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581b0f4f2f
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ff94470fb4
@ -5,7 +5,6 @@ from dash.dependencies import Input, Output
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from .app import app
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from .exam_analysis import app as exam_analysis
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from .create_exam import app as create_exam
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from .student_analysis import app as student_analysis
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app.layout = html.Div(
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@ -19,8 +18,6 @@ def display_page(pathname):
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return exam_analysis.layout
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elif pathname == "/create-exam":
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return create_exam.layout
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elif pathname == "/students":
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return student_analysis.layout
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else:
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return "404"
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@ -1,217 +0,0 @@
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#!/usr/bin/env python
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# encoding: utf-8
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import dash
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import dash_html_components as html
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import dash_core_components as dcc
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import dash_table
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import plotly.graph_objects as go
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from datetime import date, datetime
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import uuid
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import pandas as pd
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import yaml
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from pathlib import Path
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from ...scripts.getconfig import config
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from ... import flat_df_students, pp_q_scores
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from ...config import NO_ST_COLUMNS
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from ..app import app
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from ...scripts.exam import Exam
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def get_students(csv):
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return list(pd.read_csv(csv).T.to_dict().values())
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QUESTION_COLUMNS = [
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{"id": "id", "name": "Question"},
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{
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"id": "competence",
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"name": "Competence",
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"presentation": "dropdown",
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},
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{"id": "theme", "name": "Domaine"},
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{"id": "comment", "name": "Commentaire"},
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{"id": "score_rate", "name": "Bareme"},
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{"id": "is_leveled", "name": "Est_nivele"},
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]
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layout = html.Div(
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[
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html.Header(
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children=[
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html.H1("Bilan des élèves"),
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],
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),
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html.Main(
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children=[
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html.Section(
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children=[
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html.Form(
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id="select-student",
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children=[
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html.Label(
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children=[
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"Classe",
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dcc.Dropdown(
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id="tribe",
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options=[
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{"label": t["name"], "value": t["name"]}
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for t in config["tribes"]
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],
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value=config["tribes"][0]["name"],
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),
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]
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),
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html.Label(
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children=[
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"Élève",
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dcc.Dropdown(
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id="student",
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options=[
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{"label": t["Nom"], "value": t["Nom"]}
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for t in get_students(config["tribes"][0]["students"])
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],
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value=get_students(config["tribes"][0]["students"])[0]["Nom"],
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),
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]
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),
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html.Label(
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children=[
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"Trimestre",
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dcc.Dropdown(
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id="term",
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options=[
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{"label": i + 1, "value": i + 1}
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for i in range(3)
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],
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value=1,
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),
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]
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),
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],
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),
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],
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id="form",
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),
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html.Section(
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children=[
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html.H2("Évaluations"),
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html.Div(
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dash_table.DataTable(
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id="exam_scores",
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columns=[
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{"id": "Nom", "name": "Évaluations"},
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{"id": "Note", "name": "Note"},
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{"id": "Bareme", "name": "Barème"},
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],
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data=[],
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style_data_conditional=[
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{
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"if": {"row_index": "odd"},
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"backgroundColor": "rgb(248, 248, 248)",
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}
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],
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style_data={
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"width": "100px",
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"maxWidth": "100px",
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"minWidth": "100px",
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},
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),
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id="eval-table",
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),
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html.Div(
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id="describe",
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),
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],
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id="Évaluations",
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),
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html.Section(
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children=[
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html.Div(
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id="competences-viz",
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),
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html.Div(
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id="themes-viz",
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),
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],
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id="visualisation",
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),
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]
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),
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dcc.Store(id="student-scores"),
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]
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)
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@app.callback(
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[
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dash.dependencies.Output("student", "options"),
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dash.dependencies.Output("student", "value"),
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],
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[
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dash.dependencies.Input("tribe", "value")
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],)
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def update_students_list(tribe):
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tribe_config = [t for t in config["tribes"] if t["name"] == tribe][0]
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students = get_students(tribe_config["students"])
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options = [
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{"label": t["Nom"], "value": t["Nom"]}
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for t in students
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]
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value = students[0]["Nom"]
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return options, value
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@app.callback(
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[
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dash.dependencies.Output("student-scores", "data"),
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],
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[
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dash.dependencies.Input("tribe", "value"),
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dash.dependencies.Input("student", "value"),
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dash.dependencies.Input("term", "value"),
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],
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)
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def update_student_scores(tribe, student, term):
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tribe_config = [t for t in config["tribes"] if t["name"] == tribe][0]
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p = Path(tribe_config["name"])
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csvs = list(p.glob("*.csv"))
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dfs = []
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for csv in csvs:
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try:
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scores = pd.read_csv(csv)
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except pd.errors.ParserError:
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pass
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else:
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try:
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if scores.iloc[0]["Commentaire"] == "commentaire":
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scores.drop([0], inplace=True)
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except KeyError:
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pass
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scores = flat_df_students(scores).dropna(subset=["Score"])
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scores = scores[scores["Eleve"] == student]
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scores = scores[scores["Trimestre"] == term]
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dfs.append(scores)
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df = pd.concat(dfs)
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return [df.to_dict("records")]
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@app.callback(
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[
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dash.dependencies.Output("exam_scores", "data"),
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],
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[
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dash.dependencies.Input("student-scores", "data"),
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],
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)
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def update_exam_scores(data):
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scores = pd.DataFrame.from_records(data)
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scores = pp_q_scores(scores)
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assessment_scores = scores.groupby(["Nom"]).agg({"Note": "sum", "Bareme": "sum"})
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return [assessment_scores.reset_index().to_dict("records")]
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return [{}]
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