444 lines
26 KiB
Plaintext
444 lines
26 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 16,
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"metadata": {},
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"outputs": [],
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"source": [
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"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_clear_csv, pp_q_scores\n",
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"#import prettytable as pt\n",
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"%matplotlib inline"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {
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"tags": [
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"parameters"
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]
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},
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"outputs": [],
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"source": [
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"tribe = \"308\"\n",
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"assessment = \"DM1\"\n",
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"date = \"15/09/16\"\n",
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"csv_file = Path(f\"../sheets/{tribe}/160915_{assessment}.csv\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/markdown": [
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"# DM1 (15/09/16) pour 308"
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],
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"text/plain": [
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"<IPython.core.display.Markdown object>"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"if date is None:\n",
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" 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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]
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},
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{
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"cell_type": "code",
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"execution_count": 11,
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"metadata": {},
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"outputs": [],
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"source": [
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"stack_scores = pd.read_csv(csv_file, encoding=\"latin_1\")\n",
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"scores = flat_clear_csv(stack_scores).dropna(subset=[\"Score\"])\n",
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"scores = pp_q_scores(scores)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 12,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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" }\n",
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"\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
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" }\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th></th>\n",
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" <th>Note</th>\n",
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" <th>Bareme</th>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>Exercice</th>\n",
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" <th>Eleve</th>\n",
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" <th></th>\n",
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" <th></th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th rowspan=\"5\" valign=\"top\">1</th>\n",
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" <th>ABDOU Asmahane</th>\n",
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" <td>3.67</td>\n",
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" <td>6.0</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>ABOU Roihim</th>\n",
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" <td>0.00</td>\n",
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" <td>6.0</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>AHMED BOINALI Kouraichia</th>\n",
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" <td>1.33</td>\n",
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" <td>6.0</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>AHMED Rahada</th>\n",
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" <td>2.67</td>\n",
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" <td>6.0</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>ALI SAID Anchourati</th>\n",
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" <td>0.00</td>\n",
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" <td>6.0</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"</div>"
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],
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"text/plain": [
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" Note Bareme\n",
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"Exercice Eleve \n",
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"1 ABDOU Asmahane 3.67 6.0\n",
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" ABOU Roihim 0.00 6.0\n",
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" AHMED BOINALI Kouraichia 1.33 6.0\n",
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" AHMED Rahada 2.67 6.0\n",
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" ALI SAID Anchourati 0.00 6.0"
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]
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},
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"execution_count": 12,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"exercises_scores = scores.groupby([\"Exercice\", \"Eleve\"]).agg({\"Note\": \"sum\", \"Bareme\": \"sum\"})\n",
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"exercises_scores.head()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 15,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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" }\n",
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"\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
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" }\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>Note</th>\n",
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" <th>Bareme</th>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>Eleve</th>\n",
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" <th></th>\n",
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" <th></th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>ABDOU Asmahane</th>\n",
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" <td>5.00</td>\n",
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" <td>12.0</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>ABOU Roihim</th>\n",
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" <td>0.00</td>\n",
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" <td>12.0</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>AHMED BOINALI Kouraichia</th>\n",
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" <td>2.67</td>\n",
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" <td>12.0</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>AHMED Rahada</th>\n",
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" <td>6.33</td>\n",
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" <td>12.0</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>ALI SAID Anchourati</th>\n",
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" <td>0.00</td>\n",
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" <td>12.0</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>ASSANE Noussouraniya</th>\n",
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" <td>4.67</td>\n",
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" <td>12.0</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>BACAR Issiaka</th>\n",
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" <td>0.00</td>\n",
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" <td>12.0</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>BACAR Samina</th>\n",
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" <td>3.67</td>\n",
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" <td>12.0</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>CHAIHANE Said</th>\n",
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" <td>5.33</td>\n",
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" <td>12.0</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>COMBO Houzaimati</th>\n",
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" <td>5.00</td>\n",
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" <td>12.0</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>DAOUD Anzilati</th>\n",
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" <td>5.17</td>\n",
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" <td>12.0</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>DAOUD Talaenti</th>\n",
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" <td>5.67</td>\n",
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" <td>12.0</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>DARKAOUI Rachma</th>\n",
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" <td>5.67</td>\n",
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" <td>12.0</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>DHAKIOINE Nabaouya</th>\n",
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" <td>1.00</td>\n",
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" <td>12.0</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>DJANFAR Soioutinour</th>\n",
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" <td>5.33</td>\n",
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" <td>12.0</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>DRISSA Ibrahim</th>\n",
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" <td>0.00</td>\n",
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" <td>12.0</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>HACHIM SIDI Assani</th>\n",
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" <td>7.00</td>\n",
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" <td>12.0</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>HAFIDHUI Zalifa</th>\n",
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" <td>5.67</td>\n",
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" <td>12.0</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>HOUMADI Marie</th>\n",
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" <td>6.67</td>\n",
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" <td>12.0</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>HOUMADI Sania</th>\n",
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" <td>5.33</td>\n",
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" <td>12.0</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>MAANDHUI Halouoi</th>\n",
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" <td>7.00</td>\n",
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" <td>12.0</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>MASSONDI Nasma</th>\n",
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" <td>7.33</td>\n",
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" <td>12.0</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>SAIDALI Irichad</th>\n",
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" <td>5.00</td>\n",
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" <td>12.0</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"</div>"
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],
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"text/plain": [
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" Note Bareme\n",
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"Eleve \n",
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"ABDOU Asmahane 5.00 12.0\n",
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"ABOU Roihim 0.00 12.0\n",
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"AHMED BOINALI Kouraichia 2.67 12.0\n",
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"AHMED Rahada 6.33 12.0\n",
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"ALI SAID Anchourati 0.00 12.0\n",
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"ASSANE Noussouraniya 4.67 12.0\n",
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"BACAR Issiaka 0.00 12.0\n",
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"BACAR Samina 3.67 12.0\n",
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"CHAIHANE Said 5.33 12.0\n",
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"COMBO Houzaimati 5.00 12.0\n",
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"DAOUD Anzilati 5.17 12.0\n",
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"DAOUD Talaenti 5.67 12.0\n",
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"DARKAOUI Rachma 5.67 12.0\n",
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"DHAKIOINE Nabaouya 1.00 12.0\n",
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"DJANFAR Soioutinour 5.33 12.0\n",
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"DRISSA Ibrahim 0.00 12.0\n",
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"HACHIM SIDI Assani 7.00 12.0\n",
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"HAFIDHUI Zalifa 5.67 12.0\n",
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"HOUMADI Marie 6.67 12.0\n",
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"HOUMADI Sania 5.33 12.0\n",
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"MAANDHUI Halouoi 7.00 12.0\n",
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"MASSONDI Nasma 7.33 12.0\n",
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"SAIDALI Irichad 5.00 12.0"
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]
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},
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"execution_count": 15,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"assessment_scores = scores.groupby([\"Eleve\"]).agg({\"Note\": \"sum\", \"Bareme\": \"sum\"})\n",
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"assessment_scores"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"count 23.00\n",
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"mean 4.33\n",
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"std 2.45\n",
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"min 0.00\n",
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"25% 3.17\n",
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"50% 5.17\n",
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"75% 5.67\n",
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"max 7.33\n",
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"Name: Note, dtype: float64"
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]
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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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}
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],
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"source": [
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"assessment_scores[\"Note\"].describe()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"<matplotlib.axes._subplots.AxesSubplot at 0x7f0ae61e5cf8>"
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]
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},
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"execution_count": 8,
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"metadata": {},
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"output_type": "execute_result"
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},
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{
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"data": {
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"image/png": 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\n",
|
|
"text/plain": [
|
|
"<Figure size 432x288 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {
|
|
"needs_background": "light"
|
|
},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"assessment_scores[\"Note\"].plot.kde()\n",
|
|
"assessment_scores[\"Note\"].plot.hist(density=True)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
}
|
|
],
|
|
"metadata": {
|
|
"celltoolbar": "Tags",
|
|
"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",
|
|
"version": "3.7.3"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 2
|
|
}
|