gpa-major-example.ipynb

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   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import pandas as pd, numpy as np, random as rdm\n",
      "from qgrid import show_grid"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "This code is designed to create a dataframe with some of the core components that we need.  "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "np.random.seed(1983)\n",
      "\n",
      "majors = ['polisci', 'geography', 'biology', 'engineering']\n",
      "\n",
      "allmajor = []\n",
      "\n",
      "i = 1\n",
      "while i < 1001: \n",
      "    j = rdm.choice(majors)\n",
      "    allmajor.append(j)\n",
      "    i = i + 1\n",
      "\n",
      "semesters = ['F10', 'S10', 'F11', 'S11', 'F12', 'S12', 'F13', 'S13']\n",
      "\n",
      "students = range(1, 126) * 8\n",
      "\n",
      "students.sort()\n",
      "\n",
      "credits = np.random.randint(12, 19, size = 1000)\n",
      "\n",
      "gpa = np.random.uniform(2.0, 4.0, size = 1000)\n",
      "\n",
      "df1 = pd.DataFrame(data = {'studentid' : students, \n",
      "                           'gpa' : gpa, \n",
      "                           'credits' : credits, \n",
      "                           'semester' : semesters * 125, \n",
      "                           'major' : allmajor\n",
      "                           })\n",
      "\n",
      "df1['cumcredits'] = df1.groupby('studentid')['credits'].cumsum()\n",
      "\n",
      "df1['weightedgpa'] = df1['gpa'] * df1['credits']\n",
      "\n",
      "# df1['cumgpa'] = df1.groupby('studentid')['gpa'].apply(pd.expanding_mean)  -- This gives a rolling mean; below incorporates weights\n",
      "\n",
      "df1['cumgpa'] = df1.groupby('studentid')['weightedgpa'].cumsum() / df1['cumcredits']\n",
      "\n",
      "del df1['weightedgpa'] \n",
      "\n",
      "show_grid(df1, remote_js = True)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
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[{\"field\": null, \"type\": \"Integer\"}, {\"field\": \"credits\", \"type\": \"Integer\"}, {\"field\": \"gpa\", \"type\": \"Float\"}, {\"field\": \"major\"}, {\"field\": \"semester\"}, {\"field\": \"studentid\", \"type\": \"Integer\"}, {\"field\": \"cumcredits\", \"type\": \"Integer\"}, {\"field\": \"cumgpa\", \"type\": \"Float\"}]);\n",
        "            grid.initialize_slick_grid();\n",
        "        });\n",
        "    });\n",
        "});"
       ],
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      }
     ],
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    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We now have a data frame similar to what you're working with in your project.  I've randomized the majors - no one in reality would change majors that much - but everything else makes some degree of sense.  \n",
      "\n",
      "You've got multiple options for working with your data.  Probably the simplest is to take the final semester the student was enrolled at TCU, and work with that.  In this data frame, the final semester is always Spring of 2013; your dataset is more complicated than that.  So, I'll show you how to handle that.  \n",
      "\n",
      "The issue here is that while there is a logical order to the semesters, it is not alphabetical.  As such, we'll need to do some wrangling to create a dictionary that we can use to indicate some sort of order.  "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pre = ['F', 'S']\n",
      "\n",
      "post = []\n",
      "\n",
      "for i in xrange(4, 14):\n",
      "    j = str(i).zfill(2)\n",
      "    post.append(j)\n",
      "\n",
      "sems = []\n",
      "\n",
      "for a in post:\n",
      "    for b in pre:\n",
      "        c = b + a\n",
      "        sems.append(c)\n",
      "\n",
      "order = range(1, len(sems) + 1)\n",
      "\n",
      "semdict = dict(zip(sems, order))\n",
      "\n",
      "print(semdict)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "{'S08': 10, 'S09': 12, 'S13': 20, 'S12': 18, 'S11': 16, 'S10': 14, 'S04': 2, 'S05': 4, 'S06': 6, 'S07': 8, 'F05': 3, 'F04': 1, 'F07': 7, 'F06': 5, 'F12': 17, 'F13': 19, 'F10': 13, 'F11': 15, 'F09': 11, 'F08': 9}\n"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Excellent, we now have a dictionary with appropriate ranks.  This can be used to create a new semester rank column in our data frame.  "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df1['semorder'] = df1['semester']\n",
      "\n",
      "df1['semorder'].replace(semdict, inplace = True)\n",
      "\n",
      "show_grid(df1, remote_js = True)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
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        "<div id='5c300250-2b6f-416f-907d-e4aa491e1619' class='q-grid'></div>\n",
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        "    jquery_drag: \"https://cdn.rawgit.com/quantopian/qgrid/6988858671b6f80c7d1987e07ebff3dc810d20ce/qgrid/qgridjs//lib/jquery.event.drag-2.2\",\n",
        "    slick_core: \"https://cdn.rawgit.com/quantopian/qgrid/6988858671b6f80c7d1987e07ebff3dc810d20ce/qgrid/qgridjs//lib/slick.core.2.2\",\n",
        "    slick_data_view: \"https://cdn.rawgit.com/quantopian/qgrid/6988858671b6f80c7d1987e07ebff3dc810d20ce/qgrid/qgridjs//lib/slick.dataview.2.2\",\n",
        "    slick_grid: \"https://cdn.rawgit.com/quantopian/qgrid/6988858671b6f80c7d1987e07ebff3dc810d20ce/qgrid/qgridjs//lib/slick.grid.2.2\",\n",
        "    data_grid: \"https://cdn.rawgit.com/quantopian/qgrid/6988858671b6f80c7d1987e07ebff3dc810d20ce/qgrid/qgridjs//qgrid\",\n",
        "    date_filter: \"https://cdn.rawgit.com/quantopian/qgrid/6988858671b6f80c7d1987e07ebff3dc810d20ce/qgrid/qgridjs//qgrid.datefilter\",\n",
        "    slider_filter: \"https://cdn.rawgit.com/quantopian/qgrid/6988858671b6f80c7d1987e07ebff3dc810d20ce/qgrid/qgridjs//qgrid.sliderfilter\",\n",
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        "    handlebars: \"https://cdnjs.cloudflare.com/ajax/libs/handlebars.js/2.0.0/handlebars.min\"\n",
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        "    path_dictionary['underscore'] = \"https://cdnjs.cloudflare.com/ajax/libs/underscore.js/1.7.0/underscore-min\";\n",
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        "if (!existing_config.paths['jqueryui']){\n",
        "    path_dictionary['jqueryui'] = \"https://ajax.googleapis.com/ajax/libs/jqueryui/1.11.1/jquery-ui.min\";\n",
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        "require.config({\n",
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        "\n",
        "if (typeof jQuery === 'function') {\n",
        "    define('jquery', function() { return jQuery; });\n",
        "}\n",
        "\n",
        "require([\n",
        "    'jquery',\n",
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        "    'slick_core',\n",
        "    'slick_data_view'\n",
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        "function($){\n",
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[{\"field\": null, \"type\": \"Integer\"}, {\"field\": \"credits\", \"type\": \"Integer\"}, {\"field\": \"gpa\", \"type\": \"Float\"}, {\"field\": \"major\"}, {\"field\": \"semester\"}, {\"field\": \"studentid\", \"type\": \"Integer\"}, {\"field\": \"cumcredits\", \"type\": \"Integer\"}, {\"field\": \"cumgpa\", \"type\": \"Float\"}, {\"field\": \"semorder\", \"type\": \"Integer\"}]);\n",
        "            grid.initialize_slick_grid();\n",
        "        });\n",
        "    });\n",
        "});"
       ],
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      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "From here, we can identify first and last semesters, etc. with `groupby` methods.  Let's say we only want the student's last semester at TCU.  In the above example, the order value will always be 20 but yours will be more complex.  So, instead we take the maximum value for each student.  "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "dfmax = df1.groupby('studentid', as_index = False).apply(lambda x: x[x['semorder'] == x['semorder'].max()])\n",
      "\n",
      "show_grid(dfmax, remote_js = True)\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
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        "    slick_core: \"https://cdn.rawgit.com/quantopian/qgrid/6988858671b6f80c7d1987e07ebff3dc810d20ce/qgrid/qgridjs//lib/slick.core.2.2\",\n",
        "    slick_data_view: \"https://cdn.rawgit.com/quantopian/qgrid/6988858671b6f80c7d1987e07ebff3dc810d20ce/qgrid/qgridjs//lib/slick.dataview.2.2\",\n",
        "    slick_grid: \"https://cdn.rawgit.com/quantopian/qgrid/6988858671b6f80c7d1987e07ebff3dc810d20ce/qgrid/qgridjs//lib/slick.grid.2.2\",\n",
        "    data_grid: \"https://cdn.rawgit.com/quantopian/qgrid/6988858671b6f80c7d1987e07ebff3dc810d20ce/qgrid/qgridjs//qgrid\",\n",
        "    date_filter: \"https://cdn.rawgit.com/quantopian/qgrid/6988858671b6f80c7d1987e07ebff3dc810d20ce/qgrid/qgridjs//qgrid.datefilter\",\n",
        "    slider_filter: \"https://cdn.rawgit.com/quantopian/qgrid/6988858671b6f80c7d1987e07ebff3dc810d20ce/qgrid/qgridjs//qgrid.sliderfilter\",\n",
        "    filter_base:  \"https://cdn.rawgit.com/quantopian/qgrid/6988858671b6f80c7d1987e07ebff3dc810d20ce/qgrid/qgridjs//qgrid.filterbase\",\n",
        "    handlebars: \"https://cdnjs.cloudflare.com/ajax/libs/handlebars.js/2.0.0/handlebars.min\"\n",
        "};\n",
        "\n",
        "var existing_config = require.s.contexts._.config;\n",
        "if (!existing_config.paths['underscore']){\n",
        "    path_dictionary['underscore'] = \"https://cdnjs.cloudflare.com/ajax/libs/underscore.js/1.7.0/underscore-min\";\n",
        "}\n",
        "\n",
        "if (!existing_config.paths['moment']){\n",
        "    path_dictionary['moment'] = \"https://cdnjs.cloudflare.com/ajax/libs/moment.js/2.8.3/moment.min\";\n",
        "}\n",
        "\n",
        "if (!existing_config.paths['jqueryui']){\n",
        "    path_dictionary['jqueryui'] = \"https://ajax.googleapis.com/ajax/libs/jqueryui/1.11.1/jquery-ui.min\";\n",
        "}\n",
        "\n",
        "require.config({\n",
        "    paths: path_dictionary\n",
        "});\n",
        "\n",
        "if (typeof jQuery === 'function') {\n",
        "    define('jquery', function() { return jQuery; });\n",
        "}\n",
        "\n",
        "require([\n",
        "    'jquery',\n",
        "    'jquery_drag',\n",
        "    'slick_core',\n",
        "    'slick_data_view'\n",
        "],\n",
        "function($){\n",
        "    $('#3bf0dbe6-d99d-4cfc-b1eb-c9ef16cc2a4f').closest('.rendered_html').removeClass('rendered_html');\n",
        "    require(['slick_grid'], function(){\n",
        "        require([\"data_grid\"], function(dgrid){\n",
        "            var grid = new dgrid.QGrid('#3bf0dbe6-d99d-4cfc-b1eb-c9ef16cc2a4f', 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[{\"field\": \"level_0\", \"type\": \"Integer\"}, {\"field\": \"level_1\", \"type\": \"Integer\"}, {\"field\": \"credits\", \"type\": \"Integer\"}, {\"field\": \"gpa\", \"type\": \"Float\"}, {\"field\": \"major\"}, {\"field\": \"semester\"}, {\"field\": \"studentid\", \"type\": \"Integer\"}, {\"field\": \"cumcredits\", \"type\": \"Integer\"}, {\"field\": \"cumgpa\", \"type\": \"Float\"}, {\"field\": \"semorder\", \"type\": \"Integer\"}]);\n",
        "            grid.initialize_slick_grid();\n",
        "        });\n",
        "    });\n",
        "});"
       ],
       "metadata": {},
       "output_type": "display_data"
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "I think it is useful to walk through the above code a bit.  We are creating a new data frame, `dfmax`, by grouping our primary data frame by `studentid`, but not setting the index as `studentid`. . . \n",
      "\n",
      "```python\n",
      "\n",
      "dfmax = df1.groupby('studentid', as_index = False)\n",
      "\n",
      "```\n",
      "\n",
      ". . . and then __apply__ an expression to our data using the __lambda__ operator, which allows us to specify an inline function. \n",
      "\n",
      "```python\n",
      "\n",
      ".apply(lambda x: x[x['semorder'] == x['semorder'].max()])\n",
      "\n",
      "```\n",
      "\n",
      "In this case, we index the data frame to take those rows where the `semorder` column is equal to the group maximum of that column.  \n",
      "\n",
      "From here, we can take a look at stuff like average GPA by major, etc.  Also, as each row now represents the final semester (and in turn final GPA) of each student, it can be merged cleanly with your other data.  "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df1.groupby('major')['cumgpa'].mean()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 6,
       "text": [
        "major\n",
        "biology        3.019779\n",
        "engineering    3.016776\n",
        "geography      3.043046\n",
        "polisci        3.042790\n",
        "Name: cumgpa, dtype: float64"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Reshaping your data\n",
      "\n",
      "It is also possible that you'd want a data frame where the rows represent individual students, not student-semesters.  To do this, you'll have to perform a common data wrangling task called _reshaping_.  \n",
      "\n",
      "Right now, your data are in \"long\" or \"tidy\" format, in which each row represents the smallest divisible unit in the table (the student-semester).  Given the structure of the data, this makes the most sense for representing all of the information in the data frame.  However, what we'll do next is reshape your data into \"wide\" format, in which semesters are columns and the values are the GPA for that semester.  In Pandas, this is accomplished with the `pivot_table` function.  "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "dfsub = df1[['gpa', 'semester', 'studentid']]\n",
      "\n",
      "dfwide = pd.pivot_table(df1, values = 'gpa', index = 'studentid', columns = 'semester').reset_index()\n",
      "\n",
      "show_grid(dfwide, remote_js = True)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
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        "            grid.initialize_slick_grid();\n",
        "        });\n",
        "    });\n",
        "});"
       ],
       "metadata": {},
       "output_type": "display_data"
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 7
    }
   ],
   "metadata": {}
  }
 ]
}