intro_scicomp_python.ipynb

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  {
   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# Contributors to this notebook\n",
      "\n",
      "* Daniel Arribas-Bel [@darribas](http://twitter.com/darribas)"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import numpy as np  # 1.7 or higher\n",
      "import pandas as pd # 0.10 or higher\n",
      "from matplotlib import pyplot as plt\n",
      "\n",
      "print \"numpy version: \", np.__version__\n",
      "print \"pandas version: \", pd.__version__"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "numpy version:  1.6.2\n",
        "pandas version:  0.10.0\n"
       ]
      }
     ],
     "prompt_number": 47
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# Brief introduction to the Python stack for scientific computing\n",
      "\n",
      "In this notebook, we will do a very brief introduction of the main elements of the Python stack for scientific computing, with an eye to data analysis. Keep in mind this very introductory and only allows to to scratch the surface of what is out there. Hopefully, it is enough to get you interested and dig deeper.\n",
      "\n",
      "The main five libraries presented here are:\n",
      "\n",
      "* [IPython](http://ipython.org)\n",
      "* [Numpy](http://numpy.org)\n",
      "* [SciPy](http://scipy.org)\n",
      "* [Pandas](http://pandas.pydata.org)\n"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "There is a lot of different resources to learn about tools in Python for science, so any attempt to list will be unavoidably incomplete. However, one reference can be made to Wes McKiney's new book: [*Python for data analysis*](http://shop.oreilly.com/product/0636920023784.do), published by O'Reilly Media. It is a good first step into the world of scientific computing in Python."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from IPython.display import Image\n",
      "Image(url=\"http://akamaicovers.oreilly.com/images/0636920023784/cat.gif\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<img src=\"http://akamaicovers.oreilly.com/images/0636920023784/cat.gif\" />"
       ],
       "output_type": "pyout",
       "prompt_number": 4,
       "text": [
        "<IPython.core.display.Image at 0x3fac850>"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## IPython\n",
      "\n",
      "IPython is an enhanced interactive shell for scientific computing in Python. It includes several improvements and extensions with respect to the vanilla Python interpreter and it is particularly well suited for interactive data analysis. The project website ([http://ipython.org](http://ipython.org)) contains many resources so make sure to take a look for help.\n",
      "\n",
      "### IPython Notebook\n",
      "\n",
      "In particular, this document is written in the `IPython Notebook`, a web app that extends many of the capabilities and allows to create self-contained files (called *notebooks*) that combine Python code, Python output and text in the markdown markup language.\n",
      "\n",
      "The concept of the notebook is structured around individual cells that may contain either code or text; if they contain code, they can be run as if that chunk of code was pasted in the interpreter.\n",
      "\n",
      "Here are the main tricks to get you started with the notebook:"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "* Make sure all dependencies and the Notebook itself are installed. Then fire up a terminal and run `ipython notebook`. This will open up your default browser and start the web app at the directory where you had your terminal pointed. From the home screen, you can create a new notebook, or open any `.ipynb` files in the folder."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "* Double click on a cell to edit it. Once you are ready to run it (code) or render it (text), press `shift+enter`."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "* A code cell can contain any chunk of Python code that you could execute on the interpreter. For instance:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print \"Hello world\""
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Hello world\n"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "When you press `shift+enter`, the code is evaluated dynamically."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "* A text cell is to be written in the Markdown markup language. This includes, among others:\n",
      "\n",
      "     * Links to [websites](http://ipython.org).\n",
      "     * *Italics* and **bold**.\n",
      "     * And even the fantastic $\\LaTeX$ language for mathematical typesetting."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "* Tab completion and inline help"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pd?"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 6
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "* The Notebook includes some keyboard shortcuts for agile development. All of them are triggered by `ctr+m`. To get a full list of them, you can call the `Help` by pressing `ctr+m` and then `h`. This is the full list:"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "`Shift-Enter : run cell\n",
      "Ctrl-Enter : run cell in-place\n",
      "Ctrl-m x : cut cell\n",
      "Ctrl-m c : copy cell\n",
      "Ctrl-m v : paste cell\n",
      "Ctrl-m d : delete cell\n",
      "Ctrl-m a : insert cell above\n",
      "Ctrl-m b : insert cell below\n",
      "Ctrl-m o : toggle output\n",
      "Ctrl-m O : toggle output scroll\n",
      "Ctrl-m l : toggle line numbers\n",
      "Ctrl-m s : save notebook\n",
      "Ctrl-m j : move cell down\n",
      "Ctrl-m k : move cell up\n",
      "Ctrl-m y : code cell\n",
      "Ctrl-m m : markdown cell\n",
      "Ctrl-m t : raw cell\n",
      "Ctrl-m 1-6 : heading 1-6 cell\n",
      "Ctrl-m p : select previous\n",
      "Ctrl-m n : select next\n",
      "Ctrl-m i : interrupt kernel\n",
      "Ctrl-m . : restart kernel\n",
      "Ctrl-m h : show keyboard shortcuts`"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "* Once you are done with the session, save the notebook and exit the app. A `.ipynb` file will have been saved in the folder where you started the session. You can come back at any time and pick up where you left, re-run all or some cells and save it again. Because a `.ipynb` file is basically a text file containing a `json` object, this is multi-platform and easily inter-exchangeable. It is also great to share it with friends and collaborators."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "* Finally, notebooks are great also to share them on the internert to diseminate analysis or collaborate online. You can host the file somewhere on a server and point to it from one of the renderers for notebooks. For example, the [nbviewer](http://nbviewer.ipython.org)."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The functionality...\n",
      "\n",
      "Here are a couple of resources to show you how cool the notebook can get:\n",
      "\n",
      "* [Keynote by Fernando Perez](http://www.youtube.com/watch?v=F4rFuIb1Ie4&feature=player_embedded)\n",
      "* [Demo blog post using the Notebook](http://blog.fperez.org/2012/09/blogging-with-ipython-notebook.html)"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Numpy and Scipy\n",
      "\n",
      "**Numpy** and Scipy are the foundational libraries for any kind of numeric computing in Python. Numpy offers the efficient matrix structure denominated `array` or `ndarray` (Numpy data array) as well as some basic statistical functions that may be applied to arrays.\n",
      "\n",
      "To whet your appetite, let's first create a simple array. You can do this from a pre-existing Python list, for example:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "l = [1, 2, 3, 4]\n",
      "a = np.array(l)\n",
      "a"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 7,
       "text": [
        "array([1, 2, 3, 4])"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "At first sight, `a` is not very different from `l`; however, under the hood, it provides much more efficient structures for data manipulation (including C-optimized functions and other performance enhancements). Arrays contain only one data type and may have several dimensions, opening up the door for very fancy matrix manipulation."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print type(a[0])\n",
      "l += 'a'\n",
      "a = np.array(l)\n",
      "print type(a[0])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "<type 'numpy.int64'>\n",
        "<type 'numpy.string_'>\n"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "l = [[1, 2], [3, 4], [5, 6]]\n",
      "a = np.array(l)\n",
      "print 'Array a has a dimension of: ', a.shape\n",
      "print a"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Array a has a dimension of:  (3, 2)\n",
        "[[1 2]\n",
        " [3 4]\n",
        " [5 6]]\n"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Arrays have several basic utilities as methods:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "a.mean()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 10,
       "text": [
        "3.5"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "a.min(axis=0)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 11,
       "text": [
        "array([1, 2])"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "a.max(axis=1)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 12,
       "text": [
        "array([2, 4, 6])"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "It also includes random number generation:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "r = np.random.random(4)\n",
      "r"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 13,
       "text": [
        "array([ 0.85141035,  0.48934467,  0.82694686,  0.18799519])"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "rr = np.random.random((3, 2))\n",
      "rr"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 14,
       "text": [
        "array([[ 0.31710468,  0.93124485],\n",
        "       [ 0.39381576,  0.43668221],\n",
        "       [ 0.11553762,  0.51282671]])"
       ]
      }
     ],
     "prompt_number": 14
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Numpy even supports operations betwee arrays, such as sumation, difference, multiplication and division:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "a = np.random.random((3, 2))\n",
      "b = np.random.random((2, 3))\n",
      "a, b"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 15,
       "text": [
        "(array([[ 0.1053234 ,  0.27330955],\n",
        "       [ 0.18669486,  0.95238838],\n",
        "       [ 0.3806249 ,  0.78371884]]),\n",
        " array([[ 0.88481153,  0.99623253,  0.88403853],\n",
        "       [ 0.17156613,  0.56839029,  0.62555925]]))"
       ]
      }
     ],
     "prompt_number": 15
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Sum (note the transpose for dimensionality alignment)\n",
      "a + b.T"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 16,
       "text": [
        "array([[ 0.99013492,  0.44487568],\n",
        "       [ 1.18292739,  1.52077867],\n",
        "       [ 1.26466343,  1.40927809]])"
       ]
      }
     ],
     "prompt_number": 16
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Difference (note the transpose for dimensionality alignment)\n",
      "a - b.T"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 17,
       "text": [
        "array([[-0.77948813,  0.10174342],\n",
        "       [-0.80953768,  0.38399809],\n",
        "       [-0.50341363,  0.15815959]])"
       ]
      }
     ],
     "prompt_number": 17
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Matrix product\n",
      "c = np.dot(a, b)\n",
      "c"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 18,
       "text": [
        "array([[ 0.14008201,  0.26027309,  0.26408126],\n",
        "       [ 0.32858735,  0.7273198 ,  0.76082081],\n",
        "       [ 0.4712409 ,  0.82464909,  0.82674964]])"
       ]
      }
     ],
     "prompt_number": 18
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Matrix division by a scalar\n",
      "c = a / 2.\n",
      "c"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 19,
       "text": [
        "array([[ 0.0526617 ,  0.13665477],\n",
        "       [ 0.09334743,  0.47619419],\n",
        "       [ 0.19031245,  0.39185942]])"
       ]
      }
     ],
     "prompt_number": 19
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "**SciPy** is the sister library of Numpy and offers a wide arrange of statistical functions to operate on Numpy arrays. This provides much of the functionality that you find in the core packages of other statistical languages like R (in the r-base package) or Matlab. "
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Examples to show:\n",
      "\n",
      "* Draw from a normal distribution\n",
      "* Check a couple of other examples"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Besides the core of scipy, the project also includes additional packages called `scikits` that expand the main functionality in some particular way. Check out the [scikits website](http://scikits.appspot.com) to get a sense of what is covered."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Pandas\n",
      "\n",
      "Pandas is a fairly new library but is undergoing exciting intense development. It relies on Numpy to provide efficient data structures that ease dealing with (potentially messy) data. Its core objects are the `Series` and the `DataFrame`, which are very similar to the array except for the following main differences:\n",
      "\n",
      "* They allow different data types into one object.\n",
      "* They are labeled, opening up the floor for all kinds of efficient manipulation.\n",
      "* They support missing data.\n",
      "\n",
      "Essentially, you can think of `pandas` as a Numpy \"on steroids\" with a focus on real-world data. It encapsulates and wraps around much of the low-level functionality of `numpy`, `scipy` and `matplotlib`, exposing it to the end-user in a much friendlier way. For that reason, throughout the tutorial, we will call most of the functions from `pandas`."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Matplotlib\n",
      "\n",
      "Matplotlib is the main tool for static graphical display in Python. It provides 2D and 3D functionality to plot data in a static way. The library may not appear as very intuitive at first, but if you get over the learning curve, it is extremely flexible and it allows to tweak every aspect of a figure. Because of its focus on flexibility, the defaults may not be the prettiest, but with some work on them, Matplotlib can create beautiful figures that rival in quality with any other library for static plotting (such as R's `ggplot2`)."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# Hands-on example\n",
      "\n",
      "In this section, we are going to use real world data to get a sense of the basic capabilities of the libraries just presented. We will be using a sample of Foursquare checkins collected from Twitter in Amsterdam during 2010; the data is derived from the database originally published by [Chen et al (2011)](http://students.cse.tamu.edu/zcheng/papers/cheng_icwsm11.pdf), see this [link](http://infolab.tamu.edu/data/) for the original source and information regarding the [open license](http://creativecommons.org/licenses/by-nc/3.0/us/). The data are stored in a comma-delimitted (`.csv`) text file."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Read in and checks"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Let's start by reading the data into memory; note that, although we have the file stored on the cloud, `pandas` can read it without problem:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data_link = 'http://ubuntuone.com/4oIpVJDCpdREhzNdSRwMx8'\n",
      "db = pd.read_csv(data_link)\n",
      "db"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 20,
       "text": [
        "<class 'pandas.core.frame.DataFrame'>\n",
        "Int64Index: 77427 entries, 0 to 77426\n",
        "Data columns:\n",
        "user_id     77427  non-null values\n",
        "tweet_id    77427  non-null values\n",
        "lat         77427  non-null values\n",
        "lon         77427  non-null values\n",
        "time        77427  non-null values\n",
        "text        77427  non-null values\n",
        "place_id    75273  non-null values\n",
        "BU_CODE     77427  non-null values\n",
        "dtypes: float64(2), int64(2), object(4)"
       ]
      }
     ],
     "prompt_number": 20
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "This gives us a basic summary of the properties of the dataset we just loaded. We can also take a look at the real data:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "db.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
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        "      <td> 82837637</td>\n",
        "      <td> 14847348078</td>\n",
        "      <td> 52.333500</td>\n",
        "      <td> 4.889770</td>\n",
        "      <td> 2010-05-27 17:06:19</td>\n",
        "      <td> Mooi feest hier. Wie is er eigenlijk niet vand...</td>\n",
        "      <td> 99cdab25eddd6bce</td>\n",
        "      <td> BU03631491</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>3</strong></td>\n",
        "      <td> 82837637</td>\n",
        "      <td> 17401139685</td>\n",
        "      <td> 52.340512</td>\n",
        "      <td> 4.873362</td>\n",
        "      <td> 2010-06-30 08:54:35</td>\n",
        "      <td> @nienkehofkamp ik ben er! (@ World Trade Cente...</td>\n",
        "      <td> 99cdab25eddd6bce</td>\n",
        "      <td> BU03631459</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>4</strong></td>\n",
        "      <td> 82837637</td>\n",
        "      <td> 17554964312</td>\n",
        "      <td> 52.349976</td>\n",
        "      <td> 4.918581</td>\n",
        "      <td> 2010-07-02 07:26:45</td>\n",
        "      <td> Klein leger van 200 groen gele Braziliaantjes ...</td>\n",
        "      <td> 99cdab25eddd6bce</td>\n",
        "      <td> BU03631255</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "output_type": "pyout",
       "prompt_number": 21,
       "text": [
        "    user_id     tweet_id        lat       lon                 time  \\\n",
        "0  82837637  13416482426  52.360800  4.867410  2010-05-05 09:21:15   \n",
        "1  82837637  13419413928  52.366300  4.886320  2010-05-05 10:52:24   \n",
        "2  82837637  14847348078  52.333500  4.889770  2010-05-27 17:06:19   \n",
        "3  82837637  17401139685  52.340512  4.873362  2010-06-30 08:54:35   \n",
        "4  82837637  17554964312  52.349976  4.918581  2010-07-02 07:26:45   \n",
        "\n",
        "                                                text          place_id  \\\n",
        "0  I'm at Marqt Overtoom (Overtoom, Amsterdam). h...  99cdab25eddd6bce   \n",
        "1  I'm at Walem (Keizersgracht 449, Amsterdam). h...  99cdab25eddd6bce   \n",
        "2  Mooi feest hier. Wie is er eigenlijk niet vand...  99cdab25eddd6bce   \n",
        "3  @nienkehofkamp ik ben er! (@ World Trade Cente...  99cdab25eddd6bce   \n",
        "4  Klein leger van 200 groen gele Braziliaantjes ...  99cdab25eddd6bce   \n",
        "\n",
        "      BU_CODE  \n",
        "0  BU03630322  \n",
        "1  BU03630003  \n",
        "2  BU03631491  \n",
        "3  BU03631459  \n",
        "4  BU03631255  "
       ]
      }
     ],
     "prompt_number": 21
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "db.tail(7)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>user_id</th>\n",
        "      <th>tweet_id</th>\n",
        "      <th>lat</th>\n",
        "      <th>lon</th>\n",
        "      <th>time</th>\n",
        "      <th>text</th>\n",
        "      <th>place_id</th>\n",
        "      <th>BU_CODE</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <td><strong>77420</strong></td>\n",
        "      <td> 188743276</td>\n",
        "      <td>  6108935840862208</td>\n",
        "      <td> 52.307327</td>\n",
        "      <td> 4.944605</td>\n",
        "      <td> 2010-11-20 22:17:38</td>\n",
        "      <td> Nu aansluiten in de file vanaf parkeerplaats. ...</td>\n",
        "      <td> 536dd7e62e0b35c3</td>\n",
        "      <td> BU03631192</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>77421</strong></td>\n",
        "      <td>  25689874</td>\n",
        "      <td> 20442608845193216</td>\n",
        "      <td> 52.330856</td>\n",
        "      <td> 4.879264</td>\n",
        "      <td> 2010-12-30 11:34:32</td>\n",
        "      <td>          Lunch! :) @ Werelds http://fst.je/fgZklm</td>\n",
        "      <td> 355790f421ee5262</td>\n",
        "      <td> BU03631490</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>77422</strong></td>\n",
        "      <td>  23592731</td>\n",
        "      <td>       14773261764</td>\n",
        "      <td> 52.372175</td>\n",
        "      <td> 4.884715</td>\n",
        "      <td> 2010-05-26 16:15:24</td>\n",
        "      <td> Net met Een (electrische) Tesla Roadster door ...</td>\n",
        "      <td> 99cdab25eddd6bce</td>\n",
        "      <td> BU03630002</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>77423</strong></td>\n",
        "      <td>  48758572</td>\n",
        "      <td>  4256444949995521</td>\n",
        "      <td> 52.359700</td>\n",
        "      <td> 4.881170</td>\n",
        "      <td> 2010-11-15 19:36:30</td>\n",
        "      <td> I'm at Bilderberg Hotel Jan Luyken (Jan Luijke...</td>\n",
        "      <td> cbe7d5bad97ca45c</td>\n",
        "      <td> BU03631347</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>77424</strong></td>\n",
        "      <td>  48758572</td>\n",
        "      <td>  4506146060632064</td>\n",
        "      <td> 52.347838</td>\n",
        "      <td> 4.856291</td>\n",
        "      <td> 2010-11-16 12:08:43</td>\n",
        "      <td> I'm at Mindshare Amsterdam (Karperstraat 8, Am...</td>\n",
        "      <td> 1736a08fa21720bb</td>\n",
        "      <td> BU03631348</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>77425</strong></td>\n",
        "      <td>  48758572</td>\n",
        "      <td>  4506221956567040</td>\n",
        "      <td> 52.347838</td>\n",
        "      <td> 4.856291</td>\n",
        "      <td> 2010-11-16 12:09:01</td>\n",
        "      <td> I'm at GroupM Netherlands (Karperstraat 8, Ams...</td>\n",
        "      <td> 1736a08fa21720bb</td>\n",
        "      <td> BU03631348</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>77426</strong></td>\n",
        "      <td>  85143057</td>\n",
        "      <td>  9291006105096193</td>\n",
        "      <td> 52.361092</td>\n",
        "      <td> 4.890365</td>\n",
        "      <td> 2010-11-29 17:02:03</td>\n",
        "      <td> I'm at Dierenkliniek de Wetering. http://4sq.c...</td>\n",
        "      <td> 6ec13a20504b1599</td>\n",
        "      <td> BU03630007</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "output_type": "pyout",
       "prompt_number": 22,
       "text": [
        "         user_id           tweet_id        lat       lon                 time  \\\n",
        "77420  188743276   6108935840862208  52.307327  4.944605  2010-11-20 22:17:38   \n",
        "77421   25689874  20442608845193216  52.330856  4.879264  2010-12-30 11:34:32   \n",
        "77422   23592731        14773261764  52.372175  4.884715  2010-05-26 16:15:24   \n",
        "77423   48758572   4256444949995521  52.359700  4.881170  2010-11-15 19:36:30   \n",
        "77424   48758572   4506146060632064  52.347838  4.856291  2010-11-16 12:08:43   \n",
        "77425   48758572   4506221956567040  52.347838  4.856291  2010-11-16 12:09:01   \n",
        "77426   85143057   9291006105096193  52.361092  4.890365  2010-11-29 17:02:03   \n",
        "\n",
        "                                                    text          place_id  \\\n",
        "77420  Nu aansluiten in de file vanaf parkeerplaats. ...  536dd7e62e0b35c3   \n",
        "77421           Lunch! :) @ Werelds http://fst.je/fgZklm  355790f421ee5262   \n",
        "77422  Net met Een (electrische) Tesla Roadster door ...  99cdab25eddd6bce   \n",
        "77423  I'm at Bilderberg Hotel Jan Luyken (Jan Luijke...  cbe7d5bad97ca45c   \n",
        "77424  I'm at Mindshare Amsterdam (Karperstraat 8, Am...  1736a08fa21720bb   \n",
        "77425  I'm at GroupM Netherlands (Karperstraat 8, Ams...  1736a08fa21720bb   \n",
        "77426  I'm at Dierenkliniek de Wetering. http://4sq.c...  6ec13a20504b1599   \n",
        "\n",
        "          BU_CODE  \n",
        "77420  BU03631192  \n",
        "77421  BU03631490  \n",
        "77422  BU03630002  \n",
        "77423  BU03631347  \n",
        "77424  BU03631348  \n",
        "77425  BU03631348  \n",
        "77426  BU03630007  "
       ]
      }
     ],
     "prompt_number": 22
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "db.describe()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>user_id</th>\n",
        "      <th>tweet_id</th>\n",
        "      <th>lat</th>\n",
        "      <th>lon</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <td><strong>count</strong></td>\n",
        "      <td> 7.742700e+04</td>\n",
        "      <td> 7.742700e+04</td>\n",
        "      <td> 77427.000000</td>\n",
        "      <td> 77427.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>mean</strong></td>\n",
        "      <td> 4.180966e+07</td>\n",
        "      <td> 5.787601e+13</td>\n",
        "      <td>    52.360964</td>\n",
        "      <td>     4.893621</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>std</strong></td>\n",
        "      <td> 6.408738e+06</td>\n",
        "      <td> 1.069835e+07</td>\n",
        "      <td>     0.021956</td>\n",
        "      <td>     0.036132</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>min</strong></td>\n",
        "      <td> 1.013000e+03</td>\n",
        "      <td> 9.666602e+09</td>\n",
        "      <td>    52.280120</td>\n",
        "      <td>     4.733114</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>25%</strong></td>\n",
        "      <td> 1.421860e+07</td>\n",
        "      <td> 1.818519e+10</td>\n",
        "      <td>    52.351255</td>\n",
        "      <td>     4.875392</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>50%</strong></td>\n",
        "      <td> 2.130657e+07</td>\n",
        "      <td> 2.523498e+10</td>\n",
        "      <td>    52.364679</td>\n",
        "      <td>     4.892890</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>75%</strong></td>\n",
        "      <td> 5.049944e+07</td>\n",
        "      <td> 1.359153e+15</td>\n",
        "      <td>    52.375917</td>\n",
        "      <td>     4.911386</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>max</strong></td>\n",
        "      <td> 2.277718e+08</td>\n",
        "      <td> 2.805568e+16</td>\n",
        "      <td>    52.429774</td>\n",
        "      <td>     5.025750</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "output_type": "pyout",
       "prompt_number": 23,
       "text": [
        "            user_id      tweet_id           lat           lon\n",
        "count  7.742700e+04  7.742700e+04  77427.000000  77427.000000\n",
        "mean   4.180966e+07  5.787601e+13     52.360964      4.893621\n",
        "std    6.408738e+06  1.069835e+07      0.021956      0.036132\n",
        "min    1.013000e+03  9.666602e+09     52.280120      4.733114\n",
        "25%    1.421860e+07  1.818519e+10     52.351255      4.875392\n",
        "50%    2.130657e+07  2.523498e+10     52.364679      4.892890\n",
        "75%    5.049944e+07  1.359153e+15     52.375917      4.911386\n",
        "max    2.277718e+08  2.805568e+16     52.429774      5.025750"
       ]
      }
     ],
     "prompt_number": 23
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Indexing and slicing"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "`DataFrame` objects support various ways of indexing and slicing. By default, rows are indexed with a range of integers:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "db.index"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 24,
       "text": [
        "Int64Index([0, 1, 2, ..., 77424, 77425, 77426], dtype=int64)"
       ]
      }
     ],
     "prompt_number": 24
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "db.ix[5: 10, ['time', 'text', 'BU_CODE']]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>time</th>\n",
        "      <th>text</th>\n",
        "      <th>BU_CODE</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <td><strong>5 </strong></td>\n",
        "      <td> 2010-07-31 13:30:58</td>\n",
        "      <td> I'm at Caf\u00e9 Bloemers (Hemonystraat 70HS, Amste...</td>\n",
        "      <td> BU03631325</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>6 </strong></td>\n",
        "      <td> 2010-08-02 11:56:43</td>\n",
        "      <td> Altijd fijn lunchen hier! (@ Hotel Okura Amste...</td>\n",
        "      <td> BU03631452</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>7 </strong></td>\n",
        "      <td> 2010-08-02 14:37:30</td>\n",
        "      <td> Ik ga voor het mayorship! :-) (@ Caf\u00e9 Bloemers...</td>\n",
        "      <td> BU03631325</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>8 </strong></td>\n",
        "      <td> 2010-08-16 10:25:56</td>\n",
        "      <td> @lisaportengen. Racen! :-) Ik zie je wel komen...</td>\n",
        "      <td> BU03631324</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>9 </strong></td>\n",
        "      <td> 2010-08-21 10:43:53</td>\n",
        "      <td> VicMB1, 1 x winst, 1 x gelijk. So far so good....</td>\n",
        "      <td> BU03630984</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>10</strong></td>\n",
        "      <td> 2010-08-27 07:01:18</td>\n",
        "      <td> Aan de slag! (@ Hotel Casa 400) http://4sq.com...</td>\n",
        "      <td> BU03631255</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "output_type": "pyout",
       "prompt_number": 25,
       "text": [
        "                   time                                               text  \\\n",
        "5   2010-07-31 13:30:58  I'm at Caf\u00e9 Bloemers (Hemonystraat 70HS, Amste...   \n",
        "6   2010-08-02 11:56:43  Altijd fijn lunchen hier! (@ Hotel Okura Amste...   \n",
        "7   2010-08-02 14:37:30  Ik ga voor het mayorship! :-) (@ Caf\u00e9 Bloemers...   \n",
        "8   2010-08-16 10:25:56  @lisaportengen. Racen! :-) Ik zie je wel komen...   \n",
        "9   2010-08-21 10:43:53  VicMB1, 1 x winst, 1 x gelijk. So far so good....   \n",
        "10  2010-08-27 07:01:18  Aan de slag! (@ Hotel Casa 400) http://4sq.com...   \n",
        "\n",
        "       BU_CODE  \n",
        "5   BU03631325  \n",
        "6   BU03631452  \n",
        "7   BU03631325  \n",
        "8   BU03631324  \n",
        "9   BU03630984  \n",
        "10  BU03631255  "
       ]
      }
     ],
     "prompt_number": 25
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Clip checkins on a bounding box for (52.30, 52.40, 4.89, 4.9)\n",
      "clipped = db[(db['lat'] > 52.30) & (db.lat < 52.40) & (db.lon > 4.89) & (db['lon'] < 4.9)]\n",
      "clipped"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 26,
       "text": [
        "<class 'pandas.core.frame.DataFrame'>\n",
        "Int64Index: 15113 entries, 6 to 77426\n",
        "Data columns:\n",
        "user_id     15113  non-null values\n",
        "tweet_id    15113  non-null values\n",
        "lat         15113  non-null values\n",
        "lon         15113  non-null values\n",
        "time        15113  non-null values\n",
        "text        15113  non-null values\n",
        "place_id    14708  non-null values\n",
        "BU_CODE     15113  non-null values\n",
        "dtypes: float64(2), int64(2), object(4)"
       ]
      }
     ],
     "prompt_number": 26
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Grouping operations"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "There is also very efficient functionality to group the data based on certain characteristics. Once grouped, you can apply different functions to the grouped data and collect the output in a new `DataFrame`. This is akin to some of the \"map-reduce\" operations that have become very popular in distributed systems, with the difference that everything here happens in memory (which makes it very fast). As an example, let's group the checkins by neighborhood (`'BU_CODE'`) and count them:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "neigh = db.groupby('BU_CODE').size()\n",
      "neigh"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 27,
       "text": [
        "BU_CODE\n",
        "BU03630000    2750\n",
        "BU03630001    6620\n",
        "BU03630002    2521\n",
        "BU03630003    3468\n",
        "BU03630004    2076\n",
        "BU03630005    1434\n",
        "BU03630006    2385\n",
        "BU03630007    4209\n",
        "BU03630008    1211\n",
        "BU03630009     988\n",
        "BU03630110     554\n",
        "BU03630111    2019\n",
        "BU03630212     241\n",
        "BU03630213    1228\n",
        "BU03630214    1330\n",
        "BU03630215     424\n",
        "BU03630216     222\n",
        "BU03630317     315\n",
        "BU03630318     134\n",
        "BU03630319     672\n",
        "BU03630320     289\n",
        "BU03630321     498\n",
        "BU03630322     539\n",
        "BU03630431     420\n",
        "BU03630432     218\n",
        "BU03630433    2157\n",
        "BU03630434     144\n",
        "BU03630435     739\n",
        "BU03630451     208\n",
        "BU03630536      55\n",
        "BU03630537     660\n",
        "BU03630538     201\n",
        "BU03630539     455\n",
        "BU03630640     293\n",
        "BU03630641     100\n",
        "BU03630642     299\n",
        "BU03630643     148\n",
        "BU03630760     108\n",
        "BU03630761     163\n",
        "BU03630762      10\n",
        "BU03630763       5\n",
        "BU03630764      28\n",
        "BU03630765      44\n",
        "BU03630766      62\n",
        "BU03630767      62\n",
        "BU03630768      36\n",
        "BU03630769     163\n",
        "BU03630770      64\n",
        "BU03630771    1199\n",
        "BU03630772     136\n",
        "BU03630773      41\n",
        "BU03630875      54\n",
        "BU03630876      76\n",
        "BU03630877     151\n",
        "BU03630878     125\n",
        "BU03630879      23\n",
        "BU03630980      38\n",
        "BU03630981     501\n",
        "BU03630982     369\n",
        "BU03630983      72\n",
        "BU03630984     355\n",
        "BU03631085    1149\n",
        "BU03631086     369\n",
        "BU03631087     665\n",
        "BU03631088     872\n",
        "BU03631192    4008\n",
        "BU03631193    1231\n",
        "BU03631194     237\n",
        "BU03631195     203\n",
        "BU03631196     914\n",
        "BU03631197      46\n",
        "BU03631198       7\n",
        "BU03631227    1191\n",
        "BU03631228     535\n",
        "BU03631229     895\n",
        "BU03631230     380\n",
        "BU03631255    1874\n",
        "BU03631256    1477\n",
        "BU03631257      88\n",
        "BU03631258     971\n",
        "BU03631324    1690\n",
        "BU03631325     914\n",
        "BU03631326     262\n",
        "BU03631344     400\n",
        "BU03631345     287\n",
        "BU03631346     509\n",
        "BU03631347    2811\n",
        "BU03631348     897\n",
        "BU03631349     428\n",
        "BU03631350     190\n",
        "BU03631452    1367\n",
        "BU03631453     431\n",
        "BU03631454     483\n",
        "BU03631459     543\n",
        "BU03631490    2179\n",
        "BU03631491     815\n",
        "Length: 96"
       ]
      }
     ],
     "prompt_number": 27
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "With this"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The output is a `Series` because we applied a function that outputs a single number per group of rows, but you can apply functions that operate by column and the output will then be a `DataFrame` (in this example, `mean` is applied only to those columns where it can be applied, but not to those with text):"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": true,
     "input": [
      "neigh_several = db.groupby('BU_CODE').mean()\n",
      "neigh_several"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>user_id</th>\n",
        "      <th>tweet_id</th>\n",
        "      <th>lat</th>\n",
        "      <th>lon</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>BU_CODE</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <td><strong>BU03630000</strong></td>\n",
        "      <td> 4.089537e+07</td>\n",
        "      <td> 2.896880e+15</td>\n",
        "      <td> 52.373986</td>\n",
        "      <td> 4.897222</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>BU03630001</strong></td>\n",
        "      <td> 4.001633e+07</td>\n",
        "      <td> 2.494666e+15</td>\n",
        "      <td> 52.375894</td>\n",
        "      <td> 4.896031</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>BU03630002</strong></td>\n",
        "      <td> 2.975765e+07</td>\n",
        "      <td> 2.635145e+15</td>\n",
        "      <td> 52.374089</td>\n",
        "      <td> 4.886963</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>BU03630003</strong></td>\n",
        "      <td> 3.360024e+07</td>\n",
        "      <td> 3.197510e+15</td>\n",
        "      <td> 52.366105</td>\n",
        "      <td> 4.894889</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>BU03630004</strong></td>\n",
        "      <td> 3.696746e+07</td>\n",
        "      <td> 2.597457e+15</td>\n",
        "      <td> 52.371965</td>\n",
        "      <td> 4.903914</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>BU03630005</strong></td>\n",
        "      <td> 3.213309e+07</td>\n",
        "      <td> 3.016933e+15</td>\n",
        "      <td> 52.384250</td>\n",
        "      <td> 4.889986</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>BU03630006</strong></td>\n",
        "      <td> 3.500533e+07</td>\n",
        "      <td> 3.837788e+15</td>\n",
        "      <td> 52.375541</td>\n",
        "      <td> 4.881686</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>BU03630007</strong></td>\n",
        "      <td> 3.076569e+07</td>\n",
        "      <td> 2.026354e+15</td>\n",
        "      <td> 52.362500</td>\n",
        "      <td> 4.890146</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>BU03630008</strong></td>\n",
        "      <td> 3.885122e+07</td>\n",
        "      <td> 1.834704e+15</td>\n",
        "      <td> 52.364856</td>\n",
        "      <td> 4.909952</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>BU03630009</strong></td>\n",
        "      <td> 3.275136e+07</td>\n",
        "      <td> 1.761756e+15</td>\n",
        "      <td> 52.372387</td>\n",
        "      <td> 4.922353</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>BU03630110</strong></td>\n",
        "      <td> 4.583103e+07</td>\n",
        "      <td> 3.024124e+15</td>\n",
        "      <td> 52.403604</td>\n",
        "      <td> 4.840568</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>BU03630111</strong></td>\n",
        "      <td> 4.553697e+07</td>\n",
        "      <td> 4.043264e+15</td>\n",
        "      <td> 52.390713</td>\n",
        "      <td> 4.837820</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>BU03630212</strong></td>\n",
        "      <td> 4.653763e+07</td>\n",
        "      <td> 5.552885e+15</td>\n",
        "      <td> 52.393468</td>\n",
        "      <td> 4.885883</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>BU03630213</strong></td>\n",
        "      <td> 4.207794e+07</td>\n",
        "      <td> 1.911825e+15</td>\n",
        "      <td> 52.388810</td>\n",
        "      <td> 4.875225</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>BU03630214</strong></td>\n",
        "      <td> 2.604093e+07</td>\n",
        "      <td> 1.475080e+15</td>\n",
        "      <td> 52.383136</td>\n",
        "      <td> 4.872976</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>BU03630215</strong></td>\n",
        "      <td> 2.596567e+07</td>\n",
        "      <td> 1.881479e+15</td>\n",
        "      <td> 52.384311</td>\n",
        "      <td> 4.867759</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>BU03630216</strong></td>\n",
        "      <td> 3.409945e+07</td>\n",
        "      <td> 2.481599e+15</td>\n",
        "      <td> 52.377359</td>\n",
        "      <td> 4.874483</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>BU03630317</strong></td>\n",
        "      <td> 3.083976e+07</td>\n",
        "      <td> 9.677685e+14</td>\n",
        "      <td> 52.371689</td>\n",
        "      <td> 4.872083</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>BU03630318</strong></td>\n",
        "      <td> 3.270001e+07</td>\n",
        "      <td> 4.452065e+15</td>\n",
        "      <td> 52.369788</td>\n",
        "      <td> 4.866933</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>BU03630319</strong></td>\n",
        "      <td> 1.256780e+07</td>\n",
        "      <td> 9.603981e+15</td>\n",
        "      <td> 52.366653</td>\n",
        "      <td> 4.871118</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>BU03630320</strong></td>\n",
        "      <td> 3.580618e+07</td>\n",
        "      <td> 3.725604e+15</td>\n",
        "      <td> 52.364255</td>\n",
        "      <td> 4.872827</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>BU03630321</strong></td>\n",
        "      <td> 4.327673e+07</td>\n",
        "      <td> 2.162391e+15</td>\n",
        "      <td> 52.360449</td>\n",
        "      <td> 4.860336</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>BU03630322</strong></td>\n",
        "      <td> 3.306616e+07</td>\n",
        "      <td> 2.397359e+15</td>\n",
        "      <td> 52.362916</td>\n",
        "      <td> 4.876829</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>BU03630431</strong></td>\n",
        "      <td> 3.621605e+07</td>\n",
        "      <td> 3.166838e+15</td>\n",
        "      <td> 52.364937</td>\n",
        "      <td> 4.935627</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>BU03630432</strong></td>\n",
        "      <td> 3.705649e+07</td>\n",
        "      <td> 3.989395e+15</td>\n",
        "      <td> 52.365043</td>\n",
        "      <td> 4.946886</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>BU03630433</strong></td>\n",
        "      <td> 4.343960e+07</td>\n",
        "      <td> 1.383628e+15</td>\n",
        "      <td> 52.375096</td>\n",
        "      <td> 4.932047</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>BU03630434</strong></td>\n",
        "      <td> 5.536995e+07</td>\n",
        "      <td> 3.429937e+15</td>\n",
        "      <td> 52.366242</td>\n",
        "      <td> 4.968782</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>BU03630435</strong></td>\n",
        "      <td> 3.080973e+07</td>\n",
        "      <td> 2.029308e+15</td>\n",
        "      <td> 52.357460</td>\n",
        "      <td> 4.991925</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>BU03630451</strong></td>\n",
        "      <td> 5.232057e+07</td>\n",
        "      <td> 2.948715e+15</td>\n",
        "      <td> 52.351359</td>\n",
        "      <td> 5.004841</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>BU03630536</strong></td>\n",
        "      <td> 3.945977e+07</td>\n",
        "      <td> 3.452410e+15</td>\n",
        "      <td> 52.388011</td>\n",
        "      <td> 4.850064</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>BU03630537</strong></td>\n",
        "      <td> 3.519838e+07</td>\n",
        "      <td> 2.477468e+15</td>\n",
        "      <td> 52.382839</td>\n",
        "      <td> 4.852951</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>BU03630538</strong></td>\n",
        "      <td> 3.493221e+07</td>\n",
        "      <td> 2.787626e+15</td>\n",
        "      <td> 52.378635</td>\n",
        "      <td> 4.850743</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>BU03630539</strong></td>\n",
        "      <td> 4.352352e+07</td>\n",
        "      <td> 1.309941e+15</td>\n",
        "      <td> 52.378174</td>\n",
        "      <td> 4.842480</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>BU03630640</strong></td>\n",
        "      <td> 3.914126e+07</td>\n",
        "      <td> 3.803748e+15</td>\n",
        "      <td> 52.371784</td>\n",
        "      <td> 4.860462</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>BU03630641</strong></td>\n",
        "      <td> 4.282359e+07</td>\n",
        "      <td> 2.833880e+15</td>\n",
        "      <td> 52.372286</td>\n",
        "      <td> 4.848435</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>BU03630642</strong></td>\n",
        "      <td> 4.807170e+07</td>\n",
        "      <td> 2.098702e+15</td>\n",
        "      <td> 52.369191</td>\n",
        "      <td> 4.852857</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>BU03630643</strong></td>\n",
        "      <td> 2.546463e+07</td>\n",
        "      <td> 1.109082e+15</td>\n",
        "      <td> 52.362064</td>\n",
        "      <td> 4.854477</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>BU03630760</strong></td>\n",
        "      <td> 3.908344e+07</td>\n",
        "      <td> 1.564955e+15</td>\n",
        "      <td> 52.392179</td>\n",
        "      <td> 4.912117</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>BU03630761</strong></td>\n",
        "      <td> 3.431164e+07</td>\n",
        "      <td> 2.454694e+15</td>\n",
        "      <td> 52.386317</td>\n",
        "      <td> 4.919903</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>BU03630762</strong></td>\n",
        "      <td> 1.010210e+08</td>\n",
        "      <td> 8.829694e+15</td>\n",
        "      <td> 52.391132</td>\n",
        "      <td> 4.947516</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>BU03630763</strong></td>\n",
        "      <td> 3.912022e+07</td>\n",
        "      <td> 8.352349e+15</td>\n",
        "      <td> 52.394585</td>\n",
        "      <td> 4.928672</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>BU03630764</strong></td>\n",
        "      <td> 1.165250e+08</td>\n",
        "      <td> 9.981837e+13</td>\n",
        "      <td> 52.389851</td>\n",
        "      <td> 4.941517</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>BU03630765</strong></td>\n",
        "      <td> 2.503894e+07</td>\n",
        "      <td> 2.076387e+15</td>\n",
        "      <td> 52.407464</td>\n",
        "      <td> 4.895148</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>BU03630766</strong></td>\n",
        "      <td> 6.563517e+07</td>\n",
        "      <td> 5.165142e+15</td>\n",
        "      <td> 52.423225</td>\n",
        "      <td> 4.879855</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>BU03630767</strong></td>\n",
        "      <td> 4.283430e+07</td>\n",
        "      <td> 9.493364e+14</td>\n",
        "      <td> 52.415386</td>\n",
        "      <td> 4.908213</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>BU03630768</strong></td>\n",
        "      <td> 5.704161e+07</td>\n",
        "      <td> 2.083465e+15</td>\n",
        "      <td> 52.395175</td>\n",
        "      <td> 4.953750</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>BU03630769</strong></td>\n",
        "      <td> 4.664555e+07</td>\n",
        "      <td> 3.435446e+15</td>\n",
        "      <td> 52.401304</td>\n",
        "      <td> 4.937816</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>BU03630770</strong></td>\n",
        "      <td> 7.281672e+07</td>\n",
        "      <td> 1.315143e+15</td>\n",
        "      <td> 52.407017</td>\n",
        "      <td> 4.917439</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>BU03630771</strong></td>\n",
        "      <td> 3.789073e+07</td>\n",
        "      <td> 1.218216e+15</td>\n",
        "      <td> 52.396542</td>\n",
        "      <td> 4.898526</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>BU03630772</strong></td>\n",
        "      <td> 5.448457e+07</td>\n",
        "      <td> 1.724291e+15</td>\n",
        "      <td> 52.384922</td>\n",
        "      <td> 4.926660</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>BU03630773</strong></td>\n",
        "      <td> 5.508481e+07</td>\n",
        "      <td> 3.657312e+15</td>\n",
        "      <td> 52.394324</td>\n",
        "      <td> 4.973050</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>BU03630875</strong></td>\n",
        "      <td> 3.594713e+07</td>\n",
        "      <td> 7.794782e+15</td>\n",
        "      <td> 52.387976</td>\n",
        "      <td> 4.808582</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>BU03630876</strong></td>\n",
        "      <td> 3.349138e+07</td>\n",
        "      <td> 2.399725e+15</td>\n",
        "      <td> 52.381993</td>\n",
        "      <td> 4.829340</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>BU03630877</strong></td>\n",
        "      <td> 3.273494e+07</td>\n",
        "      <td> 1.659150e+15</td>\n",
        "      <td> 52.375512</td>\n",
        "      <td> 4.821954</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>BU03630878</strong></td>\n",
        "      <td> 1.904827e+07</td>\n",
        "      <td> 2.327387e+15</td>\n",
        "      <td> 52.378257</td>\n",
        "      <td> 4.801976</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>BU03630879</strong></td>\n",
        "      <td> 2.440559e+07</td>\n",
        "      <td> 5.435435e+15</td>\n",
        "      <td> 52.381651</td>\n",
        "      <td> 4.775801</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>BU03630980</strong></td>\n",
        "      <td> 3.414051e+07</td>\n",
        "      <td> 4.780221e+15</td>\n",
        "      <td> 52.367047</td>\n",
        "      <td> 4.785599</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>BU03630981</strong></td>\n",
        "      <td> 3.648536e+07</td>\n",
        "      <td> 2.713174e+15</td>\n",
        "      <td> 52.360207</td>\n",
        "      <td> 4.806123</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>BU03630982</strong></td>\n",
        "      <td> 3.127312e+07</td>\n",
        "      <td> 4.149097e+15</td>\n",
        "      <td> 52.353184</td>\n",
        "      <td> 4.796367</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>BU03630983</strong></td>\n",
        "      <td> 4.603978e+07</td>\n",
        "      <td> 1.870076e+15</td>\n",
        "      <td> 52.357826</td>\n",
        "      <td> 4.787147</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>BU03630984</strong></td>\n",
        "      <td> 3.940835e+07</td>\n",
        "      <td> 4.014687e+14</td>\n",
        "      <td> 52.351995</td>\n",
        "      <td> 4.781948</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>BU03631085</strong></td>\n",
        "      <td> 4.430177e+07</td>\n",
        "      <td> 2.849526e+15</td>\n",
        "      <td> 52.359106</td>\n",
        "      <td> 4.830747</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>BU03631086</strong></td>\n",
        "      <td> 4.779580e+07</td>\n",
        "      <td> 2.986380e+15</td>\n",
        "      <td> 52.366085</td>\n",
        "      <td> 4.840374</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>BU03631087</strong></td>\n",
        "      <td> 4.184698e+07</td>\n",
        "      <td> 1.974442e+15</td>\n",
        "      <td> 52.350586</td>\n",
        "      <td> 4.839453</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>BU03631088</strong></td>\n",
        "      <td> 3.869939e+07</td>\n",
        "      <td> 3.006081e+15</td>\n",
        "      <td> 52.342867</td>\n",
        "      <td> 4.820212</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>BU03631192</strong></td>\n",
        "      <td> 5.403510e+07</td>\n",
        "      <td> 4.860566e+15</td>\n",
        "      <td> 52.307826</td>\n",
        "      <td> 4.946894</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>BU03631193</strong></td>\n",
        "      <td> 7.972719e+07</td>\n",
        "      <td> 2.138244e+15</td>\n",
        "      <td> 52.317417</td>\n",
        "      <td> 4.948888</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>BU03631194</strong></td>\n",
        "      <td> 3.217954e+07</td>\n",
        "      <td> 5.126992e+15</td>\n",
        "      <td> 52.320579</td>\n",
        "      <td> 4.977246</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>BU03631195</strong></td>\n",
        "      <td> 5.940351e+07</td>\n",
        "      <td> 5.772487e+15</td>\n",
        "      <td> 52.310161</td>\n",
        "      <td> 4.988906</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>BU03631196</strong></td>\n",
        "      <td> 1.181518e+08</td>\n",
        "      <td> 1.638116e+16</td>\n",
        "      <td> 52.301250</td>\n",
        "      <td> 4.965400</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>BU03631197</strong></td>\n",
        "      <td> 1.696124e+07</td>\n",
        "      <td> 7.058894e+14</td>\n",
        "      <td> 52.294902</td>\n",
        "      <td> 4.988288</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>BU03631198</strong></td>\n",
        "      <td> 3.380559e+07</td>\n",
        "      <td> 1.352041e+15</td>\n",
        "      <td> 52.311467</td>\n",
        "      <td> 5.015063</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>BU03631227</strong></td>\n",
        "      <td> 4.789822e+07</td>\n",
        "      <td> 2.182101e+15</td>\n",
        "      <td> 52.356135</td>\n",
        "      <td> 4.910376</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>BU03631228</strong></td>\n",
        "      <td> 4.173428e+07</td>\n",
        "      <td> 2.744241e+15</td>\n",
        "      <td> 52.360448</td>\n",
        "      <td> 4.918493</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>BU03631229</strong></td>\n",
        "      <td> 2.320473e+07</td>\n",
        "      <td> 1.259262e+15</td>\n",
        "      <td> 52.363710</td>\n",
        "      <td> 4.926495</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>BU03631230</strong></td>\n",
        "      <td> 1.030016e+08</td>\n",
        "      <td> 1.930944e+15</td>\n",
        "      <td> 52.354213</td>\n",
        "      <td> 4.919484</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>BU03631255</strong></td>\n",
        "      <td> 5.286842e+07</td>\n",
        "      <td> 6.314643e+15</td>\n",
        "      <td> 52.348853</td>\n",
        "      <td> 4.919705</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>BU03631256</strong></td>\n",
        "      <td> 4.094719e+07</td>\n",
        "      <td> 2.910426e+15</td>\n",
        "      <td> 52.350648</td>\n",
        "      <td> 4.945512</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>BU03631257</strong></td>\n",
        "      <td> 2.836188e+07</td>\n",
        "      <td> 1.531634e+15</td>\n",
        "      <td> 52.343198</td>\n",
        "      <td> 4.939110</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>BU03631258</strong></td>\n",
        "      <td> 3.463629e+07</td>\n",
        "      <td> 2.752124e+15</td>\n",
        "      <td> 52.336681</td>\n",
        "      <td> 4.916669</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>BU03631324</strong></td>\n",
        "      <td> 3.998340e+07</td>\n",
        "      <td> 1.891869e+15</td>\n",
        "      <td> 52.356410</td>\n",
        "      <td> 4.893144</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>BU03631325</strong></td>\n",
        "      <td> 3.055078e+07</td>\n",
        "      <td> 2.104374e+15</td>\n",
        "      <td> 52.353144</td>\n",
        "      <td> 4.895829</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>BU03631326</strong></td>\n",
        "      <td> 3.681281e+07</td>\n",
        "      <td> 2.426239e+15</td>\n",
        "      <td> 52.352225</td>\n",
        "      <td> 4.902491</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>BU03631344</strong></td>\n",
        "      <td> 3.373319e+07</td>\n",
        "      <td> 1.908824e+15</td>\n",
        "      <td> 52.351826</td>\n",
        "      <td> 4.850016</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>BU03631345</strong></td>\n",
        "      <td> 3.490560e+07</td>\n",
        "      <td> 3.600734e+15</td>\n",
        "      <td> 52.352346</td>\n",
        "      <td> 4.855679</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>BU03631346</strong></td>\n",
        "      <td> 3.347443e+07</td>\n",
        "      <td> 2.801139e+15</td>\n",
        "      <td> 52.354236</td>\n",
        "      <td> 4.863389</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>BU03631347</strong></td>\n",
        "      <td> 3.838088e+07</td>\n",
        "      <td> 2.181407e+15</td>\n",
        "      <td> 52.358917</td>\n",
        "      <td> 4.879794</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>BU03631348</strong></td>\n",
        "      <td> 3.336986e+07</td>\n",
        "      <td> 2.238330e+15</td>\n",
        "      <td> 52.345672</td>\n",
        "      <td> 4.857665</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>BU03631349</strong></td>\n",
        "      <td> 3.858499e+07</td>\n",
        "      <td> 4.708061e+15</td>\n",
        "      <td> 52.349222</td>\n",
        "      <td> 4.875158</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>BU03631350</strong></td>\n",
        "      <td> 3.337788e+07</td>\n",
        "      <td> 2.267384e+15</td>\n",
        "      <td> 52.354644</td>\n",
        "      <td> 4.885674</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>BU03631452</strong></td>\n",
        "      <td> 4.030466e+07</td>\n",
        "      <td> 3.022871e+15</td>\n",
        "      <td> 52.343024</td>\n",
        "      <td> 4.892552</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>BU03631453</strong></td>\n",
        "      <td> 1.084910e+08</td>\n",
        "      <td> 2.076940e+16</td>\n",
        "      <td> 52.348890</td>\n",
        "      <td> 4.908481</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>BU03631454</strong></td>\n",
        "      <td> 3.481568e+07</td>\n",
        "      <td> 2.950243e+15</td>\n",
        "      <td> 52.341972</td>\n",
        "      <td> 4.907108</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>BU03631459</strong></td>\n",
        "      <td> 4.564034e+07</td>\n",
        "      <td> 2.574770e+15</td>\n",
        "      <td> 52.340828</td>\n",
        "      <td> 4.872763</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>BU03631490</strong></td>\n",
        "      <td> 5.062685e+07</td>\n",
        "      <td> 3.270327e+15</td>\n",
        "      <td> 52.335458</td>\n",
        "      <td> 4.866732</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>BU03631491</strong></td>\n",
        "      <td> 1.002436e+08</td>\n",
        "      <td> 7.937146e+15</td>\n",
        "      <td> 52.330808</td>\n",
        "      <td> 4.886211</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "output_type": "pyout",
       "prompt_number": 28,
       "text": [
        "                 user_id      tweet_id        lat       lon\n",
        "BU_CODE                                                    \n",
        "BU03630000  4.089537e+07  2.896880e+15  52.373986  4.897222\n",
        "BU03630001  4.001633e+07  2.494666e+15  52.375894  4.896031\n",
        "BU03630002  2.975765e+07  2.635145e+15  52.374089  4.886963\n",
        "BU03630003  3.360024e+07  3.197510e+15  52.366105  4.894889\n",
        "BU03630004  3.696746e+07  2.597457e+15  52.371965  4.903914\n",
        "BU03630005  3.213309e+07  3.016933e+15  52.384250  4.889986\n",
        "BU03630006  3.500533e+07  3.837788e+15  52.375541  4.881686\n",
        "BU03630007  3.076569e+07  2.026354e+15  52.362500  4.890146\n",
        "BU03630008  3.885122e+07  1.834704e+15  52.364856  4.909952\n",
        "BU03630009  3.275136e+07  1.761756e+15  52.372387  4.922353\n",
        "BU03630110  4.583103e+07  3.024124e+15  52.403604  4.840568\n",
        "BU03630111  4.553697e+07  4.043264e+15  52.390713  4.837820\n",
        "BU03630212  4.653763e+07  5.552885e+15  52.393468  4.885883\n",
        "BU03630213  4.207794e+07  1.911825e+15  52.388810  4.875225\n",
        "BU03630214  2.604093e+07  1.475080e+15  52.383136  4.872976\n",
        "BU03630215  2.596567e+07  1.881479e+15  52.384311  4.867759\n",
        "BU03630216  3.409945e+07  2.481599e+15  52.377359  4.874483\n",
        "BU03630317  3.083976e+07  9.677685e+14  52.371689  4.872083\n",
        "BU03630318  3.270001e+07  4.452065e+15  52.369788  4.866933\n",
        "BU03630319  1.256780e+07  9.603981e+15  52.366653  4.871118\n",
        "BU03630320  3.580618e+07  3.725604e+15  52.364255  4.872827\n",
        "BU03630321  4.327673e+07  2.162391e+15  52.360449  4.860336\n",
        "BU03630322  3.306616e+07  2.397359e+15  52.362916  4.876829\n",
        "BU03630431  3.621605e+07  3.166838e+15  52.364937  4.935627\n",
        "BU03630432  3.705649e+07  3.989395e+15  52.365043  4.946886\n",
        "BU03630433  4.343960e+07  1.383628e+15  52.375096  4.932047\n",
        "BU03630434  5.536995e+07  3.429937e+15  52.366242  4.968782\n",
        "BU03630435  3.080973e+07  2.029308e+15  52.357460  4.991925\n",
        "BU03630451  5.232057e+07  2.948715e+15  52.351359  5.004841\n",
        "BU03630536  3.945977e+07  3.452410e+15  52.388011  4.850064\n",
        "BU03630537  3.519838e+07  2.477468e+15  52.382839  4.852951\n",
        "BU03630538  3.493221e+07  2.787626e+15  52.378635  4.850743\n",
        "BU03630539  4.352352e+07  1.309941e+15  52.378174  4.842480\n",
        "BU03630640  3.914126e+07  3.803748e+15  52.371784  4.860462\n",
        "BU03630641  4.282359e+07  2.833880e+15  52.372286  4.848435\n",
        "BU03630642  4.807170e+07  2.098702e+15  52.369191  4.852857\n",
        "BU03630643  2.546463e+07  1.109082e+15  52.362064  4.854477\n",
        "BU03630760  3.908344e+07  1.564955e+15  52.392179  4.912117\n",
        "BU03630761  3.431164e+07  2.454694e+15  52.386317  4.919903\n",
        "BU03630762  1.010210e+08  8.829694e+15  52.391132  4.947516\n",
        "BU03630763  3.912022e+07  8.352349e+15  52.394585  4.928672\n",
        "BU03630764  1.165250e+08  9.981837e+13  52.389851  4.941517\n",
        "BU03630765  2.503894e+07  2.076387e+15  52.407464  4.895148\n",
        "BU03630766  6.563517e+07  5.165142e+15  52.423225  4.879855\n",
        "BU03630767  4.283430e+07  9.493364e+14  52.415386  4.908213\n",
        "BU03630768  5.704161e+07  2.083465e+15  52.395175  4.953750\n",
        "BU03630769  4.664555e+07  3.435446e+15  52.401304  4.937816\n",
        "BU03630770  7.281672e+07  1.315143e+15  52.407017  4.917439\n",
        "BU03630771  3.789073e+07  1.218216e+15  52.396542  4.898526\n",
        "BU03630772  5.448457e+07  1.724291e+15  52.384922  4.926660\n",
        "BU03630773  5.508481e+07  3.657312e+15  52.394324  4.973050\n",
        "BU03630875  3.594713e+07  7.794782e+15  52.387976  4.808582\n",
        "BU03630876  3.349138e+07  2.399725e+15  52.381993  4.829340\n",
        "BU03630877  3.273494e+07  1.659150e+15  52.375512  4.821954\n",
        "BU03630878  1.904827e+07  2.327387e+15  52.378257  4.801976\n",
        "BU03630879  2.440559e+07  5.435435e+15  52.381651  4.775801\n",
        "BU03630980  3.414051e+07  4.780221e+15  52.367047  4.785599\n",
        "BU03630981  3.648536e+07  2.713174e+15  52.360207  4.806123\n",
        "BU03630982  3.127312e+07  4.149097e+15  52.353184  4.796367\n",
        "BU03630983  4.603978e+07  1.870076e+15  52.357826  4.787147\n",
        "BU03630984  3.940835e+07  4.014687e+14  52.351995  4.781948\n",
        "BU03631085  4.430177e+07  2.849526e+15  52.359106  4.830747\n",
        "BU03631086  4.779580e+07  2.986380e+15  52.366085  4.840374\n",
        "BU03631087  4.184698e+07  1.974442e+15  52.350586  4.839453\n",
        "BU03631088  3.869939e+07  3.006081e+15  52.342867  4.820212\n",
        "BU03631192  5.403510e+07  4.860566e+15  52.307826  4.946894\n",
        "BU03631193  7.972719e+07  2.138244e+15  52.317417  4.948888\n",
        "BU03631194  3.217954e+07  5.126992e+15  52.320579  4.977246\n",
        "BU03631195  5.940351e+07  5.772487e+15  52.310161  4.988906\n",
        "BU03631196  1.181518e+08  1.638116e+16  52.301250  4.965400\n",
        "BU03631197  1.696124e+07  7.058894e+14  52.294902  4.988288\n",
        "BU03631198  3.380559e+07  1.352041e+15  52.311467  5.015063\n",
        "BU03631227  4.789822e+07  2.182101e+15  52.356135  4.910376\n",
        "BU03631228  4.173428e+07  2.744241e+15  52.360448  4.918493\n",
        "BU03631229  2.320473e+07  1.259262e+15  52.363710  4.926495\n",
        "BU03631230  1.030016e+08  1.930944e+15  52.354213  4.919484\n",
        "BU03631255  5.286842e+07  6.314643e+15  52.348853  4.919705\n",
        "BU03631256  4.094719e+07  2.910426e+15  52.350648  4.945512\n",
        "BU03631257  2.836188e+07  1.531634e+15  52.343198  4.939110\n",
        "BU03631258  3.463629e+07  2.752124e+15  52.336681  4.916669\n",
        "BU03631324  3.998340e+07  1.891869e+15  52.356410  4.893144\n",
        "BU03631325  3.055078e+07  2.104374e+15  52.353144  4.895829\n",
        "BU03631326  3.681281e+07  2.426239e+15  52.352225  4.902491\n",
        "BU03631344  3.373319e+07  1.908824e+15  52.351826  4.850016\n",
        "BU03631345  3.490560e+07  3.600734e+15  52.352346  4.855679\n",
        "BU03631346  3.347443e+07  2.801139e+15  52.354236  4.863389\n",
        "BU03631347  3.838088e+07  2.181407e+15  52.358917  4.879794\n",
        "BU03631348  3.336986e+07  2.238330e+15  52.345672  4.857665\n",
        "BU03631349  3.858499e+07  4.708061e+15  52.349222  4.875158\n",
        "BU03631350  3.337788e+07  2.267384e+15  52.354644  4.885674\n",
        "BU03631452  4.030466e+07  3.022871e+15  52.343024  4.892552\n",
        "BU03631453  1.084910e+08  2.076940e+16  52.348890  4.908481\n",
        "BU03631454  3.481568e+07  2.950243e+15  52.341972  4.907108\n",
        "BU03631459  4.564034e+07  2.574770e+15  52.340828  4.872763\n",
        "BU03631490  5.062685e+07  3.270327e+15  52.335458  4.866732\n",
        "BU03631491  1.002436e+08  7.937146e+15  52.330808  4.886211"
       ]
      }
     ],
     "prompt_number": 28
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "This is also useful if you want to obtain simple descriptive statistics by group. For example, you might want to know a bit more of checkins per neighborhodd:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Proportion by neighborhood\n",
      "pct = neigh * 100. / neigh.sum()\n",
      "\n",
      "# Sort them\n",
      "rk = pct.order(ascending=False)\n",
      "print '--- Top 5 most popular neighborhoods ---'\n",
      "print np.round(rk.head(), decimals=2)\n",
      "print '--- Top 5 least popular neighborhoods ---'\n",
      "print np.round(rk.tail()[::-1], decimals=2)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "--- Top 5 most popular neighborhoods ---\n",
        "BU_CODE\n",
        "BU03630001    8.55\n",
        "BU03630007    5.44\n",
        "BU03631192    5.18\n",
        "BU03630003    4.48\n",
        "BU03631347    3.63\n",
        "--- Top 5 least popular neighborhoods ---\n",
        "BU_CODE\n",
        "BU03630763    0.01\n",
        "BU03631198    0.01\n",
        "BU03630762    0.01\n",
        "BU03630879    0.03\n",
        "BU03630764    0.04\n"
       ]
      }
     ],
     "prompt_number": 29
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Date functionality"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "`pandas` inludes very nice support for dates and operations based on date. To exemplify it, we will use the column `time`. As it is read, it is taken as simple text:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "db['time'][0]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 30,
       "text": [
        "'2010-05-05 09:21:15'"
       ]
      }
     ],
     "prompt_number": 30
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "type(db['time'][0])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 31,
       "text": [
        "str"
       ]
      }
     ],
     "prompt_number": 31
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "However, you can convert it to a `Timestamp`, the class in `pandas` to deal with dates:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# This might take some time\n",
      "db['t'] = db['time'].apply(pd.Timestamp)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 32
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ts = db['t'][0]\n",
      "ts"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 33,
       "text": [
        "<Timestamp: 2010-05-05 09:21:15>"
       ]
      }
     ],
     "prompt_number": 33
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "This gives you handy many attributes:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ts.hour"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 34,
       "text": [
        "9"
       ]
      }
     ],
     "prompt_number": 34
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ts.date()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 35,
       "text": [
        "datetime.date(2010, 5, 5)"
       ]
      }
     ],
     "prompt_number": 35
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "See all of them:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "[i for i in dir(ts) if '__' not in i]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 36,
       "text": [
        "['_get_field',\n",
        " '_repr_base',\n",
        " 'asm8',\n",
        " 'astimezone',\n",
        " 'combine',\n",
        " 'ctime',\n",
        " 'date',\n",
        " 'day',\n",
        " 'dayofweek',\n",
        " 'dayofyear',\n",
        " 'dst',\n",
        " 'freq',\n",
        " 'freqstr',\n",
        " 'fromordinal',\n",
        " 'fromtimestamp',\n",
        " 'hour',\n",
        " 'isocalendar',\n",
        " 'isoformat',\n",
        " 'isoweekday',\n",
        " 'max',\n",
        " 'microsecond',\n",
        " 'min',\n",
        " 'minute',\n",
        " 'month',\n",
        " 'nanosecond',\n",
        " 'now',\n",
        " 'offset',\n",
        " 'quarter',\n",
        " 'replace',\n",
        " 'resolution',\n",
        " 'second',\n",
        " 'strftime',\n",
        " 'strptime',\n",
        " 'time',\n",
        " 'timetuple',\n",
        " 'timetz',\n",
        " 'to_datetime',\n",
        " 'to_period',\n",
        " 'to_pydatetime',\n",
        " 'today',\n",
        " 'toordinal',\n",
        " 'tz',\n",
        " 'tz_convert',\n",
        " 'tz_localize',\n",
        " 'tzinfo',\n",
        " 'tzname',\n",
        " 'utcfromtimestamp',\n",
        " 'utcnow',\n",
        " 'utcoffset',\n",
        " 'utctimetuple',\n",
        " 'value',\n",
        " 'week',\n",
        " 'weekday',\n",
        " 'weekofyear',\n",
        " 'year']"
       ]
      }
     ],
     "prompt_number": 36
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "With this, we can assign `t` as the index of the `DataFrame`, which will be useful later:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "db = db.set_index('t')\n",
      "db.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>user_id</th>\n",
        "      <th>tweet_id</th>\n",
        "      <th>lat</th>\n",
        "      <th>lon</th>\n",
        "      <th>time</th>\n",
        "      <th>text</th>\n",
        "      <th>place_id</th>\n",
        "      <th>BU_CODE</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>t</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",
        "      <td><strong>2010-05-05 09:21:15</strong></td>\n",
        "      <td> 82837637</td>\n",
        "      <td> 13416482426</td>\n",
        "      <td> 52.360800</td>\n",
        "      <td> 4.867410</td>\n",
        "      <td> 2010-05-05 09:21:15</td>\n",
        "      <td> I'm at Marqt Overtoom (Overtoom, Amsterdam). h...</td>\n",
        "      <td> 99cdab25eddd6bce</td>\n",
        "      <td> BU03630322</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>2010-05-05 10:52:24</strong></td>\n",
        "      <td> 82837637</td>\n",
        "      <td> 13419413928</td>\n",
        "      <td> 52.366300</td>\n",
        "      <td> 4.886320</td>\n",
        "      <td> 2010-05-05 10:52:24</td>\n",
        "      <td> I'm at Walem (Keizersgracht 449, Amsterdam). h...</td>\n",
        "      <td> 99cdab25eddd6bce</td>\n",
        "      <td> BU03630003</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>2010-05-27 17:06:19</strong></td>\n",
        "      <td> 82837637</td>\n",
        "      <td> 14847348078</td>\n",
        "      <td> 52.333500</td>\n",
        "      <td> 4.889770</td>\n",
        "      <td> 2010-05-27 17:06:19</td>\n",
        "      <td> Mooi feest hier. Wie is er eigenlijk niet vand...</td>\n",
        "      <td> 99cdab25eddd6bce</td>\n",
        "      <td> BU03631491</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>2010-06-30 08:54:35</strong></td>\n",
        "      <td> 82837637</td>\n",
        "      <td> 17401139685</td>\n",
        "      <td> 52.340512</td>\n",
        "      <td> 4.873362</td>\n",
        "      <td> 2010-06-30 08:54:35</td>\n",
        "      <td> @nienkehofkamp ik ben er! (@ World Trade Cente...</td>\n",
        "      <td> 99cdab25eddd6bce</td>\n",
        "      <td> BU03631459</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>2010-07-02 07:26:45</strong></td>\n",
        "      <td> 82837637</td>\n",
        "      <td> 17554964312</td>\n",
        "      <td> 52.349976</td>\n",
        "      <td> 4.918581</td>\n",
        "      <td> 2010-07-02 07:26:45</td>\n",
        "      <td> Klein leger van 200 groen gele Braziliaantjes ...</td>\n",
        "      <td> 99cdab25eddd6bce</td>\n",
        "      <td> BU03631255</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "output_type": "pyout",
       "prompt_number": 37,
       "text": [
        "                      user_id     tweet_id        lat       lon  \\\n",
        "t                                                                 \n",
        "2010-05-05 09:21:15  82837637  13416482426  52.360800  4.867410   \n",
        "2010-05-05 10:52:24  82837637  13419413928  52.366300  4.886320   \n",
        "2010-05-27 17:06:19  82837637  14847348078  52.333500  4.889770   \n",
        "2010-06-30 08:54:35  82837637  17401139685  52.340512  4.873362   \n",
        "2010-07-02 07:26:45  82837637  17554964312  52.349976  4.918581   \n",
        "\n",
        "                                    time  \\\n",
        "t                                          \n",
        "2010-05-05 09:21:15  2010-05-05 09:21:15   \n",
        "2010-05-05 10:52:24  2010-05-05 10:52:24   \n",
        "2010-05-27 17:06:19  2010-05-27 17:06:19   \n",
        "2010-06-30 08:54:35  2010-06-30 08:54:35   \n",
        "2010-07-02 07:26:45  2010-07-02 07:26:45   \n",
        "\n",
        "                                                                  text  \\\n",
        "t                                                                        \n",
        "2010-05-05 09:21:15  I'm at Marqt Overtoom (Overtoom, Amsterdam). h...   \n",
        "2010-05-05 10:52:24  I'm at Walem (Keizersgracht 449, Amsterdam). h...   \n",
        "2010-05-27 17:06:19  Mooi feest hier. Wie is er eigenlijk niet vand...   \n",
        "2010-06-30 08:54:35  @nienkehofkamp ik ben er! (@ World Trade Cente...   \n",
        "2010-07-02 07:26:45  Klein leger van 200 groen gele Braziliaantjes ...   \n",
        "\n",
        "                             place_id     BU_CODE  \n",
        "t                                                  \n",
        "2010-05-05 09:21:15  99cdab25eddd6bce  BU03630322  \n",
        "2010-05-05 10:52:24  99cdab25eddd6bce  BU03630003  \n",
        "2010-05-27 17:06:19  99cdab25eddd6bce  BU03631491  \n",
        "2010-06-30 08:54:35  99cdab25eddd6bce  BU03631459  \n",
        "2010-07-02 07:26:45  99cdab25eddd6bce  BU03631255  "
       ]
      }
     ],
     "prompt_number": 37
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Once we have it, it is easier to group the rows by day and get the average location, for example:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "days = db.groupby(lambda i: i.date())[['lat', 'lon']].mean()\n",
      "days.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<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",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <td><strong>2010-02-26</strong></td>\n",
        "      <td> 52.362463</td>\n",
        "      <td> 4.882973</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>2010-02-27</strong></td>\n",
        "      <td> 52.367031</td>\n",
        "      <td> 4.879173</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>2010-02-28</strong></td>\n",
        "      <td> 52.360148</td>\n",
        "      <td> 4.857665</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>2010-03-01</strong></td>\n",
        "      <td> 52.365379</td>\n",
        "      <td> 4.877875</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>2010-03-02</strong></td>\n",
        "      <td> 52.364598</td>\n",
        "      <td> 4.891863</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "output_type": "pyout",
       "prompt_number": 38,
       "text": [
        "                  lat       lon\n",
        "2010-02-26  52.362463  4.882973\n",
        "2010-02-27  52.367031  4.879173\n",
        "2010-02-28  52.360148  4.857665\n",
        "2010-03-01  52.365379  4.877875\n",
        "2010-03-02  52.364598  4.891863"
       ]
      }
     ],
     "prompt_number": 38
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Another one-liner is to get the volume of checkins by hour of the day:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "by_hour = db.groupby(lambda i: i.hour).size()\n",
      "by_hour"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 39,
       "text": [
        "0      778\n",
        "1      463\n",
        "2      225\n",
        "3      159\n",
        "4      375\n",
        "5     1168\n",
        "6     3049\n",
        "7     4601\n",
        "8     4463\n",
        "9     3921\n",
        "10    4164\n",
        "11    4683\n",
        "12    4284\n",
        "13    4247\n",
        "14    4426\n",
        "15    4748\n",
        "16    5349\n",
        "17    5686\n",
        "18    5613\n",
        "19    4583\n",
        "20    3743\n",
        "21    3107\n",
        "22    2215\n",
        "23    1377"
       ]
      }
     ],
     "prompt_number": 39
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "And we can do similar by hour of the day by neighborhood:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "by_neigh_by_hour = db.groupby(['BU_CODE', lambda i: i.hour]).size()\n",
      "by_neigh_by_hour"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 40,
       "text": [
        "BU_CODE       \n",
        "BU03630000  0      30\n",
        "            1      12\n",
        "            2      13\n",
        "            3       9\n",
        "            4       3\n",
        "            5      11\n",
        "            6      48\n",
        "            7      90\n",
        "            8     117\n",
        "            9     111\n",
        "            10    136\n",
        "            11    155\n",
        "            12    163\n",
        "            13    149\n",
        "            14    195\n",
        "...\n",
        "BU03631491  9     39\n",
        "            10    43\n",
        "            11    39\n",
        "            12    41\n",
        "            13    25\n",
        "            14    46\n",
        "            15    45\n",
        "            16    65\n",
        "            17    45\n",
        "            18    37\n",
        "            19    27\n",
        "            20    37\n",
        "            21    48\n",
        "            22    58\n",
        "            23    24\n",
        "Length: 1986"
       ]
      }
     ],
     "prompt_number": 40
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "This creates a `Series` object but with a hierarchical index that contains two levels: one for the neighborhood and one for the hour of the day. We can quickly reshape this into a `DataFrame`:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "bnbh_df = by_neigh_by_hour.unstack()\n",
      "# Add 'h' to every column to make it clear that is the hour of the day\n",
      "bnbh_df = bnbh_df.rename(columns=lambda x: 'h'+str(x))\n",
      "bnbh_df"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 41,
       "text": [
        "<class 'pandas.core.frame.DataFrame'>\n",
        "Index: 96 entries, BU03630000 to BU03631491\n",
        "Data columns:\n",
        "h0     72  non-null values\n",
        "h1     59  non-null values\n",
        "h2     53  non-null values\n",
        "h3     43  non-null values\n",
        "h4     59  non-null values\n",
        "h5     80  non-null values\n",
        "h6     90  non-null values\n",
        "h7     93  non-null values\n",
        "h8     93  non-null values\n",
        "h9     91  non-null values\n",
        "h10    93  non-null values\n",
        "h11    94  non-null values\n",
        "h12    93  non-null values\n",
        "h13    92  non-null values\n",
        "h14    94  non-null values\n",
        "h15    90  non-null values\n",
        "h16    91  non-null values\n",
        "h17    90  non-null values\n",
        "h18    89  non-null values\n",
        "h19    89  non-null values\n",
        "h20    86  non-null values\n",
        "h21    85  non-null values\n",
        "h22    88  non-null values\n",
        "h23    79  non-null values\n",
        "dtypes: float64(24)"
       ]
      }
     ],
     "prompt_number": 41
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Plotting\n",
      "\n",
      "Another nice addition of `pandas` is that it leverages the power of `matplotlib` considerably lowering the access and difficulty. It includes a few canned graphics that are one-liners and very helpful when interacting with your data. Let's walk through the main four ones: bar and line plots, histograms and density plots."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "To see the bar plots, let's explore the proportions of checkins by neighborhood:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "neigh.order(ascending=False).plot(kind='bar')\n",
      "# Note the notebook allows you to manually resize the figure if it is too small"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 42,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x51088d0>"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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y7u7Vmr+Era0vZnVFHvFa5zDlrknVl1w0qfrqLq0zOvxkP2fOHBw4cADnzp1D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\nt7jFLW75HYi72LvFLW5xy+9A/h8Lf8ywOkG7YgAAAABJRU5ErkJggg==\n"
      }
     ],
     "prompt_number": 42
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Another useful simple plot is using lines instead of bars. In this case, let's examine the evolution of checkins by hour of the day:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "by_hour.plot()\n",
      "title('Checkins by hour of the day')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 43,
       "text": [
        "<matplotlib.text.Text at 0x6fb6910>"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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HDs7ZXrdueq6Wyto/SulJ2sLD4frr9bkH5V3CUHgvme3TMepc7gFPP/00b775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\n"
      }
     ],
     "prompt_number": 43
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We can also think of what the distribution of checkins is over the whole period. To that, we can pull the values of the each `Timestamp` and get a histogram:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "db['t'] = db.index\n",
      "deltas = db['t'].apply(lambda x: pd.Timestamp(x).value)\n",
      "deltas.hist(bins=50)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 44,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x5f1ae50>"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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NjY1wOBwjXbUhQh2BREQ0Fo2oMFy9ehXt7e0AgP/85z84ePAgUlNTkZ2djfLy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\nFgYiIgry/wG01LfSsz1I8AAAAABJRU5ErkJggg==\n"
      }
     ],
     "prompt_number": 44
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Or a density kernel, which is a more elegant continious version of the histogram:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "(deltas * 1.).plot(kind='kde')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 45,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x72b7350>"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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rr7/Ot99+y9atW5k5cyZ79+41ajhhQV98oaZzCv9Wo4ZaivvHH82ORFzKsOLf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\nhBB+SIq/EEL4ISn+Qgjhh6T4CyGEH/p/sYHQhlvCZJ8AAAAASUVORK5CYII=\n"
      }
     ],
     "prompt_number": 45
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Finally, a useful graph that is not included in `pandas` but is very simple from `matplotlib` is a scatter plot. In this case, let's plot the spatial distribution of the checkins:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "scatter(db['lon'].values, db['lat'].values)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 46,
       "text": [
        "<matplotlib.collections.PathCollection at 0x82be890>"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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8UUpjOJj2IsL48eNLPkuWLPktUmM4wli9ejXDh4/miiuuYfny5YCq8//4x300atSOli27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\nhMfTG40kSkATwJINI9oXNQ/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HM00zTLMmThmLPTiF6RIMY62Mk4QYj8gSM8/R+198jhOq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\nMX0zLJbN6Nv3eq0UapLKWrw1a9aMaWlpp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      }
     ],
     "prompt_number": 46
    }
   ],
   "metadata": {}
  }
 ]
}