Using the tips dataset again, let’s find the average gratuity by size of the party and sex of the server. In Excel, we use the following configuration for the PivotTable: The equivalent in pandas: In highlight the maximum in the third and fourth columns with red text. We will highlight the subset sliced region in yellow. [34]: slice_ = ['c3', 'c4'] df3.style.apply(highlight_max, props='color:red;', axis=0 output Loading only visible sheets GH19842#issuecomment-892150745 HTML Reading HTML tables from a server that cannot handle the default request header 1026 Chapter 2. User Guide pandas: powerful Python

Using the tips dataset again, let’s find the average gratuity by size of the party and sex of the server. In Excel, we use the following configuration for the PivotTable: The equivalent in pandas: In highlight the maximum in the third and fourth columns with red text. We will highlight the subset sliced region in yellow. [30]: slice_ = ['c3', 'c4'] df3.style.apply(highlight_max, props='color:red;', axis=0 from a filelike handle Modifying formatting in XlsxWriter output HTML Reading HTML tables from a server that cannot handle the default request header HDFStore The HDFStores docs Simple queries with

Using the tips dataset again, let’s find the average gratuity by size of the party and sex of the server. In Excel, we use the following configuration for the PivotTable: The equivalent in pandas: In highlight the maximum in the third and fourth columns with red text. We will highlight the subset sliced region in yellow. [30]: slice_ = ['c3', 'c4'] df3.style.apply(highlight_max, props='color:red;', axis=0 1011 pandas: powerful Python data analysis toolkit, Release 1.3.3 HTML Reading HTML tables from a server that cannot handle the default request header HDFStore The HDFStores docs Simple queries with

Using the tips dataset again, let’s find the average gratuity by size of the party and sex of the server. In Excel, we use the following configuration for the PivotTable: The equivalent in pandas: In highlight the maximum in the third and fourth columns with red text. We will highlight the subset sliced region in yellow. [30]: slice_ = ['c3', 'c4'] df3.style.apply(highlight_max, props='color:red;', axis=0 Guide pandas: powerful Python data analysis toolkit, Release 1.3.4 HTML Reading HTML tables from a server that cannot handle the default request header HDFStore The HDFStores docs Simple queries with

Using the tips dataset again, let’s find the average gratuity by size of the party and sex of the server. In Excel, we use the following configuration for the PivotTable: The equivalent in pandas: In highlight the maximum in the third and fourth columns with red text. We will highlight the subset sliced region in yellow. [34]: slice_ = ['c3', 'c4'] df3.style.apply(highlight_max, props='color:red;', axis=0 output Loading only visible sheets GH19842#issuecomment-892150745 HTML Reading HTML tables from a server that cannot handle the default request header HDFStore The HDFStores docs Simple queries with

Using the tips dataset again, let’s find the average gratuity by size of the party and sex of the server. In Excel, we use the following configuration for the PivotTable: The equivalent in pandas: In highlight the maximum in the third and fourth columns with red text. We will highlight the subset sliced region in yellow. [34]: slice_ = ['c3', 'c4'] df3.style.apply(highlight_max, props='color:red;', axis=0 output Loading only visible sheets GH19842#issuecomment-892150745 HTML Reading HTML tables from a server that cannot handle the default request header HDFStore The HDFStores docs Simple queries with

from a filelike handle Modifying formatting in XlsxWriter output HTML Reading HTML tables from a server that cannot handle the default request header HDFStore The HDFStores docs Simple queries with account private key in JSON format. Can be file path or string contents. This is useful for remote server authentication (eg. Jupyter/IPython notebook on remote host). verbose [None, deprecated] Deprecated of the string. repl [str, optional] String for replacement. If not specified (None), the sliced region is re- placed with an empty string. Returns Series or Index Same type as the original object.

pandas: powerful Python data analysis toolkit, Release 0.25.1 HTML Reading HTML tables from a server that cannot handle the default request header HDFStore The HDFStores docs Simple queries with account private key in JSON format. Can be file path or string contents. This is useful for remote server authentication (eg. Jupyter/IPython notebook on remote host). verbose [None, deprecated] Deprecated of the string. repl [str, optional] String for replacement. If not specified (None), the sliced region is re- placed with an empty string. Returns Series or Index Same type as the original object.

from a filelike handle Modifying formatting in XlsxWriter output HTML Reading HTML tables from a server that cannot handle the default request header HDFStore The HDFStores docs Simple Queries with account private key in JSON format. Can be file path or string contents. This is useful for remote server authentication (eg. Jupyter/IPython notebook on remote host). verbose [None, deprecated] Deprecated of the string. repl [str, optional] String for replacement. If not specified (None), the sliced region is re- placed with an empty string. Returns replaced [Series or Index] Same type as the original

from a filelike handle Modifying formatting in XlsxWriter output HTML Reading HTML tables from a server that cannot handle the default request header HDFStore The HDFStores docs Simple queries with of the string. repl [str, optional] String for replacement. If not specified (None), the sliced region is re- placed with an empty string. Returns Series or Index Same type as the original object. See All databases supported by SQLAlchemy can be used, such as PostgreSQL, MySQL, Oracle, Microsoft SQL server (see documentation of SQLAlchemy on included dialects). The functionality of providing DBAPI connection