compute_eval_marks manage onver the time evaluations
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@ -294,9 +294,6 @@ def compute_eval_marks(df):
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3 E2 N2 1 4.0 01/10/2016 3.5 Total 0
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"""
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#exo = df[df["Question"] == "Total"]
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#eval_pt = pd.pivot_table(exo,
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def date_format(dates):
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date_l = list(dates.unique())
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if len(date_l) == 1:
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@ -304,16 +301,25 @@ def compute_eval_marks(df):
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else:
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return "Trimestre"
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eval_pt = pd.pivot_table(df,
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#index = [ "Eleve", "Nom", "Date", "Trimestre"],
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index = [ "Eleve", "Nom", "Trimestre"],
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values = ["Bareme", "Mark", "Date"],
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aggfunc={"Bareme": np.sum, "Mark": np.sum, "Date": date_format},
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).applymap(round_half_point)
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eval_m = pd.DataFrame()
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for eval_name in df["Nom"].unique():
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eval_df = df[df["Nom"] == eval_name]
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dates = eval_df["Date"].unique()
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if len(dates) > 1:
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# Les devoirs sur la durée, les NaN ne sont pas pénalisants
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# On les enlèves
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eval_df = eval_df.dropna(subset=["Mark"])
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dates = ["Trimestre"]
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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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)
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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_pt.reset_index()
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eval_m["Exercice"] = "Total"
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eval_m["Niveau"] = 0
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return eval_m
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def digest_flat_df(flat_df):
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