Lab06/Lab06_Visualization.ipynb

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    "# Lab 06 Data visualization with matplotlib and seaborn\n",
    "\n",
    "Nice visualization libraries:\n",
    "- matplotlib: https://matplotlib.org/stable/index.html\n",
    "- seaborn: http:https://seaborn.pydata.org/\n",
    "- bokeh: http:https://bokeh.pydata.org/en/latest/\n",
    "- folium (geospatial visualization): https://python-visualization.github.io/folium/\n",
    "- kepler.gl (large-scale geospatial visualization): https://kepler.gl/\n",
    "\n",
    "## Please submit your finished lab06 jupyter notebook via pull request in GitHub"
   ]
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    "# magic command to display matplotlib plots inline within the ipython notebook webpage\n",
    "%matplotlib inline\n",
    "\n",
    "# import necessary modules\n",
    "import pandas as pd, numpy as np, matplotlib.pyplot as plt\n",
    "import matplotlib.cm as cm, matplotlib.font_manager as fm"
   ]
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   "source": [
    "## load the data sets"
   ]
  },
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    "# load the gps coordinate data, using the date as the full set's index\n",
    "# the data files are encoded as utf-8: specify so to prevent matplotlib from choking on diacritics\n",
    "df = pd.read_csv('data/summer-travel-gps-full.csv', encoding='utf-8', index_col='date', parse_dates=True)\n",
    "rs = pd.read_csv('data/summer-travel-gps-dbscan.csv', encoding='utf-8')"
   ]
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       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>lat</th>\n",
       "      <th>lon</th>\n",
       "      <th>city</th>\n",
       "      <th>country</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2014-05-14 09:07:00</th>\n",
       "      <td>51.481292</td>\n",
       "      <td>-0.451011</td>\n",
       "      <td>West Drayton</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-05-14 09:22:00</th>\n",
       "      <td>51.474005</td>\n",
       "      <td>-0.450999</td>\n",
       "      <td>Hounslow</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-05-14 10:51:00</th>\n",
       "      <td>51.478199</td>\n",
       "      <td>-0.446081</td>\n",
       "      <td>Hounslow</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-05-14 11:24:00</th>\n",
       "      <td>51.478199</td>\n",
       "      <td>-0.446081</td>\n",
       "      <td>Hounslow</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-05-14 11:38:00</th>\n",
       "      <td>51.474146</td>\n",
       "      <td>-0.451562</td>\n",
       "      <td>Hounslow</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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       "                           lat       lon          city         country\n",
       "date                                                                  \n",
       "2014-05-14 09:07:00  51.481292 -0.451011  West Drayton  United Kingdom\n",
       "2014-05-14 09:22:00  51.474005 -0.450999      Hounslow  United Kingdom\n",
       "2014-05-14 10:51:00  51.478199 -0.446081      Hounslow  United Kingdom\n",
       "2014-05-14 11:24:00  51.478199 -0.446081      Hounslow  United Kingdom\n",
       "2014-05-14 11:38:00  51.474146 -0.451562      Hounslow  United Kingdom"
      ]
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   "source": [
    "df.head()"
   ]
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       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>lat</th>\n",
       "      <th>lon</th>\n",
       "      <th>date</th>\n",
       "      <th>city</th>\n",
       "      <th>country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>51.478199</td>\n",
       "      <td>-0.446081</td>\n",
       "      <td>05/14/2014 10:51</td>\n",
       "      <td>Hounslow</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>38.781775</td>\n",
       "      <td>-9.137544</td>\n",
       "      <td>05/14/2014 15:11</td>\n",
       "      <td>Lisbon</td>\n",
       "      <td>Portugal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>38.771939</td>\n",
       "      <td>-9.128911</td>\n",
       "      <td>05/14/2014 15:41</td>\n",
       "      <td>Lisbon</td>\n",
       "      <td>Portugal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>38.742987</td>\n",
       "      <td>-9.147780</td>\n",
       "      <td>05/14/2014 16:11</td>\n",
       "      <td>Lisbon</td>\n",
       "      <td>Portugal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>38.693780</td>\n",
       "      <td>-9.206363</td>\n",
       "      <td>05/15/2014 14:19</td>\n",
       "      <td>Lisbon</td>\n",
       "      <td>Portugal</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "         lat       lon              date      city         country\n",
       "0  51.478199 -0.446081  05/14/2014 10:51  Hounslow  United Kingdom\n",
       "1  38.781775 -9.137544  05/14/2014 15:11    Lisbon        Portugal\n",
       "2  38.771939 -9.128911  05/14/2014 15:41    Lisbon        Portugal\n",
       "3  38.742987 -9.147780  05/14/2014 16:11    Lisbon        Portugal\n",
       "4  38.693780 -9.206363  05/15/2014 14:19    Lisbon        Portugal"
      ]
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   "source": [
    "rs.head()"
   ]
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   "source": [
    "## First up: Bar Charts"
   ]
  },
  {
   "cell_type": "code",
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   "source": [
    "# Q1: Can you draw a bar chart for the most visited countries by using matplotlib and seaborn? You can use the df dataframe.\n",
    "# Please style the chart to make it look nicer\n",
    "# such as figsize, width, alpha, color, edgecolor, grid, xlim, and ylim"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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   "source": [
    "Now you try: re-create the plot above, but make the bars orange with maroon edges."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
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   "outputs": [],
   "source": [
    "# Q2: Draw the same bar chart, but now for the most visited cities instead of countries"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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    "editable": true
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   "source": [
    "## Next: scatter plots as simple maps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
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   "source": [
    "# Q3: Draw a scatter plot the longitude and latitude data, with matplotlib and seaborn defaults. \n",
    "# You can use the rs dataframe.\n",
    "# Please style the chart to make it look nicer with good size and alpha settings. "
   ]
  },
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   "cell_type": "markdown",
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   "source": [
    "## Line charts, with annotation"
   ]
  },
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   "cell_type": "code",
   "execution_count": 8,
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "2014-05-14    36\n",
       "2014-05-15    61\n",
       "2014-05-16    57\n",
       "2014-05-17    64\n",
       "2014-05-18    50\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
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     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get the count of records by date\n",
    "countdata = df.groupby(df.index.date).size()\n",
    "countdata.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
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    "deletable": true,
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   },
   "outputs": [],
   "source": [
    "# Q4: Draw a  matplotlib and seaborn line chart from countdata. \n",
    "# Please style the chart to make it look nicer with good size and alpha settings. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "deletable": true,
    "editable": true
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   "source": [
    "# Q5: Now you try: re-create the plot above with different line widths, colors, and point markers. \n",
    "# Play with moving the annotation labels around. Make the vertical tick marks 3 days apart instead of 5.\n",
    "# experiment with a more advanced challenge like smoothing the line:\n",
    "# - https://stackoverflow.com/questions/5283649/plot-smooth-line-with-pyplot\n",
    "# - https://stackoverflow.com/questions/25825946/generating-smooth-line-graph-using-matplotlib"
   ]
  },
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   "cell_type": "markdown",
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    "deletable": true,
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   "source": [
    "## Pie charts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "113\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Barcelona    632\n",
       "Lisbon       158\n",
       "Tübingen     131\n",
       "Prague        97\n",
       "Istanbul      94\n",
       "Name: city, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cities = df['city'].value_counts()\n",
    "print(cities.count())\n",
    "cities.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "jupyter": {
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   },
   "outputs": [],
   "source": [
    "# Q6: draw a pie chart for cities.\n",
    "# Please style the chart to make it look nicer. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Extra task: Kepler.gl\n",
    "Some examples: https://docs.kepler.gl/docs/keplergl-jupyter and https://jamesopacich.medium.com/display-a-kepler-gl-map-69d807dae082"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Q7: can you visualize the GPS points by using Kepler.gl? \n"
   ]
  }
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