However consider the fact that many tables on the web are not big enough for the parsing algorithm runtime to matter. It is more likely that the bottleneck will be in the process of reading the raw text from and BeautifulSoup4. Note: Unless you have both: • A strong restriction on the upper bound of the runtime of some code that incorporates read_html() • Complete knowledge that the HTML you will be parsing level in Series and DataFrame (GH341) • Raise exception if dateutil 2.0 installed on Python 2.x runtime (GH346) • Significant GroupBy performance enhancement with multiple keys with many “empty” combinations

to run the check yourself: cpplint --extensions=c,h --headers=h --filter=-readability/casting,-runtime/int,- ˓→build/include_subdir modified-c-file You can also run this command on an entire directory directory if necessary: cpplint --extensions=c,h --headers=h --filter=-readability/casting,-runtime/int,- ˓→build/include_subdir --recursive modified-c-directory To make your commits compliant with this standard However consider the fact that many tables on the web are not big enough for the parsing algorithm runtime to matter. It is more likely that the bottleneck will be in the process of reading the raw text from

to run the check yourself: cpplint --extensions=c,h --headers=h --filter=-readability/casting,-runtime/int,- ˓→build/include_subdir modified-c-file You can also run this command on an entire directory directory if necessary: cpplint --extensions=c,h --headers=h --filter=-readability/casting,-runtime/int,- ˓→build/include_subdir --recursive modified-c-directory To make your commits compliant with this standard However consider the fact that many tables on the web are not big enough for the parsing algorithm runtime to matter. It is more likely that the bottleneck will be in the process of reading the raw text from

However consider the fact that many tables on the web are not big enough for the parsing algorithm runtime to matter. It is more likely that the bottleneck will be in the process of reading the raw text from and BeautifulSoup4. Note: Unless you have both: • A strong restriction on the upper bound of the runtime of some code that incorporates read_html() • Complete knowledge that the HTML you will be parsing level in Series and DataFrame (GH341) • Raise exception if dateutil 2.0 installed on Python 2.x runtime (GH346) • Significant GroupBy performance enhancement with multiple keys with many “empty” combinations

to run the check yourself: cpplint --extensions=c,h --headers=h --filter=-readability/casting,-runtime/int,- ˓→build/include_subdir modified-c-file You can also run this command on an entire directory directory if necessary: cpplint --extensions=c,h --headers=h --filter=-readability/casting,-runtime/int,- ˓→build/include_subdir --recursive modified-c-directory To make your commits compliant with this standard However consider the fact that many tables on the web are not big enough for the parsing algorithm runtime to matter. It is more likely that the bottleneck will be in the process of reading the raw text from

works by generating optimized machine code using the LLVM compiler infrastructure at import time, runtime, or statically (using the included pycc tool). Numba supports compilation of Python to run on either However consider the fact that many tables on the web are not big enough for the parsing algorithm runtime to matter. It is more likely that the bottleneck will be in the process of reading the raw text from and BeautifulSoup4. Note: Unless you have both: • A strong restriction on the upper bound of the runtime of some code that incorporates read_html() • Complete knowledge that the HTML you will be parsing

However consider the fact that many tables on the web are not big enough for the parsing algorithm runtime to matter. It is more likely that the bottleneck will be in the process of reading the raw text from and BeautifulSoup4. Note: Unless you have both: • A strong restriction on the upper bound of the runtime of some code that incorporates read_html() • Complete knowledge that the HTML you will be parsing level in Series and DataFrame (GH341) • Raise exception if dateutil 2.0 installed on Python 2.x runtime (GH346) • Significant GroupBy performance enhancement with multiple keys with many “empty” combinations

works by generating optimized machine code using the LLVM compiler infrastructure at import time, runtime, or statically (using the included pycc tool). Numba supports compilation of Python to run on either However consider the fact that many tables on the web are not big enough for the parsing algorithm runtime to matter. It is more likely that the 29.7. Differences with NumPy 1041 pandas: powerful Python and BeautifulSoup4. Note: Unless you have both: • A strong restriction on the upper bound of the runtime of some code that incorporates read_html() • Complete knowledge that the HTML you will be parsing

works by generating optimized machine code using the LLVM compiler infrastructure at import time, runtime, or statically (using the included pycc tool). Numba supports compilation of Python to run on either However consider the fact that many tables on the web are not big enough for the parsing algorithm runtime to matter. It is more likely that the 29.7. Differences with NumPy 1043 pandas: powerful Python and BeautifulSoup4. Note: Unless you have both: • A strong restriction on the upper bound of the runtime of some code that incorporates read_html() • Complete knowledge that the HTML you will be parsing

However consider the fact that many tables on the web are not big enough for the parsing algorithm runtime to matter. It is more likely that the bottleneck will be in the process of reading the raw text from and BeautifulSoup4. Note: Unless you have both: • A strong restriction on the upper bound of the runtime of some code that incorporates read_html() • Complete knowledge that the HTML you will be parsing level in Series and DataFrame (GH341) • Raise exception if dateutil 2.0 installed on Python 2.x runtime (GH346) • Significant GroupBy performance enhancement with multiple keys with many “empty” combinations