我如何使一个简单的轮廓图的熊猫DataFrame与数字单元格值为Z和标记行/列为X和Y坐标？

Question

我有一个Pandas DataFrame，luminance_df，看起来像这样：

到目前为止，我正在构建一个单一的多图表HTML文件，如下所示：

with open(os.path.join(cwd, 'testout.html'), 'w') as outfile:
    outfile.write("<p>&nbsp;</p><hr/><p>&nbsp;</p>".join(['<h1>Colors</h1>'+hex_styler.to_html(), '<h1>Hue</h1>'+hue_styler.to_html(), '<h1>Saturation</h1>'+saturation_styler.to_html(
    ), '<h1>Luminance</h1>'+luminance_styler.to_html(), '<h1>Perceived Brightness</h1>'+perceived_brightness_pivot_styler.to_html(), '<h1>Base Data</h1>'+basic_df.to_html()]))

我想在Luminance\u styler.to\u html（）之后显示亮度的高程/等高线样式图，很像我在Excel中制作的这张图：

我希望颜色作为y轴上的值保持“从上到下”的排序，而深色作为x轴上的值保持“从左到右”的排序，就像上面的例子一样。

我不是数据科学家，也不经常使用Python。我为自己首先制作了luminance_df而自豪，但我并没有，就我的一生而言，弄清楚如何简单地制作Python...处理DataFrame中的数字单元格值，其双向标签都是字符串....作为z轴，并制作它的等高线图。

我在谷歌搜索的每件事都会引出非常复杂的数据科学问题。

有没有人能给我一个基本的“hello world”代码，让我在Python中使用luminance\u df的数据时，至少能像我在Excel中使用“插入图表”按钮时一样，使用它，让我走上正确的轨道？

如果你能让我得到一个img=BytesIO（），那就是image_base64=bas64. b64encode（img.read（））. decode（"utf-8"）-able，我可以f'

我在Windows上，已将自己设置为能够pip安装。

>

公平地说，我发现这些等高线图表比Excel制作的更有吸引力，也更容易阅读，但是我可以接受某种“野蛮”的东西——看起来像Excel版本，只要它让“上升”

虽然我希望图表的颜色遵循某种“彩虹”（因为我个人觉得它们很容易阅读），但图表上的任何“彩虹阴影”都应该完全忽略y轴标签恰好描述颜色这一事实。没有任何关联。我只是在16到98之间绘制数字事实；图表的颜色应该仅仅表示这两个极端之间“海拔”的变化。

到目前为止，我发现的唯一一个类似的“简单”问题是将pandas DataFrame转换为3d图形，使用索引和列作为X，Y，值作为Z？，但这段代码对我根本不起作用，所以我甚至不知道它在视觉上输出了什么，所以我不知道它是否相关：

from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plt
lumX = luminance_df.columns
lumY = luminance_df.index
lumZ = luminance_df.values
fig = plt.figure()
ax = plt.axes(projection = '3d')
ax.contour3D(lumX,lumY,lumZ)

我的脚本出现了一条错误消息：ValueError:无法将字符串转换为float:“orange”，我不知道我在做什么，以适应以下事实：这个答案似乎是围绕数字X轴和Y轴键的假设编写的。（此外，它可能不会生成我希望的图表类型——正如我所说，无法判断，因为它甚至不会执行，而且答案中也没有视觉样本。）

准备<代码>熊猫。数据帧（）：

{"barelyvisible":{"orange":96,"gold":96,"yellow":95,"chartreuse":95,"green":97,"forest":96,"aqua":97,"teal":96,"lightblue":97,"blue":97,"indigo":97,"purple":98,"royalpurple":98,"magenta":98,"pink":97,"dustypink":97,"red":97},"ultralight":{"orange":92,"gold":89,"yellow":88,"chartreuse":89,"green":93,"forest":90,"aqua":89,"teal":90,"lightblue":94,"blue":93,"indigo":94,"purple":95,"royalpurple":95,"magenta":95,"pink":95,"dustypink":95,"red":94},"light":{"orange":83,"gold":77,"yellow":77,"chartreuse":80,"green":85,"forest":80,"aqua":78,"teal":82,"lightblue":86,"blue":87,"indigo":89,"purple":92,"royalpurple":92,"magenta":91,"pink":90,"dustypink":90,"red":89},"abitlight":{"orange":72,"gold":65,"yellow":64,"chartreuse":67,"green":73,"forest":67,"aqua":64,"teal":69,"lightblue":74,"blue":78,"indigo":82,"purple":85,"royalpurple":85,"magenta":83,"pink":82,"dustypink":82,"red":82},"medium":{"orange":61,"gold":56,"yellow":53,"chartreuse":55,"green":58,"forest":52,"aqua":50,"teal":53,"lightblue":60,"blue":68,"indigo":74,"purple":76,"royalpurple":75,"magenta":73,"pink":70,"dustypink":71,"red":71},"abitdark":{"orange":53,"gold":50,"yellow":47,"chartreuse":44,"green":45,"forest":39,"aqua":40,"teal":43,"lightblue":48,"blue":60,"indigo":67,"purple":66,"royalpurple":65,"magenta":61,"pink":60,"dustypink":60,"red":60},"dark":{"orange":48,"gold":44,"yellow":40,"chartreuse":35,"green":36,"forest":30,"aqua":32,"teal":36,"lightblue":39,"blue":53,"indigo":59,"purple":58,"royalpurple":56,"magenta":49,"pink":51,"dustypink":50,"red":51},"evendarker":{"orange":40,"gold":37,"yellow":33,"chartreuse":27,"green":29,"forest":24,"aqua":26,"teal":31,"lightblue":32,"blue":48,"indigo":51,"purple":50,"royalpurple":47,"magenta":40,"pink":42,"dustypink":41,"red":42},"ultradark":{"orange":34,"gold":31,"yellow":29,"chartreuse":23,"green":24,"forest":20,"aqua":22,"teal":27,"lightblue":27,"blue":40,"indigo":41,"purple":42,"royalpurple":39,"magenta":33,"pink":35,"dustypink":35,"red":35},"almostblack":{"orange":28,"gold":26,"yellow":26,"chartreuse":20,"green":20,"forest":16,"aqua":19,"teal":24,"lightblue":24,"blue":33,"indigo":34,"purple":35,"royalpurple":32,"magenta":28,"pink":30,"dustypink":30,"red":31}}

Answer 1

我相信你只需要做一个countourf：

plt.contourf(df, cmap='RdYlBu')
plt.xticks(range(df.shape[1]), df.columns, rotation=90)
plt.yticks(range(df.shape[0]), df.index)
plt.show()

输出：

或热图：

import seaborn as sns
sns.heatmap(df, cmap='RdYlBu')

输出：