117 lines
3.3 KiB
Python
117 lines
3.3 KiB
Python
#!/usr/bin/env python
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# encoding: utf-8
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from .plottings import radar_graph, pivot_table_to_pie
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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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import logging
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logger = logging.getLogger(__name__)
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__all__ = ["radar_on",
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"pie_pivot_table",
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"marks_hist",
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"parallel_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_pivot_table(df, pies_per_lines = 3, **kwargs):
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""" Plot a pie plot of the pivot_table of df
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:param df: the dataframe.
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:param pies_per_lines: Number of pies per line.
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:param kwargs: arguments to pass to pd.pivot_table.
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"""
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logger.debug(f"pie_pivot_table avec les arguments {kwargs}")
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pv = pd.pivot_table(df, **kwargs)
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return pivot_table_to_pie(pv, pies_per_lines)
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def marks_hist(df, **kwargs):
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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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:param kwargs: argument to pass to hist
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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), **kwargs)
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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 hist_boxplot(df, kwargs_hist=[], kwargs_box=[]):
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f, (ax_hist, ax_box) = plt.subplots(2, sharex=True,
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gridspec_kw={"height_ratios": (.85, .15)})
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marks_hist(df, ax = ax_hist, rwidth=0.9)
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ev_desc = df["Mark"].describe()
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m = round(ev_desc["mean"], 1)
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ax_hist.plot([m,m], ax_hist.get_ylim())
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ax_hist.annotate(round(ev_desc["mean"],1),
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xy=(ev_desc["mean"] + 0.2, ax_hist.get_ylim()[1]-0.2))
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df["Mark"].plot.box(ax = ax_box, vert=False, widths = 0.6)
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ax_box.set_yticklabels("")
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for e in ["min", "25%", "50%", "75%", "max"]:
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ax_box.annotate(round(ev_desc[e], 1),
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xy=(ev_desc[e] - 0.2, ax_box.get_ylim()[1]))
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return f, (ax_hist, ax_box)
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def parallel_on(df, index, student=None):
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""" Plot parallel 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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