plotszoo
This repository contains a collection of classes to easily make some common plots as well as retrieving data from multiple sources.
This project mostly fits my needs and it doesn't want to be in any way complete nor general purpose.
Feel free to use it, submit your own plots, bugfixes, requests or ideas. Any PR is very welcomed.
Repository under active development, braking changes may (and will) occur
Installation
pip install plotszoo
Examples
Parallel Coordinates Plot
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.datasets import load_iris
import plotszoo
iris = load_iris()
scalars = pd.DataFrame(data= np.c_[iris["data"], iris["target"]], columns=iris["feature_names"] + ["target"])
data = plotszoo.data.DataCollection()
data.set_scalars(scalars)
fig, axes = plt.subplots(1, len(iris["feature_names"]), sharey=False)
parallel_plot = plotszoo.scalars.ScalarsParallelCoordinates(data, iris["feature_names"], "target")
parallel_plot.plot(axes, cmap="tab10")
fig.set_size_inches(30, 10)
fig.show()
Grouped Series Parade
import numpy as np
import pandas as pd
import os
import matplotlib.pyplot as plt
import plotszoo
np.random.seed(0)
num_series = 10
samples = 100
noise_level = 0.8
x = np.linspace(0, np.pi*2, samples)
types = []
series = {}
for _ in range(0, num_series):
noisy_sin = np.sin(x) + np.random.rand(samples)*noise_level
series[len(types)] = pd.DataFrame(noisy_sin, columns=["value"])
types.append("sin")
noisy_cos = np.cos(x) + np.random.rand(samples)*noise_level
series[len(types)] = pd.DataFrame(noisy_cos, columns=["value"])
types.append("cos")
data = plotszoo.data.DataCollection()
data.set_scalars(pd.DataFrame(types, columns=["type"]))
data.set_series(series)
fig, ax = plt.subplots()
series_parade = plotszoo.series.grouped.GroupedSeriesParade(data, ["type"], "value")
series_parade.plot(ax)
fig.show()
GroupedScalarsBarchart
import numpy as np
import pandas as pd
import os
import matplotlib.pyplot as plt
from sklearn.datasets import load_iris
import plotszoo
iris = load_iris()
scalars = pd.DataFrame(data= np.c_[iris["data"], iris["target"]], columns=iris["feature_names"] + ["target"])
data = plotszoo.data.DataCollection()
data.set_scalars(scalars)
fig, axes = plt.subplots(2, 2)
yticks_fn = lambda x: iris["target_names"][int(x)]
for feature, ax in zip(iris["feature_names"], axes.flatten()):
barchart = plotszoo.scalars.grouped.GroupedScalarsBarchart(data, ["target"], feature)
barchart.plot(ax, title=feature, nbins=20, grid=True, yticks_fn=yticks_fn)
fig.set_size_inches(20, 10)
fig.show()
Contributions and license
The code is released as Free Software under the GNU/GPLv3 license. Copying, adapting e republishing it is not only consent but also encouraged.
For any further question feel free to reach me at federico.galatolo@ing.unipi.it or on Telegram @galatolo
