500 lines
18 KiB
Python
500 lines
18 KiB
Python
#!/usr/bin/env python
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# encoding: utf-8
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import pandas as pd
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import numpy as np
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from math import ceil, floor
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import logging
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logger = logging.getLogger(__name__)
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NOANSWER = "na"
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NORATED = ""
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# Values manipulations
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def round_half_point(val):
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try:
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return 0.5 * ceil(2.0 * val)
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except ValueError:
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return val
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except TypeError:
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return val
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def num_format(num):
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""" Tranform a number into an appropriate string """
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try:
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if int(num) == num:
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return str(int(num))
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except ValueError:
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pass
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return f"{num:.1f}".replace(".", ",")
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latex_caract = ["\\NoRep", "\\RepZ", "\\RepU", "\\RepD", "\\RepT"]
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def note_to_rep(x):
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r""" Transform a Note to the latex caracter
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:param x: dictionnary with "Niveau" and "Note" keys
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>>> d = {"Eleve":["E1"]*6 + ["E2"]*6,
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... "Nom": ["N1"]*4+["N2"]*2 + ["N1"]*4+["N2"]*2,
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... "Exercice":["Ex1"]*2+["Ex2"]*2+["Ex1"]+["Ex2"] + ["Ex1"]*2+["Ex2"]*2+["Ex1"]+["Ex2"],
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... "Question":["Q1"]+["Q2"]+["Q1"]+["Q2"]+["Q1"]+["Q1"] + ["Q1"]+["Q2"]+["Q1"]+["Q2"]+["Q1"]+["Q1"],
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... "Date":["16/09/2016"]*4+["01/10/2016"]*2 + ["16/09/2016"]*4+["01/10/2016"]*2,
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... "Trimestre": ["1"]*12,
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... "Bareme":[1]*2+[2]*2+[2]*2 + [1]*2+[2]*2+[2]*2,
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... "Niveau":[0]*4+[1]*2 + [0]*4+[1]*2,
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... "Note":[1, 0.33, 2, 1.5, 1, 3, 0.67, 1, 1.5, 1, 2, 3],
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... }
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>>> df = pd.DataFrame(d)
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>>> note_to_rep(df.loc[0])
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1.0
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>>> note_to_rep(df.loc[4])
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'\\RepU'
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"""
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if x["Niveau"]:
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if x["Note"] == NOANSWER:
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return latex_caract[0]
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elif x["Note"] in range(4):
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return latex_caract[int(x["Note"])+1]
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return x["Note"]
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def note_to_mark(x):
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""" Compute the mark when it is a "Niveau" note
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:param x: dictionnary with "Niveau", "Note" and "Bareme" keys
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>>> d = {"Eleve":["E1"]*6 + ["E2"]*6,
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... "Nom": ["N1"]*4+["N2"]*2 + ["N1"]*4+["N2"]*2,
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... "Exercice":["Ex1"]*2+["Ex2"]*2+["Ex1"]+["Ex2"] + ["Ex1"]*2+["Ex2"]*2+["Ex1"]+["Ex2"],
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... "Question":["Q1"]+["Q2"]+["Q1"]+["Q2"]+["Q1"]+["Q1"] + ["Q1"]+["Q2"]+["Q1"]+["Q2"]+["Q1"]+["Q1"],
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... "Date":["16/09/2016"]*4+["01/10/2016"]*2 + ["16/09/2016"]*4+["01/10/2016"]*2,
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... "Trimestre": ["1"]*12,
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... "Bareme":[1]*2+[2]*2+[2]*2 + [1]*2+[2]*2+[2]*2,
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... "Niveau":[0]*4+[1]*2 + [0]*4+[1]*2,
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... "Note":[1, 0.33, 2, 1.5, 1, 3, 0.666, 1, 1.5, 1, 2, 3],
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... }
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>>> df = pd.DataFrame(d)
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>>> note_to_mark(df.loc[0])
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1.0
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>>> note_to_mark(df.loc[10])
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1.3333333333333333
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"""
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if x["Niveau"]:
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if x["Note"] == NOANSWER:
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return 0
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return x["Note"] * x["Bareme"] / 3
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if x["Note"] > x["Bareme"]:
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logger.warning(f"The note ({x['Note']}) is greated than the rating scale ({x['Bareme']}) at {x}")
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return x["Note"]
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def note_to_level(x):
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""" Compute the level ("na",0,1,2,3).
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"na" correspond to "no answer"
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:param x: dictionnary with "Niveau", "Note" and "Bareme" keys
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>>> d = {"Eleve":["E1"]*6 + ["E2"]*6,
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... "Nom": ["N1"]*4+["N2"]*2 + ["N1"]*4+["N2"]*2,
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... "Exercice":["Ex1"]*2+["Ex2"]*2+["Ex1"]+["Ex2"] + ["Ex1"]*2+["Ex2"]*2+["Ex1"]+["Ex2"],
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... "Question":["Q1"]+["Q2"]+["Q1"]+["Q2"]+["Q1"]+["Q1"] + ["Q1"]+["Q2"]+["Q1"]+["Q2"]+["Q1"]+["Q1"],
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... "Date":["16/09/2016"]*4+["01/10/2016"]*2 + ["16/09/2016"]*4+["01/10/2016"]*2,
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... "Trimestre": ["1"]*12,
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... "Bareme":[1]*2+[2]*2+[2]*2 + [1]*2+[2]*2+[2]*2,
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... "Niveau":[0]*4+[1]*2 + [0]*4+[1]*2,
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... "Note":[1, 0.33, np.nan, 1.5, 1, 3, 0.666, 1, 1.5, 1, 2, 3],
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... }
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>>> df = pd.DataFrame(d)
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>>> note_to_level(df.loc[0])
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3
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>>> note_to_level(df.loc[1])
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1
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>>> note_to_level(df.loc[2])
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'na'
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>>> note_to_level(df.loc[3])
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3
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>>> note_to_level(df.loc[5])
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3
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>>> note_to_level(df.loc[10])
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2
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"""
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if x["Note"] == NOANSWER:
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return "na"
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if pd.isnull(x["Bareme"]) or x["Bareme"] == 0:
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return "na"
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if x["Niveau"]:
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return int(x["Note"])
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else:
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return int(ceil(x["Note"] / x["Bareme"] * 3))
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def mark_bareme_formater(row):
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""" Create m/b string """
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return f"{num_format(row['Mark'])} / {num_format(row['Bareme'])}"
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def question_uniq_formater(row):
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""" Create a kind of unique description of the question
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>>> d = {"Eleve":["E1"]*6 + ["E2"]*6,
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... "Nom": ["N1"]*4+["N2"]*2 + ["N1"]*4+["N2"]*2,
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... "Exercice":["Ex1"]*2+["Ex2"]*2+["Ex1"]+["Ex2"] + ["Ex1"]*2+["Ex2"]*2+["Ex1"]+["Ex2"],
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... "Question":["Q1"]+["Q2"]+["Q1"]+["Q2"]+["Q1"]+["Q1"] + ["Q1"]+["Q2"]+["Q1"]+["Q2"]+["Q1"]+["Q1"],
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... "Date":["16/09/2016"]*4+["01/10/2016"]*2 + ["16/09/2016"]*4+["01/10/2016"]*2,
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... "Trimestre": ["1"]*12,
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... "Bareme":[1]*2+[2]*2+[2]*2 + [1]*2+[2]*2+[2]*2,
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... "Niveau":[0]*4+[1]*2 + [0]*4+[1]*2,
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... "Note":[1, 0.33, 2, 1.5, 1, 3, 0.666, 1, 1.5, 1, 2, 3],
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... }
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>>> df = pd.DataFrame(d)
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>>> question_uniq_formater(df.loc[0])
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'Ex1 Q1'
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>>> question_uniq_formater(df.loc[10])
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'Ex1 Q1'
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"""
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ans = ""
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try:
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int(row['Exercice'])
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except ValueError:
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ans += str(row["Exercice"])
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else:
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ans += "Exo"+str(row["Exercice"])
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ans += " "
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try:
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int(row["Question"])
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except ValueError:
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if not pd.isnull(row["Question"]):
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ans += str(row["Question"])
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else:
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ans += "Qu"+str(row["Question"])
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try:
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row["Commentaire"]
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except KeyError:
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pass
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else:
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if not pd.isnull(row["Commentaire"]):
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ans += " ({})".format(row["Commentaire"])
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return ans
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# DataFrame columns manipulations
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def compute_marks(df):
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""" Add Mark column to df
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:param df: DataFrame with "Note", "Niveau" and "Bareme" columns.
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>>> d = {"Eleve":["E1"]*6 + ["E2"]*6,
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... "Nom": ["N1"]*4+["N2"]*2 + ["N1"]*4+["N2"]*2,
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... "Exercice":["Ex1"]*2+["Ex2"]*2+["Ex1"]+["Ex2"] + ["Ex1"]*2+["Ex2"]*2+["Ex1"]+["Ex2"],
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... "Question":["Q1"]+["Q2"]+["Q1"]+["Q2"]+["Q1"]+["Q1"] + ["Q1"]+["Q2"]+["Q1"]+["Q2"]+["Q1"]+["Q1"],
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... "Date":["16/09/2016"]*4+["01/10/2016"]*2 + ["16/09/2016"]*4+["01/10/2016"]*2,
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... "Trimestre": ["1"]*12,
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... "Bareme":[1]*2+[2]*2+[2]*2 + [1]*2+[2]*2+[2]*2,
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... "Niveau":[0]*4+[1]*2 + [0]*4+[1]*2,
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... "Note":[1, 0.33, 2, 1.5, 1, 3, 0.666, 1, 1.5, 1, 2, 3],
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... }
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>>> df = pd.DataFrame(d)
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>>> compute_marks(df)
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0 1.00
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1 0.33
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2 2.00
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3 1.50
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4 0.67
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5 2.00
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6 0.67
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7 1.00
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8 1.50
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9 1.00
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10 1.33
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11 2.00
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dtype: float64
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"""
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return df[["Note", "Niveau", "Bareme"]].apply(note_to_mark, axis=1)
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def compute_level(df):
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""" Add Mark column to df
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:param df: DataFrame with "Note", "Niveau" and "Bareme" columns.
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>>> d = {"Eleve":["E1"]*6 + ["E2"]*6,
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... "Nom": ["N1"]*4+["N2"]*2 + ["N1"]*4+["N2"]*2,
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... "Exercice":["Ex1"]*2+["Ex2"]*2+["Ex1"]+["Ex2"] + ["Ex1"]*2+["Ex2"]*2+["Ex1"]+["Ex2"],
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... "Question":["Q1"]+["Q2"]+["Q1"]+["Q2"]+["Q1"]+["Q1"] + ["Q1"]+["Q2"]+["Q1"]+["Q2"]+["Q1"]+["Q1"],
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... "Date":["16/09/2016"]*4+["01/10/2016"]*2 + ["16/09/2016"]*4+["01/10/2016"]*2,
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... "Trimestre": ["1"]*12,
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... "Bareme":[1]*2+[2]*2+[2]*2 + [1]*2+[2]*2+[2]*2,
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... "Niveau":[0]*4+[1]*2 + [0]*4+[1]*2,
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... "Note":[np.nan, 0.33, 2, 1.5, 1, 3, 0.666, 1, 1.5, 1, 2, 3],
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... }
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>>> df = pd.DataFrame(d)
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>>> compute_level(df)
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0 na
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1 1
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2 3
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3 3
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4 1
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5 3
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6 2
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7 3
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8 3
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9 2
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10 2
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11 3
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dtype: object
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"""
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return df[["Note", "Niveau", "Bareme"]].apply(note_to_level, axis=1)
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def compute_latex_rep(df):
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""" Add Latex_rep column to df
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:param df: DataFrame with "Note" and "Niveau" columns.
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>>> d = {"Eleve":["E1"]*6 + ["E2"]*6,
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... "Nom": ["N1"]*4+["N2"]*2 + ["N1"]*4+["N2"]*2,
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... "Exercice":["Ex1"]*2+["Ex2"]*2+["Ex1"]+["Ex2"] + ["Ex1"]*2+["Ex2"]*2+["Ex1"]+["Ex2"],
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... "Question":["Q1"]+["Q2"]+["Q1"]+["Q2"]+["Q1"]+["Q1"] + ["Q1"]+["Q2"]+["Q1"]+["Q2"]+["Q1"]+["Q1"],
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... "Date":["16/09/2016"]*4+["01/10/2016"]*2 + ["16/09/2016"]*4+["01/10/2016"]*2,
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... "Trimestre": ["1"]*12,
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... "Bareme":[1]*2+[2]*2+[2]*2 + [1]*2+[2]*2+[2]*2,
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... "Niveau":[0]*4+[1]*2 + [0]*4+[1]*2,
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... "Note":[1, 0.33, 2, 1.5, 1, 3, 0.666, 1, 1.5, 1, 2, 3],
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... }
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>>> df = pd.DataFrame(d)
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>>> compute_latex_rep(df)
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0 1
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1 0.33
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2 2
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3 1.5
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4 \RepU
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5 \RepT
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6 0.67
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7 1
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8 1.5
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9 1
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10 \RepD
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11 \RepT
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dtype: object
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"""
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return df[["Note", "Niveau"]].apply(note_to_rep, axis=1).fillna("??")
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def compute_normalized(df):
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""" Compute the normalized mark (Mark / Bareme)
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:param df: DataFrame with "Mark" and "Bareme" columns
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>>> d = {"Eleve":["E1"]*6 + ["E2"]*6,
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... "Nom": ["N1"]*4+["N2"]*2 + ["N1"]*4+["N2"]*2,
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... "Exercice":["Ex1"]*2+["Ex2"]*2+["Ex1"]+["Ex2"] + ["Ex1"]*2+["Ex2"]*2+["Ex1"]+["Ex2"],
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... "Question":["Q1"]+["Q2"]+["Q1"]+["Q2"]+["Q1"]+["Q1"] + ["Q1"]+["Q2"]+["Q1"]+["Q2"]+["Q1"]+["Q1"],
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... "Date":["16/09/2016"]*4+["01/10/2016"]*2 + ["16/09/2016"]*4+["01/10/2016"]*2,
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... "Trimestre": ["1"]*12,
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... "Bareme":[1]*2+[2]*2+[2]*2 + [1]*2+[2]*2+[2]*2,
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... "Niveau":[0]*4+[1]*2 + [0]*4+[1]*2,
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... "Note":[1, 0.33, 2, 1.5, 1, 3, 0.666, 1, 1.5, 1, 2, 3],
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... }
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>>> df = pd.DataFrame(d)
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>>> df["Mark"] = compute_marks(df)
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>>> compute_normalized(df)
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0 1.00
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1 0.33
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2 1.00
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3 0.75
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4 0.33
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5 1.00
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6 0.67
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7 1.00
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8 0.75
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9 0.50
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10 0.67
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11 1.00
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dtype: float64
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"""
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return df["Mark"] / df["Bareme"]
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def compute_mark_barem(df):
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""" Build the string mark m/b """
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return df.apply(mark_bareme_formater, axis=1)
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def compute_question_description(df):
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""" Compute the unique description of a question """
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return df.apply(question_uniq_formater, axis = 1)
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# Computing custom values
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def compute_exo_marks(df):
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""" Compute Exercice level marks
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:param df: the original marks
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:returns: DataFrame with computed marks
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>>> d = {"Eleve":["E1"]*6 + ["E2"]*6,
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... "Nom": ["N1"]*4+["N2"]*2 + ["N1"]*4+["N2"]*2,
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... "Exercice":["Ex1"]*2+["Ex2"]*2+["Ex1"]+["Ex2"] + ["Ex1"]*2+["Ex2"]*2+["Ex1"]+["Ex2"],
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... "Question":["Q1"]+["Q2"]+["Q1"]+["Q2"]+["Q1"]+["Q1"] + ["Q1"]+["Q2"]+["Q1"]+["Q2"]+["Q1"]+["Q1"],
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... "Date":["16/09/2016"]*4+["01/10/2016"]*2 + ["16/09/2016"]*4+["01/10/2016"]*2,
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... "Trimestre": ["1"]*12,
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... "Bareme":[1]*2+[2]*2+[2]*2 + [1]*2+[2]*2+[2]*2,
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... "Niveau":[0]*4+[1]*2 + [0]*4+[1]*2,
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... "Note":[1, 0.33, 2, 1.5, 1, 3, 0.67, 1, 1.5, 1, 2, 3],
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... }
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>>> df = pd.DataFrame(d)
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>>> df["Mark"] = compute_marks(df)
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>>> compute_exo_marks(df)
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Eleve Nom Exercice Date Trimestre Bareme Mark Question Niveau
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0 E1 N1 Ex1 16/09/2016 1 2.0 1.5 Total 0
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1 E1 N1 Ex2 16/09/2016 1 4.0 3.5 Total 0
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2 E1 N2 Ex1 01/10/2016 1 2.0 1.0 Total 0
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3 E1 N2 Ex2 01/10/2016 1 2.0 2.0 Total 0
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4 E2 N1 Ex1 16/09/2016 1 2.0 2.0 Total 0
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5 E2 N1 Ex2 16/09/2016 1 4.0 2.5 Total 0
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6 E2 N2 Ex1 01/10/2016 1 2.0 1.5 Total 0
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7 E2 N2 Ex2 01/10/2016 1 2.0 2.0 Total 0
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"""
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exo_pt = pd.pivot_table(df,
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index = [ "Eleve", "Nom", "Exercice", "Date", "Trimestre"],
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values = ["Bareme", "Mark"],
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aggfunc=np.sum,
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).applymap(round_half_point)
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exo = exo_pt.reset_index()
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exo["Question"] = "Total"
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exo["Niveau"] = 0
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return exo
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def compute_eval_marks(df):
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""" Compute Nom level marks from the dataframe using only row with Total in Question
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:param df: DataFrame with value Total in Question column
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:returns: DataFrame with evaluation marks
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>>> d = {"Eleve":["E1"]*6 + ["E2"]*6,
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... "Nom": ["N1"]*4+["N2"]*2 + ["N1"]*4+["N2"]*2,
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... "Exercice":["Ex1"]*2+["Ex2"]*2+["Ex1"]+["Ex2"] + ["Ex1"]*2+["Ex2"]*2+["Ex1"]+["Ex2"],
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... "Question":["Q1"]+["Q2"]+["Q1"]+["Q2"]+["Q1"]+["Q1"] + ["Q1"]+["Q2"]+["Q1"]+["Q2"]+["Q1"]+["Q1"],
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... "Date":["16/09/2016"]*4+["01/10/2016"]*2 + ["16/09/2016"]*4+["01/10/2016"]*2,
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... "Trimestre": ["1"]*12,
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... "Bareme":[1]*2+[2]*2+[2]*2 + [1]*2+[2]*2+[2]*2,
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... "Niveau":[0]*4+[1]*2 + [0]*4+[1]*2,
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... "Note":[1, 0.33, 2, 1.5, 1, 3, 0.67, 1, 1.5, 1, 2, 3],
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... }
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>>> df = pd.DataFrame(d)
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>>> df["Mark"] = compute_marks(df)
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>>> df_exo = compute_exo_marks(df)
|
|
>>> compute_eval_marks(df_exo)
|
|
index Eleve Nom Trimestre Bareme Date Mark
|
|
0 0 E1 N1 1 6.0 16/09/2016 5.0
|
|
1 1 E2 N1 1 6.0 16/09/2016 4.5
|
|
2 0 E1 N2 1 4.0 01/10/2016 3.0
|
|
3 1 E2 N2 1 4.0 01/10/2016 3.5
|
|
|
|
|
|
"""
|
|
def date_format(dates):
|
|
date_l = list(dates.unique())
|
|
if len(date_l) == 1:
|
|
return date_l[0]
|
|
else:
|
|
return "Trimestre"
|
|
|
|
eval_m = pd.DataFrame()
|
|
for eval_name in df["Nom"].unique():
|
|
logger.debug(f"Compute marks for {eval_name}")
|
|
eval_df = df[df["Nom"] == eval_name]
|
|
dates = eval_df["Date"].unique()
|
|
logger.debug(f"Find those dates: {dates}")
|
|
if len(dates) > 1 or dates[0] == "Trimestre":
|
|
# Les devoirs sur la durée, les NaN ne sont pas pénalisants
|
|
# On les enlèves
|
|
eval_df = eval_df.dropna(subset=["Mark"])
|
|
dates = ["Trimestre"]
|
|
|
|
eval_pt = pd.pivot_table(eval_df,
|
|
index = [ "Eleve", "Nom", "Trimestre"],
|
|
values = ["Bareme", "Mark", "Date"],
|
|
aggfunc={"Bareme": np.sum, "Mark": np.sum, "Date":lambda x:dates[0]},
|
|
)
|
|
eval_pt = eval_pt.reset_index()
|
|
eval_m = pd.concat([eval_m, eval_pt])
|
|
|
|
eval_m = eval_m.reset_index()
|
|
|
|
return eval_m
|
|
|
|
def digest_flat_df(flat_df):
|
|
r""" Compute necessary element to make a flat df usable for analysis.
|
|
|
|
>>> from numpy import nan
|
|
>>> d = {"Eleve":["E1"]*6 + ["E2"]*6,
|
|
... "Nom": ["N1"]*4+["N2"]*2 + ["N1"]*4+["N2"]*2,
|
|
... "Exercice":["Ex1"]*2+["Ex2"]*2+["Ex1"]+["Ex2"] + ["Ex1"]*2+["Ex2"]*2+["Ex1"]+["Ex2"],
|
|
... "Question":["Q1"]+["Q2"]+["Q1"]+["Q2"]+["Q1"]+["Q1"] + ["Q1"]+["Q2"]+["Q1"]+["Q2"]+["Q1"]+["Q1"],
|
|
... "Date":["16/09/2016"]*4+["01/10/2016"]*2 + ["16/09/2016"]*4+["01/10/2016"]*2,
|
|
... "Trimestre": ["1"]*12,
|
|
... "Bareme":[1]*2+[2]*2+[2]*2 + [1]*2+[2]*2+[2]*2,
|
|
... "Niveau":[0]*4+[1]*2 + [0]*4+[1]*2,
|
|
... "Note":[1, 0.33, 2, 1.5, 1, 3, np.nan, 0, 0, np.nan, np.nan, np.nan],
|
|
... }
|
|
>>> df = pd.DataFrame(d)
|
|
>>> quest_df, exo_df, eval_df = digest_flat_df(df)
|
|
>>> quest_df[['Eleve', "Nom", "Mark", "Latex_rep", "Normalized", "Uniq_quest", "Level"]]
|
|
Eleve Nom Mark Latex_rep Normalized Uniq_quest Level
|
|
0 E1 N1 1.00 1 1.00 Ex1 Q1 3
|
|
1 E1 N1 0.33 0.33 0.33 Ex1 Q2 1
|
|
2 E1 N1 2.00 2 1.00 Ex2 Q1 3
|
|
3 E1 N1 1.50 1.5 0.75 Ex2 Q2 3
|
|
4 E1 N2 0.67 \RepU 0.33 Ex1 Q1 1
|
|
5 E1 N2 2.00 \RepT 1.00 Ex2 Q1 3
|
|
6 E2 N1 NaN ?? NaN Ex1 Q1 na
|
|
7 E2 N1 0.00 0 0.00 Ex1 Q2 0
|
|
8 E2 N1 0.00 0 0.00 Ex2 Q1 0
|
|
9 E2 N1 NaN ?? NaN Ex2 Q2 na
|
|
10 E2 N2 NaN \NoRep NaN Ex1 Q1 na
|
|
11 E2 N2 NaN \NoRep NaN Ex2 Q1 na
|
|
>>> exo_df[['Eleve', "Nom", "Exercice", "Mark", "Normalized"]]
|
|
Eleve Nom Exercice Mark Normalized
|
|
0 E1 N1 Ex1 1.5 0.75
|
|
1 E1 N1 Ex2 3.5 0.88
|
|
2 E1 N2 Ex1 1.0 0.50
|
|
3 E1 N2 Ex2 2.0 1.00
|
|
4 E2 N1 Ex1 0.0 0.00
|
|
5 E2 N1 Ex2 0.0 0.00
|
|
6 E2 N2 Ex1 NaN NaN
|
|
7 E2 N2 Ex2 NaN NaN
|
|
>>> eval_df
|
|
index Eleve Nom Trimestre Bareme Date Mark Normalized
|
|
0 0 E1 N1 1 6.0 16/09/2016 5.0 0.83
|
|
1 1 E2 N1 1 6.0 16/09/2016 0.0 0.00
|
|
2 0 E1 N2 1 4.0 01/10/2016 3.0 0.75
|
|
3 1 E2 N2 1 4.0 01/10/2016 NaN NaN
|
|
"""
|
|
df = flat_df.dropna(subset=["Note"])
|
|
|
|
df["Mark"] = compute_marks(df)
|
|
df["Level"] = compute_level(df)
|
|
df["Latex_rep"] = compute_latex_rep(df)
|
|
df["Normalized"] = compute_normalized(df)
|
|
df["Uniq_quest"] = compute_question_description(df)
|
|
|
|
exo_df = compute_exo_marks(df)
|
|
exo_df["Normalized"] = compute_normalized(exo_df)
|
|
exo_df["Mark_barem"] = compute_mark_barem(exo_df)
|
|
eval_df = compute_eval_marks(exo_df)
|
|
eval_df["Normalized"] = compute_normalized(eval_df)
|
|
eval_df["Mark_barem"] = compute_mark_barem(eval_df)
|
|
|
|
return df, exo_df, eval_df
|
|
|
|
|
|
# -----------------------------
|
|
# 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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