pandas 行和列的熊猫风格背景渐变
声明：本页面是StackOverFlow热门问题的中英对照翻译，遵循CC BY-SA 4.0协议，如果您需要使用它，必须同样遵循CC BY-SA许可，注明原文地址和作者信息，同时你必须将它归于原作者（不是我）：StackOverFlow
原文地址: http://stackoverflow.com/questions/38931566/
Warning: these are provided under cc-by-sa 4.0 license. You are free to use/share it, But you must attribute it to the original authors (not me):
StackOverFlow
pandas style background gradient both rows and columns
提问by Peter9192
The pandas style optionto add a background gradient is great for quickly inspecting my output table. However, it is applied either row-wise or columns-wise. Would it be possible to apply it to the whole dataframe at once?
用于添加背景渐变的pandas 样式选项非常适合快速检查我的输出表。但是，它可以按行或按列应用。是否可以一次将它应用于整个数据帧？
EDIT: A minimum working example:
编辑：最低工作示例：
df = pd.DataFrame([[3,2,10,4],[20,1,3,2],[5,4,6,1]])
df.style.background_gradient()
回答by Guilherme Beltramini
Currently you can't set the background_gradientfor both the rows/columns simultaneously as pointed by Nickil Maveli. The trick is to customize the pandas function background_gradient:
目前，您不能background_gradient像Nickil Maveli指出的那样同时设置两个行/列。诀窍是自定义Pandas函数 background_gradient：
import pandas as pd
import matplotlib.pyplot as plt
from matplotlib import colors
def background_gradient(s, m, M, cmap='PuBu', low=0, high=0):
rng = M - m
norm = colors.Normalize(m - (rng * low),
M + (rng * high))
normed = norm(s.values)
c = [colors.rgb2hex(x) for x in plt.cm.get_cmap(cmap)(normed)]
return ['background-color: %s' % color for color in c]
df = pd.DataFrame([[3,2,10,4],[20,1,3,2],[5,4,6,1]])
df.style.apply(background_gradient,
cmap='PuBu',
m=df.min().min(),
M=df.max().max(),
low=0,
high=0.2)
回答by Andreas Mueller
You can use axis=Noneto get rid of the min and max computations in the call:
您可以使用axis=None来摆脱调用中的最小和最大计算：
def background_gradient(s, m=None, M=None, cmap='PuBu', low=0, high=0):
print(s.shape)
if m is None:
m = s.min().min()
if M is None:
M = s.max().max()
rng = M - m
norm = colors.Normalize(m - (rng * low),
M + (rng * high))
normed = s.apply(norm)
cm = plt.cm.get_cmap(cmap)
c = normed.applymap(lambda x: colors.rgb2hex(cm(x)))
ret = c.applymap(lambda x: 'background-color: %s' % x)
return ret
df.style.apply(background_gradient, axis=None)
Edit: You may need to use normed = s.apply(lambda x: norm(x.values))for this to work on matplotlib 2.2
编辑：您可能需要使用normed = s.apply(lambda x: norm(x.values))的这个工作对matplotlib 2.2
