Grade_Analysis/Student Achievement.ipynb

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   "cell_type": "code",
   "execution_count": 3,
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   "source": [
    "%matplotlib inline \n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib\n",
    "matplotlib.style.use('ggplot')\n",
    "import math\n",
    "import hashlib"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "bf43ff83-d0d3-45f5-a35c-aad87d00816e"
    }
   },
   "source": [
    "## Read in Data and Split into months"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_math_sept = pd.read_excel('Data/StATActivity-Data&Analysis.xlsx', sheetname=1)\n",
    "df_math_dec = pd.read_excel('Data/StATActivity-Data&Analysis.xlsx', sheetname=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "f76a7066-731a-4b39-81e3-7085bdac5ca9"
    }
   },
   "source": [
    "## Anonymizing Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "array([-1028273951365604154, -1633040034837318058,   784310327575974077])"
      ]
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     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_math_sept['First Name'] = [hash(val)for val in df_math_sept['First Name']]\n",
    "df_math_sept['Class'] = [hash(val) for val in df_math_sept['Class']]\n",
    "df_math_sept['Class'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-5639959969792992056, -8550569062478399911,   784310327575974077])"
      ]
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     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_math_dec['FirstName'] = [hash(val)for val in df_math_dec['FirstName']]\n",
    "df_math_dec['Class'] = [hash(val) for val in df_math_dec['Class']]\n",
    "df_math_dec['Class'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false,
    "nbpresent": {
     "id": "38687dc7-72be-471b-85c1-f2d4345cae18"
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   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Student ID</th>\n",
       "      <th>First Name</th>\n",
       "      <th>Class</th>\n",
       "      <th>Category</th>\n",
       "      <th>% Correct</th>\n",
       "      <th>Proficiency Level:</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>102090</td>\n",
       "      <td>1f7bf9ce6d583e92ead7003090c52263</td>\n",
       "      <td>8eadc5d3e1f1919c527b6d865e226a88</td>\n",
       "      <td>[Sep 10-11] Gr1 IA Math 10-11</td>\n",
       "      <td>49.1</td>\n",
       "      <td>Below</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>101906</td>\n",
       "      <td>0f24b4817e0eb462de94ddfbc47a332e</td>\n",
       "      <td>c8b287075ce4f11c834d2a0ada967ddc</td>\n",
       "      <td>[Sep 10-11] Gr1 IA Math 10-11</td>\n",
       "      <td>43.5</td>\n",
       "      <td>Below</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>101982</td>\n",
       "      <td>fad5df872a99d10a80fc2337d1ea275b</td>\n",
       "      <td>43a9616a147c80fbefdfe2dbc988896c</td>\n",
       "      <td>[Sep 10-11] Gr1 IA Math 10-11</td>\n",
       "      <td>68.2</td>\n",
       "      <td>Approaching</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>101947</td>\n",
       "      <td>f2b4ffb1fce6be175baac966150f9390</td>\n",
       "      <td>8eadc5d3e1f1919c527b6d865e226a88</td>\n",
       "      <td>[Sep 10-11] Gr1 IA Math 10-11</td>\n",
       "      <td>47.8</td>\n",
       "      <td>Below</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>101898</td>\n",
       "      <td>a08372b70196c21a9229cf04db6b7ceb</td>\n",
       "      <td>c8b287075ce4f11c834d2a0ada967ddc</td>\n",
       "      <td>[Sep 10-11] Gr1 IA Math 10-11</td>\n",
       "      <td>37.4</td>\n",
       "      <td>Below</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Student ID                        First Name  \\\n",
       "0      102090  1f7bf9ce6d583e92ead7003090c52263   \n",
       "1      101906  0f24b4817e0eb462de94ddfbc47a332e   \n",
       "2      101982  fad5df872a99d10a80fc2337d1ea275b   \n",
       "3      101947  f2b4ffb1fce6be175baac966150f9390   \n",
       "4      101898  a08372b70196c21a9229cf04db6b7ceb   \n",
       "\n",
       "                              Class                       Category  % Correct  \\\n",
       "0  8eadc5d3e1f1919c527b6d865e226a88  [Sep 10-11] Gr1 IA Math 10-11       49.1   \n",
       "1  c8b287075ce4f11c834d2a0ada967ddc  [Sep 10-11] Gr1 IA Math 10-11       43.5   \n",
       "2  43a9616a147c80fbefdfe2dbc988896c  [Sep 10-11] Gr1 IA Math 10-11       68.2   \n",
       "3  8eadc5d3e1f1919c527b6d865e226a88  [Sep 10-11] Gr1 IA Math 10-11       47.8   \n",
       "4  c8b287075ce4f11c834d2a0ada967ddc  [Sep 10-11] Gr1 IA Math 10-11       37.4   \n",
       "\n",
       "  Proficiency Level:  \n",
       "0              Below  \n",
       "1              Below  \n",
       "2        Approaching  \n",
       "3              Below  \n",
       "4              Below  "
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_math_sept['First Name'] = [hashlib.md5(val.encode('utf-8')).hexdigest() for val in df_math_sept['First Name']]\n",
    "df_math_dec['FirstName'] = [hashlib.md5(val.encode('utf-8')).hexdigest() for val in df_math_dec['FirstName']]\n",
    "df_math_sept['Class'] = [hashlib.md5(val.encode('utf-8')).hexdigest() for val in df_math_sept['Class']]\n",
    "df_math_dec['Class'] = [hashlib.md5(val.encode('utf-8')).hexdigest() for val in df_math_dec['Class']]\n",
    "df_math_sept.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false,
    "nbpresent": {
     "id": "a1a4d9c0-fb89-42df-aceb-15bd5f74fee7"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Student ID</th>\n",
       "      <th>FirstName</th>\n",
       "      <th>Class</th>\n",
       "      <th>Category</th>\n",
       "      <th>% Correct</th>\n",
       "      <th>Proficiency Level:</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>101932</td>\n",
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       "      <td>8eadc5d3e1f1919c527b6d865e226a88</td>\n",
       "      <td>[Dec 10-11] Gr1 IA Math 10-11</td>\n",
       "      <td>48.5</td>\n",
       "      <td>Below</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>101927</td>\n",
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       "      <td>8eadc5d3e1f1919c527b6d865e226a88</td>\n",
       "      <td>[Dec 10-11] Gr1 IA Math 10-11</td>\n",
       "      <td>64.5</td>\n",
       "      <td>Below</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
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       "      <td>c8b287075ce4f11c834d2a0ada967ddc</td>\n",
       "      <td>[Dec 10-11] Gr1 IA Math 10-11</td>\n",
       "      <td>37.5</td>\n",
       "      <td>Below</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>103234</td>\n",
       "      <td>c98387889a60cef1feb72222ee635d27</td>\n",
       "      <td>8eadc5d3e1f1919c527b6d865e226a88</td>\n",
       "      <td>[Dec 10-11] Gr1 IA Math 10-11</td>\n",
       "      <td>50.6</td>\n",
       "      <td>Below</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>101955</td>\n",
       "      <td>07e556728e29ff6dc6bf0042aaead2c5</td>\n",
       "      <td>c8b287075ce4f11c834d2a0ada967ddc</td>\n",
       "      <td>[Dec 10-11] Gr1 IA Math 10-11</td>\n",
       "      <td>40.3</td>\n",
       "      <td>Below</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Student ID                         FirstName  \\\n",
       "0      101932  88068e33c78eb72f1b371c7110846085   \n",
       "1      101927  8af7fdc6cb5f743b599a3e03b4461e93   \n",
       "2      101908  71f22cde790e93efe5d7cb77bbc8eae1   \n",
       "3      103234  c98387889a60cef1feb72222ee635d27   \n",
       "4      101955  07e556728e29ff6dc6bf0042aaead2c5   \n",
       "\n",
       "                              Class                       Category  % Correct  \\\n",
       "0  8eadc5d3e1f1919c527b6d865e226a88  [Dec 10-11] Gr1 IA Math 10-11       48.5   \n",
       "1  8eadc5d3e1f1919c527b6d865e226a88  [Dec 10-11] Gr1 IA Math 10-11       64.5   \n",
       "2  c8b287075ce4f11c834d2a0ada967ddc  [Dec 10-11] Gr1 IA Math 10-11       37.5   \n",
       "3  8eadc5d3e1f1919c527b6d865e226a88  [Dec 10-11] Gr1 IA Math 10-11       50.6   \n",
       "4  c8b287075ce4f11c834d2a0ada967ddc  [Dec 10-11] Gr1 IA Math 10-11       40.3   \n",
       "\n",
       "  Proficiency Level:  \n",
       "0              Below  \n",
       "1              Below  \n",
       "2              Below  \n",
       "3              Below  \n",
       "4              Below  "
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_math_dec.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# determine hashed Class Names\n",
    "A,B,C = df_math_sept['Class'].unique()\n",
    "D,E,F = df_math_dec['Class'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "assert A == D\n",
    "assert B == E\n",
    "assert C == F"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "3655c50c-d5bc-4f6f-8690-c8c4bd19f219"
    }
   },
   "source": [
    "### Plot Scores from September and December in Histogram Form"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false,
    "nbpresent": {
     "id": "5a480643-c2b4-48e6-8730-e6b562d384c4"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1007c6d30>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11347d0f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "df_math_sept['% Correct'].hist(bins=20, alpha=0.8, label='Sept.')\n",
    "df_math_dec['% Correct'].hist(bins=20, alpha=0.8, label='Dec.')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "3d49effb-edc4-4e32-97f5-71edceffad2b"
    }
   },
   "source": [
    "Noticable shift from Sept -> Dec"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false,
    "nbpresent": {
     "id": "f2a11858-89c9-4a7c-ac0c-926c2b1a9041"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Change in\n",
      "Mean: 11.81\n",
      "Median: 12.15\n",
      "Range: -6.6\n"
     ]
    }
   ],
   "source": [
    "sept_attr = (df_math_sept['% Correct'].mean(), df_math_sept['% Correct'].median(),max(df_math_sept['% Correct'])-min(df_math_sept['% Correct']))\n",
    "dec_attr = (df_math_dec['% Correct'].mean(), df_math_dec['% Correct'].median(),max(df_math_dec['% Correct'])-min(df_math_dec['% Correct']))\n",
    "\n",
    "print('Change in\\nMean: {}\\nMedian: {}\\nRange: {}'.format(*[round(x-y,2) for x, y in zip(dec_attr,sept_attr)]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "3fc3762c-ed08-49e4-8d82-7a3e8e18ee4b"
    }
   },
   "source": [
    "## Use Student ID and Work with Entire Grade"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false,
    "nbpresent": {
     "id": "4663ef62-1927-416a-8b7d-31f811ddbbae"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sept % Correct</th>\n",
       "      <th>Dec % Correct</th>\n",
       "      <th>% Change</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Student ID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>101986</th>\n",
       "      <td>28.7</td>\n",
       "      <td>95.7</td>\n",
       "      <td>67.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101898</th>\n",
       "      <td>37.4</td>\n",
       "      <td>95.7</td>\n",
       "      <td>58.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103167</th>\n",
       "      <td>40.1</td>\n",
       "      <td>95.7</td>\n",
       "      <td>55.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101938</th>\n",
       "      <td>45.5</td>\n",
       "      <td>95.7</td>\n",
       "      <td>50.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101914</th>\n",
       "      <td>33.0</td>\n",
       "      <td>78.2</td>\n",
       "      <td>45.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Sept % Correct  Dec % Correct  % Change\n",
       "Student ID                                         \n",
       "101986                28.7           95.7      67.0\n",
       "101898                37.4           95.7      58.3\n",
       "103167                40.1           95.7      55.6\n",
       "101938                45.5           95.7      50.2\n",
       "101914                33.0           78.2      45.2"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sept_grades = df_math_sept[['Student ID','% Correct']].set_index('Student ID')\n",
    "sept_grades.rename(columns={'% Correct': 'Sept % Correct'}, inplace=True)\n",
    "dec_grades = df_math_dec[['Student ID', '% Correct']].set_index('Student ID')\n",
    "dec_grades.rename(columns={'% Correct': 'Dec % Correct'}, inplace=True)\n",
    "grades = sept_grades.join(dec_grades, how='left')\n",
    "grades['% Change'] = grades['Dec % Correct'] - grades['Sept % Correct']\n",
    "grades.sort_values('% Change', ascending=False, axis=0, inplace=True);\n",
    "grades.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sept % Correct    53.919444\n",
       "Dec % Correct     65.729167\n",
       "% Change          11.809722\n",
       "dtype: float64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grades.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false,
    "nbpresent": {
     "id": "c692bbd3-6db1-40da-8fde-d4086c9b729b"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11347d240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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yyjEoTp8+bfC+Kq+5a9euVXt3BQClSFDTnRbVqcxl6nWpUqkQHh6OZcuWISsrC7m5uVi9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\nVQoYrC7XDbMap8KAKnil6ir3tqguWU3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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1138600b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "ax=grades.plot(kind='hist', subplots=True, alpha=0.8, bins = 20, legend=False,\\\n",
    "            layout=(1,3),figsize=(15,4),title=['Sept. % Correct', 'Dec. % Correct', 'Difference'])\n",
    "plt.suptitle('Grade 1 Math Scores',fontsize=24,y=1.11)\n",
    "for i in range(len(ax[0])):\n",
    "    ax[0][i].set_xlabel('Score')\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "77a51ed0-868a-480e-bfae-fd2872c45374"
    }
   },
   "source": [
    "### Split into classes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": false,
    "nbpresent": {
     "id": "96842dda-2d86-4e83-abc0-c50e11b326a5"
    }
   },
   "outputs": [],
   "source": [
    "A_math = df_math_sept.set_index('Student ID').join(df_math_dec.set_index('Student ID'), how='left', rsuffix='_Dec')\n",
    "C_math = A_math[['Class','First Name','% Correct','% Correct_Dec']][A_math['Class']== C]\n",
    "B_math = A_math[A_math[['Class','First Name','% Correct','% Correct_Dec']]['Class']== B]\n",
    "A_math = A_math[A_math[['Class','First Name','% Correct','% Correct_Dec']]['Class']== A]\n",
    "\n",
    "C_math['Diff'] = C_math['% Correct_Dec'] - C_math['% Correct']\n",
    "B_math['Diff'] = B_math['% Correct_Dec'] - B_math['% Correct']\n",
    "A_math['Diff'] = A_math['% Correct_Dec'] - A_math['% Correct']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": false,
    "nbpresent": {
     "id": "e89d6f94-7450-4235-a7f5-df4eb95e128e"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "B improved by 10.2%\n",
      "A improved by 16.8%\n",
      "C improved by 8.5%\n"
     ]
    }
   ],
   "source": [
    "class_improvement = {'C':C_math.Diff.mean(),'B': B_math.Diff.mean(),\\\n",
    "                     'A':A_math.Diff.mean()}\n",
    "for k,v in class_improvement.items():\n",
    "    print('{} improved by {}%'.format(k,round(v,1)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "% Correct        54.65\n",
       "% Correct_Dec    69.40\n",
       "Diff             16.95\n",
       "dtype: float64"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A_math.median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "% Correct        53.5\n",
       "% Correct_Dec    63.4\n",
       "Diff             12.1\n",
       "dtype: float64"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "B_math.median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "% Correct        47.80\n",
       "% Correct_Dec    63.20\n",
       "Diff              9.25\n",
       "dtype: float64"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "C_math.median()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "8ee7d535-9874-48f6-8ff5-9679027310c6"
    }
   },
   "source": [
    "### Plot each class histogram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": false,
    "nbpresent": {
     "id": "cb60b12c-d2aa-41c4-b4ce-303ef9d5a803"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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RwWuvvYbXX3/d+rxOp0NQUJDLv16TiOwxs0Seg3klcl81NsgREREAgEWLFmlS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\nNshERERERDbYIBMRERER2fg/Z1qJByQQM64AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1151e2b70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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SpBgiahpmlsh9MK9ErkvRXSzKysrwxRdf4Pjx46iqqrKbZvtbLxG5BmaWyH0w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\ngw0yEREREZENNshERERERDb+P2N1thxgtj0gAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x114cb75f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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zRK6DeSXSriob5AULFkBE8Prrr+ONN96wPq/T6eDn5wdPT0+nF0hEyjGzRK6D\neSXSriob5ODgYADAokWLVCmGiOqGmSVyHcwrkXYpuotFQUEB1q1bh+PHj6O4uNhumu1vvUSkDcws\nketgXom0R1GDPHfuXJjNZtx+++38kw+RC2BmiVwH80qkPYoa5PT0dCxduhQeHh7OroeIHICZJXId\nzCuR9ii6D3LTpk2Rk5Pj7FqIyEGYWSLXwbwSaY+iM8i33nor3n77bfTr1w8BAQF20/r3769ooOef\nfx7e3t5wc3ODu7s7EhMTa14tESlS18wyr0Tq4TGWSHsUNciHDh1CUFAQ9u3bV26a0vACQEJCAvz8\n/JRXR0S14ojMMq9E6uAxlkh7FDXICQkJzq6DiByImSVyHcwrkfYoapABID8/H7t370ZeXh7uv/9+\n5ObmQkQQFBSkeLBr3yt/5513IjY2tubVEpFidc0s80qkHh5jibRFJ7Zf/l6JAwcOYPbs2YiKikJa\nWhpWrFiBAwcOYN26dYiPj1c0UG5uLgwGAy5evIgZM2Zg1KhRuOWWW+zmMZlMMJlMAIDExESUlpZW\nujy9Xg+z2axobGfTUi3LPGJQ3Us62rxdlVq0tF1cqRZH3Oaprpl1dF4B13oNbrQ6AMfVcrv+9iqn\nb1ew/3G17VLXzKpxjK1pXtWipdf6ka8eqfb4ufqx1arUoqXtcr3VojSvis4gL1++HJMmTUK7du0w\natQoAEDLli1x5MgRxQUZDAYAgL+/P7p27YqMjIxyB9zY2Fi733qzs7MrXZ7RaKxyupq0VIs0lmrf\nPGrVqqXt4kq1hIWF1XmMumbW0XkFXOs1uNHqABxXi9lY9/2Pq22XumZWjWNsTfOqFi291iI8flbk\neqtFaV4V3eYtKysL7dq1s3tOr9ejrKxM0SDFxcW4fPmy9f979+5F06ZNFf0sEdVcXTLLvBKpi8dY\nIu1RdAY5IiICe/bsQceOHa3P7du3T3EAL168iPfeew8AUFZWhl69etkti4gcqy6ZZV6J1MVjLJH2\nKGqQhw8fjnfeeQe33XYbSktL8fHHH2Pnzp2YOnWqokFCQ0Mxa9asOhVKRMrVJbPMK5G6eIwl0h5F\nDXKrVq0wa9Ys/Pzzz/D29obRaMTbb79do0/XEpF6mFki18G8EmmP4tu8GQwGPPDAA86shYgciJkl\nch3MK5G2VNogz58/HzqdrtoFjB8/3qEFEVHtMLNEroN5JdK2Su9i0bhxY4SGhiI0NBS+vr5ISUmB\nxWKBwWAqMeSSAAAbfElEQVSAxWJBSkoKfH191ayViKrAzBK5DuaVSNsqPYP8yCOPWP//1ltvIT4+\nHm3atLE+d+jQIXzzzTfOrY6IFGNmiVwH80qkbYrug5yeno7o6Gi751q2bIn09HSnFEVEdcPMErkO\n5pVIexQ1yJGRkfjiiy+sX01ZWlqKL7/8Es2bN3dmbURUS8wsketgXom0R9FdLMaNG4d58+ZhxIgR\naNiwIQoKCtCiRQtMnDjR2fURUS0ws0Sug3kl0h5FDXJISAhmzJiB7OxsXLhwAYGBgTAajc6ujYhq\niZklch3MK5H2KGqQLRYLgKv3aTQYDHbPubkpukqDiFTEzBK5DuaVSHsUNciPP/54pdO++uorhxVD\nRI7BzBK5DuaVSHsUNcgLFiywe3zhwgWsXbsWXbp0cUpRRFQ3zCyR62BeibRH0d9ugoOD7f61atUK\n48ePx3fffVejwSwWC6ZNm4bExMRaFUtEyjgis8wrkTp4jCXSnlpf3FRUVIRLly7V6Ge+//57hIeH\n13ZIIqqDmmaWeSWqPzzGEtUvRZdY/P0740tKSnDw4EH07t1b8UA5OTnYtWsXBg8ejA0bNtS8UiJS\nrK6ZZV6J1MNjLJH2KGqQGzdubPfYy8sLd955J9q3b694oOXLl+OJJ57A5cuXK53HZDLBZDIBABIT\nE6u8zY1er9fMbXDUquVT/e3VzqPT6aDXV/2yrmt8X5XTR5u316iuyijZLkrWqTpK6r3R3i91zayj\n8wrceK+BK9UBADEeMZDGUuU82xVmrSpK1ldL28UV8gpUn9ma5lUtWnqtlRw/ldT68JcP17mWtU+s\n1cx20dJrpGYtihrkjh07lvsaTADIyMhAy5Ytq/35nTt3wt/fH1FRUUhNTa10vtjYWMTGxlofZ2dn\nVzqv0Wiscrqa1KrFbDRXO49er4fZXP18VXHUuijZLkrWqTpK6nWl90tYWFidx6hLZp2RV8C1XoMb\nrQ4AkMZS7b5DSa3VZfp6yytQ98yqcYytaV7VoqXXWsRBGajjMfjaMrSyXbT0GjmiFqV5VXQN8owZ\nMyp8/q233lI0SFpaGnbs2IHnn38ec+bMwf79+zFv3jxFP0tENVeXzDKvROriMZZIe6o8g3ztRuUi\nYv13zfnz5+Hu7q5okKFDh2Lo0KEAgNTUVKxfv55foUnkBI7ILPNKpA4eY4m0q8oG2fbm5Y899pjd\nNDc3Nzz44IPOqYqIaoWZJXIdzCuRdlXZIC9YsAAigtdffx1vvPGG9XmdTgc/Pz94enrWeMC2bdui\nbdu2Na+UiKrl6Mwyr0TOw2MskXZV2SAHBwcDABYtWmT3fEFBQa2CS0TOxcwSuQ7mlUi7qmyQt27d\nCn9/f3Ts2BEAcOTIEbz33nvIzc1F48aNERcX55BP3BORYzCzRK6DeSXSrirvYrF+/XoEBARYH3/8\n8cdo164d3nvvPbRr1w5JSUlOL5CIlGNmiVwH80qkXVU2yDk5OWjatCmAq/f+O3HiBJ588kk0adIE\nw4YNQ0ZGhipFEpEyzCyR62BeibSrygbZzc3NesPr9PR0hIWFoWHDhgCuftNPaWmp8yskIsWYWSLX\nwbwSaVeVDfItt9yCL7/8EsePH8cPP/yAzp07W6edPn3a7k9DRFT/mFki18G8EmlXlQ3yqFGjcOzY\nMbz66qvw8vLCoEGDrNO2bduGDh06OL1AIlKOmSVyHcwrkXZVeRcLg8GAhISECqcNGzbMKQURUe0x\ns0Sug3kl0q4qzyATEREREd1o2CATEREREdlgg0xEREREZKPSa5CTkpIwfPhwAMD+/ftx66231nqQ\n0tJSJCQkwGw2o6ysDD169MCQIUNqvTwiKs9RmWVeiZyPx1gibav0DLLJZLL+f9asWXUaxMPDAwkJ\nCZg1axbeffdd7NmzB+np6XVaJhHZc1RmmVci5+MxlkjbKj2D3Lx5c8yePRsRERG4cuUKvvrqqwrn\ne/TRR6sdRKfTwdvbGwBQVlaGsrIy6HS6WpZMRBVxVGaZVyLn4zGWSNsqbZBffPFFmEwmZGVlQUSQ\nk5NTp4EsFgvi4uJw7tw5/POf/0R0dHSdlkdE9hyZWeaVyLl4jCXStkobZH9/fzz00EMArgZv3Lhx\ndRrIzc0Ns2bNQmFhId577z2cOHHC+h3015hMJuufnRITE2E0GisvXK+vcrqa1KpFr6/yttUArp5J\nUDJfVRy1Lkq2S11rBZTVeyO8XxyZWUfnFbgxXoP6quN2/e11XoaSfYfSrDliGVp4fQDXyCtQfWZr\nmle1aOm1VisDSmhpu9yotSh6FceNG4eCggLs3LkTubm5MBgM6Ny5s/U742uiQYMGaNu2Lfbs2VPu\ngBsbG4vY2Fjr4+zs7EqXYzQaq5yuJrVqMRvN1c6j1+thNlc/X1UctS5KtouSdaqOknpd6f0SFhZW\n5zEclVlH5RVwrdfA1epwRI6U7DuU1FpdLddbXoG6Z1aNY2xN86oWLb3WIuKYDNTxGHxtGVrZLlp6\njRxRi9K8KrrNW3p6OiZMmIAff/wRx48fh8lkwoQJExR/CODSpUsoLCwEcPXTtnv37kV4eLiinyWi\nmqtLZplXInXxGEukPYrOIC9fvhxPP/00evbsaX0uOTkZy5Ytw8yZM6v9+QsXLmDhwoWwWCwQEdx+\n++3o3Llz7asmoirVJbPMK5G6eIwl0h5FDfLZs2dx++3217j16NEDS5YsUTRIs2bN8O6779a8OiKq\nlbpklnklUhePsUTao+gSi8aNGyM5Odnuue3btyM0NNQpRRFR3TCzRK6DeSXSHkVnkEeOHInExET8\n8MMPMBqNyMrKwtmzZxEfH+/s+oioFphZItfBvBJpj6IG+eabb8b8+fOxa9cuXLhwAZ07d0anTp1q\n9QlbInI+ZpbIdTCvRNqj+GZ9DRs2RJ8+fZxZCxE5EDNL5DqYVyJtUXQNMhERERHRjYINMhERERGR\nDTbIREREREQ2FDfIWVlZzqyDiByMmSVyHcwrkbYobpCnTZsGAPj++++dVgwROQ4zS+Q6mFcibany\nLhZxcXGIiopCZGQkLBYLAODrr7/GwIEDVSmOiGqGmSVyHcwrkXZVeQZ58uTJ6NChA7KyslBaWoq4\nuDiYzWbs378fRUVFatVIRAoxs0Sug3kl0q4qG2SLxYIePXpg2LBh8Pb2xtSpUyEi+N///V9MnToV\nEydOVKtOIlKAmSVyHcwrkXZVeYnFvHnzkJ2djYiICFy5cgWFhYXw8PDAlClTAAAFBQWKBsnOzsbC\nhQuRl5cHnU6H2NhY/gmJyAkckVnmlUgdPMYSaVeVDfLbb7+NsrIynDhxAq+99ho+/fRTFBcXY8mS\nJYiMjERUVJSir8J0d3fH8OHDERUVhcuXLyM+Ph7t27dHRESEw1aEiByTWeaVSB08xhJpV7V3sXB3\nd0dkZCT0ej3eeOMNeHl5oW3btjh37hw+//xzRYMEBgYiKioKAODj44Pw8HDk5ubWrXIiqlBdM8u8\nEqmHx1gibaryDLKtESNGAAB0Oh1iYmIQExNTqwEzMzNx7NgxtGzZslY/T0TKOCKzzCuROniMJdIW\nnYhITX6goKBA0Z98KlJcXIyEhAQMHjwY3bt3LzfdZDLBZDIBABITE1FaWlrpsvR6Pcxmc63qcDS1\navlUf3u18+h0OtTwJS1ntHm7Q2p5FinVbhcly6mOknpd6f3i6enp0PFqm1lH5hVwrddALTEeMXXO\nq6M4Yt+hxHYFeVWyXZQsxxGUvFccmVlnHWNrmle1OCqLD3/5cJXTVz+2utplPPLVI9W+75Qsp7pa\nlHBEHpXUqoRW9peAY2pRmlfFZ5CvqW1wzWYzZs+ejd69e1d4sAWA2NhYxMbGWh9nZ2dXujyj0Vjl\ndDWpVYvZWP2bwhFvHiXroqQWM8zVLkvJcqqjpF5Xer+EhYU5dLzaZNbReQVc6zVQizSW6+rAo4SS\n7a5ku6j1+il5rzgys846xtY0r2pxVBYd8X4Rccz7zhE5UutYroRW9peAY2pRmlfF36RXFyKCxYsX\nIzw8HPfee68aQxJRLTGvRK6FmSVyvBqfQa6NtLQ0bNu2DU2bNsXUqVMBAI8//jg6deqkxvBEVAPM\nK5FrYWaJHE+VBrl169ZYtWqVGkMRUR0xr0SuhZklcjxVLrEgIiIiInIVbJCJiIiIiGywQSYiIiIi\nssEGmYiIiIjIBhtkIiIiIiIbbJCJiIiIiGywQSYiIiIissEGmYiIiIjIBhtkIiIiIiIbbJCJiIiI\niGywQSYiIiIisqFXY5BFixZh165d8Pf3x+zZs9UYkojqgJklch3MK5HjqXIGuV+/fnj55ZfVGIqI\nHICZJXIdzCuR46nSIN9yyy1o2LChGkMRkQMws0Sug3klcjxeg0xEREREZEOVa5CVMplMMJlMAIDE\nxEQYjcZK59Xr9VVOV5Natej11b9cOp1O0XxVUbIuSsbQo/rtUtdaAWBd4/uqnedZpNxw7xdnq0le\nAW2tt1ZqcUReHUWtWpRsdyW1OOL1u11/e7XzpGho31EXNc2rWh756hGISJXzrH5sdbXLqe79MsY0\nptplKHnfKVmOI3Kk1rFcCa3sLwF1a9HGnvn/i42NRWxsrPVxdnZ2pfMajcYqp6tJrVrMRnO18+j1\nepjN1c9XFSXroqQWM8zVLkvJchxBSS1qqe79EhYWpmI1tVeTvAI3ZmarI42lznl1FEfsO5RQst2V\nbBdHvH6O2o+5QmZrmle1iDjmtXbEe1etDCih1rFcCa3sLwHH1KI0r7zEgoiIiIjIhipnkOfMmYMD\nBw4gPz8fY8aMwZAhQ9C/f381hiaiWmBmiVwH80rkeKo0yJMmTVJjGCJyEGaWyHUwr0SOx0ssiIiI\niIhssEEmIiIiIrLBBpmIiIiIyAYbZCIiIiIiG2yQiYiIiIhssEEmIiIiIrLBBpmIiIiIyAYbZCIi\nIiIiG2yQiYiIiIhssEEmIiIiIrLBBpmIiIiIyAYbZCIiIiIiG3q1BtqzZw+WLVsGi8WCAQMGYNCg\nQWoNTUQ1xLwSuRZmlsixVDmDbLFY8Mknn+Dll1/GBx98gF9//RWnTp1SY2giqiHmlci1MLNEjqdK\ng5yRkYHGjRsjNDQUer0eMTExSElJUWNoIqoh5pXItTCzRI6nSoOcm5uLoKAg6+OgoCDk5uaqMTQR\n1RDzSuRamFkix1PtGmQlTCYTTCYTACAxMRFhYWFVzl/ddDWpUct4/KlsRs86DqRgVZTWUt12UbxO\nDnCjvV+craZ5BbS13lqoZSd21j2vjqRGLQo2u6Lt4oCX708H7cdcQW3yqoZ1T65zyHK+H/29Q5ZD\nFdPK+wVQrxZVziAbDAbk5ORYH+fk5MBgMJSbLzY2FomJiUhMTKx2mfHx8Q6tsS5YS8VYS8W0VEtF\nnJFXQFvrrZVatFIHwFoqo6VaKqMks7Z51dI6sZaKsZaKqVmLKg1yixYtcPbsWWRmZsJsNiM5ORld\nunRRY2giqiHmlci1MLNEjqfKJRbu7u4YPXo03nrrLVgsFtxxxx1o0qSJGkMTUQ0xr0SuhZklcjz3\n119//XU1Brrppptw9913Y+DAgWjTpo1DlhkVFeWQ5TgCa6kYa6mYlmqpiDPyCmhrvbVSi1bqAFhL\nZbRUS2VqmlktrRNrqRhrqZhatehERFQZiYiIiIjIBfCrpomIiIiIbGjqNm9K1edXamZnZ2PhwoXI\ny8uDTqdDbGwsBg4ciIKCAnzwwQfIyspCcHAw/vWvf6Fhw4ZOr8disSA+Ph4GgwHx8fHIzMzEnDlz\nkJ+fj6ioKEyYMAF6vfNf5sLCQixevBgnT56ETqfD2LFjERYWVi/bZMOGDdi8eTN0Oh2aNGmCcePG\nIS8vT5XtsmjRIuzatQv+/v6YPXs2AFT63hARLFu2DLt374aXlxfGjRunqT9jOQrzao+ZLY+ZVdf6\n9euRlJSEpUuXws/Pr17WKykpCTt37oRer0doaCjGjRuHBg0aAADWrFmDzZs3w83NDaNGjULHjh2d\nWgtQf/sp7qMqV+/7KHExZWVlMn78eDl37pxcuXJFpkyZIidPnlRt/NzcXDly5IiIiBQVFcnEiRPl\n5MmTkpSUJGvWrBERkTVr1khSUpIq9axfv17mzJkjM2fOFBGR2bNnyy+//CIiIh999JFs2rRJlTrm\nz58vJpNJRESuXLkiBQUF9bJNcnJyZNy4cVJSUiIiV7fHli1bVNsuqampcuTIEXnxxRetz1W2HXbu\n3ClvvfWWWCwWSUtLk5deeskpNdUn5rU8ZtYeM6uurKwsmTFjhowdO1YuXrwoIvWzXnv27BGz2Swi\nV7f3tW188uRJmTJlipSWlsr58+dl/PjxUlZW5tRa6nM/xX1U5ep7H+Vyl1jU91dqBgYGWn+z9vHx\nQXh4OHJzc5GSkoK+ffsCAPr27atKTTk5Odi1axcGDBgAABARpKamokePHgCAfv36qVJHUVERDh48\niP79+wMA9Ho9GjRoUC/bBLj6229paSnKyspQWlqKgIAA1bbLLbfcUu632cq2w44dO9CnTx/odDq0\natUKhYWFuHDhglPqqi/Mqz1mtmLMrHo+++wzDBs2DDqdzvpcfaxXhw4d4O7uDgBo1aqV9Zv/UlJS\nEBMTAw8PD4SEhKBx48bIyMhwai31uZ/iPqpiWthHudwlFhV9pebhw4frpZbMzEwcO3YMLVu2xMWL\nFxEYGAgACAgIwMWLF50+/vLly/HEE0/g8uXLAID8/Hz4+vpadzoGg0GVrxvNzMyEn58fFi1ahOPH\njyMqKgojR46sl21iMBhw3333YezYsfD09ESHDh0QFRVVL9vlmsq2Q25uLoxGo3W+a18Pe23e6wHz\nao+ZLY+ZVU9KSgoMBgOaN29u93x9r9fmzZsRExNjrSU6Oto6TY3XXiv7Ke6j/o8W9lEu1yBrRXFx\nMWbPno2RI0fC19fXbppOp7P77dwZdu7cCX9/f0RFRSE1NdWpY1WnrKwMx44dw+jRoxEdHY1ly5Zh\n7dq1dvOosU2Aq9cOpqSkYOHChfD19cX777+PPXv2OH1cpdTaDmSvvvMKMLOVYWYd680330ReXl65\n5x977DGsWbMGr7zyiiZq6dq1KwDg22+/hbu7O3r37q1aXVrEfZQ9LeyjXK5BVvo1uM5kNpsxe/Zs\n9O7dG927dwcA+Pv748KFCwgMDMSFCxfg5+fn1BrS0tKwY8cO7N69G6Wlpbh8+TKWL1+OoqIilJWV\nwd3dHbm5uapsm6CgIAQFBVl/6+/RowfWrl2r+jYBgH379iEkJMQ6Vvfu3ZGWllYv2+WayraDwWBA\ndna2db76eC87G/P6f5jZijGzjvXqq69W+PyJEyeQmZmJqVOnArhae1xcHGbOnOm09aqslmt++ukn\n7Ny5E6+99pq10fn7PkON176+91PcR5WnhX2Uy12DXN9fqSkiWLx4McLDw3Hvvfdan+/SpQu2bt0K\nANi6dav1t2NnGTp0KBYvXoyFCxdi0qRJuPXWWzFx4kS0bdsWv/32G4CrOx81tk1AQACCgoJw5swZ\nAFcPeBEREapvEwAwGo04fPgwSkpKICLWWupju1xT2Xbo0qULtm3bBhFBeno6fH19XeZPtUoxr/+H\nma0YM6uOpk2bYunSpVi4cCEWLlyIoKAgvPPOOwgICKiX9dqzZw++++47xMXFwcvLy/p8ly5dkJyc\njCtXriAzMxNnz55Fy5YtnVpLfe6nuI+qmBb2US75RSG7du3CZ599Zv1KzcGDB6s29qFDh/Daa6+h\nadOm1t94H3/8cURHR+ODDz5Adna26rdkSU1Nxfr16xEfH4/z589jzpw5KCgoQGRkJCZMmAAPDw+n\n1/DXX39h8eLFMJvNCAkJwbhx4yAi9bJNVq1aheTkZLi7u6N58+YYM2YMcnNzVdkuc+bMwYEDB5Cf\nnw9/f38MGTIEXbt2rXA7iAg++eQT/Pnnn/D09MS4cePQokULh9dU35jX8phZe8ys+p5//nnMnDnT\neps3tddrwoQJMJvN1vdXdHQ0nn32WQBXL7vYsmUL3NzcMHLkSNx2221OrQWov/0U91GVq+99lEs2\nyEREREREzuJyl1gQERERETkTG2QiIiIiIhtskImIiIiIbLBBJiIiIiKywQaZiIiIiMgGG2QiIiIi\nIhsu9016pMyhQ4ewcuVKnDx5Em5uboiIiMCIESOcfsN1IqodZpbIdTCv1z82yNehoqIiJCYm4umn\nn0ZMTAzMZjMOHjzo0Bt7WywWuLnxDxBEjsDMErkO5vXGwAb5OnT27FkAQK9evQAAnp6e6NChg3W6\nyWTCxo0bkZOTg6CgIEyYMAFRUVE4deoUli5dir/++gsGgwFDhw61fqXkwoUL4enpiezsbBw4cABT\np05FmzZt8MUXX2D79u0wm83o2rUrRo4cCU9PT/VXmsiFMbNEroN5vUEIXXcKCwtl1KhRMn/+fNm1\na5fk5+dbpyUnJ8uzzz4rhw8fFovFImfPnpXMzEy5cuWKjB8/Xr755hu5cuWK7Nu3T4YPHy6nT58W\nEZEFCxbIk08+KQcPHpSysjIpKSmRZcuWSWJiouTn50tRUZHMnDlTPv/88/pabSKXxcwSuQ7m9cbA\n8/fXIV9fX0yfPh06nQ4fffQRnn76abzzzjvIy8vD5s2b8cADD6Bly5bQ6XRo3LgxgoODcfjwYRQX\nF2PQoEHQ6/W49dZb0alTJ/zyyy/W5Xbt2hWtW7eGm5sbPDw88N///hcjRoxAw4YN4ePjg8GDB+PX\nX3+txzUnck3MLJHrYF5vDLzE4joVERGB559/HgBw+vRpzJ8/H8uXL0d2djZCQ0PLzX/hwgUYjUa7\na56Cg4ORm5trfRwUFGT9/6VLl1BSUoL4+HjrcyICi8XijNUhuu4xs0Sug3m9/rFBvgGEh4ejX79+\n+PHHH2E0GnH+/Ply8wQGBiI7O9vugwHZ2dm46aabrPPodDrr/xs1agRPT0+8//77MBgMzl8JohsI\nM0vkOpjX6xMvsbgOnT59GuvXr0dOTg6AqyH89ddfER0djf79+2P9+vU4evQoRATnzp1DVlYWoqOj\n4eXlhXXr1sFsNiM1NRU7d+5Ez549KxzDzc0NAwYMwPLly3Hx4kUAQG5uLvbs2aPaehJdL5hZItfB\nvN4YeAb5OuTj44PDhw9jw4YNKCoqgq+vLzp37ownnngCvr6+yM/Px9y5c5Gbm4uQkBCMHz8ewcHB\niIuLw9KlS7FmzRoYDAaMHz8e4eHhlY4zbNgwrF69Gv/+97+Rn58Pg8GAO++8Ex07dlRxbYlcHzNL\n5DqY1xuDTkSkvosgIiIiItIKXmJBRERERGSDDTIRERERkQ02yERERERENtggExERERHZYINMRERE\nRGSDDTIRERERkQ02yERERERENtggExERERHZYINMRERERGTj/wFuJSlgfoFt6gAAAABJRU5ErkJg\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x114b7af98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1,3,figsize=(10,3.3),subplot_kw={'ylim': (0,8)})\n",
    "fig.suptitle('A',fontsize=24, y=1.08)\n",
    "for axis in ax:\n",
    "    axis.set(xlabel=\"Score\", ylabel=\"# of Students\")\n",
    "ax[0].set_title('Sept. % Correct')\n",
    "ax[1].set_title('Dec. % Correct')\n",
    "ax[2].set_title('Difference')\n",
    "A_math['% Correct'].hist(color='deepskyblue',alpha=0.8, bins = 20,\\\n",
    "                           label = 'A',range=[0, 100],  ax= ax[0])\n",
    "A_math['% Correct_Dec'].hist(color='cadetblue',alpha=0.8, bins = 20,\\\n",
    "                           label = 'A',range=[0, 100], ax= ax[1])\n",
    "A_math['Diff'].hist(color='teal',alpha=0.8, bins = 20,\\\n",
    "                           label = 'A',range=[-35, 65], ax= ax[2])\n",
    "fig.tight_layout()\n",
    "\n",
    "\n",
    "fig, ax = plt.subplots(1,3,figsize=(10,3.3),subplot_kw={'ylim': (0,8)})\n",
    "fig.suptitle('B',fontsize=24, y=1.08)\n",
    "for axis in ax:\n",
    "    axis.set(xlabel=\"Score\", ylabel=\"# of Students\")\n",
    "    ax[0].set_title('Sept. % Correct')\n",
    "ax[1].set_title('Dec. % Correct')\n",
    "ax[2].set_title('Difference')\n",
    "B_math['% Correct'].hist(color='coral',alpha=0.8, bins = 20,\\\n",
    "                           label = 'B',range=[0, 100], ax= ax[0])\n",
    "B_math['% Correct_Dec'].hist(color='orangered',alpha=0.8, bins = 20,\\\n",
    "                           label = 'B',range=[0, 100], ax= ax[1])\n",
    "B_math['Diff'].hist(color='darkred',alpha=0.8, bins = 20,\\\n",
    "                           label = 'B',range=[-35, 65],  ax= ax[2])\n",
    "fig.tight_layout()\n",
    "\n",
    "\n",
    "fig, ax = plt.subplots(1,3,figsize=(10,3.3), subplot_kw={'ylim': (0,8)})\n",
    "fig.suptitle('C',fontsize=24, y=1.08)\n",
    "for axis in ax:\n",
    "    axis.set(xlabel=\"Score\", ylabel=\"# of Students\")\n",
    "ax[0].set_title('Sept. % Correct')\n",
    "ax[1].set_title('Dec. % Correct')\n",
    "ax[2].set_title('Difference')\n",
    "C_math['% Correct'].hist(color='chartreuse',alpha=0.8, bins = 20,\\\n",
    "                           label='C',range=[0, 100], ax= ax[0])\n",
    "C_math['% Correct_Dec'].hist(color='lime',alpha=0.8, bins = 20,\\\n",
    "                           label='C',range=[0, 100], ax= ax[1])\n",
    "C_math['Diff'].hist(color='forestgreen',alpha=0.8, bins = 20,\\\n",
    "                           label='C',range=[-35, 65],  ax= ax[2])\n",
    "fig.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "e881330e-7280-4948-b4ce-0298f6f9ea23"
    }
   },
   "source": [
    "### Further Graphical Analysis\n",
    "Further grahical analysis can be found here: http://bit.ly/SABronx1GA\n",
    "The analysis includes growth curves and be looked at on the grade level and by class."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "002e15c7-d1e5-40a8-90be-acf97b85450f"
    }
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   "source": [
    "## Analysis of Progress"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "58 students have shown improvement and 14 have shown regression.\n"
     ]
    }
   ],
   "source": [
    "print('{} students have shown improvement and {} have shown regression.'.format(\\\n",
    "            grades['% Change'][grades['% Change'] > 0].count(),grades['% Change'][grades['% Change'] < 0].count()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": false,
    "nbpresent": {
     "id": "aaa6c2f8-8088-43b4-a610-9e9b88b64e11"
    }
   },
   "outputs": [],
   "source": [
    "most_prog = df_math_sept[['First Name', 'Class', 'Student ID']][df_math_sept['Student ID'] == grades.index[0]]\n",
    "least_prog = df_math_sept[['First Name', 'Class', 'Student ID']][df_math_sept['Student ID'] == grades.index[-1]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": false,
    "nbpresent": {
     "id": "0a7e6284-17d7-4697-b462-c442c4a456f8"
    }
   },
   "outputs": [],
   "source": [
    "most_prog['Improvement'] = grades['% Change'].loc[most_prog['Student ID']].values\n",
    "least_prog['Improvement'] = grades['% Change'].loc[least_prog['Student ID']].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": false,
    "nbpresent": {
     "id": "f57c774b-e5d3-4f61-85e2-baab68e55078"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Most Progress:\n",
      "c02bf7384c92a9eee646650ad8e5ff91 in c8b287075ce4f11c834d2a0ada967ddc Class: 67.0\n",
      "Least Progress:\n",
      "f54c99dd1f73b138f90e529dfbf8931f in c8b287075ce4f11c834d2a0ada967ddc: -29.400000000000006\n"
     ]
    }
   ],
   "source": [
    "print('Most Progress:\\n{} in {} Class: {}\\nLeast Progress:\\n{} in {}: {}'.\\\n",
    "      format(*[most_prog.values[0][i] for i in [0,1,-1]]+[least_prog.values[0][i] for i in [0,1,-1]]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "top_five = df_math_sept[['First Name', 'Class', 'Student ID']][df_math_sept['Student ID'].isin(grades.index[:5])].set_index('Student ID')\n",
    "bottom_five = df_math_sept[['First Name', 'Class', 'Student ID']][df_math_sept['Student ID'].isin(grades.index[-5:])].set_index('Student ID')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "top_five = top_five.join(grades['% Change']).sort_values('% Change', ascending=False, axis=0)\n",
    "bottom_five = bottom_five.join(grades['% Change']).sort_values('% Change', ascending=False, axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
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       "                                  First Name  \\\n",
       "Student ID                                     \n",
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    "top_five"
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   "cell_type": "code",
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       "                                  First Name  \\\n",
       "Student ID                                     \n",
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