96 lines
2.6 KiB
Python
96 lines
2.6 KiB
Python
#!/usr/bin/env python
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# encoding: utf-8
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from .plottings import radar_graph
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from .skills_tools import count_levels, count_skill_evaluation
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import matplotlib.pyplot as plt
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import pandas as pd
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import numpy as np
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__all__ = ["radar_on",
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"pie_skill_evaluation",
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"pies_skills_level",
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"marks_hist",
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"parallele_on",
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]
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def radar_on(df, index, optimum = None):
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""" Plot the radar graph concerning index column of the df
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:param df: DataFrame with index and "Normalized" column
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:returns: exes with radar plot
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"""
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comp_pt = pd.pivot_table(df,
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index = [index],
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values = ["Normalized"],
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aggfunc=np.mean,
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)
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labels = list(comp_pt.index)
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values = [i[0] for i in comp_pt.values]
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if optimum is None:
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optimum = [1]*len(values)
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fig, ax = radar_graph(labels, values, optimum)
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return fig, ax
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def pie_skill_evaluation(df, skill):
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""" Plot a pie plot with the repartition of skill evaluations
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"""
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ax = count_skill_evaluation(df, skill).plot.pie(autopct='%.0f')
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return ax
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def pies_skills_level(df, skill):
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""" Plot series of pies (one by different skill) with level repartition """
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levels_counts = count_levels(df, skill)
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fig, ax = plt.subplots(nrows=1, ncols=len(levels_counts), figsize=(16,3))
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plots = levels_counts.T.plot(ax=ax ,kind="pie", subplots=True, legend=False)
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return fig, ax
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def marks_hist(df):
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""" Return axe for the histogramme of the dataframe
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:param df: Dataframe with "Mark" and "Bareme" columns. If it has "Nom" column, it is use in title.
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"""
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bareme = df["Bareme"].max()
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bins = int(bareme*2)
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ax = df["Mark"].hist(bins = bins, range=(0,bareme))
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try:
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nom = df["Nom"].unique()
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except KeyError:
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title="Histogramme"
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else:
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title="Histogramme pour {}".format(" ".join(nom))
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ax.set_title(title)
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return ax
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def parallele_on(df, index, student=None):
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""" Plot parallele one line by student
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:param df: TODO
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:param index: TODO
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:returns: TODO
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"""
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pt = pd.pivot_table(df,
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index = [index],
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values = ["Normalized"],
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columns = ["Eleve"],
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aggfunc = np.mean,
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)["Normalized"]
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ax = pt.plot(color="b", figsize=(10,5), legend=False)
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pt.T.describe().T[["min", "25%","50%", "75%", "max"]].plot(ax=ax,
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kind='area', stacked=False, alpha=0.2)
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if not student is None:
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pt.ix[:,student].plot(ax=ax, color="r")
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return ax
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# -----------------------------
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# 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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