{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "nbpresent": {
     "id": "04352eb2-a708-4e9d-be5e-140d1fb82590"
    },
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "<CENTER>\n",
    "  <header>\n",
    "    <h1>Pandas Hands-on</h1>\n",
    "    <h3>Universitat Pompeu Fabra (UPF) - Barcelona</h3>\n",
    "    <h2>Massimo Quadrana</h2>\n",
    "  </header>\n",
    "</CENTER>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "fae371fd-868d-45d5-9b11-a5a41bdac839"
    },
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# About me: Massimo Quadrana\n",
    "\n",
    "- PhD student at Politecnico di Milano\n",
    "- Working on Recommendation Systems\n",
    "->\n",
    "\n",
    "- https://github.com/mquad\n",
    "- [@mquad](https://twitter.com/mquad)\n",
    "\n",
    "\n",
    "Originally Licensed under [CC BY 4.0 Creative Commons](http://creativecommons.org/licenses/by/4.0/)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "8317c7f2-0f11-4e8c-a58b-1f9f6dc7b126"
    },
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "\n",
    "# Content of this talk\n",
    "\n",
    "- Why do you need pandas?\n",
    "- Basic introduction to:\n",
    "    - Data structures and basic operations\n",
    "    - Indexing and selecting data\n",
    "    - Groupby operation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Material\n",
    "\n",
    "- All materials (notebook, data, link to nbviewer): https://github.com/mquad/pandas-tutorial\n",
    "- The complete tutorial is available at https://github.com/jorisvandenbossche/pandas-tutorial\n",
    "- You need `pandas` >= 0.15.2 (easy solution is using Anaconda)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "5c11d3c6-daee-4080-b8f5-0d7cd5ade8f7"
    },
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Why do you need pandas?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "a175be5b-cf10-487f-8146-3166eb5e60b5"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Why do you need pandas?\n",
    "\n",
    "When working with *tabular or structured data* (like R dataframe, SQL table, Excel spreadsheet, ...):\n",
    "\n",
    "- Import data\n",
    "- Clean up messy data\n",
    "- Explore data, gain insight into data\n",
    "- Process and prepare your data for analysis\n",
    "- Analyse your data (together with scikit-learn, statsmodels, __Keras__...)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "nbpresent": {
     "id": "431d8d39-3deb-4f1f-b6aa-142e5001436a"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "# Pandas: data analysis in python\n",
    "\n",
    "For data-intensive work in Python the [Pandas](http://pandas.pydata.org) library has become essential.\n",
    "\n",
    "What is ``pandas``?\n",
    "\n",
    "* Pandas can be thought of as NumPy arrays with labels for rows and columns, and better support for heterogeneous data types, but it's also much, much more than that.\n",
    "* Pandas can also be thought of as `R`'s `data.frame` in Python.\n",
    "* Powerful for working with missing data, working with time series data, for reading and writing your data, for reshaping, grouping, merging your data, ...\n",
    "\n",
    "It's documentation: http://pandas.pydata.org/pandas-docs/stable/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "02d99173-0900-4675-b5e6-1ee49ea908a0"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Key features\n",
    "\n",
    "* Fast, easy and flexible input/output for a lot of different data formats\n",
    "* Working with missing data (`.dropna()`, `pd.isnull()`)\n",
    "* Merging and joining (`concat`, `join`)\n",
    "* Grouping: `groupby` functionality\n",
    "* Reshaping (`stack`, `pivot`) [ADVANCED]\n",
    "* Powerful time series manipulation (resampling, timezones, ..) [ADVANCED]\n",
    "* Easy plotting"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "nbpresent": {
     "id": "1a1c4fdb-a27c-4369-a0e8-a2a9b9ca6b61"
    },
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Further reading\n",
    "\n",
    "- the documentation: http://pandas.pydata.org/pandas-docs/stable/\n",
    "- Wes McKinney's book \"Python for Data Analysis\"\n",
    "- Jake VanderPlas's Python Data Science Handbook: https://github.com/jakevdp/PythonDataScienceHandbook\n",
    "- Tom Augspurger's series on modern idiomatic pandas: https://tomaugspurger.github.io/modern-1.html\n",
    "- lots of tutorials on the internet, eg http://github.com/jvns/pandas-cookbook, https://github.com/brandon-rhodes/pycon-pandas-tutorial/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "nbpresent": {
     "id": "b8672524-da19-4038-9396-584415c29a33"
    }
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "celltoolbar": "Slideshow",
  "kernelspec": {
   "display_name": "Python [Root]",
   "language": "python",
   "name": "Python [Root]"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  },
  "nbpresent": {
   "slides": {
    "18d518b8-3fcf-40e8-b015-c03ae6d4f203": {
     "id": "18d518b8-3fcf-40e8-b015-c03ae6d4f203",
     "prev": "2901b664-7a34-47e2-b2df-4109e915930d",
     "regions": {
      "9e4bb7d7-eb82-4d26-9922-92ca8934b494": {
       "attrs": {
        "height": 0.8,
        "width": 0.8,
        "x": 0.1,
        "y": 0.1
       },
       "content": {
        "cell": "fae371fd-868d-45d5-9b11-a5a41bdac839",
        "part": "whole"
       },
       "id": "9e4bb7d7-eb82-4d26-9922-92ca8934b494"
      }
     }
    },
    "1b1d69a3-4c2f-4243-8baf-e035163abc55": {
     "id": "1b1d69a3-4c2f-4243-8baf-e035163abc55",
     "prev": "18d518b8-3fcf-40e8-b015-c03ae6d4f203",
     "regions": {
      "559338df-d665-4c96-9a3b-dd1ea12fee39": {
       "attrs": {
        "height": 0.8,
        "width": 0.8,
        "x": 0.1,
        "y": 0.1
       },
       "content": {
        "cell": "8317c7f2-0f11-4e8c-a58b-1f9f6dc7b126",
        "part": "whole"
       },
       "id": "559338df-d665-4c96-9a3b-dd1ea12fee39"
      }
     }
    },
    "239b9ea7-1483-44f8-9eee-ff4ca4a2c90a": {
     "id": "239b9ea7-1483-44f8-9eee-ff4ca4a2c90a",
     "prev": "a02063d5-d8c0-4690-a152-a50479080f88",
     "regions": {
      "fd8ca7e3-c5e0-41d6-9626-6c8b231d112e": {
       "attrs": {
        "height": 0.8,
        "width": 0.8,
        "x": 0.1,
        "y": 0.1
       },
       "content": {
        "cell": "a175be5b-cf10-487f-8146-3166eb5e60b5",
        "part": "whole"
       },
       "id": "fd8ca7e3-c5e0-41d6-9626-6c8b231d112e"
      }
     }
    },
    "2901b664-7a34-47e2-b2df-4109e915930d": {
     "id": "2901b664-7a34-47e2-b2df-4109e915930d",
     "prev": null,
     "regions": {
      "99a84244-deb7-4b3f-877f-43332b5686e1": {
       "attrs": {
        "height": 0.8,
        "width": 0.8,
        "x": 0.1,
        "y": 0.1
       },
       "content": {
        "cell": "04352eb2-a708-4e9d-be5e-140d1fb82590",
        "part": "whole"
       },
       "id": "99a84244-deb7-4b3f-877f-43332b5686e1"
      }
     }
    },
    "516ce62c-55fd-44f1-b182-8db658b45c05": {
     "id": "516ce62c-55fd-44f1-b182-8db658b45c05",
     "prev": "239b9ea7-1483-44f8-9eee-ff4ca4a2c90a",
     "regions": {
      "4647a68a-626d-42c7-be88-84a54b599ba1": {
       "attrs": {
        "height": 0.8,
        "width": 0.8,
        "x": 0.1,
        "y": 0.1
       },
       "content": {
        "cell": "431d8d39-3deb-4f1f-b6aa-142e5001436a",
        "part": "whole"
       },
       "id": "4647a68a-626d-42c7-be88-84a54b599ba1"
      }
     }
    },
    "97c8f96f-6f45-4d66-8064-6fc091070068": {
     "id": "97c8f96f-6f45-4d66-8064-6fc091070068",
     "prev": "e01c8c16-065a-40e9-9467-cbd241b2a133",
     "regions": {
      "8ccc2152-7dae-48b2-8ad7-3d89073c2dff": {
       "attrs": {
        "height": 0.8,
        "width": 0.8,
        "x": 0.1,
        "y": 0.1
       },
       "content": {
        "cell": "1a1c4fdb-a27c-4369-a0e8-a2a9b9ca6b61",
        "part": "whole"
       },
       "id": "8ccc2152-7dae-48b2-8ad7-3d89073c2dff"
      }
     }
    },
    "a02063d5-d8c0-4690-a152-a50479080f88": {
     "id": "a02063d5-d8c0-4690-a152-a50479080f88",
     "prev": "1b1d69a3-4c2f-4243-8baf-e035163abc55",
     "regions": {
      "7b7c9c8f-7b9f-4945-88f7-372403ae0ef2": {
       "attrs": {
        "height": 0.8,
        "width": 0.8,
        "x": 0.1,
        "y": 0.1
       },
       "content": {
        "cell": "5c11d3c6-daee-4080-b8f5-0d7cd5ade8f7",
        "part": "whole"
       },
       "id": "7b7c9c8f-7b9f-4945-88f7-372403ae0ef2"
      }
     }
    },
    "a5b57b6d-28c0-4cac-96d7-43b58c53b778": {
     "id": "a5b57b6d-28c0-4cac-96d7-43b58c53b778",
     "prev": "97c8f96f-6f45-4d66-8064-6fc091070068",
     "regions": {
      "9e2296ce-3fce-4713-a093-0da51aaf0db7": {
       "attrs": {
        "height": 0.8,
        "width": 0.8,
        "x": 0.1,
        "y": 0.1
       },
       "content": {
        "cell": "b8672524-da19-4038-9396-584415c29a33",
        "part": "whole"
       },
       "id": "9e2296ce-3fce-4713-a093-0da51aaf0db7"
      }
     }
    },
    "e01c8c16-065a-40e9-9467-cbd241b2a133": {
     "id": "e01c8c16-065a-40e9-9467-cbd241b2a133",
     "prev": "516ce62c-55fd-44f1-b182-8db658b45c05",
     "regions": {
      "35721f06-8e1d-41f7-a582-9a674da526d8": {
       "attrs": {
        "height": 0.8,
        "width": 0.8,
        "x": 0.1,
        "y": 0.1
       },
       "content": {
        "cell": "02d99173-0900-4675-b5e6-1ee49ea908a0",
        "part": "whole"
       },
       "id": "35721f06-8e1d-41f7-a582-9a674da526d8"
      }
     }
    }
   },
   "themes": {}
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}