Python 使用 to_html 将 CSS 类应用于 Pandas DataFrame
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Apply CSS class to Pandas DataFrame using to_html
提问by sparrow
I'm having trouble applying "classes" argument with Pandas "to_html" method to style a DataFrame.
我在使用 Pandas 的“to_html”方法应用“classes”参数来设置 DataFrame 样式时遇到问题。
"classes : str or list or tuple, default None CSS class(es) to apply to the resulting html table" from: https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.to_html.html
"classes : str or list or tuple, default None CSS class(es) to apply to the result html table" from: https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.to_html。 html
I am able to render a styled DataFrame like this (for example):
我能够渲染这样的样式数据帧（例如）：
df = pd.DataFrame([[1, 2], [1, 3], [4, 6]], columns=['A', 'B'])
myhtml = df.style.set_properties(**{'font-size': '11pt', 'font-family': 'Calibri','border-collapse': 'collapse','border': '1px solid black'}).render()
with open('myhtml.html','w') as f:
f.write(myhtml)
How can I style html output from a DataFrame using "classes" with "to_html" like this:
如何使用带有“to_html”的“类”来设置来自 DataFrame 的 html 输出的样式，如下所示：
df.to_html('myhtml.html',classes=<something here>)
回答by Parfait
Pandas' to_htmlsimply outputs a large string containing HTML table markup. The classes argument is a convenience handler to give the <table>a classattribute that will be referenced in a previously createdCSS document that styles it. Therefore, incorporate to_htmlinto a wider HTML document build that references an external CSS.
Pandasto_html只是输出一个包含 HTML 表格标记的大字符串。classes 参数是一个方便的处理程序，用于提供<table>一个class属性，该属性将在先前创建的CSS 文档中引用它的样式。因此，合并to_html到引用外部 CSS 的更广泛的 HTML 文档构建中。
Interestingly, to_htmladds dual classes <table class="dataframe mystyle">which can be referenced in CSS individually, .dataframe {...} .mystyle{...}, or together .dataframe.mystyle {...}. Below demonstrates with random data.
有趣的是，to_html添加了<table class="dataframe mystyle">可以在 CSS 中单独.dataframe {...} .mystyle{...}、或一起引用的双类.dataframe.mystyle {...}。下面用随机数据演示。
Data
数据
import pandas as pd
import numpy as np
pd.set_option('display.width', 1000)
pd.set_option('colheader_justify', 'center')
np.random.seed(6182018)
demo_df = pd.DataFrame({'date': np.random.choice(pd.date_range('2018-01-01', '2018-06-18', freq='D'), 50),
'analysis_tool': np.random.choice(['pandas', 'r', 'julia', 'sas', 'stata', 'spss'],50),
'database': np.random.choice(['postgres', 'mysql', 'sqlite', 'oracle', 'sql server', 'db2'],50),
'os': np.random.choice(['windows 10', 'ubuntu', 'mac os', 'android', 'ios', 'windows 7', 'debian'],50),
'num1': np.random.randn(50)*100,
'num2': np.random.uniform(0,1,50),
'num3': np.random.randint(100, size=50),
'bool': np.random.choice([True, False], 50)
},
columns=['date', 'analysis_tool', 'num1', 'database', 'num2', 'os', 'num3', 'bool']
)
print(demo_df.head(10))
# date analysis_tool num1 database num2 os num3 bool
# 0 2018-04-21 pandas 153.474246 mysql 0.658533 ios 74 True
# 1 2018-04-13 sas 199.461669 sqlite 0.656985 windows 7 11 False
# 2 2018-06-09 stata 12.918608 oracle 0.495707 android 25 False
# 3 2018-04-24 spss 88.562111 sql server 0.113580 windows 7 42 False
# 4 2018-05-05 spss 110.231277 oracle 0.660977 windows 10 76 True
# 5 2018-04-05 sas -68.140295 sql server 0.346894 windows 10 0 True
# 6 2018-05-07 julia 12.874660 postgres 0.195217 ios 79 True
# 7 2018-01-22 r 189.410928 mysql 0.234815 windows 10 56 False
# 8 2018-01-12 pandas -111.412564 sql server 0.580253 debian 30 False
# 9 2018-04-12 r 38.963967 postgres 0.266604 windows 7 46 False
CSS(save as df_style.css)
CSS （另存为 df_style.css）
/* includes alternating gray and white with on-hover color */
.mystyle {
font-size: 11pt;
font-family: Arial;
border-collapse: collapse;
border: 1px solid silver;
}
.mystyle td, th {
padding: 5px;
}
.mystyle tr:nth-child(even) {
background: #E0E0E0;
}
.mystyle tr:hover {
background: silver;
cursor: pointer;
}
Pandas
熊猫
pd.set_option('colheader_justify', 'center') # FOR TABLE <th>
html_string = '''
<html>
<head><title>HTML Pandas Dataframe with CSS</title></head>
<link rel="stylesheet" type="text/css" href="df_style.css"/>
<body>
{table}
</body>
</html>.
'''
# OUTPUT AN HTML FILE
with open('myhtml.html', 'w') as f:
f.write(html_string.format(table=demo_df.to_html(classes='mystyle')))
OUTPUT
输出
HTML(references df_style.css, assumed in same directory; see class argument in table)
HTML （引用 df_style.css，假定在同一目录中；请参阅表中的类参数）
<html>
<head><title>HTML Pandas Dataframe with CSS</title></head>
<link rel="stylesheet" type="text/css" href="df_style.css"/>
<body>
<table border="1" class="dataframe mystyle">
<thead>
<tr style="text-align: center;">
<th></th>
<th>date</th>
<th>analysis_tool</th>
<th>num1</th>
<th>database</th>
<th>num2</th>
<th>os</th>
<th>num3</th>
<th>bool</th>
</tr>
</thead>
<tbody>
<tr>
<th>0</th>
<td>2018-04-21</td>
<td>pandas</td>
<td>153.474246</td>
<td>mysql</td>
<td>0.658533</td>
<td>ios</td>
<td>74</td>
<td>True</td>
</tr>
<tr>
<th>1</th>
<td>2018-04-13</td>
<td>sas</td>
<td>199.461669</td>
<td>sqlite</td>
<td>0.656985</td>
<td>windows 7</td>
<td>11</td>
<td>False</td>
</tr>
<tr>
<th>2</th>
<td>2018-06-09</td>
<td>stata</td>
<td>12.918608</td>
<td>oracle</td>
<td>0.495707</td>
<td>android</td>
<td>25</td>
<td>False</td>
</tr>
<tr>
<th>3</th>
<td>2018-04-24</td>
<td>spss</td>
<td>88.562111</td>
<td>sql server</td>
<td>0.113580</td>
<td>windows 7</td>
<td>42</td>
<td>False</td>
</tr>
<tr>
<th>4</th>
<td>2018-05-05</td>
<td>spss</td>
<td>110.231277</td>
<td>oracle</td>
<td>0.660977</td>
<td>windows 10</td>
<td>76</td>
<td>True</td>
</tr>
...
</tbody>
</table>
</body>
</html>
回答by Shubham Chourasia
Here's how I did it
这是我如何做到的
Create a text file for css code and write down your css code here, say css_style.txt Now read this txt file as a string in your python file
为 css 代码创建一个文本文件并在这里写下你的 css 代码，比如 css_style.txt 现在将这个 txt 文件作为你的 python 文件中的字符串读取
with open('css_style.txt', 'r') as myfile:
style = myfile.read()
with open('css_style.txt', 'r') as myfile:
style = myfile.read()
Now in HTML code
现在在 HTML 代码中
"""<html><head>Something Something</head>{1}<div>{0}</div></html>""".format(some_panda_dataframe.to_html,style)
Here in my case css_style.txt file is
在我的例子中 css_style.txt 文件是
<style>
table {
border-collapse: collapse;
width: 100%;
}
th {
text-align: center;
padding: 8px;
}
td {
text-align: left;
padding: 8px;
}
tr:nth-child(even){background-color: #FFD5D5}
th {
background-color: #0000FF;
color: white;
}
</style>
回答by hui chen
Essentially, the pandas.to_html() just exports a plain HTML table. You can insert the table wherever you want in the body and control the style via CSS in the style section.
本质上，pandas.to_html() 只是导出一个普通的 HTML 表格。您可以在正文中的任何位置插入表格，并通过样式部分中的 CSS 控制样式。
<html>
<head>
<style>
table, th, td {{font-size:10pt; border:1px solid black; border-collapse:collapse; text-align:left;}}
th, td {{padding: 5px;}}
</style>
</head>
<body>
{
pandas.to_html()
}
</body>
</html>
