doctest fixes
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@ -287,11 +287,12 @@ def compute_eval_marks(df):
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>>> df["Mark"] = compute_marks(df)
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>>> df_exo = compute_exo_marks(df)
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>>> compute_eval_marks(df_exo)
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Eleve Nom Trimestre Bareme Date Mark Exercice Niveau
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0 E1 N1 1 6.0 16/09/2016 5.0 Total 0
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1 E1 N2 1 4.0 01/10/2016 3.0 Total 0
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2 E2 N1 1 6.0 16/09/2016 4.5 Total 0
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3 E2 N2 1 4.0 01/10/2016 3.5 Total 0
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index Eleve Nom Trimestre Bareme Date Mark
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0 0 E1 N1 1 6.0 16/09/2016 5.0
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1 1 E2 N1 1 6.0 16/09/2016 4.5
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2 0 E1 N2 1 4.0 01/10/2016 3.0
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3 1 E2 N2 1 4.0 01/10/2016 3.5
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"""
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def date_format(dates):
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@ -313,12 +314,13 @@ def compute_eval_marks(df):
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eval_pt = pd.pivot_table(eval_df,
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index = [ "Eleve", "Nom", "Trimestre"],
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values = ["Bareme", "Mark", "Normalized", "Date"],
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aggfunc={"Bareme": np.sum, "Mark": np.sum,"Normalized":np.mean, "Date":lambda x:dates[0]},
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values = ["Bareme", "Mark", "Date"],
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aggfunc={"Bareme": np.sum, "Mark": np.sum, "Date":lambda x:dates[0]},
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)
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eval_pt = eval_pt.reset_index()
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eval_m = pd.concat([eval_m, eval_pt])
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eval_m = eval_m.reset_index()
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return eval_m
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@ -362,12 +364,12 @@ def digest_flat_df(flat_df):
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5 E2 N1 Ex2 0.0 0.000
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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[['Eleve', "Nom", "Mark", "Normalized"]]
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Eleve Nom Mark Normalized
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0 E1 N1 5.0 0.833333
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1 E1 N2 3.0 0.750000
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2 E2 N1 0.0 0.000000
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3 E2 N2 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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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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df["Mark"] = compute_marks(flat_df)
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