recopytex/templates/tpl_evaluation.ipynb

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{
"cells": [
{
"cell_type": "code",
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"execution_count": 1,
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"outputs": [],
"source": [
"from IPython.display import Markdown as md\n",
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"from IPython.display import display\n",
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"import pandas as pd\n",
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"from pathlib import Path\n",
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"from datetime import datetime\n",
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"from recopytex import flat_df_students, pp_q_scores\n",
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"#import prettytable as pt\n",
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"%matplotlib inline"
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]
},
{
"cell_type": "code",
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"execution_count": 2,
"metadata": {
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"tags": [
"parameters"
]
},
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"outputs": [],
"source": [
"tribe = \"308\"\n",
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"assessment = \"DM1\"\n",
"date = \"15/09/16\"\n",
"csv_file = Path(f\"../sheets/{tribe}/160915_{assessment}.csv\")"
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]
},
{
"cell_type": "code",
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"execution_count": 3,
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"outputs": [
{
"data": {
"text/markdown": [
"# DM1 (15/09/16) pour 308"
],
"text/plain": [
"<IPython.core.display.Markdown object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
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"source": [
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"if date is None:\n",
" display(md(f\"# {assessment} pour {tribe}\"))\n",
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"else:\n",
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" display(md(f\"# {assessment} ({date}) pour {tribe}\"))"
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]
},
{
"cell_type": "code",
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"execution_count": 5,
"metadata": {
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},
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"outputs": [],
"source": [
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"stack_scores = pd.read_csv(csv_file, encoding=\"latin_1\")\n",
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"scores = flat_df_students(stack_scores).dropna(subset=[\"Score\"])\n",
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"scores = pp_q_scores(scores)"
]
},
{
"cell_type": "code",
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"execution_count": 6,
"metadata": {
"extensions": {
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}
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},
"outputs": [],
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"source": [
"exercises_scores = scores.groupby([\"Exercice\", \"Eleve\"]).agg({\"Note\": \"sum\", \"Bareme\": \"sum\"})\n",
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"#exercises_scores.head()"
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]
},
{
"cell_type": "code",
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"execution_count": 7,
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"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
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"\n",
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" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Note</th>\n",
" <th>Bareme</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Eleve</th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>ABDOU Asmahane</th>\n",
" <td>5.00</td>\n",
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" <td>12</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>ABOU Roihim</th>\n",
" <td>0.00</td>\n",
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" <td>12</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>AHMED BOINALI Kouraichia</th>\n",
" <td>2.67</td>\n",
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" <td>12</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>AHMED Rahada</th>\n",
" <td>6.33</td>\n",
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" <td>12</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>ALI SAID Anchourati</th>\n",
" <td>0.00</td>\n",
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" <td>12</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>ASSANE Noussouraniya</th>\n",
" <td>4.67</td>\n",
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" <td>12</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>BACAR Issiaka</th>\n",
" <td>0.00</td>\n",
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" <td>12</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>BACAR Samina</th>\n",
" <td>3.67</td>\n",
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" <td>12</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>CHAIHANE Said</th>\n",
" <td>5.33</td>\n",
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" <td>12</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>COMBO Houzaimati</th>\n",
" <td>5.00</td>\n",
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" <td>12</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>DAOUD Anzilati</th>\n",
" <td>5.17</td>\n",
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" <td>12</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>DAOUD Talaenti</th>\n",
" <td>5.67</td>\n",
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" <td>12</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>DARKAOUI Rachma</th>\n",
" <td>5.67</td>\n",
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" <td>12</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>DHAKIOINE Nabaouya</th>\n",
" <td>1.00</td>\n",
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" <td>12</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>DJANFAR Soioutinour</th>\n",
" <td>5.33</td>\n",
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" <td>12</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>DRISSA Ibrahim</th>\n",
" <td>0.00</td>\n",
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" <td>12</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>HACHIM SIDI Assani</th>\n",
" <td>7.00</td>\n",
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" <td>12</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>HAFIDHUI Zalifa</th>\n",
" <td>5.67</td>\n",
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" <td>12</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>HOUMADI Marie</th>\n",
" <td>6.67</td>\n",
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" <td>12</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>HOUMADI Sania</th>\n",
" <td>5.33</td>\n",
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" <td>12</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>MAANDHUI Halouoi</th>\n",
" <td>7.00</td>\n",
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" <td>12</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>MASSONDI Nasma</th>\n",
" <td>7.33</td>\n",
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" <td>12</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>SAIDALI Irichad</th>\n",
" <td>5.00</td>\n",
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" <td>12</td>\n",
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" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Note Bareme\n",
"Eleve \n",
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"ABDOU Asmahane 5.00 12\n",
"ABOU Roihim 0.00 12\n",
"AHMED BOINALI Kouraichia 2.67 12\n",
"AHMED Rahada 6.33 12\n",
"ALI SAID Anchourati 0.00 12\n",
"ASSANE Noussouraniya 4.67 12\n",
"BACAR Issiaka 0.00 12\n",
"BACAR Samina 3.67 12\n",
"CHAIHANE Said 5.33 12\n",
"COMBO Houzaimati 5.00 12\n",
"DAOUD Anzilati 5.17 12\n",
"DAOUD Talaenti 5.67 12\n",
"DARKAOUI Rachma 5.67 12\n",
"DHAKIOINE Nabaouya 1.00 12\n",
"DJANFAR Soioutinour 5.33 12\n",
"DRISSA Ibrahim 0.00 12\n",
"HACHIM SIDI Assani 7.00 12\n",
"HAFIDHUI Zalifa 5.67 12\n",
"HOUMADI Marie 6.67 12\n",
"HOUMADI Sania 5.33 12\n",
"MAANDHUI Halouoi 7.00 12\n",
"MASSONDI Nasma 7.33 12\n",
"SAIDALI Irichad 5.00 12"
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]
},
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"execution_count": 7,
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"metadata": {},
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"output_type": "execute_result"
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}
],
"source": [
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"assessment_scores = scores.groupby([\"Eleve\"]).agg({\"Note\": \"sum\", \"Bareme\": \"sum\"})\n",
"assessment_scores"
]
},
{
"cell_type": "code",
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"execution_count": 8,
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"outputs": [
{
"data": {
"text/plain": [
"count 23.00\n",
"mean 4.33\n",
"std 2.45\n",
"min 0.00\n",
"25% 3.17\n",
"50% 5.17\n",
"75% 5.67\n",
"max 7.33\n",
"Name: Note, dtype: float64"
]
},
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"execution_count": 8,
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"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"assessment_scores[\"Note\"].describe()"
]
},
{
"cell_type": "code",
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"execution_count": 9,
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},
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"outputs": [
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{
"name": "stderr",
"output_type": "stream",
"text": [
"/usr/lib/python3.7/importlib/_bootstrap.py:219: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192 from C header, got 216 from PyObject\n",
" return f(*args, **kwds)\n",
"/usr/lib/python3.7/importlib/_bootstrap.py:219: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192 from C header, got 216 from PyObject\n",
" return f(*args, **kwds)\n"
]
},
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{
"data": {
"text/plain": [
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"<matplotlib.axes._subplots.AxesSubplot at 0x7f104b318090>"
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]
},
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"execution_count": 9,
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"metadata": {},
"output_type": "execute_result"
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{
"data": {
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"image/png": "iVBORw0KGgoAAAANSUhEUgAAAYgAAAD4CAYAAAD2FnFTAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAAgAElEQVR4nO3dd3yV5d3H8c8vi0DYEFAImyBbRgRX1SoqjpLWx4GjtdZKteKofZy11OJjtWqdpVbqntSBNlaUOttaQRKWEIaGIRsiG0LI+j1/nIM9xhM4kZzcGd/363Ve3OtKvhmcX+77uu/rMndHRESksoSgA4iISN2kAiEiIlGpQIiISFQqECIiEpUKhIiIRJUUdICa0r59e+/evXvQMURE6pXZs2d/6e7p0fY1mALRvXt38vLygo4hIlKvmNkXVe3TJSYREYlKBUJERKJSgRARkahUIEREJCoVCBERiUoFQkREolKBEBGRqFQgREQkKhUIERGJqsE8SS0iUdzW6iDbb6+ZHFIv6QxCRESiUoEQEZGoVCBERCSquBYIMxttZkvNrMDMbtrPcf9jZm5mWRHbbg63W2pmp8Yzp4iIfFPcOqnNLBGYBJwMrAFyzSzH3RdVOq4FcA3wScS2/sBYYADQCXjXzPq4e3m88oqIyNfF8wxiBFDg7svdvQSYAmRHOe524PdAccS2bGCKu+919xVAQfjjiYhILYlngegMrI5YXxPe9hUzGwZ0cfc3q9s23H6cmeWZWV5hYWHNpBYRESDATmozSwDuA375bT+Gu0929yx3z0pPjzpjnoiIfEvxfFBuLdAlYj0jvG2fFsBA4EMzAzgEyDGzMTG0FRGROIvnGUQukGlmPcwshVCnc86+ne6+3d3bu3t3d+8OzATGuHte+LixZtbEzHoAmcCsOGYVEZFK4nYG4e5lZjYemA4kAk+4e76ZTQTy3D1nP23zzewlYBFQBlypO5hERGpXXMdicvdpwLRK2yZUcewJldbvAO6IWzgREdkvPUktIiJRqUCIiEhUKhAiIhKVCoSIiESlAiEiIlGpQIiISFQqECIiEpUKhIiIRKUCISIiUalAiIhIVCoQIiISlQqEiIhEpQIhIiJRqUCIiEhUKhAiIhKVCoSIiEQV1wJhZqPNbKmZFZjZTVH2X25mC8xsnpl9ZGb9w9u7m9me8PZ5ZvbneOYUEZFvituMcmaWCEwCTgbWALlmluPuiyIOe8Hd/xw+fgxwHzA6vG+Zuw+JVz4REdm/eJ5BjAAK3H25u5cAU4DsyAPcfUfEahrgccwjIiLVEM8C0RlYHbG+Jrzta8zsSjNbBtwNXB2xq4eZzTWzf5rZd6J9AjMbZ2Z5ZpZXWFhYk9lFRBq9wDup3X2Su/cCbgRuDW9eD3R196HAdcALZtYyStvJ7p7l7lnp6em1F1pEpBGIZ4FYC3SJWM8Ib6vKFOD7AO6+1903h5dnA8uAPnHKKSIiUcSzQOQCmWbWw8xSgLFATuQBZpYZsXoG8Hl4e3q4kxsz6wlkAsvjmFVERCqJ211M7l5mZuOB6UAi8IS755vZRCDP3XOA8WY2CigFtgIXh5sfB0w0s1KgArjc3bfEK6uIiHxT3AoEgLtPA6ZV2jYhYvmaKtq9Crwaz2wiIrJ/gXdSi4hI3aQCISIiUalAiIhIVCoQIiISlQqEiIhEpQIhIiJRqUCIiEhUKhAiIhKVCoSIiESlAiEiIlGpQIiISFQqECIiEpUKhIiIRKUCISIiUalAiIhIVCoQIiISVVwLhJmNNrOlZlZgZjdF2X+5mS0ws3lm9pGZ9Y/Yd3O43VIzOzWeOUVE5JviViDCc0pPAk4D+gPnRxaAsBfcfZC7DwHuBu4Lt+1PaA7rAcBo4E/75qgWEZHaEc8ziBFAgbsvd/cSYAqQHXmAu++IWE0DPLycDUxx973uvgIoCH88ERGpJfGck7ozsDpifQ0wsvJBZnYlcB2QApwY0XZmpbado7QdB4wD6Nq1a42EFhGRkMA7qd19krv3Am4Ebq1m28nunuXuWenp6fEJKCLSSMWzQKwFukSsZ4S3VWUK8P1v2VZERGpYPAtELpBpZj3MLIVQp3NO5AFmlhmxegbweXg5BxhrZk3MrAeQCcyKY1YREakkbn0Q7l5mZuOB6UAi8IS755vZRCDP3XOA8WY2CigFtgIXh9vmm9lLwCKgDLjS3cvjlVVERL4pnp3UuPs0YFqlbRMilq/ZT9s7gDvil05ERPYn8E5qERGpm1QgREQkKhUIERGJSgVCRESiUoEQEZGoVCBERCQqFQgREYlKBUJERKJSgRARkahUIEREJCoVCBERiSqmAmFmg+IdRERE6pZYzyD+ZGazzOznZtYqrolERKROiKlAuPt3gAsJTeIz28xeMLOT45pMREQCFXMfhLt/TmhK0BuB44GHzGyJmZ0Vr3AiIhKcWPsgBpvZ/cBi4ETge+7eL7x8fxzziYhIQGI9g3gYmAMc7u5XuvscAHdfR+isIiozG21mS82swMxuirL/OjNbZGafmtl7ZtYtYl+5mc0Lv3IqtxURkfiKdUa5M4A9+6b9NLMEINXdi9z92WgNzCwRmAScDKwBcs0sx90XRRw2F8hy9yIzuwK4GzgvvG+Puw+p/pckIiI1IdYziHeBphHrzcLb9mcEUODuy929BJgCZEce4O4fuHtReHUmkBFjHhERibNYC0Squ+/atxJebnaANp2B1RHra8LbqnIp8Fbk5zSzPDObaWbfj9bAzMaFj8krLCw8QBwREamOWAvEbjMbtm/FzIYDe2oqhJldBGQB90Rs7ubuWcAFwANm1qtyO3ef7O5Z7p6Vnp5eU3FERITY+yCuBV42s3WAAYfw376Cqqwl9NzEPhnhbV9jZqOAXwHHu/vefdvdfW343+Vm9iEwFFgWY14RETlIMRUId881s77AYeFNS9299ADNcoFMM+tBqDCMJXQ28BUzGwo8Cox2900R29sARe6+18zaA8cQ6sAWEZFaEusZBMARQPdwm2Fmhrs/U9XB7l5mZuOB6UAi8IS755vZRCDP3XMIXVJqTujsBGCVu48B+gGPmlkFoctgd1W6+0lEROIspgJhZs8CvYB5QHl4swNVFggAd58GTKu0bULE8qgq2n0MaIBAEZEAxXoGkQX0d3ePZxipR247iDEbb9teczkag4P5XgdJvyP1Xqx3MS0k1DEtIiKNRKxnEO2BRWY2C4i802hMXFKJiEjgYi0Qt8UzhIiI1D2x3ub6z/BAepnu/q6ZNSN0Z5KIiDRQsQ73fRnwCqFnFiA0ZMbr8QolIiLBi7WT+kpCD6vtgK8mD+oQr1AiIhK8WAvE3vCIrACYWRKh5yBERKSBirVA/NPMbgGahueifhl4I36xREQkaLEWiJuAQmAB8DNCT0dXOZOciIjUf7HexVQB/CX8EhGRRiDWsZhWEKXPwd171ngiERGpE6ozFtM+qcA5QNuajyMiInVFTH0Q7r454rXW3R8AzohzNhERCVCsl5iGRawmEDqjqM5cEiIiUs/E+ib/h4jlMmAlcG6NpxERkToj1ktM3414nezul7n70gO1M7PRZrbUzArM7KYo+68zs0Vm9qmZvRce72nfvovN7PPw6+LqfVkiInKwYr3EdN3+9rv7fVHaJAKTgJOBNUCumeVUmjp0LpDl7kVmdgWheafPM7O2wG8IXcpyYHa47dZY8oqIyMGL9UG5LOAKQoP0dQYuB4YBLcKvaEYABe6+PDxMxxQgO/IAd//A3YvCqzOBjPDyqcA77r4lXBTeAUbHmFVERGpArH0QGcAwd98JYGa3AW+6+0X7adMZWB2xvgYYuZ/jLwXe2k/bzpUbmNk4YBxA165d9/8ViIhItcR6BtERKIlYLwlvqxFmdhGhs5R7qtPO3Se7e5a7Z6Wnp9dUHBERIfYziGeAWWb2Wnj9+8DTB2izFugSsZ4R3vY1ZjYK+BVwvLvvjWh7QqW2H8aYVUREakCsdzHdAVwCbA2/LnH33x2gWS6QaWY9zCwFGAvkRB5gZkMJTUI0xt03ReyaDpxiZm3MrA1wSnibiIj
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"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"assessment_scores[\"Note\"].plot.kde()\n",
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"assessment_scores[\"Note\"].plot.hist(density=True)"
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]
},
{
"cell_type": "code",
"execution_count": null,
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"metadata": {
"extensions": {
"jupyter_dashboards": {
"version": 1,
"views": {
"grid_default": {},
"report_default": {
"hidden": true
}
}
}
}
},
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"outputs": [],
"source": []
}
],
"metadata": {
"celltoolbar": "Tags",
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"extensions": {
"jupyter_dashboards": {
"activeView": "grid_default",
"version": 1,
"views": {
"grid_default": {
"cellMargin": 10,
"defaultCellHeight": 20,
"maxColumns": 12,
"name": "grid",
"type": "grid"
},
"report_default": {
"name": "report",
"type": "report"
}
}
}
},
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"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
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"version": "3.7.4"
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}
},
"nbformat": 4,
"nbformat_minor": 2
}