are used to parse HTML tables in the top-level pandas io function read_html. Issues with lxml • Benefits – lxml is very fast. – lxml requires Cython to install correctly. • Drawbacks – lxml does not just a wrapper around a parser backend. Issues with BeautifulSoup4 using html5lib as a backend • Benefits – html5lib is far more lenient than lxml and consequently deals with real-life markup in a much pandas: powerful Python data analysis toolkit, Release 0.25.3 Note: You will only see the performance benefits of using the numexpr engine with DataFrame.query() if your frame has more than approximately 200

numexpr is slightly faster than Python for large frames Note: You will only see the performance benefits of using the numexpr engine with DataFrame.query() if your frame has more than approximately 200 not useful, except for testing other eval() engines against it. You will acheive no performance benefits using eval() with engine=’python’. You can see this by using eval() with the ’python’ engine is are used to parse HTML tables in the top-level pandas io function read_html. Issues with lxml • Benefits – lxml is very fast – lxml requires Cython to install correctly. • Drawbacks – lxml does not

are used to parse HTML tables in the top-level pandas io function read_html. Issues with lxml • Benefits – lxml is very fast. – lxml requires Cython to install correctly. 336 Chapter 2. User Guide just a wrapper around a parser backend. Issues with BeautifulSoup4 using html5lib as a backend • Benefits – html5lib is far more lenient than lxml and consequently deals with real-life markup in a much numexpr is slightly faster than Python for large frames. Note: You will only see the performance benefits of using the numexpr engine with DataFrame.query() if your frame has more than approximately 200

are used to parse HTML tables in the top-level pandas io function read_html. Issues with lxml • Benefits – lxml is very fast. – lxml requires Cython to install correctly. • Drawbacks – lxml does not just a wrapper around a parser backend. Issues with BeautifulSoup4 using html5lib as a backend • Benefits – html5lib is far more lenient than lxml and consequently deals with real-life markup in a much numexpr is slightly faster than Python for large frames. Note: You will only see the performance benefits of using the numexpr engine with DataFrame.query() if your frame has more than approximately 200

pandas: powerful Python data analysis toolkit, Release 0.14.0 Note: You will only see the performance benefits of using the numexpr engine with DataFrame.query() if your frame has more than approximately 200 useful, except for testing other evaluation engines against it. You will acheive no performance benefits using eval() with engine=’python’ and in fact may incur a performance hit. You can see this by are used to parse HTML tables in the top-level pandas io function read_html. Issues with lxml • Benefits – lxml is very fast – lxml requires Cython to install correctly. • Drawbacks – lxml does not

are used to parse HTML tables in the top-level pandas io function read_html. Issues with lxml • Benefits – lxml is very fast. – lxml requires Cython to install correctly. • Drawbacks – lxml does not just a wrapper around a parser backend. Issues with BeautifulSoup4 using html5lib as a backend • Benefits – html5lib is far more lenient than lxml and consequently deals with real-life markup in a much pandas: powerful Python data analysis toolkit, Release 1.3.2 Note: You will only see the performance benefits of using the numexpr engine with DataFrame.query() if your frame has more than approximately 200

are used to parse HTML tables in the top-level pandas io function read_html. Issues with lxml • Benefits – lxml is very fast. – lxml requires Cython to install correctly. • Drawbacks – lxml does not just a wrapper around a parser backend. Issues with BeautifulSoup4 using html5lib as a backend • Benefits – html5lib is far more lenient than lxml and consequently deals with real-life markup in a much numexpr is slightly faster than Python for large frames. Note: You will only see the performance benefits of using the numexpr engine with DataFrame.query() if your frame has more than approximately 200

are used to parse HTML tables in the top-level pandas io function read_html. Issues with lxml • Benefits – lxml is very fast. – lxml requires Cython to install correctly. • Drawbacks – lxml does not just a wrapper around a parser backend. Issues with BeautifulSoup4 using html5lib as a backend • Benefits – html5lib is far more lenient than lxml and consequently deals with real-life markup in a much numexpr is slightly faster than Python for large frames. Note: You will only see the performance benefits of using the numexpr engine with DataFrame.query() if your frame has more than approximately 200

are used to parse HTML tables in the top-level pandas io function read_html. Issues with lxml • Benefits – lxml is very fast. – lxml requires Cython to install correctly. • Drawbacks – lxml does not just a wrapper around a parser backend. Issues with BeautifulSoup4 using html5lib as a backend • Benefits – html5lib is far more lenient than lxml and consequently deals with real-life markup in a much numexpr is slightly faster than Python for large frames. Note: You will only see the performance benefits of using the numexpr engine with DataFrame.query() if your frame has more than approximately 200

are used to parse HTML tables in the top-level pandas io function read_html. Issues with lxml • Benefits – lxml is very fast – lxml requires Cython to install correctly. • Drawbacks – lxml does not data analysis toolkit, Release 0.12.0 Issues with BeautifulSoup4 using html5lib as a backend • Benefits – html5lib is far more lenient than lxml and consequently deals with real-life markup in a much