The read_html() function of the pandas DataFrame module reads the pandas.DataFrame.read_html() can be used for data wrangling or data scraping. Let's take a closer look at the syntax, parameters, and return values.
pandas.read_html(io,match='.+',flavor=None,header=None,index_col=None,skiprows=None,attrs=None,parse_dates=False,thousands=',',encoding=None,decimal='.',converters=None,na_values=None,keep_default_na=True,displayed_only=True)
Here are some argument values:
io: This is a string or path-like object. It can also be a URL or an HTML file itself.match: This can be a string or a regular expression. It filters data based on match conditions or REs. The default value is .+, which means any non-empty string match.header: A list-like object or integer value is used to create the starting column(s) as a header. The default value for this parameter is None.index_col: A list-like object or integer value is used to create the index. The default value is None.skiprows: This can be a list-like object or an integer showing the indexes skipped. The default is None.attrs: This shows a Python dictionary containing the attributes of the table to filter. Also, the default value is None.na_values: This is used to handle null, empty, or NaN values.dfs: This returns a list of DataFrames.
In the below code snippet, we are going to use the pd.read_html() function to parse an HTML file into a pandas DataFrame.
import pandas as pd# invoking read_html() to load employee.html filedf_list = pd.read_html("employee.html")# print out parsed html file data as data framesprint(df_list)
main.pypd.read_html("employee.html") keyword will load theemployee.html file as a list of data frames. It is used to parse each table tag as a different data frame.print(df_list) keyword will print the list of DataFrames.employee.htmlThis file contains records of three employees as an HTML document.