Pandas version checks
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[X] I have checked that this issue has not already been reported.
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[X] I have confirmed this issue exists on the latest version of pandas.
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[ ] I have confirmed this issue exists on the main branch of pandas.
Reproducible Example
import pandas as pd
import numpy as np
from io import StringIO
import pandas.testing as pdt
from time import time
# Creating HTML-Table Strings, 10 Tables, with 15 columns and 300 rows each
dataframes = [pd.DataFrame(np.random.randint(0,100, (300, 15)), columns=[f"Col-{i}" for i in range(15)])]*10
html_strings = [df.to_html() for df in dataframes]
# Testing the runtime when all HTML Tables are combined into a single string
combined_html = "\n".join(html_strings)
start = time()
combined = pd.read_html(StringIO(combined_html))
combined_time = time()-start
print(f"Took {combined_time} Seconds to load the combined data")
# Took 23.394442081451416 Seconds to load the combined data
# Testing the runtime when each HTML Table is read in separately
start = time()
chunked = [pd.read_html(StringIO(string))[0] for string in html_strings]
chunked_time = time()-start
print(f"Took {chunked_time} Seconds to load the data in chunks")
# Took 0.5712969303131104 Seconds to load the data in chunks
print(f"Chunked was {round(combined_time/chunked_time, 2)} faster than the combined data")
# Chunked was 40.95 faster than the combined data
# Checking if the resulting tables are the same
combined_df = pd.concat(combined).reset_index(drop=True)
chunked_df = pd.concat(chunked).reset_index(drop=True)
pdt.assert_frame_equal(combined_df, chunked_df) # No Assertion => data is the same
Installed Versions
INSTALLED VERSIONS
------------------
commit : 8dab54d6573f7186ff0c3b6364d5e4dd635ff3e7
python : 3.10.8.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.19045
machine : AMD64
processor : AMD64 Family 23 Model 113 Stepping 0, AuthenticAMD
byteorder : little
LC_ALL : None
LANG : None
LOCALE : de_DE.cp1252
pandas : 1.5.2
numpy : 1.23.5
pytz : 2022.6
dateutil : 2.8.2
setuptools : 65.6.3
pip : 22.3
Cython : None
pytest : 7.2.0
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.9.1
html5lib : None
pymysql : None
psycopg2 : 2.9.5
jinja2 : 3.1.2
IPython : 8.6.0
pandas_datareader: None
bs4 : 4.11.1
bottleneck : None
brotli : None
fastparquet : 0.8.3
fsspec : 2022.11.0
gcsfs : None
matplotlib : None
numba : None
numexpr : None
odfpy : None
openpyxl : 3.0.10
pandas_gbq : None
pyarrow : None
pyreadstat : None
pyxlsb : None
s3fs : None
scipy : None
snappy : None
sqlalchemy : 1.4.44
tables : None
tabulate : 0.9.0
xarray : None
xlrd : None
xlwt : None
zstandard : None
tzdata : None
Prior Performance
No response
Comment From: asishm
The issue seems to either be with lxml or the xpath expression pandas is using (not an xpath expert - but leaning towards the former)
https://github.com/pandas-dev/pandas/blob/094b2c0a183a96411b7ee2bc26a98e6d82c8cbb9/pandas/io/html.py#L748
that xpath expression takes ~ 20 seconds on the combined html (seems to be exponential in nature) compared to sub second timings for the individual htmls