Feat: csv extraction and flattening
This commit is contained in:
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9358c10b47
commit
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#!/usr/bin/env python
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# encoding: utf-8
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NO_STUDENT_COLUMNS = [
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"Trimestre",
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"Nom",
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"Date",
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"Exercice",
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"Question",
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"Competence",
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"Domaine",
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"Commentaire",
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"Bareme",
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"Niveau",
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]
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@ -0,0 +1,75 @@
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#!/usr/bin/env python
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# encoding: utf-8
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""" Extracting data from xlsx files """
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import pandas as pd
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from . import NO_STUDENT_COLUMNS
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pd.set_option("Precision", 2)
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def extract_students(df, no_student_columns=NO_STUDENT_COLUMNS):
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""" Extract the list of students from df
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:param df: the dataframe
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:param no_student_columns: columns that are not students
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:return: list of students
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"""
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students = df.columns.difference(no_student_columns)
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return students
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def flat_df_students(df, no_student_columns=NO_STUDENT_COLUMNS):
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""" Flat the ws for students
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:param df: the dataframe (one row per questions)
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:param no_student_columns: columns that are not students
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:return: dataframe with one row per questions and students
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Columns of csv files:
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- NO_STUDENT_COLUMNS
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- one for each students
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This function flat student's columns to "student" and "score"
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"""
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students = extract_students(df, no_student_columns)
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scores = []
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for st in students:
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scores.append(
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pd.melt(
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df,
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id_vars=no_student_columns,
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value_vars=st,
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var_name="student",
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value_name="score",
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)
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)
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return pd.concat(scores)
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def flat_clear_csv(csv_df, no_student_columns=NO_STUDENT_COLUMNS):
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""" Flat and clear the dataframe extracted from csv
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:param csv_df: data frame read from csv
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:param no_student_columns: columns that are not students
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:return: dataframe with one row per questions and students
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"""
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df = flat_df_students(csv_df)
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df.columns = df.columns.map(lambda x: x.lower())
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df["question"].fillna("", inplace=True)
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df["exercice"].fillna("", inplace=True)
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df["commentaire"].fillna("", inplace=True)
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df["competence"].fillna("", inplace=True)
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return df
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# -----------------------------
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# Reglages pour 'vim'
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# vim:set autoindent expandtab tabstop=4 shiftwidth=4:
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# cursor: 16 del
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@ -2,7 +2,7 @@
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"cells": [
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"cells": [
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{
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{
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"cell_type": "code",
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"cell_type": "code",
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"execution_count": 4,
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"execution_count": 1,
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"metadata": {},
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"metadata": {},
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"outputs": [],
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"outputs": [],
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"source": [
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"source": [
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@ -10,12 +10,13 @@
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"from IPython.display import DisplayHandle\n",
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"from IPython.display import DisplayHandle\n",
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"import pandas as pd\n",
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"import pandas as pd\n",
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"from pathlib import Path\n",
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"from pathlib import Path\n",
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"from datetime import datetime"
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"from datetime import datetime\n",
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"from recopytex.csv_extraction import flat_clear_csv"
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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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"cell_type": "code",
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"cell_type": "code",
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"execution_count": 8,
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"execution_count": 2,
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"metadata": {
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"metadata": {
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"tags": [
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"tags": [
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"parameters"
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"parameters"
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@ -24,35 +25,20 @@
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"outputs": [],
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"outputs": [],
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"source": [
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"source": [
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"tribe = \"308\"\n",
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"tribe = \"308\"\n",
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"assessment = \"161114_dm2\"\n",
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"assessment = \"DM1\"\n",
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"csv_file = Path(f\"./sheets/{tribe}/{assessment}.csv\")"
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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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},
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{
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{
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"cell_type": "code",
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"cell_type": "code",
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"execution_count": 9,
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"execution_count": 3,
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"metadata": {},
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"outputs": [],
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"source": [
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"split_ass = assessment.split(\"_\")\n",
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"if len(split_ass) > 1:\n",
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" date, *assessment = assessment.split(\"_\")\n",
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" date = datetime.strptime(date, \"%y%m%d\")\n",
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" assessment = ' '.join(assessment)\n",
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"else:\n",
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" date = None\n",
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" assessment = split_ass[0]"
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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": 16,
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"metadata": {},
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"metadata": {},
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"outputs": [
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"outputs": [
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{
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{
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"data": {
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"data": {
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"text/markdown": [
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"text/markdown": [
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"# dm2 (14/11/2016) pour 308"
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"# DM1 (15/09/16) pour 308"
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],
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],
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"text/plain": [
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"text/plain": [
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"<IPython.core.display.Markdown object>"
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"<IPython.core.display.Markdown object>"
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@ -66,7 +52,154 @@
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"if date is None:\n",
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"if date is None:\n",
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" display(md(f\"# {assessment} pour {tribe}\"))\n",
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" display(md(f\"# {assessment} pour {tribe}\"))\n",
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"else:\n",
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"else:\n",
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" display(md(f\"# {assessment} ({date:%d/%m/%Y}) pour {tribe}\"))"
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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": 4,
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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>trimestre</th>\n",
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" <th>nom</th>\n",
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" <th>date</th>\n",
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" <th>exercice</th>\n",
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" <th>question</th>\n",
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" <th>competence</th>\n",
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" <th>domaine</th>\n",
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" <th>commentaire</th>\n",
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" <th>bareme</th>\n",
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" <th>niveau</th>\n",
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" <th>student</th>\n",
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" <th>score</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>0</th>\n",
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" <td>1</td>\n",
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" <td>DM1</td>\n",
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" <td>15/09/16</td>\n",
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" <td>1</td>\n",
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" <td>1.1</td>\n",
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" <td>Cal</td>\n",
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" <td>Prio</td>\n",
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" <td></td>\n",
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" <td>1.0</td>\n",
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" <td>1</td>\n",
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" <td>ABDOU Asmahane</td>\n",
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" <td>2</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>1</th>\n",
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" <td>1</td>\n",
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" <td>DM1</td>\n",
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" <td>15/09/16</td>\n",
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" <td>1</td>\n",
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" <td>1.2</td>\n",
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" <td>Cal</td>\n",
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" <td>Prio</td>\n",
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" <td></td>\n",
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" <td>1.0</td>\n",
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" <td>1</td>\n",
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" <td>ABDOU Asmahane</td>\n",
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" <td>3</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>2</th>\n",
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" <td>1</td>\n",
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" <td>DM1</td>\n",
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" <td>15/09/16</td>\n",
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" <td>1</td>\n",
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" <td>1.3</td>\n",
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" <td>Cal</td>\n",
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" <td>Prio</td>\n",
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" <td></td>\n",
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" <td>1.0</td>\n",
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" <td>1</td>\n",
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" <td>ABDOU Asmahane</td>\n",
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" <td>2</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>3</th>\n",
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" <td>1</td>\n",
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" <td>DM1</td>\n",
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" <td>15/09/16</td>\n",
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" <td>1</td>\n",
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" <td>1.4</td>\n",
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" <td>Cal</td>\n",
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" <td>Prio</td>\n",
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" <td></td>\n",
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" <td>1.0</td>\n",
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" <td>1</td>\n",
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" <td>ABDOU Asmahane</td>\n",
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" <td>2</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>4</th>\n",
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" <td>1</td>\n",
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" <td>DM1</td>\n",
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" <td>15/09/16</td>\n",
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" <td>1</td>\n",
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" <td>1.5</td>\n",
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" <td>Cal</td>\n",
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" <td>Prio</td>\n",
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" <td></td>\n",
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" <td>1.0</td>\n",
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" <td>1</td>\n",
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" <td>ABDOU Asmahane</td>\n",
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" <td>2</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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" trimestre nom date exercice question competence domaine commentaire \\\n",
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"0 1 DM1 15/09/16 1 1.1 Cal Prio \n",
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"1 1 DM1 15/09/16 1 1.2 Cal Prio \n",
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"2 1 DM1 15/09/16 1 1.3 Cal Prio \n",
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"3 1 DM1 15/09/16 1 1.4 Cal Prio \n",
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"4 1 DM1 15/09/16 1 1.5 Cal Prio \n",
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"\n",
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" bareme niveau student score \n",
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"0 1.0 1 ABDOU Asmahane 2 \n",
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"1 1.0 1 ABDOU Asmahane 3 \n",
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"2 1.0 1 ABDOU Asmahane 2 \n",
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"3 1.0 1 ABDOU Asmahane 2 \n",
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"4 1.0 1 ABDOU Asmahane 2 "
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]
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},
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"execution_count": 4,
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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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"stack_scores = pd.read_csv(csv_file)\n",
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"scores = flat_clear_csv(stack_scores)\n",
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"scores.head()"
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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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