. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 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. read_csv()/read_table() will now be noiser w.r.t invalid options rather than falling back to the PythonParser. • Raise ValueError when sep specified with delim_whitespace=True in read_csv()/read_table() (GH6607)

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 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. function, determines whether vertical lines will be printed, default is True • Added ability to read table footers to read_html (GH8552) • to_sql now infers datatypes of non-NA values for columns that contain

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 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. schema to read from/write to with read_sql_table and to_sql (GH7441, GH7952). For example: df.to_sql(’table’, engine, schema=’other_schema’) pd.read_sql_table(’table’, engine, schema=’other_schema’) • Added

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 558 23.10 HTML Table Parsing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 558 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. reads like inf (“inf”, “Inf”, “-Inf”, “iNf”, etc.) as infinity. (GH4220, GH4219), affecting read_table, read_csv, etc. • pandas now is Python 2/3 compatible without the need for 2to3 thanks to @jtratner

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 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. automatically create a table/dataset using the pandas.io.gbq.to_gbq() function if the destination table/dataset does not exist. (GH8325, GH11121). • Added ability to replace an existing table and schema when

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 Formatting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 581 11.8 Table Schema Display . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 427 21.9 HTML Table Parsing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 427 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. for tables In [25]: path = ’store_iterator.h5’ In [26]: DataFrame(randn(10,2)).to_hdf(path,’df’,table=True) In [27]: for df in read_hdf(path,’df’, chunksize=3): ....: print df ....: 0 1 0 1.129167

GroupBy on Categoricals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 1.2.1.9 Table Schema Output . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 1.2.1.10 SciPy intersection and inner join now preserve the order of the left Index . . . . . . 31 1.2.2.15 Pivot Table always returns a DataFrame . . . . . . . . . . . . . . . . . . . . . . . 32 1.2.2.16 Other API Changes Formatting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 579 11.8 Table Schema Display . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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]

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]