repytex/notes_tools/tools/marks_plottings.py

#!/usr/bin/env python
# encoding: utf-8

from .plottings import radar_graph
from .skills_tools import count_levels, count_skill_evaluation
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np

__all__ = ["radar_on", 
        "pie_skill_evaluation",
        "pies_skills_level",
        "marks_hist",
        "parallele_on",
        ]

def radar_on(df, index, optimum = None):
    """ Plot the radar graph concerning index column of the df

    :param df: DataFrame with index and "Normalized" column
    :returns: exes with radar plot

    """
    comp_pt = pd.pivot_table(df,
            index = [index],
            values = ["Normalized"],
            aggfunc=np.mean,
            )
    labels = list(comp_pt.index)
    values = [i[0] for i in comp_pt.values]
    if optimum is None:
        optimum = [1]*len(values)
    fig, ax = radar_graph(labels, values, optimum)
    return fig, ax

def pie_skill_evaluation(df, skill):
    """ Plot a pie plot with the repartition of skill evaluations
    """
    ax = count_skill_evaluation(df, skill).plot.pie(autopct='%.0f')
    return ax

def pies_skills_level(df, skill):
    """ Plot series of pies (one by different skill) with level repartition """
    levels_counts = count_levels(df, skill)
    fig, ax = plt.subplots(nrows=1, ncols=len(levels_counts), figsize=(16,3))
    plots = levels_counts.T.plot(ax=ax ,kind="pie", subplots=True, legend=False)
    return fig, ax

def marks_hist(df):
    """ Return axe for the histogramme of the dataframe  

    :param df: Dataframe with "Mark" and "Bareme" columns. If it has "Nom" column, it is use in title.
    """
    bareme = df["Bareme"].max()
    bins = int(bareme*2)

    ax = df["Mark"].hist(bins = bins, range=(0,bareme))

    try:
        nom = df["Nom"].unique()
    except KeyError:
        title="Histogramme"
    else:
        title="Histogramme pour {}".format(" ".join(nom))

    ax.set_title(title)

    return ax

def parallele_on(df, index, student=None):
    """ Plot parallele one line by student

    :param df: TODO
    :param index: TODO
    :returns: TODO

    """
    pt = pd.pivot_table(df,
            index = [index],
            values = ["Normalized"],
            columns = ["Eleve"],
            aggfunc = np.mean,
            )["Normalized"]
    ax = pt.plot(color="b", figsize=(10,5), legend=False)
    pt.T.describe().T[["min", "25%","50%", "75%", "max"]].plot(ax=ax,
            kind='area', stacked=False, alpha=0.2)
    if not student is None:
        pt.ix[:,student].plot(ax=ax, color="r")
    return ax


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