pass doctests
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@ -29,7 +29,7 @@ def note_to_rep(x):
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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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... "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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@ -136,18 +136,18 @@ def compute_marks(df):
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... }
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>>> df = pd.DataFrame(d)
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>>> compute_marks(df)
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0 1.000000
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1 0.330000
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2 2.000000
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3 1.500000
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4 0.666667
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5 2.000000
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6 0.666000
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7 1.000000
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8 1.500000
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9 1.000000
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10 1.333333
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11 2.000000
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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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@ -175,7 +175,7 @@ def compute_latex_rep(df):
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3 1.5
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4 \RepU
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5 \RepT
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6 0.666
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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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@ -203,18 +203,18 @@ def compute_normalized(df):
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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 1.000000
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1 0.330000
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2 1.000000
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3 0.750000
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4 0.333333
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5 1.000000
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6 0.666000
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7 1.000000
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8 0.750000
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9 0.500000
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10 0.666667
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11 1.000000
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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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@ -239,7 +239,7 @@ def compute_exo_marks(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, 0.666, 1, 1.5, 1, 2, 3],
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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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@ -281,7 +281,7 @@ def compute_eval_marks(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, 0.666, 1, 1.5, 1, 2, 3],
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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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@ -341,34 +341,34 @@ def digest_flat_df(flat_df):
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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 1.000000 1 1.000000 Ex1 Q1
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1 E1 N1 0.330000 0.33 0.330000 Ex1 Q2
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2 E1 N1 2.000000 2 1.000000 Ex2 Q1
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3 E1 N1 1.500000 1.5 0.750000 Ex2 Q2
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4 E1 N2 0.666667 \RepU 0.333333 Ex1 Q1
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5 E1 N2 2.000000 \RepT 1.000000 Ex2 Q1
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6 E2 N1 NaN ?? NaN Ex1 Q1
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7 E2 N1 0.000000 0 0.000000 Ex1 Q2
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8 E2 N1 0.000000 0 0.000000 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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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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>>> 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 0.750
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1 E1 N1 Ex2 3.5 0.875
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2 E1 N2 Ex1 1.0 0.500
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3 E1 N2 Ex2 2.0 1.000
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4 E2 N1 Ex1 0.0 0.000
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5 E2 N1 Ex2 0.0 0.000
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0 E1 N1 Ex1 1.5 0.75
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1 E1 N1 Ex2 3.5 0.88
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2 E1 N2 Ex1 1.0 0.50
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3 E1 N2 Ex2 2.0 1.00
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4 E2 N1 Ex1 0.0 0.00
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5 E2 N1 Ex2 0.0 0.00
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6 E2 N2 Ex1 NaN NaN
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7 E2 N2 Ex2 NaN NaN
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>>> eval_df
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index Eleve Nom Trimestre Bareme Date Mark Normalized
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0 0 E1 N1 1 6.0 16/09/2016 5.0 0.833333
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1 1 E2 N1 1 6.0 16/09/2016 0.0 0.000000
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2 0 E1 N2 1 4.0 01/10/2016 3.0 0.750000
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0 0 E1 N1 1 6.0 16/09/2016 5.0 0.83
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1 1 E2 N1 1 6.0 16/09/2016 0.0 0.00
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2 0 E1 N2 1 4.0 01/10/2016 3.0 0.75
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3 1 E2 N2 1 4.0 01/10/2016 NaN NaN
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"""
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df = flat_df.copy()
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@ -50,6 +50,7 @@ def keep_only_presents(quest_df, exo_df, eval_df, presents):
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def students_pov(quest_df, exo_df, eval_df):
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"""
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>>> from .df_marks_manip import digest_flat_df
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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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