[393]: store.keys() Out[393]: ['/df', '/food/apple', '/food/orange', '/foo/bar/bah'] # remove all nodes under this level In [394]: store.remove('food') In [395]: store Out[395]: nodes) and adding again, WILL TEND TO INCREASE THE FILE SIZE. To repack and clean the file, use ptrepack datasets from corresponding nodes and # insert them into one data.frame object. library(rhdf5) loadhdf5data <- function(h5File) { listing <- h5ls(h5File) # Find all data nodes, values are stored in *_values

version 1.3.0. The top-level read_xml() function can accept an XML string/file/URL and will parse nodes and attributes into a pandas DataFrame. Note: Since there is no standard XML structure where design full-featured XML library that extends Python’s ElementTree API. One powerful tool is ability to query nodes selectively or conditionally with more expressive XPath: In [333]: df = pd.read_xml(file_path, x contains a namespace with prefix, doc, and URI at https://example.com. In order to parse doc:row nodes, namespaces must be used. In [339]: xml = """ .....:

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version 1.3.0. The top-level read_xml() function can accept an XML string/file/URL and will parse nodes and attributes into a pandas DataFrame. Note: Since there is no standard XML structure where design full-featured XML library that extends Python’s ElementTree API. One powerful tool is ability to query nodes selectively or conditionally with more expressive XPath: In [333]: df = pd.read_xml(file_path, x contains a namespace with prefix, doc, and URI at https://example.com. In order to parse doc:row nodes, namespaces must be used. In [339]: xml = """ .....:

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version 1.3.0. The top-level read_xml() function can accept an XML string/file/URL and will parse nodes and attributes into a pandas DataFrame. Note: Since there is no standard XML structure where design full-featured XML library that extends Python’s ElementTree API. One powerful tool is ability to query nodes selectively or conditionally with more expressive XPath: In [333]: df = pd.read_xml(file_path, x contains a namespace with prefix, doc, and URI at https://example.com. In order to parse doc:row nodes, namespaces must be used. In [339]: xml = """ .....:

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version 1.3.0. The top-level read_xml() function can accept an XML string/file/URL and will parse nodes and attributes into a pandas DataFrame. 2.2. Guides 343 pandas: powerful Python data analysis toolkit full-featured XML library that extends Python’s ElementTree API. One powerful tool is ability to query nodes selectively or conditionally with more expressive XPath: In [375]: df = pd.read_xml(file_path, x contains a namespace with prefix, doc, and URI at https://example.com. In order to parse doc:row nodes, namespaces must be used. In [381]: xml = """ .....:

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version 1.3.0. The top-level read_xml() function can accept an XML string/file/URL and will parse nodes and attributes into a pandas DataFrame. Note: Since there is no standard XML structure where design full-featured XML library that extends Python’s ElementTree API. One powerful tool is ability to query nodes selectively or conditionally with more expressive XPath: 340 Chapter 2. User Guide pandas: powerful contains a namespace with prefix, doc, and URI at https://example.com. In order to parse doc:row nodes, namespaces must be used. In [343]: xml = """ .....:

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version 1.3.0. The top-level read_xml() function can accept an XML string/file/URL and will parse nodes and attributes into a pandas DataFrame. Note: Since there is no standard XML structure where design full-featured XML library that extends Python’s ElementTree API. One powerful tool is ability to query nodes selectively or conditionally with more expressive XPath: In [376]: df = pd.read_xml(file_path, x contains a namespace with prefix, doc, and URI at https://example.com. In order to parse doc:row nodes, namespaces must be used. In [382]: xml = """ .....:

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/wp wide_table (typ->appendable,nrows->12,ncols->2,indexers->[major_axis,minor_axis]) # remove all nodes under this level In [62]: store.remove('food') In [63]: store Out[63]: nodes under this level In [307]: store.remove('food') In [308]: store Out[308]: nodes) and adding again WILL TEND TO INCREASE THE FILE SIZE. To clean the file, use ptrepack 24.8.8 Notes

wide_table (typ->appendable,nrows->12,ncols->2, ˓→indexers->[major_axis,minor_axis]) # remove all nodes under this level In [57]: store.remove('food') In [58]: store Out[58]: nodes under this level In [335]: store.remove('food') In [336]: store Out[336]: nodes) and adding again, WILL TEND TO INCREASE THE FILE SIZE. To repack and clean the file, use ptrepack

wide_table (typ->appendable,nrows->12,ncols->2, ˓→indexers->[major_axis,minor_axis]) # remove all nodes under this level In [57]: store.remove('food') In [58]: store Out[58]: nodes under this level In [335]: store.remove('food') In [336]: store Out[336]: nodes) and adding again, WILL TEND TO INCREASE THE FILE SIZE. To repack and clean the file, use ptrepack