Lab05/Lab05_Pandas.ipynb

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   "source": [
    "# Lab05 Python Data Science with Pandas\n",
    "\n",
    "## Please submit your finished lab05 jupyter notebook via pull request in GitHub"
   ]
  },
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   "source": [
    "# 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"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
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   },
   "source": [
    "## Part 1 <br /> Basics of Selecting and Slicing Data"
   ]
  },
  {
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       "  <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>51.481292</td>\n",
       "      <td>-0.451011</td>\n",
       "      <td>05/14/2014 09:07</td>\n",
       "      <td>West Drayton</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>51.474005</td>\n",
       "      <td>-0.450999</td>\n",
       "      <td>05/14/2014 09:22</td>\n",
       "      <td>Hounslow</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</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>3</th>\n",
       "      <td>51.478199</td>\n",
       "      <td>-0.446081</td>\n",
       "      <td>05/14/2014 11:24</td>\n",
       "      <td>Hounslow</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>51.474146</td>\n",
       "      <td>-0.451562</td>\n",
       "      <td>05/14/2014 11:38</td>\n",
       "      <td>Hounslow</td>\n",
       "      <td>United Kingdom</td>\n",
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       "         lat       lon              date          city         country\n",
       "0  51.481292 -0.451011  05/14/2014 09:07  West Drayton  United Kingdom\n",
       "1  51.474005 -0.450999  05/14/2014 09:22      Hounslow  United Kingdom\n",
       "2  51.478199 -0.446081  05/14/2014 10:51      Hounslow  United Kingdom\n",
       "3  51.478199 -0.446081  05/14/2014 11:24      Hounslow  United Kingdom\n",
       "4  51.474146 -0.451562  05/14/2014 11:38      Hounslow  United Kingdom"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create a pandas dataframe from the location data set\n",
    "df = pd.read_csv('data/summer-travel-gps-full.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
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    "deletable": true,
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   },
   "outputs": [],
   "source": [
    "# Q1: how to get 2 columns from the dataframe (city and country)?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "To get a single \"cell's\" value out of a dataframe, pass a column name, then a row label. This is equivalent to slicing the dataframe down to a single series, then slicing a single value out of that series using [ ] indexing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "jupyter": {
     "outputs_hidden": false
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   },
   "outputs": [],
   "source": [
    "# Q2: how to get the first 5 rows of the \"city\" column?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Using .loc[ ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "# Q3: how to use .loc to select the third row of the dataframe?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "# Q4: how to use .loc to select the first row in \"country\" column?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "jupyter": {
     "outputs_hidden": false
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   },
   "outputs": [],
   "source": [
    "# Q5: how to select the first 4 rows of ['city', 'date'] columns?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Using .iloc[ ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "jupyter": {
     "outputs_hidden": false
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   },
   "outputs": [],
   "source": [
    "# use .iloc for integer position based indexing\n",
    "# Q6: how to get the value from the row in position 3 and the column in position 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "jupyter": {
     "outputs_hidden": false
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   },
   "outputs": [],
   "source": [
    "# Q7: how to use iloc to select every 300th row from a data set"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Part 2 <br /> How to select rows by some value(s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
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       "  <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>173</th>\n",
       "      <td>41.044556</td>\n",
       "      <td>28.983286</td>\n",
       "      <td>07/08/2014 16:44</td>\n",
       "      <td>Istanbul</td>\n",
       "      <td>Turkey</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>174</th>\n",
       "      <td>41.008992</td>\n",
       "      <td>28.968268</td>\n",
       "      <td>07/08/2014 20:03</td>\n",
       "      <td>Istanbul</td>\n",
       "      <td>Turkey</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>175</th>\n",
       "      <td>41.043487</td>\n",
       "      <td>28.985488</td>\n",
       "      <td>07/08/2014 22:18</td>\n",
       "      <td>Istanbul</td>\n",
       "      <td>Turkey</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176</th>\n",
       "      <td>40.977637</td>\n",
       "      <td>28.823879</td>\n",
       "      <td>07/09/2014 09:03</td>\n",
       "      <td>Istanbul</td>\n",
       "      <td>Turkey</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>177</th>\n",
       "      <td>48.357110</td>\n",
       "      <td>11.791346</td>\n",
       "      <td>07/09/2014 13:20</td>\n",
       "      <td>Munich</td>\n",
       "      <td>Germany</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "           lat        lon              date      city  country\n",
       "173  41.044556  28.983286  07/08/2014 16:44  Istanbul   Turkey\n",
       "174  41.008992  28.968268  07/08/2014 20:03  Istanbul   Turkey\n",
       "175  41.043487  28.985488  07/08/2014 22:18  Istanbul   Turkey\n",
       "176  40.977637  28.823879  07/09/2014 09:03  Istanbul   Turkey\n",
       "177  48.357110  11.791346  07/09/2014 13:20    Munich  Germany"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# load a reduced set of gps data\n",
    "df = pd.read_csv('data/summer-travel-gps-simplified.csv')\n",
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "jupyter": {
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   },
   "outputs": [],
   "source": [
    "# Q9: create a Series of true/false, indicating if each \"city\" row in the column is equal to \"Munich\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "jupyter": {
     "outputs_hidden": false
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   },
   "outputs": [],
   "source": [
    "# pandas logical operators are: | for or, & for and, ~ for not\n",
    "# these must be grouped by using parentheses\n",
    "# Q10: what cities were visited in spain that were not barcelona? Create a dataframe for it. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "# Q11: select rows where either the city is munich, or the country is serbia"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "# Q12: how many observations are west of the prime meridian?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "# Q13: get all rows that contain a city that starts with the letter G"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Q14: how many unique cities and countries in the dataset? \n",
    "# Also can you check missing values for the dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Q15: group by country name and show the city names in each of the country"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Part 3 <br /> How to select based on a date-time values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
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    "deletable": true,
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   "outputs": [
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       "      <th></th>\n",
       "      <th>lat</th>\n",
       "      <th>lon</th>\n",
       "      <th>city</th>\n",
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       "      <th>date</th>\n",
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       "    <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",
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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"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# load the location data set, indexed by the date field\n",
    "# and, parse the dates so they're no longer strings but now rather Python datetime objects\n",
    "# this lets us do date and time based operations on the data set\n",
    "dt = pd.read_csv('data/summer-travel-gps-full.csv', index_col='date', parse_dates=True)\n",
    "dt.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "# Q16: is the timestamp index unique? How can you use code to find it? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "# Q17: drop duplicate index "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "# Q18: create a weekday and a weekend dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "# Q19: calculate and plot the number of observations each day of the week has"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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