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更新:
pandas v1.2.4
Chrome v90.0.4430.93 (Official Build) (64-bit)
Edge v90.0.818.56 (Official build) (64-bit)
474 rows x 35 columns
,所应用的样式将停止显示。jupyterlab v3.0.11
中测试PyCharm 2021.1 (Professional Edition) Build #PY-211.6693.115, built on April 6, 2021
中进行了测试,将重新渲染的样式器保存到文件中具有相同的结果,因此,这不仅仅是jupyter
的问题。 471 rows × 35 columns
或474 rows × 34 columns
,则所有行均正确显示突出显示。 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.iloc[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)
引用
pd.show_versions()
的输出
None
的所有软件包,以节省空间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
cat_mean.iloc[:237, :].style.apply(color, axis=None)
cat_mean.iloc[237:, :].style.apply(color, axis=None)
保存到Excel
test = cat_mean.style.apply(color, axis=None)
test.to_excel('test.xlsx', engine='openpyxl')
最佳答案
回答:为什么这在Chrome或Edge中不起作用?
<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>
uuid_len=0
,cell_ids=False
使文件大小稍小,但不能解决此问题。s4 = Styler(cat_mean, uuid_len=0, cell_ids=False).apply(color, axis=None)
id="T_5409d_level0_row0"
,但是使用uuid_len=0
,它看起来像id="T__level0_row0"
table_styles
:这些将类添加到行或列(而不是单个单元格),因此,如果您具有这些分组,则最好使用它们。table_styles
可能不是有效的选项set_td_classes
方法,它允许您引用外部CSS类。 (在1.3.0 中存在错误修正)class
的代码应该可以使用,但是会受到bug #39317 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)
class=
,而不是将所有样式化的行id放在HTML的顶部。 <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>
<td id="T_b3f37_row0_col0" class="data row0 col0 cls-1 " >5.782609</td>
<td id="T_b3f37_row0_col1" class="data row0 col1 cls-1 " >5.652174</td>
<td id="T_b3f37_row0_col2" class="data row0 col2 cls-1 " >6.130435</td>
<td id="T_b3f37_row0_col3" class="data row0 col3 cls-1 " >6.086957</td>
<td id="T_b3f37_row0_col4" class="data row0 col4" >4.478261</td>
<td id="T_b3f37_row0_col5" class="data row0 col5" >4.565217</td>
<td id="T_b3f37_row0_col6" class="data row0 col6" >5.826087</td>
<td id="T_b3f37_row0_col7" class="data row0 col7" >5.956522</td>
<td id="T_b3f37_row0_col8" class="data row0 col8" >4.782609</td>
<td id="T_b3f37_row0_col9" class="data row0 col9" >5.347826</td>
<td id="T_b3f37_row0_col10" class="data row0 col10" >5.260870</td>
<td id="T_b3f37_row0_col11" class="data row0 col11" >5.130435</td>
<td id="T_b3f37_row0_col12" class="data row0 col12" >5.217391</td>
<td id="T_b3f37_row0_col13" class="data row0 col13" >6.173913</td>
<td id="T_b3f37_row0_col14" class="data row0 col14" >5.043478</td>
<td id="T_b3f37_row0_col15" class="data row0 col15" >6.391304</td>
<td id="T_b3f37_row0_col16" class="data row0 col16" >5.217391</td>
<td id="T_b3f37_row0_col17" class="data row0 col17" >5.913043</td>
<td id="T_b3f37_row0_col18" class="data row0 col18" >5.608696</td>
<td id="T_b3f37_row0_col19" class="data row0 col19" >5.869565</td>
<td id="T_b3f37_row0_col20" class="data row0 col20" >6.086957</td>
<td id="T_b3f37_row0_col21" class="data row0 col21" >4.826087</td>
<td id="T_b3f37_row0_col22" class="data row0 col22" >5.739130</td>
<td id="T_b3f37_row0_col23" class="data row0 col23" >6.304348</td>
<td id="T_b3f37_row0_col24" class="data row0 col24" >5.347826</td>
<td id="T_b3f37_row0_col25" class="data row0 col25" >5.173913</td>
<td id="T_b3f37_row0_col26" class="data row0 col26" >4.608696</td>
<td id="T_b3f37_row0_col27" class="data row0 col27" >5.391304</td>
<td id="T_b3f37_row0_col28" class="data row0 col28" >5.652174</td>
<td id="T_b3f37_row0_col29" class="data row0 col29" >5.434783</td>
<td id="T_b3f37_row0_col30" class="data row0 col30" >5.565217</td>
<td id="T_b3f37_row0_col31" class="data row0 col31" >5.956522</td>
<td id="T_b3f37_row0_col32" class="data row0 col32" >6.043478</td>
<td id="T_b3f37_row0_col33" class="data row0 col33" >5.217391</td>
<td id="T_b3f37_row0_col34" class="data row0 col34 cls-1 " >5.521739</td>
</tr>
关于python - 在Chrome或Edge中,并非大数据框中的所有行都显示 Pandas 样式,我们在Stack Overflow上找到一个类似的问题: https://stackoverflow.com/questions/67065785/
pandas.crosstab 和 Pandas 数据透视表似乎都提供了完全相同的功能。有什么不同吗? 最佳答案 pivot_table没有 normalize争论,不幸的是。 在 crosstab
我能找到的最接近的答案似乎太复杂:How I can create an interval column in pandas? 如果我有一个如下所示的 pandas 数据框: +-------+ |
这是我用来将某一行的一列值移动到同一行的另一列的当前代码: #Move 2014/15 column ValB to column ValA df.loc[(df.Survey_year == 201
我有一个以下格式的 Pandas 数据框: df = pd.DataFrame({'a' : [0,1,2,3,4,5,6], 'b' : [-0.5, 0.0, 1.0, 1.2, 1.4,
所以我有这两个数据框,我想得到一个新的数据框,它由两个数据框的行的克罗内克积组成。正确的做法是什么? 举个例子:数据框1 c1 c2 0 10 100 1 11 110 2 12
TL;DR:在 pandas 中,如何绘制条形图以使其 x 轴刻度标签看起来像折线图? 我制作了一个间隔均匀的时间序列(每天一个项目),并且可以像这样很好地绘制它: intensity[350:450
我有以下两个时间列,“Time1”和“Time2”。我必须计算 Pandas 中的“差异”列,即 (Time2-Time1): Time1 Time2
从这个 df 去的正确方法是什么: >>> df=pd.DataFrame({'a':['jeff','bob','jill'], 'b':['bob','jeff','mike']}) >>> df
我想按周从 Pandas 框架中的列中累积计算唯一值。例如,假设我有这样的数据: df = pd.DataFrame({'user_id':[1,1,1,2,2,2],'week':[1,1,2,1,
数据透视表的表示形式看起来不像我在寻找的东西,更具体地说,结果行的顺序。 我不知道如何以正确的方式进行更改。 df示例: test_df = pd.DataFrame({'name':['name_1
我有一个数据框,如下所示。 Category Actual Predicted 1 1 1 1 0
我有一个 df,如下所示。 df: ID open_date limit 1 2020-06-03 100 1 2020-06-23 500
我有一个 df ,其中包含与唯一值关联的各种字符串。对于这些唯一值,我想删除不等于单独列表的行,最后一行除外。 下面使用 Label 中的各种字符串值与 Item 相关联.所以对于每个唯一的 Item
考虑以下具有相同名称的列的数据框(显然,这确实发生了,目前我有一个像这样的数据集!:() >>> df = pd.DataFrame({"a":range(10,15),"b":range(5,10)
我在 Pandas 中有一个 DF,它看起来像: Letters Numbers A 1 A 3 A 2 A 1 B 1 B 2
如何减去两列之间的时间并将其转换为分钟 Date Time Ordered Time Delivered 0 1/11/19 9:25:00 am 10:58:00 am
我试图理解 pandas 中的下/上百分位数计算,但有点困惑。这是它的示例代码和输出。 test = pd.Series([7, 15, 36, 39, 40, 41]) test.describe(
我有一个多索引数据框,如下所示: TQ bought HT Detailed Instru
我需要从包含值“低”,“中”或“高”的数据框列创建直方图。当我尝试执行通常的df.column.hist()时,出现以下错误。 ex3.Severity.value_counts() Out[85]:
我试图根据另一列的长度对一列进行子串,但结果集是 NaN .我究竟做错了什么? import pandas as pd df = pd.DataFrame([['abcdefghi','xyz'],
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