Python 3 破坏了 pandas 的渐变风格， 'SingleBlockManager' 是原因

Question

从 python 2.7 迁移到 python 3.x，此代码破坏了源代码:

pandas style background gradient both rows and columns

import pandas as pd
import matplotlib.pyplot as plt
from matplotlib import colors

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 = pd.DataFrame([[3,2,10.3,4],[20,1,3.5,2],[5,4,6.9,1]])
df.style.apply(background_gradient, axis=None)

这是堆栈跟踪的最后一行:

TypeError: ("float() argument must be a string or a number, not 'SingleBlockManager'", 'occurred at index 0')

Answer 1

显然，在 matplotlib 2.2 中您无法再使用数据帧调用 matplotlib.colors.Normalize 。

解决方案是使用数据帧的值来调用它，将 normed = s.apply(norm) 更改为

 normed = s.apply(lambda x: norm(x.values))

完整代码

import pandas as pd
import matplotlib.pyplot as plt
from matplotlib import colors

def background_gradient(s, m=None, M=None, cmap='PuBu', low=0, high=0):
    if m is None:
        m = s.min().min()
    if M is None:
        M = s.max().max()
    rng = M - m
    norm = colors.Normalize(m ,M)
    normed = s.apply(lambda x: norm(x.values))
    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 = pd.DataFrame([[3,2,10.3,4],[20,1,3.5,2],[5,4,6.9,1]])
df.style.apply(background_gradient, axis=None)

制作