443 lines
29 KiB
Plaintext
443 lines
29 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 20,
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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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"%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": 21,
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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": 22,
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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": 23,
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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)\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": 24,
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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": 24,
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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": 25,
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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": 25,
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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": 26,
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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": 26,
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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": 30,
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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 0x7f3ce24e90d0>"
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]
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},
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"execution_count": 30,
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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.kde()"
|
|
]
|
|
},
|
|
{
|
|
"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": "3.7.4"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 2
|
|
}
|