可视化篇(五)——— python绘制热力图及案例
可视化篇(五)——— python绘制热力图及案例摘要效果图python代码摘要本文演示了如何通过python绘制热力图,并给出了其应用于展示数据之间相关性的案例供读者参考。效果图python代码from matplotlib import font_managerimport matplotlibimport matplotlib.pyplot as pltimport numpy as npi
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摘要
本文演示了如何通过python绘制热力图,并给出了其应用于展示数据之间相关性的案例供读者参考。
效果图
python代码
from matplotlib import font_manager
import matplotlib
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
class CyrusPlot(object):
def __init__(self,dpi=72,fig_size=[30,20]):
"""
实列化该类,然后直接调用cyrus_heat_map方法
:param dpi:
:param fig_size:
"""
self.dpi = dpi
self.fig_size = fig_size
self.font = font_manager.FontProperties(fname="C:\Windows\Fonts\simhei.ttf", size=30)
def cyrus_heat_map(self,datas,x_ticks = [],y_ticks = [],bar_label = "bar label",show = True,save_name = ""):
figure = plt.figure(figsize=self.fig_size, dpi=self.dpi)
ax = figure.add_subplot(111)
if not x_ticks:
x_ticks = ["x"+str(i) for i in range(datas.shape[1])]
y_ticks = ["y" + str(i) for i in range(datas.shape[0])]
im, _ = self.heatmap(np.array(datas), x_ticks, y_ticks,
cmap="RdBu", cbarlabel=bar_label,ax=ax) # plt.cm.RdBu PuOr
self.annotate_heatmap(im, valfmt="{x:.2f}", size=16)
if save_name:
plt.savefig("./figure/" + save_name + ".jpg")
if show:
plt.show()
def heatmap(self,data, row_labels, col_labels, ax=None,
cbar_kw={}, cbarlabel="", **kwargs):
if not ax:
ax = plt.gca()
im = ax.imshow(data, **kwargs)
cbar = ax.figure.colorbar(im, ax=ax, **cbar_kw)
cbar.ax.set_ylabel(cbarlabel, rotation=-90, va="bottom",fontproperties=font_manager.FontProperties(fname="C:\Windows\Fonts\simhei.ttf", size=30))
ax.set_xticks(np.arange(data.shape[1]))
ax.set_yticks(np.arange(data.shape[0]))
ax.set_xticklabels(col_labels,fontproperties=self.font)
ax.set_yticklabels(row_labels,fontproperties=self.font)
ax.tick_params(top=True, bottom=False,
labeltop=True, labelbottom=False)
plt.setp(ax.get_xticklabels(), rotation=-30, ha="right",
rotation_mode="anchor")
for edge, spine in ax.spines.items():
spine.set_visible(False)
ax.set_xticks(np.arange(data.shape[1] + 1) - .5, minor=True)
ax.set_yticks(np.arange(data.shape[0] + 1) - .5, minor=True)
ax.grid(which="minor", color="w", linestyle='-', linewidth=3)
ax.tick_params(which="minor", bottom=False, left=False)
return im, cbar
def annotate_heatmap(self,im, data=None, valfmt="{x:.2f}",
textcolors=("black", "white"),
threshold=None, **textkw):
if not isinstance(data, (list, np.ndarray)):
data = im.get_array()
if threshold is not None:
threshold = im.norm(threshold)
else:
threshold = im.norm(data.max()) / 2.
kw = dict(horizontalalignment="center",
verticalalignment="center",
)
kw.update(textkw)
if isinstance(valfmt, str):
valfmt = matplotlib.ticker.StrMethodFormatter(valfmt)
texts = []
for i in range(data.shape[0]):
for j in range(data.shape[1]):
kw.update(color=textcolors[abs(data[i, j]) > 0.5])
text = im.axes.text(j, i, valfmt(data[i, j], None), **kw)
texts.append(text)
return texts
实列化该类,然后直接调用cyrus_heat_map方法。
if __name__ == '__main__':
# 构造数据集并计算其pearson相关系数
data = pd.DataFrame(np.random.randn(10,10))
pearson = data.corr()
plot_tool = CyrusPlot()
plot_tool.cyrus_heat_map(pearson,show=True)
by CyrusMay 2021 01 27
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