1 高亮缺失值
1.0 数据
import pandas as pd
import numpy as np
data=[{'a':1,'b':2},
{'a':3,'c':4},
{'a':10,'b':-2,'c':5}]
df1=pd.DataFrame(data)
df1
1.1 highlight_null
df.style.highlight_null(
color: 'str' = 'red',
subset: 'Subset | None' = None,
props: 'str | None' = None,
)1.1.1 默认情况
df1.style.highlight_null()
1.1.2 color
调整高亮的颜色（后同）
df1.style.highlight_null(color='green')
1.1.3 subset
指定操作的列（后同）
df1.style.highlight_null(subset='b')
1.1.4 props
突出显示的CSS属性（后同）
df1.style.highlight_null(props='color:pink; background-color:blue') 
2 高亮最大/最小值
2.0 数据
# Visual Python: Data Analysis > File
vp_df = pd.read_csv('https://raw.githubusercontent.com/visualpython/visualpython/main/visualpython/data/sample_csv/iris.csv')
vp_df=vp_df[:5]
vp_df
2.1 highlight_max
df.style.highlight_max(
subset: 'Subset | None' = None,
color: 'str' = 'yellow',
axis: 'Axis | None' = 0,
props: 'str | None' = None,
)subset color props 同1.1
2.1.1 axis
默认为0，表示列
axis=1，表示行
vp_df.style.highlight_max(axis=1,
subset=['sepal_length','sepal_width','petal_length','petal_width'])
axis=None, dataFrame中最大的那个
2.2 highlight_min
参数和使用方法和max一样
2.2.1 链式调用min和max
vp_df.style.highlight_max(color='purple').highlight_min(color='green')
3 高亮区间值
还是使用vp_df
df.style.highlight_between(
subset: 'Subset | None' = None,
color: 'str' = 'yellow',
axis: 'Axis | None' = 0,
left: 'Scalar | Sequence | None' = None,
right: 'Scalar | Sequence | None' = None,
inclusive: 'str' = 'both',
props: 'str | None' = None,
)subset、color、axis、props和前面的min、max一样
3.1 left、right
指定区间最小值和最大值
vp_df.style.highlight_between(left=3,
right=4.9,
subset=['sepal_length','sepal_width','petal_length','petal_width'])
3.2 inclusive
用于确定是否左右闭包，可选'both', 'neither', 'left', 'right' （保留哪个）
vp_df.style.highlight_between(left=3,
right=4.9,
inclusive='both',
subset=['sepal_length','sepal_width','petal_length','petal_width'])
vp_df.style.highlight_between(left=3,
right=4.9,
inclusive='left',
subset=['sepal_length','sepal_width','petal_length','petal_width'])
vp_df.style.highlight_between(left=3,
right=4.9,
inclusive='right',
subset=['sepal_length','sepal_width','petal_length','petal_width'])
vp_df.style.highlight_between(left=3,
right=4.9,
inclusive='neither',
subset=['sepal_length','sepal_width','petal_length','petal_width'])
3.3 不同的列不同的区间
vp_df.style.highlight_between(subset=['sepal_length','sepal_width','petal_length','petal_width'],
left=[4.6,3.1,1.33,0.2],
right=[4.9,3.5,1.5,0.3],
axis=1,
color='green'
)
4 高亮分位数
df.style.highlight_quantile(
subset: 'Subset | None' = None,
color: 'str' = 'yellow',
axis: 'Axis | None' = 0,
q_left: 'float' = 0.0,
q_right: 'float' = 1.0,
interpolation: 'str' = 'linear',
inclusive: 'str' = 'both',
props: 'str | None' = None,
) subset、color、axis、inclusive、props和之前的一样
4.1 q_left、q_right
用于指定分位数左边界和右边界
vp_df.style.highlight_quantile(subset=['sepal_length','sepal_width','petal_length','petal_width'],
q_left=0.1,
q_right=0.8,
color='green'
)
5 背景渐变色
df.style.background_gradient(
cmap='PuBu',
low: 'float' = 0,
high: 'float' = 0,
axis: 'Axis | None' = 0,
subset: 'Subset | None' = None,
text_color_threshold: 'float' = 0.408,
vmin: 'float | None' = None,
vmax: 'float | None' = None,
gmap: 'Sequence | None' = None,
) | cmap |  |
| low、high | 指定最小最大值颜色边界 区间为[0,1] |
| axis | 指定行、列或全部 |
| subset | 指定操作的列或行 |
| text_color_threshold | 指定文本颜色亮度，区间[0, 1] |
| vmin,vmax | 指定与cmap最小最大值对应的单元格最小最大值 |
vp_df.style.background_gradient()
vp_df.style.background_gradient(vmin=3,vmax=4.9)
6 文本渐变色
vp_df.style.text_gradient(cmap='RdYlGn')
7 数据条
df.style.bar(
subset: 'Subset | None' = None,
axis: 'Axis | None' = 0,
color='#d65f5f',
width: 'float' = 100,
align: 'str' = 'left',
vmin: 'float | None' = None,
vmax: 'float | None' = None,
)| axis | 指定行、列或全部 |
| subset | 指定操作的列或行 |
| color | 数据条颜色 |
| width | 指定数据条长度 |
| vmin、vmax | 指定与数据条最小最大值对应的单元格最小最大值 |
| align | 数据条与单元格对齐方式，默认是left左对齐，还有zero居中和mid位于（max-min）/2 |
vp_df.style.bar(subset='sepal_length',
color='green',
vmin=4.5,
vmax=5.1,
align='left').bar(subset='petal_length',
color='blue',
vmin=1.3,
vmax=1.5,
align='zero',
width=10)
8 逐元素条件着色
# Visual Python: Data Analysis > File
import pandas as pd
vp_df = pd.read_csv('https://raw.githubusercontent.com/visualpython/visualpython/main/visualpython/data/sample_csv/iris.csv')
vp_df=vp_df[:5][['sepal_length','sepal_width','petal_length','petal_width']]
vp_df
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def color_red(val):
color = 'purple' if val < 3.5 else 'green'
return 'color: %s' % color
vp_df.style.applymap(color_red)
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