[英]Python - plot a NxN matrix as a gradient colors grid
I want to visualize the correlation between columns that I get with datafrome.corr()
method. 我想可视化使用
datafrome.corr()
方法获得的列之间的相关性。
The result looks like: 结果看起来像:
What I am trying to do here is to draw that matrix with gradient colors based on the values of the data frame. 我在这里要做的是根据数据帧的值用渐变颜色绘制该矩阵。
Something like (Just an example from the web): 类似的东西(仅是网络示例):
If you can import your data into numpy here is a simple solution using matplotlib and should produce a heatmap similar to what you posted. 如果您可以将数据导入numpy,那么这是使用matplotlib的简单解决方案,并且会产生类似于您发布的热图。 You will just need to replace the dummy data with your data.
您只需要用数据替换虚拟数据即可。
import numpy as np
import matplotlib.pyplot as plt
# Generate some test data
data = np.arange(100).reshape((10,10))
plt.title('Actual Function')
heatmap = plt.pcolor(data)
plt.show()
Edit: Here is a bit fancier version with your x and y axis labels. 编辑:这是带有您的x和y轴标签的更高级的版本。 I chose to put them into two lists so that you could change each one independently.
我选择将它们分为两个列表,以便您可以分别更改每个列表。
import numpy as np
import matplotlib.pyplot as plt
# Generate some test data
data = np.arange(100).reshape((10,10))
xlabels = ['capacity', 'failure_rate', 'id', 'margin', 'price', 'prod_cost', 'product_type', 'quality', 'warranty', 'market_share', 'attractiveness']
ylabels = ['capacity', 'failure_rate', 'id', 'margin', 'price', 'prod_cost', 'product_type', 'quality', 'warranty', 'market_share', 'attractiveness']
fig, ax = plt.subplots()
ax.set_xticks(np.arange(data.shape[1]) + 0.5, minor=False)
ax.set_yticks(np.arange(data.shape[0]) + 0.5, minor=False)
ax.xaxis.tick_top()
plt.xticks(rotation=90)
ax.set_xticklabels(xlabels, minor=False)
ax.set_yticklabels(ylabels, minor=False)
heatmap = ax.pcolor(data)
ax = plt.gca()
for t in ax.xaxis.get_major_ticks():
t.tick1On = False
t.tick2On = False
for t in ax.yaxis.get_major_ticks():
t.tick1On = False
t.tick2On = False
plt.show()
声明:本站的技术帖子网页,遵循CC BY-SA 4.0协议,如果您需要转载,请注明本站网址或者原文地址。任何问题请咨询:yoyou2525@163.com.