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 1.0.0 Note: You will only see the performance benefits of using the numexpr engine with DataFrame.query() if your frame has more than approximately 200

(GH17352) 1.5 Performance improvements • Significant speedup in SparseArray initialization that benefits most operations, fixing performance regres- sion introduced in v0.20.0 (GH24985) • DataFrame.to_stata() datetime64[ns, tz] dtype (GH26649) 1.6.16 Sparse • Significant speedup in SparseArray initialization that benefits most operations, fixing performance regres- sion introduced in v0.20.0 (GH24985) • Bug in SparseFrame 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

(GH17352) 1.5 Performance improvements • Significant speedup in SparseArray initialization that benefits most operations, fixing performance regres- sion introduced in v0.20.0 (GH24985) • DataFrame.to_stata() datetime64[ns, tz] dtype (GH26649) 1.6.16 Sparse • Significant speedup in SparseArray initialization that benefits most operations, fixing performance regres- sion introduced in v0.20.0 (GH24985) • Bug in SparseFrame 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. 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

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 1.0.5 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 pandas: powerful Python data analysis toolkit, Release 1.0.4 Note: You will only see the performance benefits of using the numexpr engine with DataFrame.query() if your frame has more than approximately 200