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 pandas: powerful Python data analysis toolkit, Release 0.21.1 7.9.4 HTML Reading HTML tables from a server that cannot handle the default request header 7.9.5 HDFStore The HDFStores docs Simple Queries and data. The list of supported compression libraries: • zlib: The default compression library. A classic in terms of compression, achieves good com- pression rates but is somewhat slow. • lzo: Fast compression

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 powerful Python data analysis toolkit, Release 1.5.0rc0 – zlib: The default compression library. A classic in terms of compression, achieves good compression rates but is somewhat slow. – lzo: Fast compression the expense of speed. ∗ blosc:snappy: A popular compressor used in many places. ∗ blosc:zlib: A classic; somewhat slower than the previous ones, but achieving better compression ratios. ∗ blosc:zstd:

and data. The list of supported compression libraries: • zlib: The default compression library. A classic in terms of compression, achieves good com- pression rates but is somewhat slow. • lzo: Fast compression the expense of speed. • blosc:snappy: A popular compressor used in many places. • blosc:zlib: A classic; somewhat slower than the previous ones, but achieving better compression ratios. • blosc:zstd: 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

and data. The list of supported compression libraries: • zlib: The default compression library. A classic in terms of compression, achieves good com- pression rates but is somewhat slow. • lzo: Fast compression the expense of speed. • blosc:snappy: A popular compressor used in many places. • blosc:zlib: A classic; somewhat slower than the previous ones, but achieving better compression ratios. • blosc:zstd: 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

and data. The list of supported compression libraries: • zlib: The default compression library. A classic in terms of compression, achieves good com- pression rates but is somewhat slow. • lzo: Fast compression the expense of speed. • blosc:snappy: A popular compressor used in many places. • blosc:zlib: A classic; somewhat slower than the previous ones, but achieving better compression ratios. • blosc:zstd:

and data. The list of supported compression libraries: • zlib: The default compression library. A classic in terms of compression, achieves good com- pression rates but is somewhat slow. • lzo: Fast compression the expense of speed. • blosc:snappy: A popular compressor used in many places. • blosc:zlib: A classic; somewhat slower than the previous ones, but achieving better compression ratios. • blosc:zstd: 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 and data. The list of supported compression libraries: – zlib: The default compression library. A classic in terms of compression, achieves good compression rates but is somewhat slow. – lzo: Fast compression the expense of speed. * blosc:snappy: A popular compressor used in many places. * blosc:zlib: A classic; somewhat slower than the previous ones, but achieving better compression ratios. * blosc:zstd:

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 and data. The list of supported compression libraries: – zlib: The default compression library. A classic in terms of compression, achieves good compression rates but is somewhat slow. 2.4. IO tools (text the expense of speed. ∗ blosc:snappy: A popular compressor used in many places. ∗ blosc:zlib: A classic; somewhat slower than the previous ones, but achieving better compression ratios. ∗ blosc:zstd:

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 and data. The list of supported compression libraries: – zlib: The default compression library. A classic in terms of compression, achieves good compression rates but is somewhat slow. 2.4. IO tools (text the expense of speed. ∗ blosc:snappy: A popular compressor used in many places. ∗ blosc:zlib: A classic; somewhat slower than the previous ones, but achieving better compression ratios. ∗ blosc:zstd:

and data. The list of supported compression libraries: – zlib: The default compression library. A classic in terms of compression, achieves good compression rates but is somewhat slow. – lzo: Fast compression the expense of speed. * blosc:snappy: A popular compressor used in many places. * blosc:zlib: A classic; somewhat slower than the previous ones, but achieving better compression ratios. * blosc:zstd: 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