. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 890 28.10 HTML Table Parsing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 890 for many different kinds of data: • Tabular data with heterogeneously-typed columns, as in an SQL table or Excel spreadsheet • Ordered and unordered (not necessarily fixed-frequency) time series data. usually sacrifices performance. So if you focus on one feature for your application you may be able to create a faster specialized tool. • pandas is a dependency of statsmodels, making it an important part

combination). 5 pandas: powerful Python data analysis toolkit, Release 0.25.3 Miniconda allows you to create a minimal self contained Python installation, and then use the Conda command to install additional running the Miniconda will do this for you. The installer can be found here The next step is to create a new conda environment. A conda environment is like a virtualenv that allows you to specify a specific set of libraries. Run the following commands from a terminal window: conda create -n name_of_my_env python This will create a minimal environment with only Python installed in it. To put your self inside

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 618 23.10 HTML Table Parsing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 618 for many different kinds of data: • Tabular data with heterogeneously-typed columns, as in an SQL table or Excel spreadsheet • Ordered and unordered (not necessarily fixed-frequency) time series data. usually sacrifices performance. So if you focus on one feature for your application you may be able to create a faster specialized tool. • pandas is a dependency of statsmodels, making it an important part

Format . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 956 25.8.3 Table Format . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 956 25 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 958 Storing Mixed Types in a Table . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 958 Storing Multi-Index DataFrames . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 960 Querying a Table . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 961 Using timedelta64[ns]

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 768 27.10 HTML Table Parsing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 768 for many different kinds of data: • Tabular data with heterogeneously-typed columns, as in an SQL table or Excel spreadsheet • Ordered and unordered (not necessarily fixed-frequency) time series data. usually sacrifices performance. So if you focus on one feature for your application you may be able to create a faster specialized tool. • pandas is a dependency of statsmodels, making it an important part

. . . . . . . . . . . . . . . . . . . . . . . . 9 1.2.1.3 Improved warnings when attempting to create columns . . . . . . . . . . . . . . . 9 1.2.1.4 drop now also accepts index/columns keywords . GroupBy on Categoricals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44 ii 1.5.1.9 Table Schema Output . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45 1.5.1.10 SciPy intersection and inner join now preserve the order of the left Index . . . . . . 61 1.5.2.15 Pivot Table always returns a DataFrame . . . . . . . . . . . . . . . . . . . . . . . 62 1.5.2.16 Other API Changes

Format . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 958 25.8.3 Table Format . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 959 25 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 961 Storing Mixed Types in a Table . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 961 Storing Multi-Index DataFrames . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 963 Querying a Table . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 963 Using timedelta64[ns]

GroupBy on Categoricals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 1.3.1.9 Table Schema Output . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 1.3.1.10 SciPy intersection and inner join now preserve the order of the left Index . . . . . . 32 1.3.2.15 Pivot Table always returns a DataFrame . . . . . . . . . . . . . . . . . . . . . . . 33 1.3.2.16 Other API Changes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 452 7.9.1.1 Reading multiple files to create a single DataFrame . . . . . . . . . . . . . . . . . 453 7.9.1.2 Parsing date components in multi-columns

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 754 27.10 HTML Table Parsing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 754 for many different kinds of data: • Tabular data with heterogeneously-typed columns, as in an SQL table or Excel spreadsheet • Ordered and unordered (not necessarily fixed-frequency) time series data. usually sacrifices performance. So if you focus on one feature for your application you may be able to create a faster specialized tool. • pandas is a dependency of statsmodels, making it an important part

backends . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 717 2.16 Table Visualization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Add Styles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 720 2.16.4 Table Styles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 720 formatting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 895 2.22.8 Table schema display . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 896 2