add level column
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Benjamin Bertrand
parent
0abd2be854
commit
0fba0017fe
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@ -3,7 +3,7 @@
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import pandas as pd
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import numpy as np
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from math import ceil
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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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@ -47,7 +47,7 @@ def note_to_rep(x):
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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 "Nivea" note
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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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@ -72,6 +72,46 @@ def note_to_mark(x):
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return x["Note"] * x["Bareme"] / 3
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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 pd.isnull(x["Note"]):
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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 question_uniq_formater(row):
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""" Create a kind of unique description of the question
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@ -154,6 +194,39 @@ def compute_marks(df):
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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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@ -340,24 +413,24 @@ def digest_flat_df(flat_df):
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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, nan, 0, 0, nan, nan, nan],
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... "Note":[1, 0.33, 2, 1.5, 1, 3, np.nan, 0, 0, np.nan, np.nan, np.nan],
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... }
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>>> df = pd.DataFrame(d)
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>>> quest_df, exo_df, eval_df = digest_flat_df(df)
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>>> quest_df[['Eleve', "Nom", "Mark", "Latex_rep", "Normalized", "Uniq_quest"]]
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Eleve Nom Mark Latex_rep Normalized Uniq_quest
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0 E1 N1 1.00 1 1.00 Ex1 Q1
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1 E1 N1 0.33 0.33 0.33 Ex1 Q2
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2 E1 N1 2.00 2 1.00 Ex2 Q1
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3 E1 N1 1.50 1.5 0.75 Ex2 Q2
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4 E1 N2 0.67 \RepU 0.33 Ex1 Q1
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5 E1 N2 2.00 \RepT 1.00 Ex2 Q1
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6 E2 N1 NaN ?? NaN Ex1 Q1
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7 E2 N1 0.00 0 0.00 Ex1 Q2
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8 E2 N1 0.00 0 0.00 Ex2 Q1
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9 E2 N1 NaN ?? NaN Ex2 Q2
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10 E2 N2 NaN \NoRep NaN Ex1 Q1
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11 E2 N2 NaN \NoRep NaN Ex2 Q1
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>>> quest_df[['Eleve', "Nom", "Mark", "Latex_rep", "Normalized", "Uniq_quest", "Level"]]
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Eleve Nom Mark Latex_rep Normalized Uniq_quest Level
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0 E1 N1 1.00 1 1.00 Ex1 Q1 3
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1 E1 N1 0.33 0.33 0.33 Ex1 Q2 1
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2 E1 N1 2.00 2 1.00 Ex2 Q1 3
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3 E1 N1 1.50 1.5 0.75 Ex2 Q2 3
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4 E1 N2 0.67 \RepU 0.33 Ex1 Q1 1
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5 E1 N2 2.00 \RepT 1.00 Ex2 Q1 3
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6 E2 N1 NaN ?? NaN Ex1 Q1 na
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7 E2 N1 0.00 0 0.00 Ex1 Q2 0
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8 E2 N1 0.00 0 0.00 Ex2 Q1 0
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9 E2 N1 NaN ?? NaN Ex2 Q2 na
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10 E2 N2 NaN \NoRep NaN Ex1 Q1 na
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11 E2 N2 NaN \NoRep NaN Ex2 Q1 na
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>>> exo_df[['Eleve', "Nom", "Exercice", "Mark", "Normalized"]]
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Eleve Nom Exercice Mark Normalized
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0 E1 N1 Ex1 1.5 0.75
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@ -376,8 +449,9 @@ def digest_flat_df(flat_df):
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3 1 E2 N2 1 4.0 01/10/2016 NaN NaN
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"""
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# Remove data with "nn" (non notés)
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df = flat_df.copy()[flat_df["Note"] != "nn"]
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df = flat_df.copy()[flat_df["Note"].astype("object") != "nn"]
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df["Mark"] = compute_marks(df)
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df["Level"] = compute_level(df)
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df["Latex_rep"] = compute_latex_rep(df)
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df["Normalized"] = compute_normalized(df)
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df["Uniq_quest"] = compute_question_description(df)
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