93 lines
2.8 KiB
Python
93 lines
2.8 KiB
Python
|
# Sebastian Raschka 2014-2020
|
||
|
|
# mlxtend Machine Learning Library Extensions
|
||
|
|
#
|
||
|
|
# Matplotlib wrapper for creating scatterplots from data w. mult. categories.
|
||
|
|
# Author: Sebastian Raschka <sebastianraschka.com>
|
||
|
|
#
|
||
|
|
# License: BSD 3 clause
|
||
|
|
|
||
|
|
|
||
|
|
import matplotlib.pyplot as plt
|
||
|
|
import pandas as pd
|
||
|
|
import numpy as np
|
||
|
|
from itertools import cycle
|
||
|
|
|
||
|
|
|
||
|
|
def category_scatter(x, y, label_col, data,
|
||
|
|
markers='sxo^v',
|
||
|
|
colors=('blue', 'green', 'red', 'purple', 'gray', 'cyan'),
|
||
|
|
alpha=0.7, markersize=20.0, legend_loc='best'):
|
||
|
|
""" Scatter plot to plot categories in different colors/markerstyles.
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
x : str or int
|
||
|
|
DataFrame column name of the x-axis values or
|
||
|
|
integer for the numpy ndarray column index.
|
||
|
|
y : str
|
||
|
|
DataFrame column name of the y-axis values or
|
||
|
|
integer for the numpy ndarray column index
|
||
|
|
data : Pandas DataFrame object or NumPy ndarray.
|
||
|
|
markers : str
|
||
|
|
Markers that are cycled through the label category.
|
||
|
|
colors : tuple
|
||
|
|
Colors that are cycled through the label category.
|
||
|
|
alpha : float (default: 0.7)
|
||
|
|
Parameter to control the transparency.
|
||
|
|
markersize : float (default : 20.0)
|
||
|
|
Parameter to control the marker size.
|
||
|
|
legend_loc : str (default: 'best')
|
||
|
|
Location of the plot legend
|
||
|
|
{best, upper left, upper right, lower left, lower right}
|
||
|
|
No legend if legend_loc=False
|
||
|
|
|
||
|
|
Returns
|
||
|
|
---------
|
||
|
|
fig : matplotlig.pyplot figure object
|
||
|
|
|
||
|
|
Examples
|
||
|
|
-----------
|
||
|
|
For usage examples, please see
|
||
|
|
http://rasbt.github.io/mlxtend/user_guide/plotting/category_scatter/
|
||
|
|
|
||
|
|
"""
|
||
|
|
fig = plt.figure()
|
||
|
|
color_gen = cycle(colors)
|
||
|
|
marker_gen = cycle(markers)
|
||
|
|
|
||
|
|
if isinstance(data, pd.DataFrame):
|
||
|
|
for i in (x, y, label_col):
|
||
|
|
assert(isinstance(i, str))
|
||
|
|
frame = True
|
||
|
|
labels = np.unique(data.loc[:, label_col])
|
||
|
|
|
||
|
|
elif isinstance(data, np.ndarray):
|
||
|
|
for i in (x, y, label_col):
|
||
|
|
assert(isinstance(i, int))
|
||
|
|
frame = False
|
||
|
|
labels = np.unique(data[:, label_col])
|
||
|
|
|
||
|
|
else:
|
||
|
|
raise ValueError('df must be pandas.DataFrame or numpy.ndarray object')
|
||
|
|
|
||
|
|
for lab in labels:
|
||
|
|
|
||
|
|
if frame:
|
||
|
|
x_dat = data.loc[data.loc[:, label_col] == lab, x]
|
||
|
|
y_dat = data.loc[data.loc[:, label_col] == lab, y]
|
||
|
|
else:
|
||
|
|
x_dat = data[data[:, label_col] == lab, x]
|
||
|
|
y_dat = data[data[:, label_col] == lab, y]
|
||
|
|
|
||
|
|
plt.scatter(x_dat,
|
||
|
|
y_dat,
|
||
|
|
c=next(color_gen),
|
||
|
|
marker=next(marker_gen),
|
||
|
|
label=lab,
|
||
|
|
alpha=alpha,
|
||
|
|
s=markersize)
|
||
|
|
|
||
|
|
if legend_loc:
|
||
|
|
plt.legend(loc=legend_loc, scatterpoints=1)
|
||
|
|
return fig
|