jvns/gpa_vs_team_size.ipynb

## gpa_vs_team_size.ipynb
{
 "metadata": {
  "name": "",
  "signature": "sha256:d7b2c4618b15555d6ac622c1be48c3ac236a425d66bfbe7767715888dc1febb2"
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 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import pandas as pd\n",
      "import numpy as np\n",
      "import matplotlib.pyplot as plt\n",
      "pd.set_option('display.mpl_style', 'default')\n",
      "%matplotlib inline"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df = pd.read_csv('GPA.txt')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# The GPAs"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df['GPA'].value_counts()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 3,
       "text": [
        "4.000000    1708\n",
        "0.000000     221\n",
        "3.000000      19\n",
        "1.000000      17\n",
        "2.000000      17\n",
        "3.333333       5\n",
        "3.500000       5\n",
        "3.800000       5\n",
        "2.137266       4\n",
        "0.768703       4\n",
        "2.039216       3\n",
        "3.446780       3\n",
        "3.400000       3\n",
        "3.666667       3\n",
        "2.666667       3\n",
        "...\n",
        "3.966339    1\n",
        "3.163170    1\n",
        "3.379522    1\n",
        "3.264674    1\n",
        "2.989474    1\n",
        "3.141395    1\n",
        "3.476923    1\n",
        "2.501581    1\n",
        "3.950600    1\n",
        "0.955029    1\n",
        "3.639726    1\n",
        "0.146689    1\n",
        "2.687596    1\n",
        "2.995737    1\n",
        "3.673895    1\n",
        "Length: 8153, dtype: int64"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "This tells us something about the underlying algorithm: 4.0 is *by far* the most common score, so this algorithm doesn't output a smooth score. The most common scores are the round numbers: 0.0, 1.0, 2.0, and 3.0. Interesting. (weird?)\n",
      "\n",
      "I'd be interested to know what's going on here.\n",
      "\n",
      "Before I start, how much data do we have available? Let's see!"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# Total data points, by author count"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "totals_by_author_count = df.groupby('AuthorCount').aggregate(lambda x: len(x))\n",
      "totals_by_author_count.plot(kind='bar', figsize=(13, 3))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 4,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x517e7d0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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Y1+7bl2x97b59pvS1+/YlW1+7b99g97X79iVbX7tvnwl9k2H7XC6XehLzfQAOHTqkxsZG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\nrkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x517e490>"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "So let's just look at author counts up to 10, say. (also, what's an author count of 0? Interesting.)"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "There are a ton of 4s, so this is obviously a score that this GPA algorithm thinks is important. Notice that as we go down, the percentage of 4s allocated decreases *dramatically*."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# Percentage of 4s, by author count"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fours_by_author_count = df.groupby('AuthorCount').aggregate(lambda x: float(np.count_nonzero(x['GPA'] == 4)) / len(x))\n",
      "fours_by_author_count[:11].plot(kind='bar', figsize=(13, 3))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 5,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x570ca90>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x59aac10>"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Let's look at the median!"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# Median score, by author count"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "medians_by_author_count = df.groupby('AuthorCount').aggregate(lambda x: np.median(x['GPA']))\n",
      "medians_by_author_count[:11].plot(kind='bar', figsize=(13, 3))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 6,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x5bb25d0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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Tk7766quQjp8wYYJSU1Nls9k0dOhQtbW19ej9AQCdcUUfAGJs+/bt+t3vfue/\nWn3PPfdENeh+/zmJhmGEtKY/OztbDQ0NnZ4YKZ29Qt/b83ZVEwAgfFzRB4AY+uyzz5SamtphSUpe\nXp527Nghh8Oh5uZmSdKJEyfU1NTU6Xi73a4TJ05ICi0sp6amatiwYf5vy2lqalJ9fb1Gjx4d9Ngr\nrrhC27ZtU0tLiyTpzJkzOn78uCRp9OjRqq+v17FjxyRJbrdbw4cPV3JysiTJ4XDI6/VKkurr64O+\n1/f1tE8AAFf0ASCm3G638vPzO+zLz8/Xiy++qNtuu02PPvqoNmzYoPT0dJ133nmdjr/yyiv1hz/8\nQUOHDtXkyZNVWFgoqeMa+O9fVb/zzjv11FNP6YUXXpDP59Odd94ph8MRcPx3RowYoblz5+rhhx9W\nYuLZj4+f/exnmjJlijIyMnT77bdr9erVstlsSktL04IFC/zHzpw5U88++6zeffdd5eTkBF2zH2qf\nAIDAbAaXRgAAAADTYekOAAAAYEIEfQAAAMCECPoAAACACRH0AQAAABMi6AMAAAAmRNAHAAAATOj/\nAbWG/HsdjoCUAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x5bbc7d0>"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "This is pretty interesting. It's quite different from the \"percentage of 4s\" graph -- it's much much flatter. The highest median GPA is about 3.4, going down to 2.7 or so.\n",
      "\n",
      "I wonder what happens if we find the median GPA, excluding all the 4s?"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# Let's ignore GPAs of 4 entirely."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "medians_by_author_count_without_4 = df.groupby('AuthorCount').aggregate(lambda x: np.median(x['GPA'][x['GPA'] != 4]))\n",
      "medians_by_author_count_without_4[:11].plot(kind='bar', figsize=(13, 3))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 7,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x5bd1510>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x5e8b810>"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "This distribution is super different! It's hard to tell from the graph, but the maximum is actually at 4 authors, and then it goes down around 9 or 10. So it seems like the author count influences whether your GPA is 4 or not really strongly, but if your GPA is smaller than 4 it doesn't have as strong of an effect."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Let's look more closely at the GPA distributions for a few different author counts:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def draw_hist(sub_df, ax=None, title=None):\n",
      "    gpa_col = sub_df['GPA']\n",
      "    ax.set_title(title)\n",
      "    gpa_col[gpa_col != 4].hist(bins=20, figsize= (20, 10), ax=ax)\n",
      "_, axes = plt.subplots(3,2)\n",
      "draw_hist(df[df.AuthorCount == 1], ax=axes[0][0], title=\"Author count 1\")\n",
      "draw_hist(df[df.AuthorCount == 2], ax=axes[1][0], title=\"Author count 2\")\n",
      "draw_hist(df[df.AuthorCount == 3], ax=axes[2][0], title=\"Author count 3\")\n",
      "draw_hist(df[df.AuthorCount == 4], ax=axes[0][1], title=\"Author count 4\")\n",
      "draw_hist(df[df.AuthorCount == 5], ax=axes[1][1], title=\"Author count 5\")\n",
      "draw_hist(df[df.AuthorCount == 6], ax=axes[2][1], title=\"Author count 6\")\n",
      "plt.tight_layout()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x5e88e90>"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Hmm. I can't draw any really good conclusions from this. Let's try plotting a few GPA percentiles, across numbers of authors:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def get_percentile(p):\n",
      "    series = df.groupby('AuthorCount').aggregate(lambda x: np.percentile(x['GPA'][x['GPA'] != 4], p))[:11]\n",
      "    series.columns = [str(p)]\n",
      "    return series\n",
      "ax = plt.axes()\n",
      "get_percentile(90).plot(kind='line', figsize=(13, 3), ax=ax)\n",
      "get_percentile(80).plot(kind='line', figsize=(13, 3), ax=ax)\n",
      "get_percentile(70).plot(kind='line', figsize=(13, 3), ax=ax)\n",
      "get_percentile(60).plot(kind='line', figsize=(13, 3), ax=ax)\n",
      "get_percentile(50).plot(kind='line', figsize=(13, 3), ax=ax)\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 9,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x69c7fd0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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0uo0u9G1FHjAcN+l+jTeGNE2DHI7qjsC1fuPYZzoHKaNOkxXYzdkDY9Yg59xW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\nAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x5709dd0>"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "These all look pretty flat to me! Certainly the highest median GPA is at 4, but the 90th percentile seems very very flat across different numbers of authors."
     ]
    }
   ],
   "metadata": {}
  }
 ]
}