我正在尝试将[网页] [1]中的表格读入pandas DataFrames。 pandas.read_html返回空表的列表，因为HTML中的表确实是空的。它们可能是动态填充的。
有人suggested数据源可能是[XML] [3]，如下所示：
<?xml version="1.0" encoding="UTF-8" standalone="yes"?>
<Items xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">
<Item>
<ID>Content ID
Unique identifier for use of tool</ID>
<Type>Product Type
1. sample name
2. sample name
3. sample name
4. sample name
5. sample name
6. sample name
7. Accessories</Type>
<Name>Sub Category
Name of checkbox item and announcement subhead</Name>
<PubDate>Published Date
Text type - not Date
Spell out month name completely</PubDate>
<Desc>Description
Enter a full description</Desc>
<Notes>Special Notes
To appear under recommendation table</Notes>
<Image>Product Image
Enter entire URL path to image or provide the image through email.
</Image>
<LinkA>Announcement URL
Enter file name, no spaces</LinkA>
<LinkB>Product URL
Enter full URL</LinkB>
<TableA>Product SKU
Enter product number being discontinued</TableA>
<TableB>Product Description
Enter product description for original product</TableB>
<TableC>Replacement Product SKU
Enter product number to replace discontinued product
Only use this column when multiple country skus are not needed</TableC>
<TableD>Replacement Product Description
Enter product description for replacement product
Only use this column when multiple country skus are not needed</TableD>
<TableE>Custom Header 1
Use these custom headers to build a dynamic table. Used primarily for multiple skus per country</TableE>
<TableF>Custom Header 2
Use these custom headers to build a dynamic table. Used primarily for multiple skus per country</TableF>
<TableG>Custom Header 3
Use these custom headers to build a dynamic table. Used primarily for multiple skus per country</TableG>
<TableH>Custom Header 4
Use these custom headers to build a dynamic table. Used primarily for multiple skus per country</TableH>
<TableI>Custom Header 5
Use these custom headers to build a dynamic table. Used primarily for multiple skus per country</TableI>
<TableJ>Custom Header 6
Use these custom headers to build a dynamic table. Used primarily for multiple skus per country</TableJ>
</Item>
<Item>
<ID>1</ID>
<Type>1</Type>
<Name>xx sample namexx</Name>
<PubDate>June 1, 2011</PubDate>
<Desc>xx Sample Description xx.</Desc>
<Image>a3100-24.png</Image>
<LinkA>HP-A3100SI-ES-Announcement.pdf</LinkA>
</Item>
<Item>
<TableA>JD298A</TableA>
<TableB>xx Sample Table Name xx</TableB>
<TableC>N/A</TableC>
</Item>
<!-- other Item nodes -->
</Items>
如何将此XML转换为DataFrame？
答案 0 :(得分：1)
任何时候一个人使用复杂的XML并且需要更简单的结构，比如逐行二维的扁平化数据帧，人们应该考虑XSLT，这是专门用于将XML文件转换为其他XML，HTML和as的专用语言下面显示甚至文本文件！ Python的lxml可以运行XSLT 1.0脚本。
在XSLT下面生成一个带有命名列的管道分隔文本文件，然后将其导入到Pandas中。这个XML的挑战是 ID - 识别的项中的相关节点不是它的子节点而是它的兄弟节点。因此，必须针对Muenchian Grouping运行特殊键控，这是一种从XSLT Grouping Sibling @Tomalak's answer借来的策略。
XSLT （另存为.xsl文件，一个特殊的.xml文件，待导入）
由于需要映射空单元格，脚本会耗尽所有可能的列，从而耗尽其长度。
<?xml version="1.0" encoding="utf-8"?>
<xsl:stylesheet version="1.0" xmlns:xsl="http://www.w3.org/1999/XSL/Transform"
xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">
<xsl:output method="text" indent="yes" omit-xml-declaration="yes"/>
<xsl:key name="item_key" match="Item" use="generate-id(preceding-sibling::Item[count(ID) > 0][1])"/>
<xsl:template match ="/Items">
<!-- COLUMN HEADERS -->
<xsl:text>ID|Type|Name|PubDate|Desc|Notes|Image|LinkA|LinkB|TableA|TableB|TableC|TableD|TableE|TableF|TableG|TableH|TableI|TableJ
</xsl:text>
<xsl:apply-templates select="Item[count(ID) > 0 and not(contains(ID, 'Content'))]"/>
</xsl:template>
<xsl:template match ="Item">
<!-- INDICATORS TO REPEAT ACROSS RELATED ROWS -->
<xsl:variable name="ID" select="normalize-space(ID)"/>
<xsl:variable name="Type" select="normalize-space(Type)"/>
<xsl:variable name="Name" select="normalize-space(Name)"/>
<xsl:variable name="PubDate" select="normalize-space(PubDate)"/>
<xsl:variable name="Desc" select="normalize-space(Desc)"/>
<xsl:variable name="Notes" select="normalize-space(Notes)"/>
<xsl:variable name="Image" select="normalize-space(Image)"/>
<xsl:variable name="LinkA" select="normalize-space(LinkA)"/>
<xsl:variable name="LinkB" select="normalize-space(LinkB)"/>
<!-- ITEM ID NODES -->
<xsl:value-of select="$ID"/><xsl:text>|</xsl:text>
<xsl:value-of select="$Type"/><xsl:text>|</xsl:text>
<xsl:value-of select="$Name"/><xsl:text>|</xsl:text>
<xsl:value-of select="$PubDate"/><xsl:text>|</xsl:text>
<xsl:value-of select="$Desc"/><xsl:text>|</xsl:text>
<xsl:value-of select="$Notes"/><xsl:text>|</xsl:text>
<xsl:value-of select="$Image"/><xsl:text>|</xsl:text>
<xsl:value-of select="$LinkA"/><xsl:text>|</xsl:text>
<xsl:value-of select="$LinkB"/><xsl:text>|</xsl:text>
<xsl:value-of select="normalize-space(TableA)"/><xsl:text>|</xsl:text>
<xsl:value-of select="normalize-space(TableB)"/><xsl:text>|</xsl:text>
<xsl:value-of select="normalize-space(TableC)"/><xsl:text>|</xsl:text>
<xsl:value-of select="normalize-space(TableD)"/><xsl:text>|</xsl:text>
<xsl:value-of select="normalize-space(TableE)"/><xsl:text>|</xsl:text>
<xsl:value-of select="normalize-space(TableF)"/><xsl:text>|</xsl:text>
<xsl:value-of select="normalize-space(TableG)"/><xsl:text>|</xsl:text>
<xsl:value-of select="normalize-space(TableH)"/><xsl:text>|</xsl:text>
<xsl:value-of select="normalize-space(TableI)"/><xsl:text>|</xsl:text>
<xsl:value-of select="normalize-space(TableJ)"/><xsl:text>|</xsl:text>
<xsl:text>
</xsl:text> <!-- LINE BREAK -->
<!-- ALL RELATED NODES TO ITEM ID -->
<xsl:for-each select="key('item_key', generate-id())[position() != last()]" >
<xsl:value-of select="$ID"/><xsl:text>|</xsl:text>
<xsl:value-of select="$Type"/><xsl:text>|</xsl:text>
<xsl:value-of select="$Name"/><xsl:text>|</xsl:text>
<xsl:value-of select="$PubDate"/><xsl:text>|</xsl:text>
<xsl:value-of select="$Desc"/><xsl:text>|</xsl:text>
<xsl:value-of select="$Notes"/><xsl:text>|</xsl:text>
<xsl:value-of select="$Image"/><xsl:text>|</xsl:text>
<xsl:value-of select="$LinkA"/><xsl:text>|</xsl:text>
<xsl:value-of select="$LinkB"/><xsl:text>|</xsl:text>
<xsl:value-of select="normalize-space(TableA)"/><xsl:text>|</xsl:text>
<xsl:value-of select="normalize-space(TableB)"/><xsl:text>|</xsl:text>
<xsl:value-of select="normalize-space(TableC)"/><xsl:text>|</xsl:text>
<xsl:value-of select="normalize-space(TableD)"/><xsl:text>|</xsl:text>
<xsl:value-of select="normalize-space(TableE)"/><xsl:text>|</xsl:text>
<xsl:value-of select="normalize-space(TableF)"/><xsl:text>|</xsl:text>
<xsl:value-of select="normalize-space(TableG)"/><xsl:text>|</xsl:text>
<xsl:value-of select="normalize-space(TableH)"/><xsl:text>|</xsl:text>
<xsl:value-of select="normalize-space(TableI)"/><xsl:text>|</xsl:text>
<xsl:value-of select="normalize-space(TableJ)"/><xsl:text>|</xsl:text>
<xsl:text>
</xsl:text> <!-- LINE BREAK -->
</xsl:for-each>
</xsl:template>
</xsl:stylesheet>
Python （运行XSLT，保存CSV，导入Pandas）
import pandas as pd
from lxml import etree
url = "http://h17007.www1.hpe.com/data/xml/eos/eos.xml?a=0.9317168944148095.xml"
# LOAD XML AND XSL
xml = etree.parse(url)
xsl = etree.parse("XSLT_Script.xsl")
# TRANSFORM SOURCE
transformer = etree.XSLT(xsl)
result = transformer(xml)
# SAVE PIPE-DELIMITED FILE
with open("Output.txt", 'wb') as f:
f.write(result)
# IMPORT PIPE-DELIMITED FILE
hp_df = pd.read_table("Output.txt", sep="|", index_col=False)
# ALTERNATIVE: IMPORT DIRECTLY (BYPASS .TXT SAVE)
from io import StringIO
hp_df = pd.read_table(StringIO(str(result)), sep="|", index_col=False)
输出 （在导入Pandas之前）
ID|Type|Name|PubDate|Desc|Notes|Image|LinkA|LinkB|TableA|TableB|TableC|TableD|TableE|TableF|TableG|TableH|TableI|TableJ
1|1|Select HP A3100 series switches|June 1, 2011|The HP A3100 SI series switches indicated below have served H3C and HP Customers with Fast Ethernet for many years. Due to market factors, we are announcing the end of sale of the devices effective Jul 1, 2011.||a3100-24.png|HP-A3100SI-ES-Announcement.pdf||||||||||||
1|1|Select HP A3100 series switches|June 1, 2011|The HP A3100 SI series switches indicated below have served H3C and HP Customers with Fast Ethernet for many years. Due to market factors, we are announcing the end of sale of the devices effective Jul 1, 2011.||a3100-24.png|HP-A3100SI-ES-Announcement.pdf||JD298A|HP 1 Port Gig-T 3100 SI Module|N/A||||||||
1|1|Select HP A3100 series switches|June 1, 2011|The HP A3100 SI series switches indicated below have served H3C and HP Customers with Fast Ethernet for many years. Due to market factors, we are announcing the end of sale of the devices effective Jul 1, 2011.||a3100-24.png|HP-A3100SI-ES-Announcement.pdf||JD299A|HP 1 Port Gig-LX SC 3100 SI Module|N/A||||||||
1|1|Select HP A3100 series switches|June 1, 2011|The HP A3100 SI series switches indicated below have served H3C and HP Customers with Fast Ethernet for many years. Due to market factors, we are announcing the end of sale of the devices effective Jul 1, 2011.||a3100-24.png|HP-A3100SI-ES-Announcement.pdf||JD300A|HP 1 Port Gig-SX SC 3100 SI Module|N/A||||||||
1|1|Select HP A3100 series switches|June 1, 2011|The HP A3100 SI series switches indicated below have served H3C and HP Customers with Fast Ethernet for many years. Due to market factors, we are announcing the end of sale of the devices effective Jul 1, 2011.||a3100-24.png|HP-A3100SI-ES-Announcement.pdf||JD301A|HP 1-Port 10/100Base-T POE 3100 SI Module|N/A||||||||
1|1|Select HP A3100 series switches|June 1, 2011|The HP A3100 SI series switches indicated below have served H3C and HP Customers with Fast Ethernet for many years. Due to market factors, we are announcing the end of sale of the devices effective Jul 1, 2011.||a3100-24.png|HP-A3100SI-ES-Announcement.pdf||JD308A|HP A3100-16 SI Switch with 2 Module Slots|JD305A|HP A3100-16 SI Switch|||||||
1|1|Select HP A3100 series switches|June 1, 2011|The HP A3100 SI series switches indicated below have served H3C and HP Customers with Fast Ethernet for many years. Due to market factors, we are announcing the end of sale of the devices effective Jul 1, 2011.||a3100-24.png|HP-A3100SI-ES-Announcement.pdf||JD309A|HP A3100-24 SI Switch with 2 Slots|JD306A|HP A3100-24 SI Switch|||||||
1|1|Select HP A3100 series switches|June 1, 2011|The HP A3100 SI series switches indicated below have served H3C and HP Customers with Fast Ethernet for many years. Due to market factors, we are announcing the end of sale of the devices effective Jul 1, 2011.||a3100-24.png|HP-A3100SI-ES-Announcement.pdf||JF444A|3100 series module|N/A||||||||
2|2|HP V10ag Wireless Access Point (NA only)|July 26, 2010|The HP V10ag Wireless Access Point has provided secure, reliable 802.11a and 802.11b/g wireless connectivity for small business networks since 2007. Due to the availability of the next generation 802.11n technology and the introduction of the HP V-M200 802.11n Access Point, HP networking is announcing the End of Sale of the HP V10ag Wireless Access Point (J9140A). For specific product rollover details see the announcement.||WAP10ag-1.png|10agAnnouncement-AM-only.pdf|http://h10010.www1.hp.com/wwpc/us/en/sm/WF05a/12883-12883-1137927-3836040-4172284-3637595.html?jumpid=reg_R1002_USEN|||||||||||
3|2|HP ProCurve Mobility Access Point Series - M110|September 2, 2009|<b>MAC Address Schema Change:</b> We are finalizing the integration of Colubris (previous acquisition) products by transitioning MAC Address assignments to HP ProCurve MAC address assignments. HP will be doing a Product Roll to support this requirement. <b>HP ProCurve Statement on New DFS EU Standards</b> As of July 1st 2010, all wireless devices sold in the EU countries and any country that participates in the EU free market, must meet stringent Dynamic Frequency Selection (DFS) requirements for radar detection and avoidance. HP will be doing a Product Roll to support this requirement. For specific product roll details see our MAC Address A-to-B Roll and DFS Disablement Announcement.||M110_100x100.png|A-to-BRollforVariousHPProCurveAccessPoints.pdf|http://h20195.www2.hp.com/v2/GetDocument.aspx?docname=4AA0-8273ENW&cc=en&lc=en|||||||||||
要过滤掉这个主数据帧，请使用pandas方法：
# SPECIFIC PRODUCT WITH [...]
filtered_df = hp_df[hp_df['Name'] == 'HPE 1410 Fast Ethernet Switches']
# SPECIFIC PRODUCT WITH .query()
filtered_df = hp_df.query("Name == 'HPE FlexNetwork 5940 Switch Series'")
# PASS A LIST WITH .isin()
filtered_df = hp_df[hp_df['Name'].isin(['HPE FlexNetwork 5120 SI Switch Series',
'HPE 1410 Fast Ethernet Switches',
'HPE OfficeConnect 1910 Switch Series'])]