repytex/notes_tools/tools/marks_plottings.py
2017-01-31 14:06:45 +03:00

74 lines
1.9 KiB
Python

#!/usr/bin/env python
# encoding: utf-8
from .plottings import radar_graph
import pandas as pd
import numpy as np
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 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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