【问题标题】：pandas styling doesn't display for all rows in large dataframes in Chrome or Edge在 Chrome 或 Edge 中，大数据框中的所有行都不会显示 pandas 样式
【发布时间】：2021-10-23 15:14:37
【问题描述】：

更新：

问题似乎在于在 Google Chrome 和 Microsoft Edge 中使用 pandas 呈现的样式显示 HTML。
Firefox 中的 JupyterLab 正确显示所有样式化的行并正确呈现输出 HTML 文件。
更新的问题是
1. 为什么 pandas 渲染的 HTML 不能完全显示 Chrome 或 Edge 中的所有样式？
2. 是否有更有效的方法来应用 pandas 完成的样式，以便 HTML 也适用于 Chrome 和 Edge？
版本：
- pandas v1.2.4
- Chrome v90.0.4430.93 (Official Build) (64-bit)
- Edge v90.0.818.56 (Official build) (64-bit)

原文：

问题 - 它们是相互关联的：
1. 为什么不是所有行都在 Jupyter 中显示背景样式或写入 HTML？
  - 所有行都应具有绿色样式，但最后 5 行不显示样式。
2. 如何使所有行都显示背景样式？
给定一个大数据框，在本例中为474 rows x 35 columns，应用的样式将停止显示。
如果行数或列数增加到超过此大小，则不会显示更多行。
我们可以从样式映射中看到，行已正确映射为背景颜色，但未显示。
如果行数或列数减少，则所有行都显示正确的样式。
在jupyterlab v3.0.11 中测试
在 PyCharm 2021.1 (Professional Edition) Build #PY-211.6693.115, built on April 6, 2021 中测试，将重新渲染的样式器保存到文件具有相同的结果，因此这不仅仅是 jupyter 的问题。
在控制台中测试
这个问题可以在我尝试过的两个不同系统上重现。
如果形状缩小为471 rows × 35 columns 或474 rows × 34 columns，则所有行都正确显示突出显示。
相关的 pandas 错误报告：40913

可重现数据帧

import pandas as pd
import numpy as np
from faker import Faker  # conda install -c conda-forge faker or pip install Faker

# for fake names
fake = Faker()

# test data
np.random.seed(365)
rows = 11000

# change 36 or 158 to test where the rows stop appearing
vals = {f'val{i}': np.random.randint(1, 11, size=(rows)) for i in range(1, 36)}
data = {'name': np.random.choice([fake.unique.name() for i in range(158)], size=rows),
        'cat': np.random.randint(1, 4, size=(rows))}
data.update(vals)

df = pd.DataFrame(data)

# used to create the mask for the background color
mean = df.groupby('cat').mean().round(2)

# calculate the mean for each name and cat
cat_mean = df.groupby(['name', 'cat']).mean()


def color(x):
    """Function to apply background color"""
    c1 = 'background-color: green'
    c = '' 
    # compare columns
    mask1 = x.gt(mean)
    # DataFrame with same index and columns names as original filled empty strings
    df1 =  pd.DataFrame(c, index=x.index, columns=x.columns)
    # modify values of df1 column by boolean mask
    df1[mask1] = c1
    display(df1)

    return df1


# Last line in notebook displays the styled dataframe
cat_mean.style.apply(color, axis=None)

# Last line in PyCharm saving rendered styler to file - comment out when in Jupyter
cm = cat_mean.style.apply(color, axis=None).set_precision(3).render()

# save the output to an html file
with open('cm_test.html', 'w') as f:
    f.write(cm)

参考

此answer 被引用用于应用背景颜色的函数。
此question 类似，但没有答案。

`pd.show_versions()` 的输出

为了节省空间，所有 None 的包都没有显示出来

INSTALLED VERSIONS
------------------
commit           : f2c8480af2f25efdbd803218b9d87980f416563e
python           : 3.8.8.final.0 or 3.9.2.final.0
python-bits      : 64
OS               : Windows
OS-release       : 10
Version          : 10.0.19041
machine          : AMD64
processor        : Intel64 Family 6 Model 60 Stepping 3, GenuineIntel
byteorder        : little
LOCALE           : English_United States.1252

pandas           : 1.2.3 or 1.2.4
numpy            : 1.19.2
pytz             : 2021.1
dateutil         : 2.8.1
pip              : 21.0.1
setuptools       : 52.0.0.post20210125
Cython           : 0.29.22
pytest           : 6.2.3
sphinx           : 3.5.3
xlsxwriter       : 1.3.8
lxml.etree       : 4.6.3
html5lib         : 1.1
jinja2           : 2.11.3
IPython          : 7.22.0
pandas_datareader: 0.9.0
bs4              : 4.9.3
bottleneck       : 1.3.2
fsspec           : 0.9.0
matplotlib       : 3.3.4
numexpr          : 2.7.3
openpyxl         : 3.0.7
scipy            : 1.6.2
sqlalchemy       : 1.4.5
tables           : 3.6.1
tabulate         : 0.8.9
xlrd             : 2.0.1
xlwt             : 1.3.0
numba            : 0.53.1

解决方法

拆分数据帧

令人不满意，但拆分 DataFrame 并应用样式会起作用，因为 if 会减小整体大小。

cat_mean.iloc[:237, :].style.apply(color, axis=None)
cat_mean.iloc[237:, :].style.apply(color, axis=None)

保存到 Excel

保存到 Excel 时，所有行都以突出显示颜色正确显示

test = cat_mean.style.apply(color, axis=None)
test.to_excel('test.xlsx', engine='openpyxl')

【问题讨论】：

标签： python pandas google-chrome microsoft-edge pandas-styles

【解决方案1】：

更新

截至pandas 1.3.0，以下实现正确突出显示了OP 中(474, 35) 测试数据的所有预期列和行。
- 还使用形状为 (7760, 35) 的数据框成功测试。
这个实现来自pandas Table Visualization User Guide: Set classes instead of using Styler functions

# also use code previous to list line for the test data
# calculate the mean for each name and cat
cat_mean = df.groupby(['name', 'cat']).mean()

def test(s, props=''):
    t = np.where(s.gt(mean[s.name]), props, '')
    return t


build = lambda x: pd.DataFrame(x, index=cat_mean.index, columns=cat_mean.columns)
cls1 = build(cat_mean.apply(test, props='cls-1 ', axis=0))

test = cat_mean.style.set_table_styles([{'selector': '.cls-1', 'props': [('color', 'white'), ('background-color', 'darkblue')]}]).set_td_classes(cls1)

# save the output to an html file
with open('cm_test.html', 'w') as f:
    f.write(test.render())

工作实施的输出
OP 中实现的输出

回答：为什么这在 Chrome 或 Edge 中不起作用？

根据错误#39400，大型 DataFrame 会出现此问题，因为 Styler 将所有 CSS id 放在一个属性上，而并非所有浏览器都解决了这个问题。
在下面的小sn-p中，看到所有的id都在最上面。
- sn-p id 用于 5 行和 35 列，但仅包含 1 个表行的数据。

<style  type="text/css" >
#T__row0_col0,#T__row0_col1,#T__row0_col4,#T__row0_col5,#T__row0_col6,#T__row0_col11,#T__row0_col12,#T__row0_col13,#T__row0_col16,#T__row0_col19,#T__row0_col20,#T__row0_col22,#T__row0_col23,#T__row0_col24,#T__row0_col26,#T__row0_col27,#T__row0_col28,#T__row0_col30,#T__row0_col33,#T__row0_col34,#T__row1_col0,#T__row1_col1,#T__row1_col4,#T__row1_col8,#T__row1_col9,#T__row1_col11,#T__row1_col13,#T__row1_col15,#T__row1_col17,#T__row1_col21,#T__row1_col22,#T__row1_col26,#T__row1_col31,#T__row1_col34,#T__row2_col0,#T__row2_col1,#T__row2_col3,#T__row2_col7,#T__row2_col8,#T__row2_col9,#T__row2_col10,#T__row2_col12,#T__row2_col13,#T__row2_col16,#T__row2_col19,#T__row2_col20,#T__row2_col24,#T__row2_col26,#T__row2_col27,#T__row2_col28,#T__row2_col30,#T__row2_col32,#T__row3_col0,#T__row3_col3,#T__row3_col4,#T__row3_col6,#T__row3_col7,#T__row3_col8,#T__row3_col9,#T__row3_col15,#T__row3_col17,#T__row3_col20,#T__row3_col22,#T__row3_col27,#T__row3_col28,#T__row3_col29,#T__row3_col31,#T__row3_col34,#T__row4_col0,#T__row4_col1,#T__row4_col2,#T__row4_col3,#T__row4_col4,#T__row4_col6,#T__row4_col8,#T__row4_col14,#T__row4_col16,#T__row4_col17,#T__row4_col18,#T__row4_col19,#T__row4_col22,#T__row4_col23,#T__row4_col25,#T__row4_col27,#T__row4_col29,#T__row4_col32,#T__row4_col33{
            text-align:  center;
        }#T__row0_col2,#T__row0_col3,#T__row0_col7,#T__row0_col8,#T__row0_col9,#T__row0_col10,#T__row0_col14,#T__row0_col15,#T__row0_col17,#T__row0_col18,#T__row0_col21,#T__row0_col25,#T__row0_col29,#T__row0_col31,#T__row0_col32,#T__row1_col2,#T__row1_col3,#T__row1_col5,#T__row1_col6,#T__row1_col7,#T__row1_col10,#T__row1_col12,#T__row1_col14,#T__row1_col16,#T__row1_col18,#T__row1_col19,#T__row1_col20,#T__row1_col23,#T__row1_col24,#T__row1_col25,#T__row1_col27,#T__row1_col28,#T__row1_col29,#T__row1_col30,#T__row1_col32,#T__row1_col33,#T__row2_col2,#T__row2_col4,#T__row2_col5,#T__row2_col6,#T__row2_col11,#T__row2_col14,#T__row2_col15,#T__row2_col17,#T__row2_col18,#T__row2_col21,#T__row2_col22,#T__row2_col23,#T__row2_col25,#T__row2_col29,#T__row2_col31,#T__row2_col33,#T__row2_col34,#T__row3_col1,#T__row3_col2,#T__row3_col5,#T__row3_col10,#T__row3_col11,#T__row3_col12,#T__row3_col13,#T__row3_col14,#T__row3_col16,#T__row3_col18,#T__row3_col19,#T__row3_col21,#T__row3_col23,#T__row3_col24,#T__row3_col25,#T__row3_col26,#T__row3_col30,#T__row3_col32,#T__row3_col33,#T__row4_col5,#T__row4_col7,#T__row4_col9,#T__row4_col10,#T__row4_col11,#T__row4_col12,#T__row4_col13,#T__row4_col15,#T__row4_col20,#T__row4_col21,#T__row4_col24,#T__row4_col26,#T__row4_col28,#T__row4_col30,#T__row4_col31,#T__row4_col34{
            background-color:  green;
            text-align:  center;
        }</style><table id="T__" ><thead>    <tr>        <th class="blank" ></th>        <th class="blank level0" ></th>        <th class="col_heading level0 col0" >val1</th>        <th class="col_heading level0 col1" >val2</th>        <th class="col_heading level0 col2" >val3</th>        <th class="col_heading level0 col3" >val4</th>        <th class="col_heading level0 col4" >val5</th>        <th class="col_heading level0 col5" >val6</th>        <th class="col_heading level0 col6" >val7</th>        <th class="col_heading level0 col7" >val8</th>        <th class="col_heading level0 col8" >val9</th>        <th class="col_heading level0 col9" >val10</th>        <th class="col_heading level0 col10" >val11</th>        <th class="col_heading level0 col11" >val12</th>        <th class="col_heading level0 col12" >val13</th>        <th class="col_heading level0 col13" >val14</th>        <th class="col_heading level0 col14" >val15</th>        <th class="col_heading level0 col15" >val16</th>        <th class="col_heading level0 col16" >val17</th>        <th class="col_heading level0 col17" >val18</th>        <th class="col_heading level0 col18" >val19</th>        <th class="col_heading level0 col19" >val20</th>        <th class="col_heading level0 col20" >val21</th>        <th class="col_heading level0 col21" >val22</th>        <th class="col_heading level0 col22" >val23</th>        <th class="col_heading level0 col23" >val24</th>        <th class="col_heading level0 col24" >val25</th>        <th class="col_heading level0 col25" >val26</th>        <th class="col_heading level0 col26" >val27</th>        <th class="col_heading level0 col27" >val28</th>        <th class="col_heading level0 col28" >val29</th>        <th class="col_heading level0 col29" >val30</th>        <th class="col_heading level0 col30" >val31</th>        <th class="col_heading level0 col31" >val32</th>        <th class="col_heading level0 col32" >val33</th>        <th class="col_heading level0 col33" >val34</th>        <th class="col_heading level0 col34" >val35</th>    </tr>    <tr>        <th class="index_name level0" >name</th>        <th class="index_name level1" >cat</th>        <th class="blank" ></th>        <th class="blank" ></th>        <th class="blank" ></th>        <th class="blank" ></th>        <th class="blank" ></th>        <th class="blank" ></th>        <th class="blank" ></th>        <th class="blank" ></th>        <th class="blank" ></th>        <th class="blank" ></th>        <th class="blank" ></th>        <th class="blank" ></th>        <th class="blank" ></th>        <th class="blank" ></th>        <th class="blank" ></th>        <th class="blank" ></th>        <th class="blank" ></th>        <th class="blank" ></th>        <th class="blank" ></th>        <th class="blank" ></th>        <th class="blank" ></th>        <th class="blank" ></th>        <th class="blank" ></th>        <th class="blank" ></th>        <th class="blank" ></th>        <th class="blank" ></th>        <th class="blank" ></th>        <th class="blank" ></th>        <th class="blank" ></th>        <th class="blank" ></th>        <th class="blank" ></th>        <th class="blank" ></th>        <th class="blank" ></th>        <th class="blank" ></th>        <th class="blank" ></th>    </tr></thead><tbody>
                <tr>
                        <th id="T__level0_row0" class="row_heading level0 row0" rowspan="3">Alisha Ortiz</th>
                        <th id="T__level1_row0" class="row_heading level1 row0" >1</th>
                        <td id="T__row0_col0" class="data row0 col0" >4.46</td>
                        <td id="T__row0_col1" class="data row0 col1" >4.62</td>
                        <td id="T__row0_col2" class="data row0 col2" >5.73</td>
                        <td id="T__row0_col3" class="data row0 col3" >6.12</td>
                        <td id="T__row0_col4" class="data row0 col4" >4.77</td>
                        <td id="T__row0_col5" class="data row0 col5" >4.73</td>
                        <td id="T__row0_col6" class="data row0 col6" >4.50</td>
                        <td id="T__row0_col7" class="data row0 col7" >6.12</td>
                        <td id="T__row0_col8" class="data row0 col8" >5.50</td>
                        <td id="T__row0_col9" class="data row0 col9" >5.92</td>
                        <td id="T__row0_col10" class="data row0 col10" >6.08</td>
                        <td id="T__row0_col11" class="data row0 col11" >4.92</td>
                        <td id="T__row0_col12" class="data row0 col12" >5.42</td>
                        <td id="T__row0_col13" class="data row0 col13" >5.38</td>
                        <td id="T__row0_col14" class="data row0 col14" >6.08</td>
                        <td id="T__row0_col15" class="data row0 col15" >5.77</td>
                        <td id="T__row0_col16" class="data row0 col16" >5.31</td>
                        <td id="T__row0_col17" class="data row0 col17" >5.58</td>
                        <td id="T__row0_col18" class="data row0 col18" >6.12</td>
                        <td id="T__row0_col19" class="data row0 col19" >4.77</td>
                        <td id="T__row0_col20" class="data row0 col20" >5.19</td>
                        <td id="T__row0_col21" class="data row0 col21" >5.96</td>
                        <td id="T__row0_col22" class="data row0 col22" >4.88</td>
                        <td id="T__row0_col23" class="data row0 col23" >5.31</td>
                        <td id="T__row0_col24" class="data row0 col24" >4.65</td>
                        <td id="T__row0_col25" class="data row0 col25" >5.88</td>
                        <td id="T__row0_col26" class="data row0 col26" >5.38</td>
                        <td id="T__row0_col27" class="data row0 col27" >5.27</td>
                        <td id="T__row0_col28" class="data row0 col28" >4.88</td>
                        <td id="T__row0_col29" class="data row0 col29" >6.35</td>
                        <td id="T__row0_col30" class="data row0 col30" >5.19</td>
                        <td id="T__row0_col31" class="data row0 col31" >5.81</td>
                        <td id="T__row0_col32" class="data row0 col32" >5.85</td>
                        <td id="T__row0_col33" class="data row0 col33" >5.46</td>
                        <td id="T__row0_col34" class="data row0 col34" >4.50</td>
            </tr>

回答：有没有更有效的方法来应用样式？

OP 已经建议将 DataFrame 分成多个部分。
Pandas：表格可视化用户指南包含可能的Optimizations
- 设置uuid_len=0、cell_ids=False 会使文件大小稍微变小，但不能解决此问题。
  - s4 = Styler(cat_mean, uuid_len=0, cell_ids=False).apply(color, axis=None)
  - 通常，uuid 看起来像id="T_5409d_level0_row0"，但使用uuid_len=0，它看起来像id="T__level0_row0"
- 尽可能使用table_styles：这些将类添加到行或列（不是单个单元格），因此如果您有这些分组，最好使用它们。
  - 对于有很多行/列的 OP，设置行或列table_styles 可能不是有效选项
有一个方法set_td_classes，可以让你引用外部的css类。（1.3.0 中有一个错误修复）
- 以下使用class 的代码应该可以工作，但受到错误#39317 的影响

def test(s, props=''):
    t = np.where(s.gt(mean[s.name]), props, '')
    return t

build = lambda x: pd.DataFrame(x, index=cat_mean.index, columns=cat_mean.columns)
cls1 = build(cat_mean.apply(test, props='cls-1 ', axis=0))

test = cat_mean.style.set_table_styles([{'selector': '.cls-1', 'props': [('color', 'white'), ('background-color', 'darkblue')]}]).set_td_classes(cls1)

生成的 HTML 应该更像下面这样，它对每个值使用 class=，而不是将所有样式化的行 ID 放在 HTML 的顶部。
但是，如上所述，这也有一个错误，并且无法正常工作

<style  type="text/css" >
    #T_b3f37_ .cls-1 {
          color: white;
          background-color: darkblue;
    }</style><table id="T_b3f37_" ><thead>    <tr>        <th class="blank" ></th>        <th class="blank level0" ></th>        <th class="col_heading level0 col0" >val1</th>        <th class="col_heading level0 col1" >val2</th>        <th class="col_heading level0 col2" >val3</th>        <th class="col_heading level0 col3" >val4</th>        <th class="col_heading level0 col4" >val5</th>        <th class="col_heading level0 col5" >val6</th>        <th class="col_heading level0 col6" >val7</th>        <th class="col_heading level0 col7" >val8</th>        <th class="col_heading level0 col8" >val9</th>        <th class="col_heading level0 col9" >val10</th>        <th class="col_heading level0 col10" >val11</th>        <th class="col_heading level0 col11" >val12</th>        <th class="col_heading level0 col12" >val13</th>        <th class="col_heading level0 col13" >val14</th>        <th class="col_heading level0 col14" >val15</th>        <th class="col_heading level0 col15" >val16</th>        <th class="col_heading level0 col16" >val17</th>        <th class="col_heading level0 col17" >val18</th>        <th class="col_heading level0 col18" >val19</th>        <th class="col_heading level0 col19" >val20</th>        <th class="col_heading level0 col20" >val21</th>        <th class="col_heading level0 col21" >val22</th>        <th class="col_heading level0 col22" >val23</th>        <th class="col_heading level0 col23" >val24</th>        <th class="col_heading level0 col24" >val25</th>        <th class="col_heading level0 col25" >val26</th>        <th class="col_heading level0 col26" >val27</th>        <th class="col_heading level0 col27" >val28</th>        <th class="col_heading level0 col28" >val29</th>        <th class="col_heading level0 col29" >val30</th>        <th class="col_heading level0 col30" >val31</th>        <th class="col_heading level0 col31" >val32</th>        <th class="col_heading level0 col32" >val33</th>        <th class="col_heading level0 col33" >val34</th>        <th class="col_heading level0 col34" >val35</th>    </tr>    <tr>        <th class="index_name level0" >name</th>        <th class="index_name level1" >cat</th>        <th class="blank" ></th>        <th class="blank" ></th>        <th class="blank" ></th>        <th class="blank" ></th>        <th class="blank" ></th>        <th class="blank" ></th>        <th class="blank" ></th>        <th class="blank" ></th>        <th class="blank" ></th>        <th class="blank" ></th>        <th class="blank" ></th>        <th class="blank" ></th>        <th class="blank" ></th>        <th class="blank" ></th>        <th class="blank" ></th>        <th class="blank" ></th>        <th class="blank" ></th>        <th class="blank" ></th>        <th class="blank" ></th>        <th class="blank" ></th>        <th class="blank" ></th>        <th class="blank" ></th>        <th class="blank" ></th>        <th class="blank" ></th>        <th class="blank" ></th>        <th class="blank" ></th>        <th class="blank" ></th>        <th class="blank" ></th>        <th class="blank" ></th>        <th class="blank" ></th>        <th class="blank" ></th>        <th class="blank" ></th>        <th class="blank" ></th>        <th class="blank" ></th>        <th class="blank" ></th>    </tr></thead><tbody>
                <tr>
                        <th id="T_b3f37_level0_row0" class="row_heading level0 row0" rowspan="3">Adriana Mcknight</th>
                        <th id="T_b3f37_level1_row0" class="row_heading level1 row0" >1</th>
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