAOQbnMPejmU+Y7AFyjp+OmDHyt2G9tqc2R5b/DIUkKFnXtMIJLGLY1NC7ToPkh/2AsXhmnbZnvuMT/02LmRwbJcUli34yC+w8oA+ga3ILGS90w8aVOxkhuiT0kFGnnGPUg1OpcK7PY0chr++CljMHYEMxrrVBBKuw26J4CZW7mkAv43Swb57ri+oEbyzPjtdv6VdGzC48/tTs+PJQ3jGNPhGeJZe1tZCRy4aScDdJLWWtD4O1pLGdkPjaLh2BGsckXnsHj97rTcXc4Ovx6oiyBXSl2ulFqqlFqhlPqnevSZh7yHC3AvXEpB9IkaMSBx5Onx/lbJH7unTyd5HKRitbc9syV5uVON3/yKFgk/twMJC/I/T8bX6cllaQcj280DSy5a6fHx4AUgvVaAFNCTak88CImPnQgFJWiedm7RcHHbt8qgN6h/SkaWJgxLNSuRmvG8VmJZJSlnteNy6ikHdOytVlD7Bm5yczvBasZ8N2EWOHduWRy5lCIXCBdoGr9Ucj1++HWL1ax0U0IrMWoxPj/3XUvuS6+3wCYGVbsQCblQ5P7ldj9fOb/+3KDs77DJxTUI0GMDdLSWHY8YM/8UoUHXLOTcphRw8MOZWlFKlQF8DcAVAE4E8Fql1IkD7TcPvgbktiVzS74jo+BHb3wqEAhp/gs5QIH/hvqX+Lz7bV7wXA3GezApmMd/8LjG+/bvLkqPR/rQkgblc+RKmUrjbeVS8sDz+ZeUitIbE0alngXxavLKTXfA+lZMoMjh4Fl+5qnmGNIN7hZ7lF2o9nb1JsfSuErF070SJDc8BYW7l29FAO2O/ZyNGvzWPavZeac3/QqbSfBc8nygBhXRyDM5clejDDRar71cUnhozQ78v58udsYmjVGqvOR7nbiudfG5UZI6XwgCQKcvCGk3E+GoY4tFHjMh+Zm3WHrHOd62k3vjfnpuBOeIjtYyy8USjh+kKbbXkBfnoHc07/zqgXpo5AsBrNBar9JadwP4EYCr69BvJiRhSdZ8aQXlWqa/QpJ2uOegd2NZD91OoddQ4waQ1ATNWmRMO5z5+ZpxSq2kc76VJf33+++raIdj5+3dfRX8950rnfHzcifzj4GhtJKtPfEE/3KeGjh+5nKZOhv9GOOpPWql06dWoNBWhYufxMHHfLwr3j0bzwKXgNDYSQvVJ3/3dHLedHxWsjFpbr6xr8XzLPHbl24yfv1URJkLsrJS6Oztc8ajBYywt7MXj/PkWzp+v7v7KsG5+/n38zhqTv0Q/vaSYwGYexHjyP/irJmYPo6l3PAWo16PA/ev357OHnF+gMnH/ojNayPNr83XyO018ufAjx/u1MoMAOvY5/X2OwdKqXcopRYppRZt3SpoPDVC4siPnzo6aQNcgyRt52ZPGsnoAbeHHz5kM/0JK7CfAMm3QJ86cxxmThgZbX/jOUfbuWmmAZgf+TQDj+ST4GtQgFvyLHdHAGTTG+xjjF+PCnLWJnueaMe6L7vCxZKV2bHJtuEb/Zhm5Wcf5HPPOreSUjhuyhj48MuRUcQrVfzJstnw0WOpDWgmJOS27U93OzFqoieJaHXbeRk103d6XfZ29eL7D6zFt+9d45yb/6zx5Fx0XSSQzcnXZgHu0WOQcvzujqJPeN7/+65VAEx6DDo/fw6tti5q8kyyNp5KNwixt8/N125f6cyPd79s8z7s94tW+LsWP3YDrtulFCneKAyasVNrfZ3WeoHWesHkyZPzD8jvL6A3frLIaCBUlNU3SM4YPwILZk8MLiwlAaLE+vzBSHOVu1tNf+yOlnJaP7ASttMYFR1qAJTsfh9xfmzO4rkjfCjOZmldJepnt58CQMV9nbnIi+fljm+D6YWT6I1gEYkYO+Uycq7mRfP/0zOeQVGlL7l0PAXNSBq5UnKJO18YpO6fKumb86O+IdjlqYEHV2/HWpYJsKI1WkoqySy48JNpLhgpsyIA7LR5XHxqYGyHHEnM8Sm7U6BzyxIyfuQmB3mn+PnITRsZK90HPrXr+IKW74DNsa/6xv1RxWbdjgPYtq9LpD7uWLoVS+yuwlfcwtgIaSlI2ySNvbVFBe6HpZJbAYneuWbJfvgcgFns80z7XUMhaaWEfV3mAvrtbS0lx2sl+d6rRMI59vs+dAnGjWh18jxL2ldHWzlJoCS8N04VIv/Bo1zpN9gdgbRV5PBfnFNnjnMyIUoa1pPPuVtlcrMDsos/xHJ6lxTwiatPAuCm0+UvvVg/suIW+hUNjiCvFXHoQJA/vMbdAgPGi2j1tv1JzhN+XkncQIT22XlAKNCbjK2csWl99w1+FMFIGSz53EtKYW9nLy783O3J70mYCo5ATqATYLRFAPgH4sC95+nVC/jrmII/M37hA9523jGTsIAlYZPetb+52FAf63ceCM6d7i1RF4SUWnEFvZ/DHvBSNUTk7N3LTYQrGS3z/NBjcjRLYzb0j7uDBmQ7goLClDFpDpzbvfoDjeRW6iHIHwZwnFJqjlKqDcBrAPymDv1mQiO+wr3tfxeJ3yfltzwNoFRSjqWea0BTxnQElbcljbejpYQuVnYqJoR72VaQXh4KKBo/oi3pn7cDwPnHTkr+9gVcR0vZcVH0Xyw6x7TdrSAkueG96dyjsXD2xIxq8gpvOOdovPLMGY4bWC61gjRPizQ2tYvGSl+zilxjpYAnrevXFV++O5h7q7CASZ4PAJJ0t0EYt/0/LT6gg63/1LHtGGkXOX5PlwjFH2iXJxaV9gQJabiBex1RL55/f55dw9fIl23e61Er4bn96GGjdPzrr55I6DDCn5YaAfYpzz5AfRDlcr01FNP8+LX/v786m10A7Rzvozfj/PiuK2psjFA3APDw6p2iRt5WLrlJs9j4X3j1aQDcVMuZ49cBAxbkWuteAH8D4A8AngbwE631kwPtt4px890PvR8kOaIj7kSpXy4dX/3YHa0ptSItMl+73RRIXvTszkDL/fvLzHb61Fnj7Pjh/NpZrUP/xeloK+NgT+h+yMG3+jpPI7dj+25s1DdAhh2TkyK03qeCVvTVRtyHnegN47Uiq+R0alJgihlXJXlA/L4BOYWu5PkAhNkLU9rI/P/1O1Ymx/vPRFmpwO4RfWTtPWkRVidfkLzsdKPlX3PWTG9u5v+A1oF7vNQ/v5bb9rlupRV6IBjIpfXJDXtw4+INjjZNXjFbLcXpuw9OGWtoRtq1SNTKURPTeqgxjpyUGxKo/N6dd4xp6+6r5F57qZ3ysre3lgLFCwhL+fFreC4bO9Z/vVEXjlxr/Tut9fFa62O01p+sR5+5YyK8MMdNcXNA+O0+tcKfC35jfM6MsrBljd3RWkqNOwK1QQ/++p0HAoFEQT19vrsU68PJfeFxls5uAFb42h9QQQ0ehUlBO1l1NSkwJh4QlM69iy8ibG5KEMa0Tc9zPxSrwVO/kJH3wtDxUlELrpFPZFVkyCc6t28dtm7Y3ZlGduZM3t8lhX2ngmRshxGAtI33BWFbWYnHx6mHOAdufhAK0Xe/wE3Oxd3u3nL+bABupLMZ3/xPO09KXiVx4ONZfv4YR/4KmzudAnf4FC+Zb8qxdUdq+BJ2HegW2z/wQqNctZVL4o7Gp1b4NYxVGDokjJ31hsTbXWpzKRP8h6+tXEJXb5j9EHBvjP/gTRjZ5jxEWniwdx3owSa7DZe0M/JumDVhZLBVTtwA7R2XNICuyDYOMMLGj/6kY/danvKBVWnVeXIXi7sfpu3xvNnKjt2Cgz2pOxu/L2UVVnQnI3WWRg7AlpHzj0XSJiHvhcnKAlhhff/sXefiDJsXJ0lzYH8X3x6Hizefk+TS6v/O7JKkaFm6JuYzGRMPdLvusnGNPFyELmZ55v3nyS9eIRk7T7fXh8DvCS2ERLkF1E8kMpVfW77oxHZLab6bPnt82ObUHxAufWdPRby3HaxwhSwvlJPGQLPxg6jfjPHrheYV5Ag9Q6598Tznc9zY6QpSgIR8hTo37baNDG88d4Lf9x9thN/+rl5xbi84/oika3+r7FdWlwIweG1PfyEZwWidZH727wdXmeN+/PA653iFeL4TaCTaYSxMnh5aMnQm4cxIfdg37enEjxetC44vZSwiZF/IzrUSgr9wMWFL/aXUStg3YHK4UEV5ykOfG31YyXlRhUWGpzCmXRKXwf7zkLpdmv//608rkmNN3yQoIx4zbOzRHawilWc8f8WZMxyKxxf0QBroROBD0rFErfiCTCnj59/tuU/GA4Ls+XvSijTfLoFa4Vqx7zrKoZT8vnW0pn1LuynR2Ek7oiAPT3z8eqF5Bbmg9SqlkhsAyNRKV2SFbmPualE/b+YX6o9NnBwJ6pB6SfOC+OOT1pcYVZl2uOYzVwbn7mtISgnFcu0PLrDh/wsd90TjKpeVi4V46rx0riPs9eZ5YmJGyGTuKh5VagR9xI+cna+Pg919zu+X/fsVAICTjkyTR1FrVo4ZuiaUGveRJO1CfGzTd2iXmDY2rLLOf3Gw2/NDVu7LHvNDDigST7HwjZ3p8UzL9ewD/mn1VljSKx2eW4u3WHBqhdq+dOvy5Hh/fFKqzPjyInnh8ZNt3If57M+B6EIyOPvUB2AEeVb6g2Wb94r9uxp5uJtqlzhyOjePWskav15oOkH++Prd2LK301648K16xRlpLJKokXPOjLeVS0n0ps8DkxsVT3rlv0wvPMHQOn3kleKNzWsM+g8OVXv5p188DqA6PpYPT5WJJA8KyuNy5lETkt9XNPlyy2lNKcLRBK74gpzmbs+rzY3iqwjXZt2OA87xJZW++JzyobEVIu6HeUQzE7ZtLSW86MSpokGTBI1Lrbj0BAmJpMKPDl9mPi9JGG5iBZ4lnpq72Pm7JIClbBAEIYc/ts+R+4IeCO0DvG8KlKFSbtK5AcCrF8xM/o7RXXZ4M38+x5ZSWmrNtvv2gSNGtaG3oqMcOQlrbqMhUFDUTY9vBBC/fr96dIPo7ksKYSfzcuPjB7lW2HOf7jioPfv+1QNNJ8hf+tV78OIv3pVoMD7aHE3BW8GtZ4qfVhNwHfx9Tu6bd68GkNZBlIxDNz+xEQBw45INonGIagx+8+5VgaCmXCv0YvgaGEWFEnwx/zprjOUaAB3r8++mnaIrzeeosVPQiv1rI1Uz95FEzIJTCOb4797/LHYzv22tjVBQyuxQvnQrq+YOOqdgCOyzlBY/59ayG2nn164UvVZs31QO7C9t3cdsg1mPuLhzSPk8HLdNb5cEIAmT9xdPwEQov/ikqUnfUvZBfm7B8/rkJja2O68jWKHyBMK5feLlJyd/cyomCEgCzZ/PMeWYU/dDd4yOtrLdadkpxDjy3r7geCqc/MOH1ooLCT8taX7kJbZuxwHRq8aP7PTpVl6AOmv8eqGpBPkmq7nupBdH+A1/iP1nr73V9SN3jCPsxvgr9FvOmw2AGW+EvtfYKL0nntstGodIeDy5YU8q6uyPRlmtb7ot6Otr7GQ8cre6ad/H2rzcfcKOISk95QSAwBocQ83UtKdacZxaMZ/9VLLQgG+vo5JddDz1TeipeC8EUo2dtufu2OGd/869awJh7OeU9jU/vrj5PCYlVkoNo3DaAeBY5iWV5w7LhfEFx9pslywykIQtvy6TRqfRwIAryNZsP4A/PJlmXpRohWRsZrPhpdbSdve8/u0qk/Oup4/zy+E5tbeUE/ooZqg08wsXsYoGdnjZB/0xHnl2J7bv78Yy60oq7bABJNeBt/MKVRXvvgNIUiz87vGNosbfbjXyz/1hqbgIt5bDyOFgx1EYO2Ws5EV+IV+YNsYPSl4rxq/UfOYvhuS1QjjHM+xInCFtxdds3y/Ozdk2ett0evCJZvA1dj+3hq9BUc7rW5+mSDI3uhIIhZZSqYFKqquZ5gSJaORRaiVMGBb6sMtudgASWopecqdN0GxIezSJoKidNHL3ZfMr5fg8MUdQ4Ufw/CD+/cHV2wNh6M5bs8VS4ZtvWoCFcyaGaVCVcq4dX7iBuO3B3w1Ixk5qXfyRy4LjfcXjzmUmF9Ln/rA0qg0TiD6K3U8a3/SRYuveruS57Yss0KRV//JREyjuX1+6fjcu3hD0T37gFS1Tla9baHaxB7jGz34w1jEGh/OXsh/6fua+Rl4YOy0qvjASHi4uyP3WxGtFWGGlC59qdu5LLW1VSSg8sGqHODf+kMZW6AetZ4r2jvHT7Pql5p7cYLbgT9gwfC5UaAwy3tH5cVe3oKiBXShaBD/ylNgxHYfVzNMxf/M35wMA3nxuSg0lAT/sxeceN0RL3bk0TKwm0RtJ4iwW6JW64bl5tZPIzkw/ctNGnO9nb37GOZaPTSHiv3jkOdGTifKm9Fa0c89HtJUxfVyHWGeV9xFQfRkLhStk3N/5isWZnusg0VkEugab93SK/LEEX5C3lhXbSdL85WOluAsJ/m9mTxrltrMfUGqETXs6RfsENwhLOz1eMk96X9vKfupoHcqTZgsIGixweVONRi4aO3tlY6drhXaFVZnSXh5ME9n7L9VlJ01z5uY/dLSlPXfupChnNqLVM6zZH9zw0LMAkNTI9DXyJHRb6+R//9wpHDppR1qWKvRaYXUzAz9w8z/RJwFHzjSTo+2LNqLNfSl4SSwA+P4DnEM3Y2/fLxWtsGOzY8mYe+vTW9LngxZgnyO3Pci7FOrbHTOJchRe9h12jn98arNI9VHhjS6eFsK2rdy6D886SbN4q0Fo7EzbxrS7aRGyOHLfMWDetLE4wtI2Zmx3IXivzaOycuv+TLsEh69tlpRKro/vBeajIuxwJcQS0SXt7G/O2UvUCf9tnsYsCfpR7S3G1Zh2Td7xLlVL8xe7rwuaTJC7L550XdoyOPK2chm9FZ1oYj7nFWyFbFeL1hhN+dO/t7kjhL6p/mVyfOSurd91QAyAeN7sCWk+de+lv+Lk6fZ7+aH56+fPAQDMnzY2OZ5aw8yGqfYXM3byBE5RaiUJCHKr9HChIJWho3PnGhwlOZPOzR07PJcPv9TwuVecPC2grFpKchHgJE+04EdOL6tfr1FanP/+RcdnzpsE9bPb9wfCjMqAdSV57o1WyntI3WENeP9UTGPXge4kERnBpbJs2mTW3t6Semgl/TvHh0Iwz+Oi0wtIc2vImv9jPZAXlY//fevznM95iwmfI+WLP23mOFEjP9/aKV44f0qVUbtu+5iOVvRWtEMp8h+0tZSd2Iqwh/qiqQS5T15LLk8utaLENhIsTgAB08h9YUWh94mGocO+/erlsVu2bsdBcYXuaC2n7oOeoD/DCngnbwc7lnYDpPXzHcMUq7VQ0Qs6v0xjp9WQYmXBzNzMZ4laobm1Se5hGkndSgIZeWku/ju9wrrBSdeNvIF+/diGQHsM82FkaOTesT6k1MW8wLF/T4DUC8lkgbRzt22XWndVWmgpoIgP0etvzYW5UT7umEZe0bB0VXoMxVMk8AQpJXE7bdb4qrVJpz+GTbtlaoNwoLvXaOSCJCJhS8hbTKTmxet3B88sYK7R9HEdmDiqLV8jF3YUvt3KWyu9lB/Uf+b0B4SmEuRdve6qL3LkfFspUCsAEywxjdzj0MlrhXJ+V5ewK96Wrs/pj9pbysmC4fOxwby9h8ZP1M+pl5ZyCVPGtCeRinR+WcZODSTUyta9Xfi0l7uaz82vBsPnViqZUHwp3ScX5POmpoUcSB5ebRNDAcCWveSP7WrcQCqU93X1spwYpJErZ0fic+SusdM99miPf9U6fBFJ0LzoxKnBPTFzI/5eBwKRAsh4xs2SUkldV9Omk7EBd/xXnmniJbp6+wJqpM3TyH0DNBn9nUhldjxdg8XrdgUGZB8vsmkxqGyaj+Vb9oqClHD38m2BoZDAF6TqOPTYTk5u72g1qadjC+VLTjEK0res+zFvJnvWA6y8o0utCO6HhbHTwF/1pZub6bXi+Z36K2i3t4JS+yjLR/7KVuHprWgxSx1Hdqm3cK/W0VpK5uULevJp7WLuTJJ2wL1ufA3NT4JvBLls7CRqiITk/9hqLXzuSWSn4LUSs96nc3M18rTSfNr35645Dc5BYAIt8tT6G9jWcsmJUPS9Vlxjpz02qpGH93REWxljO1owY/wI9FV0YPDjRb19eqTV2x2SAX0Wy/r3husfdObNH5gXHG/c55IQcq6UMGOnRmhTaWspQWs3cjRfGxWbscp6klHwkI9e5v4n9XH/yu3oq+Rz5DEhSDYSIIu6kc9h9bb9uHFxupPz23/3uPG1v2/l9qD9vhXG0E2pM7KMnZLHU73RVIJ8/U63cks+teKi3dvq+7lWYn6fLV6kXEWHL62PajQIfuPbW8rJvPzcEuTTyv3cJQ2MhzzzdmP0cykGbnCUgmZ4PhQOfw0iHryThejzww509+EOm5/azM1cd379KB8LF6ZtLaWkks9Iu5DmucL5W+Qw54VpzzZ2xgWabFw3XKhksKPnppelUk2OE11KVZDB04xNc0u/a/d2aXFqRcOT88l14cWb4x4l4djOOcZWVYufLVqfPs9sFpT5kFIr5CmrsfF57dRajJUcFJ+St5jwdj97p8+sFcbODHzuD0udz9KFyQoISiiKXI3c1YC4TylgBJ8kyG/7hxdkzo0gGTu37esKcl9Ta5ocKNXYfb4PMO5idDxf5CSKQSHVbKVCv1Fh6UVP0svxC+vrK+Uc4UEoiQ+7tEjY/+l48t/3i+jGrm2yWNn2MJ2oGVvK8yJ5Nrx2YZq+2F+gCPu7erFy6z7RYEeVhh5duyt4meme8XNTKqR0aN7m2LT/dJcWCsLWEqdW0kWCIKVZ9YXcladOx9zJo5ghUL7or1koVyMiTXnp5r1BABwAvOZ55tr+eNG6KLXCERuffMj9/jnIHz22GHzhFhM9XIugpaAuyq3uP/dFQFAGaDtJkG5+NcZOKX/xfSu3obOn4ngrSDd+T6eJKpUE+WjmEiat/u+3qWyTiiasjYIw9nb2BMKyvdWdt294o/NKs+G5GnmQF8L2TdrUtT9fIj50QWEHICg7RqDdku+Sfv6xk1wferuI8OtHZcXCQCjSHF2uOPY+bNrd5czNTyeaRE+quCDnzwzleKcMetLzdrCnDw+tNrEDvnK6xyZBu3vFNvj8fsyuMXFUG/7wgQvTOfT2iVpxO3uW8wQhuZMSOK2jdfgsAsDoNuteZz/Huuf2DY6/umAOAGDy6HZxt3OaNeADhkfP28GG8QwhYtdgzbb9me15xxN48/suMe8y+bLzGgCA2an5imHBkVvMOcILABCuS3um+2FckJOA2n2wJ9Pl6tSP/hGArFHm8ebfsNVk/mQjMHkXZDQ62BNGmtG8KaWqv42TKsLwufv+1Em+cXYYeeRwzfS2Z1JKhPcNhIskd52M+dOa441AVEph5adeAgB4/nGTk2P5eaeeAf4iI1/n1dv22blHqBU7Nu1WfHdWPjZACZeM10wscRShTxD0LzzBFDd4ycnToudGBYj5QsGLgDy5YY9orOOLlL9w8/S4pJE77odOitewb4D8pFm0bOSat3veWoTjrIC/f9V2cbHgXlT3rtieS1VKmSp9xHogRSDXQSG3f7Yj8uhOv50/9ykl2Dg0lSDv9aJT8jVyuY00LX7h32ATU/VwD4OMuZT9DHNIc2MAssZOAoXqIfLxL7OCvKuHByyRRm5eFjKsBMatoCKMu9UO/amNRs8Xo98u2ZD2DfPi8oLOSdRoxHDkJBTzaB8nupLl7S6XDFfu10r182pnBcZw3G6jQQONnlVr57aBa3+2xEm/C7jP1MdtceldB7tztd47lm4NhB09AweE1MW+Rs4XCv7sdLSURf95olZIa+Zju1HEoRD1FwE+L8Lo9jL2dzNPoMh5c4HM4ef2Adzr51+r2EKRp6mT9w8QN4IvXr/LjOGdxeu8yl/+HChxWtqe/k1+82ld0tDYmTpnFBq5g3deeAz+9pJjk8/ShXEjO6unVtpbQi0l67pLGnkt7X7/XHv0uWCuoRGyXopQY3c5cnKl44aqJGqUOeuTjzYA7O3sTY715+6MLZyXpJHzufn+tomRuUT3pDpqhRBQK31u0AYJyq7eCn5mS7FJAu1+663w7799WvQT9xGkWbXz//hvnwoW54BaQbiToHlJi8zWfeZ+ffg3T2a6wxqNXDs2E05ZudagFKPaW6C1ceuUzo3AjY3+uMk5JNkN5T6y2r72ujPjByHdSQKhoH65dWF91OaU98f41CtOyZzDezIEOb2TW1jxDJ/68tNiFxq5xayJI3Gm5VMB+cJUZ+wMgxdSHrovSh9wxLaCJ9ggEenFuuLkaeGXND4Zr3rChSRMS5pdY5ELBcBmAQwyDCpnV3HkeJPFjgsNrkmTm6HP3wdj++6HQSUV9/dOsjJPYFHOkIM9bkkzf+HiuVyc4z0qjQJjuFD7wQPPUlNwXhNsrhC613kcqr9483H8xTkpfMCDfmzbVFaQwuGxWff0nJ02c7zo456OK+QrFzRy3wOMXG7/+nuLzLllSOH3XHQMbnj72c539DwB+bligPi1JWNiDNyjzO/CX2SycqZL8xvjOTnwOc45wngXvc8qlr6vvlFQrAIiHF9vNJUgB/KDBDKpFc/7Q/YCkF+cD1x6nNNX7MEmtzLpps2cMML57OY/ZguJZ3jzNfKYBwXB92JoYQ8VP55z+uQtwReRHsHwK/kEv+rMmUneC0loOBq5wKFzNzhz3nbeVqP9ux8vdubmX/orTpkuXAV3l0X98xwzQOrRIGnktLW++vQjoy5y3/+rVIAF7odckHtzp0Wq21lk0t+TJtrTK9eUnGxpvOOnjhYzThKM+6FrHCfhx6vj+KBiKnR9pFqihGsvn4/zvChMXkEolr2RR8bGhJwfMe2DF0yJUVtJe2ZPVaQA8PrmpRD9d7KtpZTGVggpQeqNphPk/OUQ/cgdjdxtT+rwkR85a3MEaXJ82v76s12tLybIaaGR2v2HNUbt+PmZ/YfZz37ow3cn276vO+GwAR6in/6G8p1wTpULYF4dyT+Xtpa00rjP14aVVEIOvXr/fdvunftCFrXKj19lvRVuWrIxmTvPMcMhGfXGtBuN7N9vejrKke/vTiMa/WbSai87cWoQ1LOvy7zkn//jMntu7i6LNNGePi0uMiQov37HykyN/BVfuzeg2qhi0z/8dHHynX9uK7fsdz6XByAp7l1pgmf89/XH7zwn+Tsm5EbkCHIq/AGE1yAQ5DmStNYUAG3lkqN18+NvsDn4lzH3y4JaYVjBcpJLF769JYNaKbsRkhJHbaiNcAvf5mnFUY3c/k5qPpHVjwS8hUQo9srHv/ykaUkSf+Mn7vb9qjNtdSHBQ+CpjXvcH0MH0ZXkUcM9JLgWn+QEEfr/4UNrsX1/d+Km519XZzcAL3iFVWbyc8yE3L+s2fgCgo6nlAq0E/KNnQDSKjuC1kgL//xpY6J0Fn/eJEE/Y/wIjGV+9PQTMrI+nhiRESxwABV3oP6FCSBdmDmoOARlMOTNboV6+Zq+9DR3l5OlkeeBSsf58+fURUwjj3nFpPNi1Ark5yD9HB7PdwX5GrP7A7cKkPvcUwzBuh0HoAdBkjedIJ/DAiakG+Nw5F5bEqIveK34frn+8X75qpgxk8aXdgtU1zOZn0MxMGpHyE0xfmRr8r2U8vReGzK8vzsse+WDshvy8V96qjEMcWH5r1eekLR/l1wfhbkTDnb3ia6R2SWxSk6YOodfzSbCAgAwpc8I1D3lwyZ+3zd2AsDx1k1O0vbpHJ/ZtFcUloCb2EmSddv2ddm83u7cLp5vXC7/4iwq7xcagQEkUaN8Pj6kqFP+UXvPC392Y4vERfOmOJ/zXGurQZbGG3MvbMvZCrhGf7ftXS+YG/0tgSs5tfiRA2FaD95MGUmnju1gu9zGSfKmE+QnzxyX/J3rfpjjtcKfTdIKv3f/GpFaUUphyUfT6ioxjXz7/q7o3PyXgX+i/m5cvEE0DnW0pkm1ILy4b7QGv2SRynhmsqqi89fpilOm4z//4jQAKWcqacXEj/dUKnKEGzPY+bRPKy/H5tE2o9tbMHPCiCRBVNIuXHseEZkkzUpy0KRnxf3IAZ6/Jtuz4ocPrRWvqWuzCX/Q1VsxiaE8Qy3ZZGix8eVYGzOG5nk97DnYEwpy9tlPc0tRiQuOnpAbuZmcWx0EeZag5BQVB+1UY+ALgN+/b+zM07il5mnM8BzkbvJoQTctNaP7cnZU9UDTCfK2WoydwgoKMD9y1r7Llha7fenWaJg6F8QxQU55pldv2x+0Balu+ctmB/3N4g2u75ZFe0vJ8Xn2R6ftO/HRvP0VZ8xwfssNkt94vTGqxcpSUTRt4H7I+ksq4fRpx08cCHOKiHlgfNrGo2a4sdIfm+BSHOZ/cv/7qc3lYvKhuBpp0ncSsRp/2/JexMzoyogwpoRkPgfPQ/jzPIVWb9sv0Ajp555KxZk7BQwtenZn1eHj/dHIeUIrM6fwN7Tr64mkwc3jyLfvS90PpXeSB0f1J7KTStkB4b1rbSlF89UkeXacpGGFRp7AMWYKr7RjDBVWUIC7H6bt5M41d/KoaE4P3l9eJNp1LGNgDPzGUv3Hl5wyLfBJBUwIf3dfBSu27As4TwD4o62KntQ3FOZ3oJvc+NKxrzhlOkoq9bX2NW5a/HjAD+Bei98/YYyJv3t8Y+Ah4Sdo8g1zPM2tRCVmJTPjkHZipM0tsdXoKbVB2dHImY95pO+k3zytVWge0VrGuBGt+f73PuXUQoJcR9MiEFZu3R/MjP+2t0+LlBEA6CoWMKB/rnPffNOC3D5+bTOKEiXoo7VcwmUnTsX/e/E8sf1cFhAksTAXM4pIOoXTWaoAqf0s7u7sK4ZeYizevGabMShff8/q3BxB9UDTCXIuoKQLwx9In8dOecdQI581cSSUAq46ZXr0peOLBOXRiIE4shjCrbD54nePbxKLGJAb2JL1u0RqZPlmYwReZn/HQcL9HltjUjJIBpnabJu/YEnuh1QPdClxyez3ib80ozD43Bev353UvkwWCTamnF44vPFEVXBQQBMVeEi8Vtj4YWrk2jVy0vqkY8+eOxGzJ42M+hIvpBz3XlurHezelduq8kMOIiXZ372VSnwByaCUeDm5/mjkea6DQFoOLwvXvWlBUn7ORzlDcfPnID03BzK8jgDX7dg/fvmWffj9E0aB8ss/PrvdvBN3Lt1SBATloVbjhFLKCZ31j24pKfSw/NUBtcKW/D/aHNo+iMagsmvRuWW0SdQJaQ7jRrSKeT9Om2VsB5TPOm+bH8uHkggN+4KMDFwf7fzZ8Z991akArFDyvC+ClKmVuGYi5aRw5xYXOuSX7bdNHdue3EfJ7dKnbfzjf/me85K/Y1rruh0Ho+1Pb9yDxet3i2H2QDyfNf36149tiB47lQXLBENzaqUvHkCWlQfkP645Nfk7bwdaDaRn8ry5Rwi/rF//VJgckI3RH33ZSZnHOwtBxiWQdskAsKezV3xn6o3mFuQ5s5cevvZyScxHDpgAlD6v4nmtIE+L8SNbxfbLbVm2rJvqa2cA8I+Xma2lCZ0ONfZ3X2Q0FvLC4M2k9Y1K8nr7VdfLiUHQ56F9ikYyds61+TZ6BWNnq0dn+dTLRfMmJ25+UuSmy5HbL4VLR8FD/j3fvKeL5Tu3RaVLCms+cyXmTxvDMkoiOC8AOJblB897HiRZt3mPMX4j0j/BNwJTib5L5k9JfPR9v3peJzaQ4+zvvkr4vBCWb95r5xW2j2QaeV0EufC+vsp67dQD0hzvXbE9HV84x3PnTspsz6oBzGGUr/QHL2FBatXQdgNFcwvynCsj3VijkYf5yAGTeXDj7tBVTAKl6fRx7eXz8ImXn4xL5k8R2+llzOpbyuuxymb2+8pty8Xc162JcSX0yKHtIR3ia+TtErXC2hfOmZg80FJAECXXespm6uNTa/c1cu/Upo8bgdHtVGuUxk7byyWVeMwgslMCgB89vNaOE3dS9M/7mU17xepEHNzXOY9eyEslK/VPkMqtjR/ZioPdfcm186nC02aOj47NP/KALB9/ed0Dwe8JlCgNaJxGLqVKrmf/Ha2hEZzDSTYm9Mm9Zvx7x425Pl05n/mnowp5MlA0tSDPN9CE3znUitB+4+INVeUPJpc7HyPbWvDGc46OHkveLFlT37y7M7jppJXdZ0tj+YYdeojJsOfXAwW8UHWP/vBzJ/OX4qHVO9ycIHAfSvLT/u79z6K3ryJq5N3MxZC3d7SWEi8iiQO/e/k2LLP8f9ZOKUuAE7Iq0cTqOnLEXPCokLIk7GiRo9PPemL9oXcd6MH9q7bjKzbPvN//3Mnx3QI/j96+/Ao80tzJlTXWXiukLvxdxkAgzfGLrz6dfcoeS7pG3PbS6vV//NQxSQ0C30GBdubnHTMpdxGvB5pakPfHHSxPkAOy90Twm34qElRcN+um7u3sDeb+l89LU272aR0IFSpX9Z1719j+0zY/50iQF6JcSty//PQAHEvW7xINb3z7mVAJXltsEWm3pdIAmQOfPy0tXJCVUvVjjOvkeMHxk3GajT2QKCtCrMACRywIbPo442ssNZORjnZK0m+oeERsbltthr0sYRpo5Ozv3kolaOfBXoB8bq9hFZLqIcilZ/6MWRNw0bzJ+Orrzhhw/9IUOcedm0slIi8IfvI6N5hNpq/uW7m9MHbGMMpqOf2iVsolMR85h0QfEChH9Ql869QP5G0p/aFHsNzglYoOXjzKCnnGUePt8UzQRgosEEyYvG/0C899z8Feke/jY3X19jmf1+80blg/IZ4aoUbe3VdBJVKkl/h9OhaQ76sfBUq4c9lWLHbcD9O2a86aiSOtEK7GMyQmzP7PZlDcJ1SS32nTrHYnQWhhHxS9mfeiZwXl+AZRl1oJNfKrbCRvVt8dTIj1V5BTKllAPvdSSeF/37owmE9/IJ3DA6uzOfI88PgEP01Hmw3R11qLDgqEanZ7A0VTCnLyOc198HM0cr+Ds+dMxMLZE9OXQhjgTefOxprPXNnvB5uK6/ZGQpIJWb336bBmKAkyaeqhVuy+6L5mEZuAhs7diew80ONoPnSaz24/kPTva+SAn3Uy/cH37jdCcsWWvZmBFb62JCHYiTBKqZoMdXkv4kHBF/pb96wGADywKqzETrn1KQd+Xv9ZHL1fmJwrKb2VSnA7p43rcD6LFa/YNe1viP4JTobDfnVRNaRz4OUh+yNH+XlLGjlg3mUNRF9auvcDSFeTiwF1rZT6nFLqGaXUEqXUL5VS4+s0r7pAzI6Y4X74+HO78dCaHcnnRiyg15yV+jTHcKC7ryb3QQ4K3OFVg/zUBL4fOs9QmLUb6WWumZk+zexvst7T/0GIvp1nZ09f5hZ0+eZ90XSo/BxjMFpTmCu9q4pgo6tONXOPuQEmY2S0EYfPx6eEWUvW76qycEX8B4GgZR8NR56zSAhcdZ6PdjXYuDuNjGykRgrIu4YTpvGFRB7/ebMnBCmmCQ5H7l0jx7U245283i7m9aCnYhjoGnELgJO11qcCWAbgQwOfUvVYY7W8GMQXvlwS85EDRoAC8XDqeiAWwcZx/6rtA3qpAVeb8iu2S3k9/ALH0rWjLSQQCjwqVefPrcWvu+lRCDc8ZLxNfvbn9aLHDOHztyzLpH38l8yHSQcb99bJ6vvkGYZjj+2i3nb+HADA7gNhkNhHX2qyEE6yOVV4/+Tzv/tgjxjN66MWQeBy5FXQNsJ582Li/TVKEtUHNNb9DkhtCRxZhScIP33Xebjng5eIbZzS9J9LHuxWFTU2XKkVrfUftdZEDD4AoH5OoVVgdHtOikvhwrW3ltNirJHjGmll/spty6Ntp7KEYHkj5zEJZWdLaP7uZrmT/Ux7WRw58Zz/9acVUS75b1/IIuA4bWP3k7+zhYx9gbXLCr9nd+wXjZ1UVUkhm2vMy5L31MY9gv98mtAra/H++u3Ga2TFln1CK3D3clMrdOnmMKp2pY16/YWNruX9v/Q0c11bSiW7wLmj3/6PFzmfswp+7+l0+Xn+7Pb2VXIfqLxFoprShRJ4FsVGa+TSQpuX1GwgoIRdX7XvRV73w1aQe3gbgN/HGpVS71BKLVJKLdq6deuABvrCq08D4DrdS8gvPOG2vffiY2zVD3t8A677G22RZwk8wEOa+ytZ8qu8bHRcQyXtnHKOZxV3kIyZpJUc7O7LTFxF4EJkpF1sKfeEb90n2mLWhJGisfPN580GABw1cWRmYEVrhFqh+91XqQQvW1u5BK2JMjLfSdf1dFaFRsLbM9Ix7PcMoPxlpnqT7/vho2KO+TlHuIWNpbkdOV6mBPhPYxr5dMaT5wryfr4MIxyvkcYK8ovnTQ6+qzYysz/41j2r7P+rxbQZH2LvMzDE1IpS6lal1BPCv6vZb/4FQC+AH8T60Vpfp7VeoLVeMHlyeMFrwSvPnIk1n7nSqVovQXpwnMIT3uPd3VtBX0VjlS1e0YgH75IT5EAhwNV6pJFpK+7/VgIvrExbZErMFWTac7K4me94+9ufb/I6v/6co6PUChdYvKm1XMKkUW249MQpbOy0nSqZTxnbLvLvR9s847c9syWTv48ZOz97jSmwO7q9NeTIpdqVwmWdN3V0+CVvz0jH8D62UwEAxabJK83HyrVdJAgnjo9Y6sYHv0S9faGQAYAJLM1rbjHxfgohbrtolBz7+uvPxAuOn+zQiRLq/T7vOZg+81LajNeefZTzeUjT2GqtL9Vanyz8+zUAKKXeAuAqAK/X9QzTqgOkhzMrze3NNoPgTxaZyuqNWEGzrP+nZkTqAamhFMifmzTOUlaf0hG2pXiVHgAYY4tqKNbu00597Nb7Uy+XeKpa6smA0iX89x2rAu4ecO9hVjX2PlZcmoOiRjt7+sIUu4Igl5bQEW0twXccWZGT0z3vEH5dR7NiJbGt+exJo8IvGaaM6RC/54tCT5+cNOt1TNA0SiN35tQgjfwlp0zHd9+2MPd3ecbqWvHRl3mLqHd+Pt03bKkVpdTlAK4F8DKtdbblcQighLNz0+C6oJee0F9eMAtZfY5oKyc7Buln3PhUi7GTQLmVfc+RlnIp4fskeoN47p6+StQoxxPp+3PbsrcLP3o4zXfCj59j87Rccco0QLBN9Dhaq9w/ABzsloXpGhtJ+5Xblgf+8yScupwCx2Efr104K/ySIUs+BDno2d8XHMtDvGVBl3efYwUZHI28Imv7J7HSg43c9hMGYYhMSO6h1eCmv70A1795QfD99HEureWfnn/vh7PXylcBjAFwi1LqMaXUf9dhTnVDvkbutr/O3wo14MI/LaSZ5ZBypUvzkR4Kzqlmaf5BEvySZOxM21uZm1Usgq01w/bA4fty0yJRVrJtYjqr0JLFkce8Ksj97cHVOwKNn3zUf7poXaZbZYeQIpeDrvu1l8s5s49mZeh4/05e8IjXQ563SDVPaG9EI+e5WvKETD2U2aHeruftbmI46chxQZlGIHu3JWHYauRa62O11rO01qfbf++q18TqgVjSLIJ/XV9wXMpHNmr13NuZncecIA3Pv5MWmZedlkbH5QkAfni5pJhGHmrFtCj09OmA45aQndlRrk35+VuWiblW+Hmu2LoPSsn98wIBHOfZwgOtZRXYBvZZT49NezozizeUc67l5DHtWPOZK/Gei+Sc2c8yN1m//0tPmIoTp4+N7nRi50WIpUuO7Wo4+LWNCZmjrF0mVlOzFlQTtNUIvPKMGZgypr3uitlxU8c4n3O9VoZrQNBwBV1Q6cI6gtxrO8rRnBowMQBX5njaEPUjzZ1n4pN2G5ze8BeiF504NYmyq3hGv+37urHdelBIBkVe0d0YdWrTEk+fNR7zWJFj5S0ihLy6mTct2RgVOLEiBgvnmmuy80BP4K3zMZtuoa+iWR3XsP96Umx+Vx2tJXT29kV3On65NB/jbLpkTtMA7jXMSmNLiCku173pLPzlglmOob2/iKVRaDS+8Jen46F/ubTh40jXkBdtbwRVSzgkBfnzrWYtUivlOLXC0ahtEF8sJJw8wwjbmLCktJyScsNTbvraT6Wik4AJX5iSkXdPZ48YEEQP6JduXW6MijVq5BNGtiYLqJ9Lnf+dV9IMqP1l4Pd7055O577S1vhHD6/De294JDr2qPYWvOPCubjh7WfXNLYEqbbmqq37ozudaqrsPPmxF+M7b32e8x1/fvqqCAiKCfL508bis9ecOqAdKj3Thzqka8T9+xtJrWSb45sU//2GM/HczoOiwS+LWgGA02aNx+J1uxpGreQFrlD2wNjwWdGPC1h9QX/+tz2zhfUha2iuG548gduXbq0i66T7eX93X2J0zIpEZyU9on3X+i5wQfjkhj1JJkQgjeR1+o+M/c8vOUH8vhq86MSpSd5z/7r+ZrGpWWkMcdm2hxhGtYevsX+d+lOEpV74xbvPT7I/Hmr4j1edimt/vgRAvpLRCJtb0nfDeh5CjGxrCfgrQnsGtQIAT9nSUI3aBuVtcZ/bFS8bBqTGUGl+Tg6TyJvb21cJhCnVF129bX9m0izACJxaqZWHVu/AXutn7tM6HFnpAQgD1mrY8TzfdtJ/A96IUW3xVKoUMNPVW6krned3lfc8+4FL9URbSympn3qoYSyjiwbDhTOGQ1KQZyFPI0+SGw21r1QOan1o3m4rGu3v6gtC9KmS+c//vD435abJbS2PSbuN7Eo5/dttELJO+5QZ46LV1qXjWwSpnbdI9QdZ+To+aD1dTPpf+fjPvPIUp35mNfD7ylMgpN1JgXxwOlN6Jz94OYvWbqBIOfwEubNVjV/ZwfCrlfCGc4wLZEwzpEILtYToA2ntyX3dvWKFIIC8UrINjpv3dEWFAu12+iK+aibneNzwFjN2fpRFL2YtEje+7wKx2joltQLcO+6ncpXGrgcog5409U67w9q2rzu6iLxm4VF49YJsX/YQPhdf4+EFqkI7z+UiXGQe9DVs3Q+bEXkaOaFaN8H+4DtvfR5+/u5zxTYKSoq91O12K15rSDXxqPu7egM3vGNsybD9Xb1VJcGPNRF9srdT3qY/tGZH4EfOQYUZ/Pa3cEHcj3fh+cenHh38vI6dMtrRmEz/9X/ZiKeXrun37luTzq2Ob6MvU/KEyKyJcs6WAtng8kSK3ThiFEuDUFAr9UOW+yFHNTUg+4uL503BWUdPFNvaMiI7gbRqS54NzDeSUQHj257eEoTov8Umprr5yU0sBD/ed97jGMvUsGHXwaDaOActAJnUSj9ehlNnsKyS3uHvvugY53N/CyhkgTyNpOuygeXrriet41/DvOsW80cvkA2er1xSrs6eOyn5u9DI64hq3Q+HCu05BRIeXG0KX6zbcTDzd35AENXkvHfFtiAcnC9uMY38oX95YfJ3TCgQnXP7Uje7JflCf+OOlUGIPoCk3mas4MdAwTWh/vpTDwSkkUsxNS9iedzr+Tj6XcVO63tvW4g3nxvPyFkgGz3MG0d6dtziHI2bx2EnyNt5WsshnEcMJAzzQqKpukwM/kN1+clpIJIfGMPzoMdC1ceyYKTYdXvjObPF76mogqlNGQqsm5aYXOWUqz1L2Fb6EWFYy8tUz6ruhI6Mxfk/rzkt+buhVdYjfV94/GR87OqTGzbuoY5etnOXBHlLDUrEQHDYCfKsfORAahQcKtD88jK1UZWjGHz3Q1og7lmxLfAc4RGjUtIsPi/TJj+QrzzT5EsngyyhJckJroMQfQBYvsXkn6HCDZKwpfviF1CoBo5GnrN8N1IjlzBuZGtSALqeQ/vPz1AZ7w91nMkqIEk71cG67oefIM/IRw4Ar1t4VPDdYKLNcm4xOU61Bf3Maz78/CA8OKovkg0PkJNmAX5ejsyhA0FP2QOf23VQ1MhffNI093hhbrHqPNWALxx5BkXJJXGg4O6HEhK7SB3H9B+f4UgjHgrIK1BdCPIGoT3Ha+XcYyaFXw4iKDBj+/6w/iAAfPk1ZwDIr47ka+T8gerpq0QFmpQ0K0RtiwB5xQByuta3Mq8U6XgA+PJrTs+YTzbc1MVh53wX1ogXLy86kwR5PY1hvmG1UMgbj1oKntR97EEZZRghr9r6UG9Bf/iwKUbc2SOHNJ919ASs+tRLsHCO7PVC8P3IeS7znQd6EBPGWVV4CLGm2LF56VrneVSMNLWj+5mCFHB3E9Lc32ErIAGN8Vp5dO3OzPbE86GOQ/u1LhpdZq1AY56danH4CfIcjjyr2stgYEQVSZKyXMkosjG/7JX8fVbJM0KM9qHit77BkC8qWSH6BElrHplDT1QLaadx7NTGauR5u6eUWqnf2H5QllSYuEB9MZRK4OEnyJ0aguGFr0fe5YHgnS84Jv9HGXjvxcdizWeuzP2df+5tLFWt1M6xbZ9M+8yfNgbTxnYEQTY8z8augz1VVBsPvxstJIbqD6S+d9oUvkBjtKq5R2Qb0NPUBvUb0y99d9eygRU8L5CPoUzrcVgLcumlHWrNhZffqjf+76/Suoa+MO22Arw/XiGEUe0teOCfX4hz5sbtDH5UqQRJa540uk34ZfUgbUkamd/yRmhVPB+HhLwgsP5giDeWhyUamW88D4e1IJde2nmRrImDhQNdjUtexKumx4TpDx5cm9neX5zFUuxKOGI0n1vY3p5Tbi0Pl1vPGOm8uB99I7w78oK8GmHs7I+/fYGBYd3OoStbfPgJ8nK2IB/V3oKFcybin18yP2gbDOQVnhgIsgy9tDtZvG4XANlN7wOXHtfvsd//wvRYSWBt25fSGzF59uN3nIObP/D8fo1/0+Mbo31PHStXoq8X8haHrILb/cVQ7ywPR8R2c68/+yiMH9nY6kiHZpLgDOQJcgD4yTvlhFaDgUaWw2rPsA+MHdGKHYwrloRtXv3ILDy4envyd77Akn9wdgZlUy2Goz91Sq3UUSOvR7XkAjXB9xQjfPIVp+CTrziloWMfdhp5XiX64YKzc9wL+4OszI++t4x0aZas393vsS+aNyX5O++6N/K2DLUxW0J7AwKCCkE++GhELvtqcdgJco7hKsif/vjl+H4d6kP64MEJ/qn/5F3uLkTSDncf7H9q39nMD1wyMl80b3Lm2APFh68yOc0PdDeuEk4WRre34GJ2jhxZBbf7Cz975zdef2b9Oi/g4DwbRKiDeNrBw+EtyIfhNhswId2NiAibxHIj+37mM8aPcCp+S9TKjPH9z1nNhbckyE8+MjU4NuK+jGo3O46DkUCrRuOJj70Y33nrQrGtEX7kfr6bK3J82Qv0Hy877UgAbgTzYOOwFuSNyKsxnKGUSoRxa07Fb0mkvNQ+sP0Bz/2SVxS7EbeFfNkPRjTyH73jHPzoHefUf+AqQIvm/z3wbN36PP/YI/DAh16Y/8MCA8ZfPm8WHvjQC3HqzPFDNofDztjJcZjJcQDpOZdzTl7OG9F/jdHRyIV+2nPcQgeKVCOX3TuzfN8bjgZtDKeN68D/e/E8rNza/4RjBfKhlBLLBg4mDmtBfrhp5EAaXp+Xd1tiNwYiYPnCIFEr7TmBWgNFqpEPv0gZSnD2qjNn1r1vqYZpgUMPh58kYzgM5TjW7zSVhSRhedTE1Ie93rmV25x0n+GF38hKnjUiwdOotuGrs2y3bp9DmXSpQHPjMBRlKQ5HjZwg8dRrd6SRaZJMGZBGXmL8vLAboBJ2Ax0nhpHt8Ur2Q41HnjXZEW95evMQz6RAs+LwlWQ4vHM052l/klY80IWPBLTEz3Ph3giNnPzkh+MtpzS+19rMlQUK1IrDWpAPxyi/wYIkxycy90Tpygx04aPjJX5+0qj25O9GaOQkyIfSsyAGyvUyf3pRyb5A/3BYC/ICLv7tqhOSv6VFbqAL35rthrqRqJV3viAt7tAIQT5hVBt+/u5zB1RpqFF42/lzcMvfXTigFAgFDm8UgvwwwyvPmBFt49RJI2mnR57dFXx3GtOUG1XN5qyjJ2JUnfKa1xOlksJxQ5x1s0BzY/g91QUaCgr6kbLj8cRWMWH6lvNmO94t/cGyzXuD75olB06BAsMRh6VGPn2InfeHErdaz4jbl4YVY06ZkYbJxwT5R192Et52wZwBzSEvxWohyAsUqA11EeRKqX9QSmml1BH16K/R+P37n49b//7CoZ7GkIB42Klj2oO2Y6ek23vVgCWeePDPvDI7pWchyAsUqA0Dfl2VUrMAXAZg7cCnMzgYP7LNEVqHE8ig+QIhE98elt2wETz1h644AWs+c2UuHzxck5kVKDBcUQ+O/IsArgXw6zr0VaDBOOvoiXj4Xy7FZEEjd/KVD+akPBzGcVoFCvQLA3pllFJXA3hOa724it++Qym1SCm1aOvWoqL3UEIS4oBrO2iU50g1KDTyAgVqQ65GrpS6FcA0oelfAPwzDK2SC631dQCuA4AFCxYU5UuGIeayfMpDKUsLjrxAgdqQK8i11pdK3yulTgEwB8BiGygyE8AjSqmFWutNdZ1lgUHHUGjkiz98GTbuOXhYR9wWKNAf9Jsj11o/DiApxKiUWgNggdZ6Wx3mVWCIMRRK8biRrRjX4GrjBQociijMSgUcUPj8UHLkBQoUqA11i+zUWs+uV18Fhg6/fM/5eGj1DjEfeYECBYYnihD9Ag5OnjEOJ7MIzwIFCgx/FNRKgQIFCjQ5CkFeoECBAk2OQpAXKFCgQJOjEOQFChQo0OQoBHmBAgUKNDkKQV6gQIECTY5CkBcoUKBAk6MQ5AUKFCjQ5FBaD34iQqXUVgDP9vPwIwAcKvlcinMZfjhUzgMozmW4YiDncrTWOqgKMySCfCBQSi3SWi8Y6nnUA8W5DD8cKucBFOcyXNGIcymolQIFChRochSCvECBAgWaHM0oyK8b6gnUEcW5DD8cKucBFOcyXFH3c2k6jrxAgQIFCrhoRo28QIECBQowFIK8QIECBZocTSXIlVKXK6WWKqVWKKX+aajnMxAopdYopR5XSj2mlFo01POpFkqpbyultiilnmDfTVRK3aKUWm7/nzCUc6wWkXP5qFLqOXtfHlNKvWQo51gtlFKzlFK3K6WeUko9qZR6v/2+qe5Nxnk03X1RSnUopR5SSi225/Ix+/0cpdSDVo79WCnVNuCxmoUjV0qVASwD8CIA6wE8DOC1WuunhnRi/USzFqtWSl0IYB+A72mtT7bf/QeAHVrrz9gFdoLW+oNDOc9qEDmXjwLYp7X+z6GcW61QSk0HMF1r/YhSagyAPwN4OYC3oInuTcZ5vBpNdl+UUgrAKK31PqVUK4B7ALwfwN8D+IXW+kdKqf8GsFhr/Y2BjNVMGvlCACu01qu01t0AfgTg6iGe02EHrfVdAHZ4X18N4Lv27+/CvHjDHpFzaUporTdqrR+xf+8F8DSAGWiye5NxHk0HbbDPfmy1/zSASwD8zH5fl3vSTIJ8BoB17PN6NOkNttAA/qiU+rNS6h1DPZkBYqrWeqP9exOAqUM5mTrgb5RSSyz1MqypCAlKqdkAzgDwIJr43njnATThfVFKlZVSjwHYAuAWACsB7NJa99qf1EWONZMgP9Rwgdb6TABXAHiv3eY3PbTh6pqDr5PxDQDHADgdwEYAnx/S2dQIpdRoAD8H8AGt9R7e1kz3RjiPprwvWus+rfXpAGbCsArzGzFOMwny5wDMYp9n2u+aElrr5+z/WwD8EuYmNys2W26TOM4tQzyffkNrvdm+fBUA30QT3RfLw/4cwA+01r+wXzfdvZHOo5nvCwBorXcBuB3AuQDGK6VabFNd5FgzCfKHARxnLb5tAF4D4DdDPKd+QSk1yhpyoJQaBeAyAE9kHzWs8RsAb7Z/vxnAr4dwLgMCCT2LV6BJ7os1rF0P4Gmt9RdYU1Pdm9h5NON9UUpNVkqNt3+PgHHUeBpGoF9jf1aXe9I0XisAYF2OvgSgDODbWutPDu2M+gel1FwYLRwAWgDc0CznopT6IYCLYFJxbgbwEQC/AvATAEfBpCd+tdZ62BsRI+dyEcz2XQNYA+CdjGMetlBKXQDgbgCPA6jYr/8Zhl9umnuTcR6vRZPdF6XUqTDGzDKM0vwTrfXH7fv/IwATATwK4A1a664BjdVMgrxAgQIFCoRoJmqlQIECBQoIKAR5gQIFCjQ5CkFeoECBAk2OQpAXKFCgQJOjEOQFChQo0OQoBHmBAhZKqfFKqfcM9TwKFKgVhSAvUCDFeACFIC/QdCgEeYECKT4D4Bib7/pzQz2ZAgWqRREQVKCAhc2291vKTV6gQLOg0MgLFChQoMlRCPICBQoUaHIUgrxAgRR7AYwZ6kkUKFArCkFeoICF1no7gHuVUk8Uxs4CzYTC2FmgQIECTY5CIy9QoECBJkchyAsUKFCgyVEI8gIFChRochSCvECBAgWaHIUgL1CgQIEmRyHICxQoUKDJUQjyAgUKFGhy/H9rN1J20EBCpgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot(\"t\", \"p\")\n",
    "pl.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## How to write data to csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "t = pl.linspace(0, 2 * pl.pi, 200)\n",
    "p = pl.sin(2 * pl.pi * t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pl.plot(t, p)\n",
    "pl.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pl.array([t, p])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When dealing with table data, you should always consider whether to use the .transpose() of a matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame(data.transpose(), columns=[\"t\", \"p\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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>t</th>\n",
       "      <th>p</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.031574</td>\n",
       "      <td>0.197085</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.063148</td>\n",
       "      <td>0.386439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.094721</td>\n",
       "      <td>0.560635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.126295</td>\n",
       "      <td>0.712838</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>195</th>\n",
       "      <td>6.156890</td>\n",
       "      <td>0.833697</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>6.188464</td>\n",
       "      <td>0.926180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>6.220038</td>\n",
       "      <td>0.982332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>6.251612</td>\n",
       "      <td>0.999949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>6.283185</td>\n",
       "      <td>0.978341</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>200 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            t         p\n",
       "0    0.000000  0.000000\n",
       "1    0.031574  0.197085\n",
       "2    0.063148  0.386439\n",
       "3    0.094721  0.560635\n",
       "4    0.126295  0.712838\n",
       "..        ...       ...\n",
       "195  6.156890  0.833697\n",
       "196  6.188464  0.926180\n",
       "197  6.220038  0.982332\n",
       "198  6.251612  0.999949\n",
       "199  6.283185  0.978341\n",
       "\n",
       "[200 rows x 2 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv(\"pressure_computed.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Adding a column to the existing DataFrame:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1.        ,  0.98038635,  0.92231478,  0.82806328,  0.70132909,\n",
       "        0.54708365,  0.37137759,  0.18110338, -0.01627502, -0.213015  ,\n",
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       "       -0.0812889 , -0.27612758, -0.46013452, -0.62609162, -0.76748884,\n",
       "       -0.87877953, -0.95559807, -0.99493106, -0.99523559, -0.95649971,\n",
       "       -0.88024292, -0.76945657, -0.62848651, -0.46286261, -0.27908187,\n",
       "       -0.08435349,  0.11368385,  0.30726168,  0.48878646,  0.65113746,\n",
       "        0.7879461 ,  0.89384572,  0.96468219,  0.99767677,  0.99153518,\n",
       "        0.94649833,  0.8643329 ,  0.74826202,  0.60283883,  0.4337679 ,\n",
       "        0.24768142,  0.05187907, -0.14595836, -0.33807024, -0.51692053,\n",
       "       -0.67549342, -0.80756852, -0.90796489, -0.97274423, -0.99936544,\n",
       "       -0.98678423, -0.93549413, -0.84750712, -0.72627468, -0.57655245,\n",
       "       -0.40421361, -0.21601856, -0.01934969,  0.17807823,  0.36852061,\n",
       "        0.54450692,  0.69913369,  0.82633533,  0.92112205,  0.97977564,\n",
       "        0.99999527,  0.98098778,  0.92349877,  0.8297834 ,  0.70351786,\n",
       "        0.5496552 ,  0.37423105,  0.18412683, -0.0132002 , -0.21000942,\n",
       "       -0.39858053, -0.5715164 , -0.72203322, -0.84422662, -0.93330329,\n",
       "       -0.98576898, -0.9995656 , -0.97415196, -0.91052496, -0.81118052,\n",
       "       -0.68001565, -0.52217559, -0.34385199, -0.15204001,  0.0457361 ,\n",
       "        0.2417181 ,  0.42821815,  0.59792036,  0.74416776,  0.86122346,\n",
       "        0.94449569,  0.99071789,  0.99807689,  0.96628403,  0.89658645,\n",
       "        0.79171819,  0.65579296,  0.49414274,  0.31310862,  0.1197921 ,\n",
       "       -0.07822354, -0.27317068, -0.45740207, -0.62369081, -0.76551384,\n",
       "       -0.87730783, -0.95468738, -0.99461712, -0.99553071, -0.95739231,\n",
       "       -0.88169799, -0.77141703, -0.63087545, -0.46558633, -0.28203351,\n",
       "       -0.08741728,  0.1106281 ,  0.30433384,  0.48610138,  0.64880047,\n",
       "        0.78604886,  0.89246268,  0.96386758,  0.99746256,  0.99192976,\n",
       "        0.94748624,  0.86587537,  0.75029855,  0.60528953,  0.43653664,\n",
       "        0.25065959,  0.05494983, -0.14291545, -0.33517455, -0.51428565,\n",
       "       -0.67322271, -0.80575106, -0.90667196, -0.97202656, -0.99925118,\n",
       "       -0.98727786, -0.93657629, -0.84913535, -0.72838512, -0.5790623 ,\n",
       "       -0.40702443, -0.21902008, -0.02242417,  0.17505139,  0.36566015,\n",
       "        0.54192504,  0.69693168,  0.82459956,  0.91992062,  0.97915568,\n",
       "        0.99998109,  0.98157993,  0.92467404,  0.83149567,  0.70569997,\n",
       "        0.55222156,  0.37708098,  0.18714853, -0.01012525, -0.20700185])"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "v = pl.cos(2 * pl.pi * t)\n",
    "v"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"v\"] = v"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
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       "199  6.283185  0.978341 -0.207002\n",
       "\n",
       "[200 rows x 3 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is possible to create an empty DataFrame and just add the columns whenever you like:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "empty_df = pd.DataFrame()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "empty_df[\"t\"] = t\n",
    "empty_df[\"p\"] = p\n",
    "empty_df[\"v\"] = v"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
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       "</table>\n",
       "<p>200 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
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       "198  6.251612  0.999949 -0.010125\n",
       "199  6.283185  0.978341 -0.207002\n",
       "\n",
       "[200 rows x 3 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "empty_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='t'>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "empty_df.plot(\"t\", [\"p\", \"v\"])  # pl.plot(t,p,t,v) in matplotlib"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Exercise** \n",
    "1. Create uniformly sampled time points between 0 and 30.\n",
    "2. Generate positional data in the xy-plane given by [0.4*t + cos(t), sin(t)]\n",
    "3. Create a DataFrame consisting of the three columns t, x and y\n",
    "4. plot x versus y using first the matplotlib plot function and then the DataFrame plot-method\n",
    "\n",
    "Velocity-data can be computed by $v_{x_i} = \\frac{x_{i+1} - x_i}{t_{i+1} - t_i}$, $v_{y_i} = \\frac{y_{i+1} - y_i}{t_{i+1} - t_i}$\n",
    "5. Compute the velocity data for x and y and add those as columns in the DataFrame\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## A real world example. Oslo bysykkel data\n",
    "\n",
    "We go to https://oslobysykkel.no/apne-data/historisk (you can also get there by \"oslo bysykkel data historisk\" on google). We download the September data as CSV. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "import zipfile\n",
    "from pathlib import Path\n",
    "\n",
    "import requests\n",
    "\n",
    "\n",
    "def download_file(filename, url):\n",
    "    path = Path(filename)\n",
    "    if path.exists():\n",
    "        return path\n",
    "    print(f\"Downloading {path}\")\n",
    "    with requests.get(url, stream=True) as r:\n",
    "        r.raise_for_status()\n",
    "        with path.open(\"wb\") as f:\n",
    "            for chunk in r.iter_content(chunk_size=8192):\n",
    "                f.write(chunk)\n",
    "    return path\n",
    "\n",
    "\n",
    "def download_trips(year, month):\n",
    "    dest = Path(\"data\") / f\"oslo_bike_{year}_{month:02}.csv\"\n",
    "    return download_file(\n",
    "        dest,\n",
    "        f\"https://data.urbansharing.com/oslobysykkel.no/trips/v1/{year}/{month:02}.csv\",\n",
    "    )\n",
    "\n",
    "\n",
    "trip_csv = download_trips(2021, 9)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import pylab as pl\n",
    "\n",
    "trips = pd.read_csv(trip_csv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\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>started_at</th>\n",
       "      <th>ended_at</th>\n",
       "      <th>duration</th>\n",
       "      <th>start_station_id</th>\n",
       "      <th>start_station_name</th>\n",
       "      <th>start_station_description</th>\n",
       "      <th>start_station_latitude</th>\n",
       "      <th>start_station_longitude</th>\n",
       "      <th>end_station_id</th>\n",
       "      <th>end_station_name</th>\n",
       "      <th>end_station_description</th>\n",
       "      <th>end_station_latitude</th>\n",
       "      <th>end_station_longitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2021-09-01 03:00:15.478000+00:00</td>\n",
       "      <td>2021-09-01 03:20:12.685000+00:00</td>\n",
       "      <td>1197</td>\n",
       "      <td>620</td>\n",
       "      <td>Bislettgata</td>\n",
       "      <td>ved Sofies Gate</td>\n",
       "      <td>59.923774</td>\n",
       "      <td>10.734713</td>\n",
       "      <td>735</td>\n",
       "      <td>Oslo Hospital</td>\n",
       "      <td>ved trikkestoppet</td>\n",
       "      <td>59.903213</td>\n",
       "      <td>10.767344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2021-09-01 03:03:04.080000+00:00</td>\n",
       "      <td>2021-09-01 03:06:10.732000+00:00</td>\n",
       "      <td>186</td>\n",
       "      <td>422</td>\n",
       "      <td>St. Hanshaugen</td>\n",
       "      <td>langs Waldemar Thranes gate</td>\n",
       "      <td>59.923703</td>\n",
       "      <td>10.740542</td>\n",
       "      <td>499</td>\n",
       "      <td>Bjerregaards gate</td>\n",
       "      <td>ovenfor Fredrikke Qvams gate</td>\n",
       "      <td>59.925488</td>\n",
       "      <td>10.746058</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2021-09-01 03:16:41.288000+00:00</td>\n",
       "      <td>2021-09-01 03:25:20.740000+00:00</td>\n",
       "      <td>519</td>\n",
       "      <td>424</td>\n",
       "      <td>Birkelunden</td>\n",
       "      <td>langs Seilduksgata</td>\n",
       "      <td>59.925611</td>\n",
       "      <td>10.760926</td>\n",
       "      <td>478</td>\n",
       "      <td>Jernbanetorget</td>\n",
       "      <td>Europarådets plass</td>\n",
       "      <td>59.911901</td>\n",
       "      <td>10.749929</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2021-09-01 03:21:55.708000+00:00</td>\n",
       "      <td>2021-09-01 03:28:20.138000+00:00</td>\n",
       "      <td>384</td>\n",
       "      <td>446</td>\n",
       "      <td>Bislett Stadion</td>\n",
       "      <td>ved rundkjøringen</td>\n",
       "      <td>59.925471</td>\n",
       "      <td>10.731219</td>\n",
       "      <td>478</td>\n",
       "      <td>Jernbanetorget</td>\n",
       "      <td>Europarådets plass</td>\n",
       "      <td>59.911901</td>\n",
       "      <td>10.749929</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2021-09-01 03:26:16.090000+00:00</td>\n",
       "      <td>2021-09-01 03:30:30.133000+00:00</td>\n",
       "      <td>254</td>\n",
       "      <td>514</td>\n",
       "      <td>Sofienberggata</td>\n",
       "      <td>ved Sars gate</td>\n",
       "      <td>59.921206</td>\n",
       "      <td>10.769989</td>\n",
       "      <td>542</td>\n",
       "      <td>Grünerhagen Nord</td>\n",
       "      <td>ved Sofienberggata</td>\n",
       "      <td>59.922426</td>\n",
       "      <td>10.755427</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>190000</th>\n",
       "      <td>2021-09-30 22:56:22.073000+00:00</td>\n",
       "      <td>2021-09-30 23:11:50.892000+00:00</td>\n",
       "      <td>928</td>\n",
       "      <td>415</td>\n",
       "      <td>Sinsenveien</td>\n",
       "      <td>ved Kongehellegata</td>\n",
       "      <td>59.929542</td>\n",
       "      <td>10.781053</td>\n",
       "      <td>537</td>\n",
       "      <td>St. Olavs gate</td>\n",
       "      <td>ved Pilestredet</td>\n",
       "      <td>59.917968</td>\n",
       "      <td>10.738629</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>190001</th>\n",
       "      <td>2021-09-30 22:57:17.478000+00:00</td>\n",
       "      <td>2021-09-30 23:01:52.188000+00:00</td>\n",
       "      <td>274</td>\n",
       "      <td>460</td>\n",
       "      <td>Botanisk Hage sør</td>\n",
       "      <td>langs Jens Bjelkes gate</td>\n",
       "      <td>59.915418</td>\n",
       "      <td>10.769330</td>\n",
       "      <td>475</td>\n",
       "      <td>Hausmanns bru</td>\n",
       "      <td>langs Nylandsveien</td>\n",
       "      <td>59.914651</td>\n",
       "      <td>10.759872</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>190002</th>\n",
       "      <td>2021-09-30 22:57:33.599000+00:00</td>\n",
       "      <td>2021-09-30 23:04:41.205000+00:00</td>\n",
       "      <td>427</td>\n",
       "      <td>399</td>\n",
       "      <td>Uelands gate</td>\n",
       "      <td>Ved Ulvetrappen (Ilatrappen)</td>\n",
       "      <td>59.929545</td>\n",
       "      <td>10.748986</td>\n",
       "      <td>622</td>\n",
       "      <td>Pilestredet 63</td>\n",
       "      <td>ved trikkestoppet</td>\n",
       "      <td>59.923883</td>\n",
       "      <td>10.731363</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>190003</th>\n",
       "      <td>2021-09-30 22:58:05.623000+00:00</td>\n",
       "      <td>2021-09-30 23:05:39.679000+00:00</td>\n",
       "      <td>454</td>\n",
       "      <td>465</td>\n",
       "      <td>Bjørvika</td>\n",
       "      <td>under broen Nylandsveien</td>\n",
       "      <td>59.909006</td>\n",
       "      <td>10.756180</td>\n",
       "      <td>390</td>\n",
       "      <td>Saga Kino</td>\n",
       "      <td>langs Olav Vs gate</td>\n",
       "      <td>59.914240</td>\n",
       "      <td>10.732771</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>190004</th>\n",
       "      <td>2021-09-30 22:58:36.872000+00:00</td>\n",
       "      <td>2021-09-30 23:03:06.333000+00:00</td>\n",
       "      <td>269</td>\n",
       "      <td>412</td>\n",
       "      <td>Jakob kirke</td>\n",
       "      <td>langs Torggata</td>\n",
       "      <td>59.917866</td>\n",
       "      <td>10.754898</td>\n",
       "      <td>442</td>\n",
       "      <td>Vulkan</td>\n",
       "      <td>ved Maridalsveien</td>\n",
       "      <td>59.922510</td>\n",
       "      <td>10.751010</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>190005 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                              started_at                          ended_at  \\\n",
       "0       2021-09-01 03:00:15.478000+00:00  2021-09-01 03:20:12.685000+00:00   \n",
       "1       2021-09-01 03:03:04.080000+00:00  2021-09-01 03:06:10.732000+00:00   \n",
       "2       2021-09-01 03:16:41.288000+00:00  2021-09-01 03:25:20.740000+00:00   \n",
       "3       2021-09-01 03:21:55.708000+00:00  2021-09-01 03:28:20.138000+00:00   \n",
       "4       2021-09-01 03:26:16.090000+00:00  2021-09-01 03:30:30.133000+00:00   \n",
       "...                                  ...                               ...   \n",
       "190000  2021-09-30 22:56:22.073000+00:00  2021-09-30 23:11:50.892000+00:00   \n",
       "190001  2021-09-30 22:57:17.478000+00:00  2021-09-30 23:01:52.188000+00:00   \n",
       "190002  2021-09-30 22:57:33.599000+00:00  2021-09-30 23:04:41.205000+00:00   \n",
       "190003  2021-09-30 22:58:05.623000+00:00  2021-09-30 23:05:39.679000+00:00   \n",
       "190004  2021-09-30 22:58:36.872000+00:00  2021-09-30 23:03:06.333000+00:00   \n",
       "\n",
       "        duration  start_station_id start_station_name  \\\n",
       "0           1197               620        Bislettgata   \n",
       "1            186               422     St. Hanshaugen   \n",
       "2            519               424        Birkelunden   \n",
       "3            384               446    Bislett Stadion   \n",
       "4            254               514     Sofienberggata   \n",
       "...          ...               ...                ...   \n",
       "190000       928               415        Sinsenveien   \n",
       "190001       274               460  Botanisk Hage sør   \n",
       "190002       427               399       Uelands gate   \n",
       "190003       454               465           Bjørvika   \n",
       "190004       269               412        Jakob kirke   \n",
       "\n",
       "           start_station_description  start_station_latitude  \\\n",
       "0                    ved Sofies Gate               59.923774   \n",
       "1        langs Waldemar Thranes gate               59.923703   \n",
       "2                 langs Seilduksgata               59.925611   \n",
       "3                  ved rundkjøringen               59.925471   \n",
       "4                      ved Sars gate               59.921206   \n",
       "...                              ...                     ...   \n",
       "190000            ved Kongehellegata               59.929542   \n",
       "190001       langs Jens Bjelkes gate               59.915418   \n",
       "190002  Ved Ulvetrappen (Ilatrappen)               59.929545   \n",
       "190003      under broen Nylandsveien               59.909006   \n",
       "190004                langs Torggata               59.917866   \n",
       "\n",
       "        start_station_longitude  end_station_id   end_station_name  \\\n",
       "0                     10.734713             735      Oslo Hospital   \n",
       "1                     10.740542             499  Bjerregaards gate   \n",
       "2                     10.760926             478     Jernbanetorget   \n",
       "3                     10.731219             478     Jernbanetorget   \n",
       "4                     10.769989             542   Grünerhagen Nord   \n",
       "...                         ...             ...                ...   \n",
       "190000                10.781053             537     St. Olavs gate   \n",
       "190001                10.769330             475      Hausmanns bru   \n",
       "190002                10.748986             622     Pilestredet 63   \n",
       "190003                10.756180             390          Saga Kino   \n",
       "190004                10.754898             442             Vulkan   \n",
       "\n",
       "             end_station_description  end_station_latitude  \\\n",
       "0                  ved trikkestoppet             59.903213   \n",
       "1       ovenfor Fredrikke Qvams gate             59.925488   \n",
       "2                 Europarådets plass             59.911901   \n",
       "3                 Europarådets plass             59.911901   \n",
       "4                 ved Sofienberggata             59.922426   \n",
       "...                              ...                   ...   \n",
       "190000               ved Pilestredet             59.917968   \n",
       "190001            langs Nylandsveien             59.914651   \n",
       "190002             ved trikkestoppet             59.923883   \n",
       "190003            langs Olav Vs gate             59.914240   \n",
       "190004             ved Maridalsveien             59.922510   \n",
       "\n",
       "        end_station_longitude  \n",
       "0                   10.767344  \n",
       "1                   10.746058  \n",
       "2                   10.749929  \n",
       "3                   10.749929  \n",
       "4                   10.755427  \n",
       "...                       ...  \n",
       "190000              10.738629  \n",
       "190001              10.759872  \n",
       "190002              10.731363  \n",
       "190003              10.732771  \n",
       "190004              10.751010  \n",
       "\n",
       "[190005 rows x 13 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trips"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can work with the data using normal pylab (and numpy functions):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 5658., 28023., 40349., 35711., 26288., 17542., 11016.,  6854.,\n",
       "         4433.,  2907.]),\n",
       " array([   0.,  150.,  300.,  450.,  600.,  750.,  900., 1050., 1200.,\n",
       "        1350., 1500.]),\n",
       " <BarContainer object of 10 artists>)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pl.hist(trips[\"duration\"], range=[0, 1500])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also use DataFrame built-in functions:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "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>started_at</th>\n",
       "      <th>ended_at</th>\n",
       "      <th>duration</th>\n",
       "      <th>start_station_id</th>\n",
       "      <th>start_station_name</th>\n",
       "      <th>start_station_description</th>\n",
       "      <th>start_station_latitude</th>\n",
       "      <th>start_station_longitude</th>\n",
       "      <th>end_station_id</th>\n",
       "      <th>end_station_name</th>\n",
       "      <th>end_station_description</th>\n",
       "      <th>end_station_latitude</th>\n",
       "      <th>end_station_longitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>54120</th>\n",
       "      <td>2021-09-09 06:29:40.293000+00:00</td>\n",
       "      <td>2021-09-09 06:30:42.103000+00:00</td>\n",
       "      <td>61</td>\n",
       "      <td>381</td>\n",
       "      <td>Grønlands torg</td>\n",
       "      <td>ved Tøyenbekken</td>\n",
       "      <td>59.912520</td>\n",
       "      <td>10.762240</td>\n",
       "      <td>381</td>\n",
       "      <td>Grønlands torg</td>\n",
       "      <td>ved Tøyenbekken</td>\n",
       "      <td>59.912520</td>\n",
       "      <td>10.762240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150033</th>\n",
       "      <td>2021-09-23 16:57:04.305000+00:00</td>\n",
       "      <td>2021-09-23 16:58:05.488000+00:00</td>\n",
       "      <td>61</td>\n",
       "      <td>421</td>\n",
       "      <td>Alexander Kiellands Plass</td>\n",
       "      <td>langs Maridalsveien</td>\n",
       "      <td>59.928067</td>\n",
       "      <td>10.751203</td>\n",
       "      <td>421</td>\n",
       "      <td>Alexander Kiellands Plass</td>\n",
       "      <td>langs Maridalsveien</td>\n",
       "      <td>59.928067</td>\n",
       "      <td>10.751203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53582</th>\n",
       "      <td>2021-09-09 05:53:12.313000+00:00</td>\n",
       "      <td>2021-09-09 05:54:14.047000+00:00</td>\n",
       "      <td>61</td>\n",
       "      <td>623</td>\n",
       "      <td>7 Juni Plassen</td>\n",
       "      <td>langs Henrik Ibsens gate</td>\n",
       "      <td>59.915060</td>\n",
       "      <td>10.731272</td>\n",
       "      <td>623</td>\n",
       "      <td>7 Juni Plassen</td>\n",
       "      <td>langs Henrik Ibsens gate</td>\n",
       "      <td>59.915060</td>\n",
       "      <td>10.731272</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70038</th>\n",
       "      <td>2021-09-11 09:08:26.043000+00:00</td>\n",
       "      <td>2021-09-11 09:09:27.337000+00:00</td>\n",
       "      <td>61</td>\n",
       "      <td>2304</td>\n",
       "      <td>Hedmarksgata</td>\n",
       "      <td>ved Jordal Amfi</td>\n",
       "      <td>59.911784</td>\n",
       "      <td>10.783884</td>\n",
       "      <td>2304</td>\n",
       "      <td>Hedmarksgata</td>\n",
       "      <td>ved Jordal Amfi</td>\n",
       "      <td>59.911784</td>\n",
       "      <td>10.783884</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54483</th>\n",
       "      <td>2021-09-09 06:53:24.342000+00:00</td>\n",
       "      <td>2021-09-09 06:54:25.653000+00:00</td>\n",
       "      <td>61</td>\n",
       "      <td>474</td>\n",
       "      <td>Blindern studentparkering</td>\n",
       "      <td>rett ved Blindern Studenterhjem</td>\n",
       "      <td>59.940874</td>\n",
       "      <td>10.720779</td>\n",
       "      <td>474</td>\n",
       "      <td>Blindern studentparkering</td>\n",
       "      <td>rett ved Blindern Studenterhjem</td>\n",
       "      <td>59.940874</td>\n",
       "      <td>10.720779</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52827</th>\n",
       "      <td>2021-09-08 21:45:08.196000+00:00</td>\n",
       "      <td>2021-09-09 05:06:05.736000+00:00</td>\n",
       "      <td>26457</td>\n",
       "      <td>1755</td>\n",
       "      <td>Aker Brygge</td>\n",
       "      <td>ved trikkestopp</td>\n",
       "      <td>59.911184</td>\n",
       "      <td>10.730035</td>\n",
       "      <td>1755</td>\n",
       "      <td>Aker Brygge</td>\n",
       "      <td>ved trikkestopp</td>\n",
       "      <td>59.911184</td>\n",
       "      <td>10.730035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92728</th>\n",
       "      <td>2021-09-14 22:37:30.359000+00:00</td>\n",
       "      <td>2021-09-15 06:03:14.857000+00:00</td>\n",
       "      <td>26744</td>\n",
       "      <td>525</td>\n",
       "      <td>Myraløkka Øst</td>\n",
       "      <td>ved Bentsenbrua</td>\n",
       "      <td>59.937205</td>\n",
       "      <td>10.760581</td>\n",
       "      <td>597</td>\n",
       "      <td>Fredensborg</td>\n",
       "      <td>ved rundkjøringen</td>\n",
       "      <td>59.920995</td>\n",
       "      <td>10.750358</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94955</th>\n",
       "      <td>2021-09-15 07:51:00.736000+00:00</td>\n",
       "      <td>2021-09-15 15:25:43.368000+00:00</td>\n",
       "      <td>27282</td>\n",
       "      <td>468</td>\n",
       "      <td>Skillebekk</td>\n",
       "      <td>langs Drammensveien</td>\n",
       "      <td>59.912793</td>\n",
       "      <td>10.710103</td>\n",
       "      <td>390</td>\n",
       "      <td>Saga Kino</td>\n",
       "      <td>langs Olav Vs gate</td>\n",
       "      <td>59.914240</td>\n",
       "      <td>10.732771</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8884</th>\n",
       "      <td>2021-09-02 07:15:39.199000+00:00</td>\n",
       "      <td>2021-09-02 15:05:55.621000+00:00</td>\n",
       "      <td>28216</td>\n",
       "      <td>615</td>\n",
       "      <td>Munkedamsveien</td>\n",
       "      <td>ved Haakon VIIs gate</td>\n",
       "      <td>59.913523</td>\n",
       "      <td>10.730106</td>\n",
       "      <td>580</td>\n",
       "      <td>Georg Morgenstiernes hus</td>\n",
       "      <td>ved Moltke Moes vei</td>\n",
       "      <td>59.939026</td>\n",
       "      <td>10.723003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125087</th>\n",
       "      <td>2021-09-20 06:48:08.055000+00:00</td>\n",
       "      <td>2021-09-20 16:12:09.094000+00:00</td>\n",
       "      <td>33841</td>\n",
       "      <td>506</td>\n",
       "      <td>Botanisk Hage vest</td>\n",
       "      <td>ved Blytts gate</td>\n",
       "      <td>59.920128</td>\n",
       "      <td>10.768875</td>\n",
       "      <td>569</td>\n",
       "      <td>Botanisk hage sør-vest</td>\n",
       "      <td>ved Sars' gate</td>\n",
       "      <td>59.917835</td>\n",
       "      <td>10.766374</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>190005 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                              started_at                          ended_at  \\\n",
       "54120   2021-09-09 06:29:40.293000+00:00  2021-09-09 06:30:42.103000+00:00   \n",
       "150033  2021-09-23 16:57:04.305000+00:00  2021-09-23 16:58:05.488000+00:00   \n",
       "53582   2021-09-09 05:53:12.313000+00:00  2021-09-09 05:54:14.047000+00:00   \n",
       "70038   2021-09-11 09:08:26.043000+00:00  2021-09-11 09:09:27.337000+00:00   \n",
       "54483   2021-09-09 06:53:24.342000+00:00  2021-09-09 06:54:25.653000+00:00   \n",
       "...                                  ...                               ...   \n",
       "52827   2021-09-08 21:45:08.196000+00:00  2021-09-09 05:06:05.736000+00:00   \n",
       "92728   2021-09-14 22:37:30.359000+00:00  2021-09-15 06:03:14.857000+00:00   \n",
       "94955   2021-09-15 07:51:00.736000+00:00  2021-09-15 15:25:43.368000+00:00   \n",
       "8884    2021-09-02 07:15:39.199000+00:00  2021-09-02 15:05:55.621000+00:00   \n",
       "125087  2021-09-20 06:48:08.055000+00:00  2021-09-20 16:12:09.094000+00:00   \n",
       "\n",
       "        duration  start_station_id         start_station_name  \\\n",
       "54120         61               381             Grønlands torg   \n",
       "150033        61               421  Alexander Kiellands Plass   \n",
       "53582         61               623             7 Juni Plassen   \n",
       "70038         61              2304               Hedmarksgata   \n",
       "54483         61               474  Blindern studentparkering   \n",
       "...          ...               ...                        ...   \n",
       "52827      26457              1755                Aker Brygge   \n",
       "92728      26744               525              Myraløkka Øst   \n",
       "94955      27282               468                 Skillebekk   \n",
       "8884       28216               615             Munkedamsveien   \n",
       "125087     33841               506         Botanisk Hage vest   \n",
       "\n",
       "              start_station_description  start_station_latitude  \\\n",
       "54120                   ved Tøyenbekken               59.912520   \n",
       "150033              langs Maridalsveien               59.928067   \n",
       "53582          langs Henrik Ibsens gate               59.915060   \n",
       "70038                   ved Jordal Amfi               59.911784   \n",
       "54483   rett ved Blindern Studenterhjem               59.940874   \n",
       "...                                 ...                     ...   \n",
       "52827                   ved trikkestopp               59.911184   \n",
       "92728                   ved Bentsenbrua               59.937205   \n",
       "94955               langs Drammensveien               59.912793   \n",
       "8884               ved Haakon VIIs gate               59.913523   \n",
       "125087                  ved Blytts gate               59.920128   \n",
       "\n",
       "        start_station_longitude  end_station_id           end_station_name  \\\n",
       "54120                 10.762240             381             Grønlands torg   \n",
       "150033                10.751203             421  Alexander Kiellands Plass   \n",
       "53582                 10.731272             623             7 Juni Plassen   \n",
       "70038                 10.783884            2304               Hedmarksgata   \n",
       "54483                 10.720779             474  Blindern studentparkering   \n",
       "...                         ...             ...                        ...   \n",
       "52827                 10.730035            1755                Aker Brygge   \n",
       "92728                 10.760581             597                Fredensborg   \n",
       "94955                 10.710103             390                  Saga Kino   \n",
       "8884                  10.730106             580   Georg Morgenstiernes hus   \n",
       "125087                10.768875             569     Botanisk hage sør-vest   \n",
       "\n",
       "                end_station_description  end_station_latitude  \\\n",
       "54120                   ved Tøyenbekken             59.912520   \n",
       "150033              langs Maridalsveien             59.928067   \n",
       "53582          langs Henrik Ibsens gate             59.915060   \n",
       "70038                   ved Jordal Amfi             59.911784   \n",
       "54483   rett ved Blindern Studenterhjem             59.940874   \n",
       "...                                 ...                   ...   \n",
       "52827                   ved trikkestopp             59.911184   \n",
       "92728                 ved rundkjøringen             59.920995   \n",
       "94955                langs Olav Vs gate             59.914240   \n",
       "8884                ved Moltke Moes vei             59.939026   \n",
       "125087                   ved Sars' gate             59.917835   \n",
       "\n",
       "        end_station_longitude  \n",
       "54120               10.762240  \n",
       "150033              10.751203  \n",
       "53582               10.731272  \n",
       "70038               10.783884  \n",
       "54483               10.720779  \n",
       "...                       ...  \n",
       "52827               10.730035  \n",
       "92728               10.750358  \n",
       "94955               10.732771  \n",
       "8884                10.723003  \n",
       "125087              10.766374  \n",
       "\n",
       "[190005 rows x 13 columns]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trips.sort_values(\"duration\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         59.923774\n",
       "1         59.923703\n",
       "2         59.925611\n",
       "3         59.925471\n",
       "4         59.921206\n",
       "            ...    \n",
       "190000    59.929542\n",
       "190001    59.915418\n",
       "190002    59.929545\n",
       "190003    59.909006\n",
       "190004    59.917866\n",
       "Name: start_station_latitude, Length: 190005, dtype: float64"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trips[\"start_station_latitude\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exercise\n",
    "\n",
    "1. Make a scatter-plot showing the position (longitude, latitude) of stations in Oslo. It is OK to plot a station several times. Use matplotlib or the built-in DataFrame.plot.scatter\n",
    "\n",
    "2. (Bonus) Make a scatter-plot with different size of the cirles, and let the size be dependent on how popular a station is (i.e. how many trips were started at the given station)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pl.scatter(trips[\"start_station_longitude\"], trips[\"start_station_latitude\"])\n",
    "pl.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's see if we can find information about how popular the different start stations are"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         620\n",
       "1         422\n",
       "2         424\n",
       "3         446\n",
       "4         514\n",
       "         ... \n",
       "190000    415\n",
       "190001    460\n",
       "190002    399\n",
       "190003    465\n",
       "190004    412\n",
       "Name: start_station_id, Length: 190005, dtype: int64"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trips[\"start_station_id\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's first try the numpy-way:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "stations = pl.unique(trips[\"start_station_id\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 377,  378,  380,  381,  382,  383,  384,  385,  387,  388,  389,\n",
       "        390,  391,  392,  393,  394,  396,  397,  398,  399,  400,  401,\n",
       "        402,  403,  404,  405,  406,  407,  408,  409,  410,  411,  412,\n",
       "        413,  414,  415,  416,  417,  418,  420,  421,  422,  423,  424,\n",
       "        425,  426,  427,  428,  429,  430,  431,  432,  433,  434,  435,\n",
       "        436,  437,  438,  439,  440,  441,  442,  443,  444,  445,  446,\n",
       "        447,  448,  449,  450,  451,  452,  453,  454,  455,  456,  457,\n",
       "        458,  459,  460,  461,  462,  463,  464,  465,  466,  468,  469,\n",
       "        470,  471,  472,  473,  474,  475,  476,  477,  478,  479,  480,\n",
       "        481,  482,  483,  484,  486,  487,  488,  489,  491,  493,  494,\n",
       "        495,  496,  497,  498,  499,  500,  501,  502,  503,  505,  506,\n",
       "        507,  508,  509,  511,  512,  513,  514,  516,  518,  519,  521,\n",
       "        522,  523,  524,  525,  526,  527,  529,  530,  531,  532,  533,\n",
       "        534,  535,  536,  537,  540,  541,  542,  543,  545,  547,  548,\n",
       "        549,  550,  551,  552,  553,  554,  555,  556,  557,  558,  559,\n",
       "        560,  561,  562,  563,  564,  565,  567,  568,  569,  570,  572,\n",
       "        573,  574,  575,  577,  578,  579,  580,  581,  582,  583,  584,\n",
       "        585,  586,  587,  588,  589,  590,  591,  592,  593,  594,  595,\n",
       "        596,  597,  598,  599,  600,  601,  602,  603,  605,  606,  607,\n",
       "        608,  609,  610,  611,  612,  613,  614,  615,  616,  617,  618,\n",
       "        619,  620,  621,  622,  623,  624,  625,  626,  627,  735,  737,\n",
       "        738,  739,  742,  744,  746,  748,  787,  970, 1009, 1023, 1101,\n",
       "       1755, 1919, 2270, 2280, 2304, 2305, 2306, 2307, 2308, 2309, 2315])"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         False\n",
       "1         False\n",
       "2         False\n",
       "3         False\n",
       "4         False\n",
       "          ...  \n",
       "190000    False\n",
       "190001    False\n",
       "190002    False\n",
       "190003    False\n",
       "190004    False\n",
       "Name: start_station_id, Length: 190005, dtype: bool"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stations[0] == trips[\n",
    "    \"start_station_id\"\n",
    "]  # find out if trips started at the given station"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "680"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(\n",
    "    stations[0] == trips[\"start_station_id\"]\n",
    ").sum()  # sum all trips that started at the given station"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we generalize the line above to create a list of number of trips for each station"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "number_of_trips = [\n",
    "    (stations[i] == trips[\"start_station_id\"]).sum() for i in range(len(stations))\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[680,\n",
       " 562,\n",
       " 1097,\n",
       " 927,\n",
       " 603,\n",
       " 1115,\n",
       " 1745,\n",
       " 1450,\n",
       " 585,\n",
       " 628,\n",
       " 400,\n",
       " 1134,\n",
       " 1431,\n",
       " 606,\n",
       " 822,\n",
       " 994,\n",
       " 1391,\n",
       " 1592,\n",
       " 2308,\n",
       " 777,\n",
       " 981,\n",
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       " 573,\n",
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       " 582,\n",
       " 324,\n",
       " 512,\n",
       " 567,\n",
       " 578,\n",
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       " 993,\n",
       " 644,\n",
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       " 1344,\n",
       " 1947,\n",
       " 454,\n",
       " 243,\n",
       " 548,\n",
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       " 588,\n",
       " 784,\n",
       " 703,\n",
       " 1717,\n",
       " 461,\n",
       " 1437,\n",
       " 677,\n",
       " 671,\n",
       " 755,\n",
       " 565,\n",
       " 211,\n",
       " 1532,\n",
       " 728,\n",
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       " 1046,\n",
       " 1213,\n",
       " 565,\n",
       " 599,\n",
       " 1257,\n",
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       " 313,\n",
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       " 781,\n",
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       " 1155,\n",
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       " 1440,\n",
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       " 628,\n",
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       " 634,\n",
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       " 355,\n",
       " 1147,\n",
       " 81,\n",
       " 267,\n",
       " 207,\n",
       " 426,\n",
       " 143,\n",
       " 498,\n",
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       " 1487,\n",
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       " 301,\n",
       " 215,\n",
       " 2132,\n",
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       " 477,\n",
       " 563,\n",
       " 826,\n",
       " 467,\n",
       " 480,\n",
       " 797,\n",
       " 349,\n",
       " 298,\n",
       " 769,\n",
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       " 547,\n",
       " 2056,\n",
       " 460,\n",
       " 561,\n",
       " 931,\n",
       " 704,\n",
       " 721,\n",
       " 274,\n",
       " 126,\n",
       " 1026,\n",
       " 1126,\n",
       " 311,\n",
       " 929,\n",
       " 532,\n",
       " 767,\n",
       " 193,\n",
       " 417,\n",
       " 760,\n",
       " 573,\n",
       " 117,\n",
       " 869,\n",
       " 153,\n",
       " 1203,\n",
       " 88,\n",
       " 331,\n",
       " 403,\n",
       " 425,\n",
       " 576,\n",
       " 170,\n",
       " 604,\n",
       " 342,\n",
       " 214,\n",
       " 838]"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "number_of_trips"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's try some pandas:\n",
    "For the only purpose of counting trips per station we may use .value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "number_of_trips_pandas = trips[\"start_station_id\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "421     2831\n",
       "398     2308\n",
       "551     2199\n",
       "408     2189\n",
       "607     2132\n",
       "        ... \n",
       "454       96\n",
       "1919      88\n",
       "591       81\n",
       "560       74\n",
       "573       67\n",
       "Name: start_station_id, Length: 253, dtype: int64"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "number_of_trips_pandas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's say in our case we want all the information we can get about the start station, not only the number of trips. To group the data by start_station_id and count, while still extracting other relevant data for the start station we can use groupby()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "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",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>started_at</th>\n",
       "      <th>ended_at</th>\n",
       "      <th>duration</th>\n",
       "      <th>end_station_id</th>\n",
       "      <th>end_station_name</th>\n",
       "      <th>end_station_description</th>\n",
       "      <th>end_station_latitude</th>\n",
       "      <th>end_station_longitude</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>start_station_id</th>\n",
       "      <th>start_station_name</th>\n",
       "      <th>start_station_description</th>\n",
       "      <th>start_station_latitude</th>\n",
       "      <th>start_station_longitude</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>377</th>\n",
       "      <th>Tøyenparken</th>\n",
       "      <th>ved Caltexløkka</th>\n",
       "      <th>59.915667</th>\n",
       "      <th>10.777566</th>\n",
       "      <td>680</td>\n",
       "      <td>680</td>\n",
       "      <td>680</td>\n",
       "      <td>680</td>\n",
       "      <td>680</td>\n",
       "      <td>680</td>\n",
       "      <td>680</td>\n",
       "      <td>680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>378</th>\n",
       "      <th>Colosseum Kino</th>\n",
       "      <th>langs Fridtjof Nansens vei</th>\n",
       "      <th>59.929843</th>\n",
       "      <th>10.711285</th>\n",
       "      <td>562</td>\n",
       "      <td>562</td>\n",
       "      <td>562</td>\n",
       "      <td>562</td>\n",
       "      <td>562</td>\n",
       "      <td>562</td>\n",
       "      <td>562</td>\n",
       "      <td>562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>380</th>\n",
       "      <th>Bentsebrugata</th>\n",
       "      <th>rett over busstoppet</th>\n",
       "      <th>59.939230</th>\n",
       "      <th>10.759170</th>\n",
       "      <td>1097</td>\n",
       "      <td>1097</td>\n",
       "      <td>1097</td>\n",
       "      <td>1097</td>\n",
       "      <td>1097</td>\n",
       "      <td>1097</td>\n",
       "      <td>1097</td>\n",
       "      <td>1097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>381</th>\n",
       "      <th>Grønlands torg</th>\n",
       "      <th>ved Tøyenbekken</th>\n",
       "      <th>59.912520</th>\n",
       "      <th>10.762240</th>\n",
       "      <td>927</td>\n",
       "      <td>927</td>\n",
       "      <td>927</td>\n",
       "      <td>927</td>\n",
       "      <td>927</td>\n",
       "      <td>927</td>\n",
       "      <td>927</td>\n",
       "      <td>927</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>382</th>\n",
       "      <th>Stensgata</th>\n",
       "      <th>ved trikkestoppet</th>\n",
       "      <th>59.929586</th>\n",
       "      <th>10.732839</th>\n",
       "      <td>603</td>\n",
       "      <td>603</td>\n",
       "      <td>603</td>\n",
       "      <td>603</td>\n",
       "      <td>603</td>\n",
       "      <td>603</td>\n",
       "      <td>603</td>\n",
       "      <td>603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2306</th>\n",
       "      <th>Økern Portal</th>\n",
       "      <th>ved Dag Hammarskjölds vei</th>\n",
       "      <th>59.930972</th>\n",
       "      <th>10.801830</th>\n",
       "      <td>170</td>\n",
       "      <td>170</td>\n",
       "      <td>170</td>\n",
       "      <td>170</td>\n",
       "      <td>170</td>\n",
       "      <td>170</td>\n",
       "      <td>170</td>\n",
       "      <td>170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2307</th>\n",
       "      <th>Domus Athletica</th>\n",
       "      <th>ved Vestgrensa Studentby</th>\n",
       "      <th>59.946219</th>\n",
       "      <th>10.724626</th>\n",
       "      <td>604</td>\n",
       "      <td>604</td>\n",
       "      <td>604</td>\n",
       "      <td>604</td>\n",
       "      <td>604</td>\n",
       "      <td>604</td>\n",
       "      <td>604</td>\n",
       "      <td>604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2308</th>\n",
       "      <th>Gunerius</th>\n",
       "      <th>motsatt side av Torggata fra Gunerius bygget</th>\n",
       "      <th>59.914638</th>\n",
       "      <th>10.753428</th>\n",
       "      <td>342</td>\n",
       "      <td>342</td>\n",
       "      <td>342</td>\n",
       "      <td>342</td>\n",
       "      <td>342</td>\n",
       "      <td>342</td>\n",
       "      <td>342</td>\n",
       "      <td>342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2309</th>\n",
       "      <th>Ulven Torg</th>\n",
       "      <th>ved ulvenveien</th>\n",
       "      <th>59.924960</th>\n",
       "      <th>10.812061</th>\n",
       "      <td>214</td>\n",
       "      <td>214</td>\n",
       "      <td>214</td>\n",
       "      <td>214</td>\n",
       "      <td>214</td>\n",
       "      <td>214</td>\n",
       "      <td>214</td>\n",
       "      <td>214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2315</th>\n",
       "      <th>Rostockgata</th>\n",
       "      <th>ved Operagata</th>\n",
       "      <th>59.906890</th>\n",
       "      <th>10.760307</th>\n",
       "      <td>838</td>\n",
       "      <td>838</td>\n",
       "      <td>838</td>\n",
       "      <td>838</td>\n",
       "      <td>838</td>\n",
       "      <td>838</td>\n",
       "      <td>838</td>\n",
       "      <td>838</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>254 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                                                                                 started_at  \\\n",
       "start_station_id start_station_name start_station_description                    start_station_latitude start_station_longitude               \n",
       "377              Tøyenparken        ved Caltexløkka                              59.915667              10.777566                       680   \n",
       "378              Colosseum Kino     langs Fridtjof Nansens vei                   59.929843              10.711285                       562   \n",
       "380              Bentsebrugata      rett over busstoppet                         59.939230              10.759170                      1097   \n",
       "381              Grønlands torg     ved Tøyenbekken                              59.912520              10.762240                       927   \n",
       "382              Stensgata          ved trikkestoppet                            59.929586              10.732839                       603   \n",
       "...                                                                                                                                     ...   \n",
       "2306             Økern Portal       ved Dag Hammarskjölds vei                    59.930972              10.801830                       170   \n",
       "2307             Domus Athletica    ved Vestgrensa Studentby                     59.946219              10.724626                       604   \n",
       "2308             Gunerius           motsatt side av Torggata fra Gunerius bygget 59.914638              10.753428                       342   \n",
       "2309             Ulven Torg         ved ulvenveien                               59.924960              10.812061                       214   \n",
       "2315             Rostockgata        ved Operagata                                59.906890              10.760307                       838   \n",
       "\n",
       "                                                                                                                                 ended_at  \\\n",
       "start_station_id start_station_name start_station_description                    start_station_latitude start_station_longitude             \n",
       "377              Tøyenparken        ved Caltexløkka                              59.915667              10.777566                     680   \n",
       "378              Colosseum Kino     langs Fridtjof Nansens vei                   59.929843              10.711285                     562   \n",
       "380              Bentsebrugata      rett over busstoppet                         59.939230              10.759170                    1097   \n",
       "381              Grønlands torg     ved Tøyenbekken                              59.912520              10.762240                     927   \n",
       "382              Stensgata          ved trikkestoppet                            59.929586              10.732839                     603   \n",
       "...                                                                                                                                   ...   \n",
       "2306             Økern Portal       ved Dag Hammarskjölds vei                    59.930972              10.801830                     170   \n",
       "2307             Domus Athletica    ved Vestgrensa Studentby                     59.946219              10.724626                     604   \n",
       "2308             Gunerius           motsatt side av Torggata fra Gunerius bygget 59.914638              10.753428                     342   \n",
       "2309             Ulven Torg         ved ulvenveien                               59.924960              10.812061                     214   \n",
       "2315             Rostockgata        ved Operagata                                59.906890              10.760307                     838   \n",
       "\n",
       "                                                                                                                                 duration  \\\n",
       "start_station_id start_station_name start_station_description                    start_station_latitude start_station_longitude             \n",
       "377              Tøyenparken        ved Caltexløkka                              59.915667              10.777566                     680   \n",
       "378              Colosseum Kino     langs Fridtjof Nansens vei                   59.929843              10.711285                     562   \n",
       "380              Bentsebrugata      rett over busstoppet                         59.939230              10.759170                    1097   \n",
       "381              Grønlands torg     ved Tøyenbekken                              59.912520              10.762240                     927   \n",
       "382              Stensgata          ved trikkestoppet                            59.929586              10.732839                     603   \n",
       "...                                                                                                                                   ...   \n",
       "2306             Økern Portal       ved Dag Hammarskjölds vei                    59.930972              10.801830                     170   \n",
       "2307             Domus Athletica    ved Vestgrensa Studentby                     59.946219              10.724626                     604   \n",
       "2308             Gunerius           motsatt side av Torggata fra Gunerius bygget 59.914638              10.753428                     342   \n",
       "2309             Ulven Torg         ved ulvenveien                               59.924960              10.812061                     214   \n",
       "2315             Rostockgata        ved Operagata                                59.906890              10.760307                     838   \n",
       "\n",
       "                                                                                                                                 end_station_id  \\\n",
       "start_station_id start_station_name start_station_description                    start_station_latitude start_station_longitude                   \n",
       "377              Tøyenparken        ved Caltexløkka                              59.915667              10.777566                           680   \n",
       "378              Colosseum Kino     langs Fridtjof Nansens vei                   59.929843              10.711285                           562   \n",
       "380              Bentsebrugata      rett over busstoppet                         59.939230              10.759170                          1097   \n",
       "381              Grønlands torg     ved Tøyenbekken                              59.912520              10.762240                           927   \n",
       "382              Stensgata          ved trikkestoppet                            59.929586              10.732839                           603   \n",
       "...                                                                                                                                         ...   \n",
       "2306             Økern Portal       ved Dag Hammarskjölds vei                    59.930972              10.801830                           170   \n",
       "2307             Domus Athletica    ved Vestgrensa Studentby                     59.946219              10.724626                           604   \n",
       "2308             Gunerius           motsatt side av Torggata fra Gunerius bygget 59.914638              10.753428                           342   \n",
       "2309             Ulven Torg         ved ulvenveien                               59.924960              10.812061                           214   \n",
       "2315             Rostockgata        ved Operagata                                59.906890              10.760307                           838   \n",
       "\n",
       "                                                                                                                                 end_station_name  \\\n",
       "start_station_id start_station_name start_station_description                    start_station_latitude start_station_longitude                     \n",
       "377              Tøyenparken        ved Caltexløkka                              59.915667              10.777566                             680   \n",
       "378              Colosseum Kino     langs Fridtjof Nansens vei                   59.929843              10.711285                             562   \n",
       "380              Bentsebrugata      rett over busstoppet                         59.939230              10.759170                            1097   \n",
       "381              Grønlands torg     ved Tøyenbekken                              59.912520              10.762240                             927   \n",
       "382              Stensgata          ved trikkestoppet                            59.929586              10.732839                             603   \n",
       "...                                                                                                                                           ...   \n",
       "2306             Økern Portal       ved Dag Hammarskjölds vei                    59.930972              10.801830                             170   \n",
       "2307             Domus Athletica    ved Vestgrensa Studentby                     59.946219              10.724626                             604   \n",
       "2308             Gunerius           motsatt side av Torggata fra Gunerius bygget 59.914638              10.753428                             342   \n",
       "2309             Ulven Torg         ved ulvenveien                               59.924960              10.812061                             214   \n",
       "2315             Rostockgata        ved Operagata                                59.906890              10.760307                             838   \n",
       "\n",
       "                                                                                                                                 end_station_description  \\\n",
       "start_station_id start_station_name start_station_description                    start_station_latitude start_station_longitude                            \n",
       "377              Tøyenparken        ved Caltexløkka                              59.915667              10.777566                                    680   \n",
       "378              Colosseum Kino     langs Fridtjof Nansens vei                   59.929843              10.711285                                    562   \n",
       "380              Bentsebrugata      rett over busstoppet                         59.939230              10.759170                                   1097   \n",
       "381              Grønlands torg     ved Tøyenbekken                              59.912520              10.762240                                    927   \n",
       "382              Stensgata          ved trikkestoppet                            59.929586              10.732839                                    603   \n",
       "...                                                                                                                                                  ...   \n",
       "2306             Økern Portal       ved Dag Hammarskjölds vei                    59.930972              10.801830                                    170   \n",
       "2307             Domus Athletica    ved Vestgrensa Studentby                     59.946219              10.724626                                    604   \n",
       "2308             Gunerius           motsatt side av Torggata fra Gunerius bygget 59.914638              10.753428                                    342   \n",
       "2309             Ulven Torg         ved ulvenveien                               59.924960              10.812061                                    214   \n",
       "2315             Rostockgata        ved Operagata                                59.906890              10.760307                                    838   \n",
       "\n",
       "                                                                                                                                 end_station_latitude  \\\n",
       "start_station_id start_station_name start_station_description                    start_station_latitude start_station_longitude                         \n",
       "377              Tøyenparken        ved Caltexløkka                              59.915667              10.777566                                 680   \n",
       "378              Colosseum Kino     langs Fridtjof Nansens vei                   59.929843              10.711285                                 562   \n",
       "380              Bentsebrugata      rett over busstoppet                         59.939230              10.759170                                1097   \n",
       "381              Grønlands torg     ved Tøyenbekken                              59.912520              10.762240                                 927   \n",
       "382              Stensgata          ved trikkestoppet                            59.929586              10.732839                                 603   \n",
       "...                                                                                                                                               ...   \n",
       "2306             Økern Portal       ved Dag Hammarskjölds vei                    59.930972              10.801830                                 170   \n",
       "2307             Domus Athletica    ved Vestgrensa Studentby                     59.946219              10.724626                                 604   \n",
       "2308             Gunerius           motsatt side av Torggata fra Gunerius bygget 59.914638              10.753428                                 342   \n",
       "2309             Ulven Torg         ved ulvenveien                               59.924960              10.812061                                 214   \n",
       "2315             Rostockgata        ved Operagata                                59.906890              10.760307                                 838   \n",
       "\n",
       "                                                                                                                                 end_station_longitude  \n",
       "start_station_id start_station_name start_station_description                    start_station_latitude start_station_longitude                         \n",
       "377              Tøyenparken        ved Caltexløkka                              59.915667              10.777566                                  680  \n",
       "378              Colosseum Kino     langs Fridtjof Nansens vei                   59.929843              10.711285                                  562  \n",
       "380              Bentsebrugata      rett over busstoppet                         59.939230              10.759170                                 1097  \n",
       "381              Grønlands torg     ved Tøyenbekken                              59.912520              10.762240                                  927  \n",
       "382              Stensgata          ved trikkestoppet                            59.929586              10.732839                                  603  \n",
       "...                                                                                                                                                ...  \n",
       "2306             Økern Portal       ved Dag Hammarskjölds vei                    59.930972              10.801830                                  170  \n",
       "2307             Domus Athletica    ved Vestgrensa Studentby                     59.946219              10.724626                                  604  \n",
       "2308             Gunerius           motsatt side av Torggata fra Gunerius bygget 59.914638              10.753428                                  342  \n",
       "2309             Ulven Torg         ved ulvenveien                               59.924960              10.812061                                  214  \n",
       "2315             Rostockgata        ved Operagata                                59.906890              10.760307                                  838  \n",
       "\n",
       "[254 rows x 8 columns]"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "station_data = trips.groupby(\n",
    "    [\n",
    "        \"start_station_id\",\n",
    "        \"start_station_name\",\n",
    "        \"start_station_description\",\n",
    "        \"start_station_latitude\",\n",
    "        \"start_station_longitude\",\n",
    "    ]\n",
    ").count()\n",
    "station_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "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>start_station_name</th>\n",
       "      <th>start_station_description</th>\n",
       "      <th>start_station_latitude</th>\n",
       "      <th>start_station_longitude</th>\n",
       "      <th>started_at</th>\n",
       "      <th>ended_at</th>\n",
       "      <th>duration</th>\n",
       "      <th>end_station_id</th>\n",
       "      <th>end_station_name</th>\n",
       "      <th>end_station_description</th>\n",
       "      <th>end_station_latitude</th>\n",
       "      <th>end_station_longitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>377</td>\n",
       "      <td>Tøyenparken</td>\n",
       "      <td>ved Caltexløkka</td>\n",
       "      <td>59.915667</td>\n",
       "      <td>10.777566</td>\n",
       "      <td>680</td>\n",
       "      <td>680</td>\n",
       "      <td>680</td>\n",
       "      <td>680</td>\n",
       "      <td>680</td>\n",
       "      <td>680</td>\n",
       "      <td>680</td>\n",
       "      <td>680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>378</td>\n",
       "      <td>Colosseum Kino</td>\n",
       "      <td>langs Fridtjof Nansens vei</td>\n",
       "      <td>59.929843</td>\n",
       "      <td>10.711285</td>\n",
       "      <td>562</td>\n",
       "      <td>562</td>\n",
       "      <td>562</td>\n",
       "      <td>562</td>\n",
       "      <td>562</td>\n",
       "      <td>562</td>\n",
       "      <td>562</td>\n",
       "      <td>562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>380</td>\n",
       "      <td>Bentsebrugata</td>\n",
       "      <td>rett over busstoppet</td>\n",
       "      <td>59.939230</td>\n",
       "      <td>10.759170</td>\n",
       "      <td>1097</td>\n",
       "      <td>1097</td>\n",
       "      <td>1097</td>\n",
       "      <td>1097</td>\n",
       "      <td>1097</td>\n",
       "      <td>1097</td>\n",
       "      <td>1097</td>\n",
       "      <td>1097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>381</td>\n",
       "      <td>Grønlands torg</td>\n",
       "      <td>ved Tøyenbekken</td>\n",
       "      <td>59.912520</td>\n",
       "      <td>10.762240</td>\n",
       "      <td>927</td>\n",
       "      <td>927</td>\n",
       "      <td>927</td>\n",
       "      <td>927</td>\n",
       "      <td>927</td>\n",
       "      <td>927</td>\n",
       "      <td>927</td>\n",
       "      <td>927</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>382</td>\n",
       "      <td>Stensgata</td>\n",
       "      <td>ved trikkestoppet</td>\n",
       "      <td>59.929586</td>\n",
       "      <td>10.732839</td>\n",
       "      <td>603</td>\n",
       "      <td>603</td>\n",
       "      <td>603</td>\n",
       "      <td>603</td>\n",
       "      <td>603</td>\n",
       "      <td>603</td>\n",
       "      <td>603</td>\n",
       "      <td>603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>249</th>\n",
       "      <td>2306</td>\n",
       "      <td>Økern Portal</td>\n",
       "      <td>ved Dag Hammarskjölds vei</td>\n",
       "      <td>59.930972</td>\n",
       "      <td>10.801830</td>\n",
       "      <td>170</td>\n",
       "      <td>170</td>\n",
       "      <td>170</td>\n",
       "      <td>170</td>\n",
       "      <td>170</td>\n",
       "      <td>170</td>\n",
       "      <td>170</td>\n",
       "      <td>170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>250</th>\n",
       "      <td>2307</td>\n",
       "      <td>Domus Athletica</td>\n",
       "      <td>ved Vestgrensa Studentby</td>\n",
       "      <td>59.946219</td>\n",
       "      <td>10.724626</td>\n",
       "      <td>604</td>\n",
       "      <td>604</td>\n",
       "      <td>604</td>\n",
       "      <td>604</td>\n",
       "      <td>604</td>\n",
       "      <td>604</td>\n",
       "      <td>604</td>\n",
       "      <td>604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>251</th>\n",
       "      <td>2308</td>\n",
       "      <td>Gunerius</td>\n",
       "      <td>motsatt side av Torggata fra Gunerius bygget</td>\n",
       "      <td>59.914638</td>\n",
       "      <td>10.753428</td>\n",
       "      <td>342</td>\n",
       "      <td>342</td>\n",
       "      <td>342</td>\n",
       "      <td>342</td>\n",
       "      <td>342</td>\n",
       "      <td>342</td>\n",
       "      <td>342</td>\n",
       "      <td>342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>252</th>\n",
       "      <td>2309</td>\n",
       "      <td>Ulven Torg</td>\n",
       "      <td>ved ulvenveien</td>\n",
       "      <td>59.924960</td>\n",
       "      <td>10.812061</td>\n",
       "      <td>214</td>\n",
       "      <td>214</td>\n",
       "      <td>214</td>\n",
       "      <td>214</td>\n",
       "      <td>214</td>\n",
       "      <td>214</td>\n",
       "      <td>214</td>\n",
       "      <td>214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>253</th>\n",
       "      <td>2315</td>\n",
       "      <td>Rostockgata</td>\n",
       "      <td>ved Operagata</td>\n",
       "      <td>59.906890</td>\n",
       "      <td>10.760307</td>\n",
       "      <td>838</td>\n",
       "      <td>838</td>\n",
       "      <td>838</td>\n",
       "      <td>838</td>\n",
       "      <td>838</td>\n",
       "      <td>838</td>\n",
       "      <td>838</td>\n",
       "      <td>838</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>254 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     start_station_id start_station_name  \\\n",
       "0                 377        Tøyenparken   \n",
       "1                 378     Colosseum Kino   \n",
       "2                 380      Bentsebrugata   \n",
       "3                 381     Grønlands torg   \n",
       "4                 382          Stensgata   \n",
       "..                ...                ...   \n",
       "249              2306       Økern Portal   \n",
       "250              2307    Domus Athletica   \n",
       "251              2308           Gunerius   \n",
       "252              2309         Ulven Torg   \n",
       "253              2315        Rostockgata   \n",
       "\n",
       "                        start_station_description  start_station_latitude  \\\n",
       "0                                 ved Caltexløkka               59.915667   \n",
       "1                      langs Fridtjof Nansens vei               59.929843   \n",
       "2                            rett over busstoppet               59.939230   \n",
       "3                                 ved Tøyenbekken               59.912520   \n",
       "4                               ved trikkestoppet               59.929586   \n",
       "..                                            ...                     ...   \n",
       "249                     ved Dag Hammarskjölds vei               59.930972   \n",
       "250                      ved Vestgrensa Studentby               59.946219   \n",
       "251  motsatt side av Torggata fra Gunerius bygget               59.914638   \n",
       "252                                ved ulvenveien               59.924960   \n",
       "253                                 ved Operagata               59.906890   \n",
       "\n",
       "     start_station_longitude  started_at  ended_at  duration  end_station_id  \\\n",
       "0                  10.777566         680       680       680             680   \n",
       "1                  10.711285         562       562       562             562   \n",
       "2                  10.759170        1097      1097      1097            1097   \n",
       "3                  10.762240         927       927       927             927   \n",
       "4                  10.732839         603       603       603             603   \n",
       "..                       ...         ...       ...       ...             ...   \n",
       "249                10.801830         170       170       170             170   \n",
       "250                10.724626         604       604       604             604   \n",
       "251                10.753428         342       342       342             342   \n",
       "252                10.812061         214       214       214             214   \n",
       "253                10.760307         838       838       838             838   \n",
       "\n",
       "     end_station_name  end_station_description  end_station_latitude  \\\n",
       "0                 680                      680                   680   \n",
       "1                 562                      562                   562   \n",
       "2                1097                     1097                  1097   \n",
       "3                 927                      927                   927   \n",
       "4                 603                      603                   603   \n",
       "..                ...                      ...                   ...   \n",
       "249               170                      170                   170   \n",
       "250               604                      604                   604   \n",
       "251               342                      342                   342   \n",
       "252               214                      214                   214   \n",
       "253               838                      838                   838   \n",
       "\n",
       "     end_station_longitude  \n",
       "0                      680  \n",
       "1                      562  \n",
       "2                     1097  \n",
       "3                      927  \n",
       "4                      603  \n",
       "..                     ...  \n",
       "249                    170  \n",
       "250                    604  \n",
       "251                    342  \n",
       "252                    214  \n",
       "253                    838  \n",
       "\n",
       "[254 rows x 13 columns]"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "station_data = station_data.reset_index()\n",
    "station_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe thead th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>start_station_id</th>\n",
       "      <th>start_station_name</th>\n",
       "      <th>start_station_description</th>\n",
       "      <th>start_station_latitude</th>\n",
       "      <th>start_station_longitude</th>\n",
       "      <th>started_at</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>377</td>\n",
       "      <td>Tøyenparken</td>\n",
       "      <td>ved Caltexløkka</td>\n",
       "      <td>59.915667</td>\n",
       "      <td>10.777566</td>\n",
       "      <td>680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>378</td>\n",
       "      <td>Colosseum Kino</td>\n",
       "      <td>langs Fridtjof Nansens vei</td>\n",
       "      <td>59.929843</td>\n",
       "      <td>10.711285</td>\n",
       "      <td>562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>380</td>\n",
       "      <td>Bentsebrugata</td>\n",
       "      <td>rett over busstoppet</td>\n",
       "      <td>59.939230</td>\n",
       "      <td>10.759170</td>\n",
       "      <td>1097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>381</td>\n",
       "      <td>Grønlands torg</td>\n",
       "      <td>ved Tøyenbekken</td>\n",
       "      <td>59.912520</td>\n",
       "      <td>10.762240</td>\n",
       "      <td>927</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>382</td>\n",
       "      <td>Stensgata</td>\n",
       "      <td>ved trikkestoppet</td>\n",
       "      <td>59.929586</td>\n",
       "      <td>10.732839</td>\n",
       "      <td>603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>249</th>\n",
       "      <td>2306</td>\n",
       "      <td>Økern Portal</td>\n",
       "      <td>ved Dag Hammarskjölds vei</td>\n",
       "      <td>59.930972</td>\n",
       "      <td>10.801830</td>\n",
       "      <td>170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>250</th>\n",
       "      <td>2307</td>\n",
       "      <td>Domus Athletica</td>\n",
       "      <td>ved Vestgrensa Studentby</td>\n",
       "      <td>59.946219</td>\n",
       "      <td>10.724626</td>\n",
       "      <td>604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>251</th>\n",
       "      <td>2308</td>\n",
       "      <td>Gunerius</td>\n",
       "      <td>motsatt side av Torggata fra Gunerius bygget</td>\n",
       "      <td>59.914638</td>\n",
       "      <td>10.753428</td>\n",
       "      <td>342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>252</th>\n",
       "      <td>2309</td>\n",
       "      <td>Ulven Torg</td>\n",
       "      <td>ved ulvenveien</td>\n",
       "      <td>59.924960</td>\n",
       "      <td>10.812061</td>\n",
       "      <td>214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>253</th>\n",
       "      <td>2315</td>\n",
       "      <td>Rostockgata</td>\n",
       "      <td>ved Operagata</td>\n",
       "      <td>59.906890</td>\n",
       "      <td>10.760307</td>\n",
       "      <td>838</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>254 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     start_station_id start_station_name  \\\n",
       "0                 377        Tøyenparken   \n",
       "1                 378     Colosseum Kino   \n",
       "2                 380      Bentsebrugata   \n",
       "3                 381     Grønlands torg   \n",
       "4                 382          Stensgata   \n",
       "..                ...                ...   \n",
       "249              2306       Økern Portal   \n",
       "250              2307    Domus Athletica   \n",
       "251              2308           Gunerius   \n",
       "252              2309         Ulven Torg   \n",
       "253              2315        Rostockgata   \n",
       "\n",
       "                        start_station_description  start_station_latitude  \\\n",
       "0                                 ved Caltexløkka               59.915667   \n",
       "1                      langs Fridtjof Nansens vei               59.929843   \n",
       "2                            rett over busstoppet               59.939230   \n",
       "3                                 ved Tøyenbekken               59.912520   \n",
       "4                               ved trikkestoppet               59.929586   \n",
       "..                                            ...                     ...   \n",
       "249                     ved Dag Hammarskjölds vei               59.930972   \n",
       "250                      ved Vestgrensa Studentby               59.946219   \n",
       "251  motsatt side av Torggata fra Gunerius bygget               59.914638   \n",
       "252                                ved ulvenveien               59.924960   \n",
       "253                                 ved Operagata               59.906890   \n",
       "\n",
       "     start_station_longitude  started_at  \n",
       "0                  10.777566         680  \n",
       "1                  10.711285         562  \n",
       "2                  10.759170        1097  \n",
       "3                  10.762240         927  \n",
       "4                  10.732839         603  \n",
       "..                       ...         ...  \n",
       "249                10.801830         170  \n",
       "250                10.724626         604  \n",
       "251                10.753428         342  \n",
       "252                10.812061         214  \n",
       "253                10.760307         838  \n",
       "\n",
       "[254 rows x 6 columns]"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "station_data = station_data.drop(columns=station_data.columns[-7:])\n",
    "station_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>start_station_name</th>\n",
       "      <th>start_station_description</th>\n",
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       "      <th>start_station_longitude</th>\n",
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       "    <tr>\n",
       "      <th>start_station_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>377</th>\n",
       "      <td>Tøyenparken</td>\n",
       "      <td>ved Caltexløkka</td>\n",
       "      <td>59.915667</td>\n",
       "      <td>10.777566</td>\n",
       "      <td>680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>378</th>\n",
       "      <td>Colosseum Kino</td>\n",
       "      <td>langs Fridtjof Nansens vei</td>\n",
       "      <td>59.929843</td>\n",
       "      <td>10.711285</td>\n",
       "      <td>562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>380</th>\n",
       "      <td>Bentsebrugata</td>\n",
       "      <td>rett over busstoppet</td>\n",
       "      <td>59.939230</td>\n",
       "      <td>10.759170</td>\n",
       "      <td>1097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>381</th>\n",
       "      <td>Grønlands torg</td>\n",
       "      <td>ved Tøyenbekken</td>\n",
       "      <td>59.912520</td>\n",
       "      <td>10.762240</td>\n",
       "      <td>927</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>382</th>\n",
       "      <td>Stensgata</td>\n",
       "      <td>ved trikkestoppet</td>\n",
       "      <td>59.929586</td>\n",
       "      <td>10.732839</td>\n",
       "      <td>603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2306</th>\n",
       "      <td>Økern Portal</td>\n",
       "      <td>ved Dag Hammarskjölds vei</td>\n",
       "      <td>59.930972</td>\n",
       "      <td>10.801830</td>\n",
       "      <td>170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2307</th>\n",
       "      <td>Domus Athletica</td>\n",
       "      <td>ved Vestgrensa Studentby</td>\n",
       "      <td>59.946219</td>\n",
       "      <td>10.724626</td>\n",
       "      <td>604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2308</th>\n",
       "      <td>Gunerius</td>\n",
       "      <td>motsatt side av Torggata fra Gunerius bygget</td>\n",
       "      <td>59.914638</td>\n",
       "      <td>10.753428</td>\n",
       "      <td>342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2309</th>\n",
       "      <td>Ulven Torg</td>\n",
       "      <td>ved ulvenveien</td>\n",
       "      <td>59.924960</td>\n",
       "      <td>10.812061</td>\n",
       "      <td>214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2315</th>\n",
       "      <td>Rostockgata</td>\n",
       "      <td>ved Operagata</td>\n",
       "      <td>59.906890</td>\n",
       "      <td>10.760307</td>\n",
       "      <td>838</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>254 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 start_station_name  \\\n",
       "start_station_id                      \n",
       "377                     Tøyenparken   \n",
       "378                  Colosseum Kino   \n",
       "380                   Bentsebrugata   \n",
       "381                  Grønlands torg   \n",
       "382                       Stensgata   \n",
       "...                             ...   \n",
       "2306                   Økern Portal   \n",
       "2307                Domus Athletica   \n",
       "2308                       Gunerius   \n",
       "2309                     Ulven Torg   \n",
       "2315                    Rostockgata   \n",
       "\n",
       "                                     start_station_description  \\\n",
       "start_station_id                                                 \n",
       "377                                            ved Caltexløkka   \n",
       "378                                 langs Fridtjof Nansens vei   \n",
       "380                                       rett over busstoppet   \n",
       "381                                            ved Tøyenbekken   \n",
       "382                                          ved trikkestoppet   \n",
       "...                                                        ...   \n",
       "2306                                 ved Dag Hammarskjölds vei   \n",
       "2307                                  ved Vestgrensa Studentby   \n",
       "2308              motsatt side av Torggata fra Gunerius bygget   \n",
       "2309                                            ved ulvenveien   \n",
       "2315                                             ved Operagata   \n",
       "\n",
       "                  start_station_latitude  start_station_longitude  \\\n",
       "start_station_id                                                    \n",
       "377                            59.915667                10.777566   \n",
       "378                            59.929843                10.711285   \n",
       "380                            59.939230                10.759170   \n",
       "381                            59.912520                10.762240   \n",
       "382                            59.929586                10.732839   \n",
       "...                                  ...                      ...   \n",
       "2306                           59.930972                10.801830   \n",
       "2307                           59.946219                10.724626   \n",
       "2308                           59.914638                10.753428   \n",
       "2309                           59.924960                10.812061   \n",
       "2315                           59.906890                10.760307   \n",
       "\n",
       "                  started_trips  \n",
       "start_station_id                 \n",
       "377                         680  \n",
       "378                         562  \n",
       "380                        1097  \n",
       "381                         927  \n",
       "382                         603  \n",
       "...                         ...  \n",
       "2306                        170  \n",
       "2307                        604  \n",
       "2308                        342  \n",
       "2309                        214  \n",
       "2315                        838  \n",
       "\n",
       "[254 rows x 5 columns]"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "station_data = station_data.rename(columns={\"started_at\": \"started_trips\"})\n",
    "station_data = station_data.set_index(\"start_station_id\")\n",
    "station_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>start_station_name</th>\n",
       "      <th>start_station_description</th>\n",
       "      <th>start_station_latitude</th>\n",
       "      <th>start_station_longitude</th>\n",
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       "    <tr>\n",
       "      <th>start_station_id</th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>421</th>\n",
       "      <td>Alexander Kiellands Plass</td>\n",
       "      <td>langs Maridalsveien</td>\n",
       "      <td>59.928067</td>\n",
       "      <td>10.751203</td>\n",
       "      <td>2831</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>398</th>\n",
       "      <td>Ringnes Park</td>\n",
       "      <td>ved Sannergata</td>\n",
       "      <td>59.928434</td>\n",
       "      <td>10.759430</td>\n",
       "      <td>2308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>551</th>\n",
       "      <td>Olaf Ryes plass</td>\n",
       "      <td>langs Sofienberggata</td>\n",
       "      <td>59.922425</td>\n",
       "      <td>10.758182</td>\n",
       "      <td>2198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>408</th>\n",
       "      <td>Tøyen skole</td>\n",
       "      <td>forsiden av skolebygget</td>\n",
       "      <td>59.914943</td>\n",
       "      <td>10.773977</td>\n",
       "      <td>2189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>607</th>\n",
       "      <td>Marcus Thranes gate</td>\n",
       "      <td>ved Akerselva</td>\n",
       "      <td>59.932772</td>\n",
       "      <td>10.758595</td>\n",
       "      <td>2132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>454</th>\n",
       "      <td>Furulund</td>\n",
       "      <td>langs Vækerøveien</td>\n",
       "      <td>59.919810</td>\n",
       "      <td>10.651118</td>\n",
       "      <td>96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1919</th>\n",
       "      <td>Kværnerveien</td>\n",
       "      <td>Ved Kværnerveien 5</td>\n",
       "      <td>59.905911</td>\n",
       "      <td>10.778592</td>\n",
       "      <td>88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>591</th>\n",
       "      <td>Grenseveien</td>\n",
       "      <td>ved Togbru</td>\n",
       "      <td>59.924645</td>\n",
       "      <td>10.781727</td>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>560</th>\n",
       "      <td>Gaustad T-bane</td>\n",
       "      <td>langs Slemdalsveien</td>\n",
       "      <td>59.945955</td>\n",
       "      <td>10.710392</td>\n",
       "      <td>74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>573</th>\n",
       "      <td>Tordenskiolds gate</td>\n",
       "      <td>ved Rådhusgata</td>\n",
       "      <td>59.911776</td>\n",
       "      <td>10.735113</td>\n",
       "      <td>67</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>254 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                         start_station_name start_station_description  \\\n",
       "start_station_id                                                        \n",
       "421               Alexander Kiellands Plass       langs Maridalsveien   \n",
       "398                            Ringnes Park            ved Sannergata   \n",
       "551                         Olaf Ryes plass      langs Sofienberggata   \n",
       "408                             Tøyen skole   forsiden av skolebygget   \n",
       "607                     Marcus Thranes gate             ved Akerselva   \n",
       "...                                     ...                       ...   \n",
       "454                                Furulund         langs Vækerøveien   \n",
       "1919                           Kværnerveien        Ved Kværnerveien 5   \n",
       "591                             Grenseveien                ved Togbru   \n",
       "560                          Gaustad T-bane       langs Slemdalsveien   \n",
       "573                      Tordenskiolds gate            ved Rådhusgata   \n",
       "\n",
       "                  start_station_latitude  start_station_longitude  \\\n",
       "start_station_id                                                    \n",
       "421                            59.928067                10.751203   \n",
       "398                            59.928434                10.759430   \n",
       "551                            59.922425                10.758182   \n",
       "408                            59.914943                10.773977   \n",
       "607                            59.932772                10.758595   \n",
       "...                                  ...                      ...   \n",
       "454                            59.919810                10.651118   \n",
       "1919                           59.905911                10.778592   \n",
       "591                            59.924645                10.781727   \n",
       "560                            59.945955                10.710392   \n",
       "573                            59.911776                10.735113   \n",
       "\n",
       "                  started_trips  \n",
       "start_station_id                 \n",
       "421                        2831  \n",
       "398                        2308  \n",
       "551                        2198  \n",
       "408                        2189  \n",
       "607                        2132  \n",
       "...                         ...  \n",
       "454                          96  \n",
       "1919                         88  \n",
       "591                          81  \n",
       "560                          74  \n",
       "573                          67  \n",
       "\n",
       "[254 rows x 5 columns]"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "station_data.sort_values(\"started_trips\", ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "421     2818\n",
       "551     2702\n",
       "443     2676\n",
       "489     2644\n",
       "480     2479\n",
       "        ... \n",
       "591       80\n",
       "1919      71\n",
       "498       60\n",
       "560       51\n",
       "601       43\n",
       "Name: end_station_id, Length: 253, dtype: int64"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ended_trips = trips[\"end_station_id\"].value_counts()\n",
    "ended_trips"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "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_name</th>\n",
       "      <th>start_station_description</th>\n",
       "      <th>start_station_latitude</th>\n",
       "      <th>start_station_longitude</th>\n",
       "      <th>started_trips</th>\n",
       "      <th>ended_trips</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>start_station_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>421</th>\n",
       "      <td>Alexander Kiellands Plass</td>\n",
       "      <td>langs Maridalsveien</td>\n",
       "      <td>59.928067</td>\n",
       "      <td>10.751203</td>\n",
       "      <td>2831</td>\n",
       "      <td>2818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>398</th>\n",
       "      <td>Ringnes Park</td>\n",
       "      <td>ved Sannergata</td>\n",
       "      <td>59.928434</td>\n",
       "      <td>10.759430</td>\n",
       "      <td>2308</td>\n",
       "      <td>2308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>551</th>\n",
       "      <td>Olaf Ryes plass</td>\n",
       "      <td>langs Sofienberggata</td>\n",
       "      <td>59.922425</td>\n",
       "      <td>10.758182</td>\n",
       "      <td>2198</td>\n",
       "      <td>2702</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>408</th>\n",
       "      <td>Tøyen skole</td>\n",
       "      <td>forsiden av skolebygget</td>\n",
       "      <td>59.914943</td>\n",
       "      <td>10.773977</td>\n",
       "      <td>2189</td>\n",
       "      <td>2183</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>607</th>\n",
       "      <td>Marcus Thranes gate</td>\n",
       "      <td>ved Akerselva</td>\n",
       "      <td>59.932772</td>\n",
       "      <td>10.758595</td>\n",
       "      <td>2132</td>\n",
       "      <td>1607</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>454</th>\n",
       "      <td>Furulund</td>\n",
       "      <td>langs Vækerøveien</td>\n",
       "      <td>59.919810</td>\n",
       "      <td>10.651118</td>\n",
       "      <td>96</td>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1919</th>\n",
       "      <td>Kværnerveien</td>\n",
       "      <td>Ved Kværnerveien 5</td>\n",
       "      <td>59.905911</td>\n",
       "      <td>10.778592</td>\n",
       "      <td>88</td>\n",
       "      <td>71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>591</th>\n",
       "      <td>Grenseveien</td>\n",
       "      <td>ved Togbru</td>\n",
       "      <td>59.924645</td>\n",
       "      <td>10.781727</td>\n",
       "      <td>81</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>560</th>\n",
       "      <td>Gaustad T-bane</td>\n",
       "      <td>langs Slemdalsveien</td>\n",
       "      <td>59.945955</td>\n",
       "      <td>10.710392</td>\n",
       "      <td>74</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>573</th>\n",
       "      <td>Tordenskiolds gate</td>\n",
       "      <td>ved Rådhusgata</td>\n",
       "      <td>59.911776</td>\n",
       "      <td>10.735113</td>\n",
       "      <td>67</td>\n",
       "      <td>124</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>254 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                         start_station_name start_station_description  \\\n",
       "start_station_id                                                        \n",
       "421               Alexander Kiellands Plass       langs Maridalsveien   \n",
       "398                            Ringnes Park            ved Sannergata   \n",
       "551                         Olaf Ryes plass      langs Sofienberggata   \n",
       "408                             Tøyen skole   forsiden av skolebygget   \n",
       "607                     Marcus Thranes gate             ved Akerselva   \n",
       "...                                     ...                       ...   \n",
       "454                                Furulund         langs Vækerøveien   \n",
       "1919                           Kværnerveien        Ved Kværnerveien 5   \n",
       "591                             Grenseveien                ved Togbru   \n",
       "560                          Gaustad T-bane       langs Slemdalsveien   \n",
       "573                      Tordenskiolds gate            ved Rådhusgata   \n",
       "\n",
       "                  start_station_latitude  start_station_longitude  \\\n",
       "start_station_id                                                    \n",
       "421                            59.928067                10.751203   \n",
       "398                            59.928434                10.759430   \n",
       "551                            59.922425                10.758182   \n",
       "408                            59.914943                10.773977   \n",
       "607                            59.932772                10.758595   \n",
       "...                                  ...                      ...   \n",
       "454                            59.919810                10.651118   \n",
       "1919                           59.905911                10.778592   \n",
       "591                            59.924645                10.781727   \n",
       "560                            59.945955                10.710392   \n",
       "573                            59.911776                10.735113   \n",
       "\n",
       "                  started_trips  ended_trips  \n",
       "start_station_id                              \n",
       "421                        2831         2818  \n",
       "398                        2308         2308  \n",
       "551                        2198         2702  \n",
       "408                        2189         2183  \n",
       "607                        2132         1607  \n",
       "...                         ...          ...  \n",
       "454                          96           85  \n",
       "1919                         88           71  \n",
       "591                          81           80  \n",
       "560                          74           51  \n",
       "573                          67          124  \n",
       "\n",
       "[254 rows x 6 columns]"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "station_data[\"ended_trips\"] = ended_trips\n",
    "station_data.sort_values(\"started_trips\", ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting on a map with ipyleaflet and HTML"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We saw that the scatterplot could be used to plot stations on a map:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='start_station_longitude', ylabel='start_station_latitude'>"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "station_data.plot.scatter(\"start_station_longitude\", \"start_station_latitude\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now have tools to plot the most popular bike stations as bigger circles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='start_station_longitude', ylabel='start_station_latitude'>"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "station_data.plot.scatter(\n",
    "    \"start_station_longitude\", \"start_station_latitude\", s=\"started_trips\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ipywidgets/HTML and ipyleaflet are useful tools to visualize data on maps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "from ipyleaflet import Circle, Map, Marker, Polyline, basemap_to_tiles, basemaps\n",
    "from ipywidgets import HTML"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "oslo_center = (\n",
    "    59.9127,\n",
    "    10.7461,\n",
    ")  # NB ipyleaflet uses Lat-Long (i.e. y,x, when specifying coordinates)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "oslo_map = Map(center=oslo_center, zoom=13)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "5c97563205104507ad5c9d1760e7ef35",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Map(center=[59.9127, 10.7461], controls=(ZoomControl(options=['position', 'zoom_in_text', 'zoom_in_title', 'zo…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "oslo_map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "oslo_map.save(\n",
    "    \"data/raw_oslo_map.html\"\n",
    ")  # if interactive view is not possible inline try to open this in your browser"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can add different layers to our map with a marker function. The function is written such that for a given row in the dataframe (i.e. a given station), we add one marker to the map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "def add_markers(row):\n",
    "    center = row[\"start_station_latitude\"], row[\"start_station_longitude\"]\n",
    "    marker = Circle(\n",
    "        location=center, radius=int(0.04 * row[\"started_trips\"]), color=\"green\"\n",
    "    )\n",
    "    oslo_map.add_layer(marker)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "start_station_id\n",
       "377     None\n",
       "378     None\n",
       "380     None\n",
       "381     None\n",
       "382     None\n",
       "        ... \n",
       "2306    None\n",
       "2307    None\n",
       "2308    None\n",
       "2309    None\n",
       "2315    None\n",
       "Length: 254, dtype: object"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "station_data.apply(add_markers, axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exercise\n",
    "\n",
    "Note: If you have issues with installing ipyleaflet or ipywidget with pip, just use pl.scatter() or pl.plot()\n",
    "\n",
    "1) Create the DataFrame station_data as described in the lecture\n",
    "\n",
    "2) Make a similar plot of the Oslo map with the most popular end-stations as red circles\n",
    "\n",
    "3) Add the following line as the last line in your add_markers-function: marker.popup = HTML(f\"{row['start_station_name']} Trips started: {row['started_trips']}\")  . You can also add newlines within the string with the HTML command for newline\n",
    "\n",
    "4) Try to make an Oslo map showing both started trips and ended trips in the same map\n",
    "\n",
    "5) Make a map showing which stations are most popular going from Stensgata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FremontBridge.csv\n",
      "bike-counter-locations-oslo-municipality.csv\n",
      "car-counter-locations-oslo-municipality.csv\n",
      "eustat_area.tsv\n",
      "eustat_population.tsv\n",
      "nor_population2022.csv\n",
      "oslo_bike_2021_09.csv\n",
      "oslo_bike_september_2022.csv\n",
      "pressure.csv\n",
      "pressure.png\n",
      "raw_oslo_map.html\n",
      "state-abbrevs.csv\n",
      "state-areas.csv\n",
      "state-population.csv\n",
      "used_car_sales.csv\n"
     ]
    }
   ],
   "source": [
    "ls data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%reset\n",
    "import pandas as pd\n",
    "import pylab as pl\n",
    "\n",
    "trip_csv = \"data/oslo_bike_2021_09.csv\"\n",
    "trips = pd.read_csv(trip_csv)\n",
    "station_data = trips.groupby(\n",
    "    [\n",
    "        \"start_station_id\",\n",
    "        \"start_station_longitude\",\n",
    "        \"start_station_latitude\",\n",
    "        \"start_station_name\",\n",
    "    ]\n",
    ").count()\n",
    "station_data = station_data.reset_index()\n",
    "station_data = station_data.drop(columns=station_data.columns[-7:])\n",
    "station_data = station_data.rename(columns={\"started_at\": \"started_trips\"})\n",
    "station_data = station_data.set_index(\"start_station_id\")\n",
    "station_data[\"ended_trips\"] = trips[\"end_station_id\"].value_counts()\n",
    "\n",
    "from ipyleaflet import Circle, Map, Marker, Polyline, basemap_to_tiles, basemaps\n",
    "from ipywidgets import HTML\n",
    "\n",
    "oslo_center = (\n",
    "    59.9127,\n",
    "    10.7461,\n",
    ")  # NB ipyleaflet uses Lat-Long (i.e. y,x, when specifying coordinates)\n",
    "oslo_map = Map(center=oslo_center, zoom=13)\n",
    "\n",
    "\n",
    "def add_markers(row):\n",
    "    center = row[\"start_station_latitude\"], row[\"start_station_longitude\"]\n",
    "    marker = Circle(\n",
    "        location=center, radius=int(0.04 * row[\"started_trips\"]), color=\"green\"\n",
    "    )\n",
    "    marker2 = Circle(\n",
    "        location=center, radius=int(0.04 * row[\"ended_trips\"]), color=\"red\"\n",
    "    )\n",
    "    oslo_map.add_layer(marker)\n",
    "    oslo_map.add_layer(marker2)\n",
    "    marker.popup = HTML(\n",
    "        f\"{row['start_station_name']} <br> Trips started: {row['started_trips']}\"\n",
    "    )\n",
    "\n",
    "\n",
    "station_data.apply(add_markers, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "642d4dd0be94411cae2b201f6818b50c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Map(center=[59.9127, 10.7461], controls=(ZoomControl(options=['position', 'zoom_in_text', 'zoom_in_title', 'zo…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "oslo_map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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