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seaborn.heatmap

时间: 2023-08-27 21:05:17 浏览: 184

Python-Seaborn热图绘制的实现方法

制图环境： pycharm python-3.6 Seaborn-0.8 热图 import numpy as np import seaborn as sns import matplotlib.pyplot as plt sns.set() np.random.seed(0) uniform_data = np.random.rand(10, 12) ax = sns.heatmap(uniform_data) plt.show() # 改变颜色映射的值范围 ax = sns.heatmap(uniform_data, vmin=0, vmax=1) plt.show()

seaborn.heatmap是Seaborn库中的一个函数，用于绘制热力图。热力图常用于展示一组变量的相关系数矩阵或数据分布的差异情况。该函数的语法为seaborn.heatmap(data, vmin=None, vmax=None, cmap=None, center=None, robust=False, annot=None, fmt='.2g', annot_kws=None, linewidths=0, linecolor='white', cbar=True, cbar_kws=None, cbar_ax=None, square=False, xticklabels='auto', yticklabels='auto', mask=None, ax=None, **kwargs)。其中，data是要绘制的数据，vmin和vmax用于设置颜色映射的范围，cmap用于设置颜色映射的样式，center用于设置颜色映射的中心值。通过调整这些参数，可以自定义热力图的外观和显示效果。\[1\]\[2\]\[3\] #### 引用[.reference_title] - *1* *2* [Python可视化 | Seaborn之seaborn.heatmap()热力图](https://blog.csdn.net/m0_38090787/article/details/107827167)[target="_blank" data-report-click={"spm":"1018.2226.3001.9630","extra":{"utm_source":"vip_chatgpt_common_search_pc_result","utm_medium":"distribute.pc_search_result.none-task-cask-2~all~insert_cask~default-1-null.142^v91^insertT0,239^v3^insert_chatgpt"}} ] [.reference_item] - *3* [Python数据分析与机器学习12-Seaborn之Heatmap](https://blog.csdn.net/u010520724/article/details/125761187)[target="_blank" data-report-click={"spm":"1018.2226.3001.9630","extra":{"utm_source":"vip_chatgpt_common_search_pc_result","utm_medium":"distribute.pc_search_result.none-task-cask-2~all~insert_cask~default-1-null.142^v91^insertT0,239^v3^insert_chatgpt"}} ] [.reference_item] [ .reference_list ]

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