Attribute access . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 345 i 2.2.4 Slicing ranges . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . read_html (see note) Jinja2 Conditional formatting with DataFrame.style PyQt4 Clipboard I/O PyQt5 Clipboard I/O PyTables 3.4.2 HDF5-based reading / writing SQLAlchemy 1.1.4 SQL support for databases qtpy Clipboard I/O s3fs 0.3.0 Amazon S3 access tabulate 0.8.3 Printing in Markdown-friendly format (see tabulate) xarray 0.8.2 pandas-like API for N-dimensional data xclip Clipboard I/O on linux xlrd

Attribute access . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 345 i 2.2.4 Slicing ranges . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . read_html (see note) Jinja2 Conditional formatting with DataFrame.style PyQt4 Clipboard I/O PyQt5 Clipboard I/O PyTables 3.4.2 HDF5-based reading / writing SQLAlchemy 1.1.4 SQL support for databases qtpy Clipboard I/O s3fs 0.3.0 Amazon S3 access tabulate 0.8.3 Printing in Markdown-friendly format (see tabulate) xarray 0.8.2 pandas-like API for N-dimensional data xclip Clipboard I/O on linux xlrd

SPSS formats . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 339 i 3.1.19 Other file formats . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . read_html (see note) Jinja2 Conditional formatting with DataFrame.style PyQt4 Clipboard I/O PyQt5 Clipboard I/O PyTables 3.4.2 HDF5-based reading / writing SQLAlchemy 1.1.4 SQL support for databases qtpy Clipboard I/O s3fs 0.3.0 Amazon S3 access tabulate 0.8.3 Printing in Markdown-friendly format (see tabulate) xarray 0.8.2 pandas-like API for N-dimensional data xclip Clipboard I/O on linux xlrd

analysis toolkit Release 1.0.0 Wes McKinney and the Pandas Development Team Jan 29, 2020 CONTENTS i ii pandas: powerful Python data analysis toolkit, Release 1.0.0 Date: Jan 29, 2020 Version: 1.0.0 ordered=False) pandas 1.0.0 In [50]: df.resample("2D").agg(lambda x: 'a').A.dtype Out[50]: dtype('O') This fixes an inconsistency between resample and groupby. This also fixes a potential bug, where (GH23563) • Bug in IntervalDtype where the kind attribute was incorrectly set as None instead of "O" (GH30568) • Bug in IntervalIndex, IntervalArray, and Series with interval data where equality comparisons

Vectorized string methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 209 i 2.3.11 Sorting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . read_html (see note) Jinja2 Conditional formatting with DataFrame.style PyQt4 Clipboard I/O PyQt5 Clipboard I/O PyTables 3.4.3 HDF5-based reading / writing SQLAlchemy 1.1.4 SQL support for databases qtpy Clipboard I/O s3fs 0.4.0 Amazon S3 access tabulate 0.8.3 Printing in Markdown-friendly format (see tabulate) xarray 0.8.2 pandas-like API for N-dimensional data xclip Clipboard I/O on linux xlrd

Vectorized string methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 209 i 2.3.11 Sorting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . read_html (see note) Jinja2 Conditional formatting with DataFrame.style PyQt4 Clipboard I/O PyQt5 Clipboard I/O PyTables 3.4.3 HDF5-based reading / writing SQLAlchemy 1.1.4 SQL support for databases qtpy Clipboard I/O s3fs 0.4.0 Amazon S3 access tabulate 0.8.3 Printing in Markdown-friendly format (see tabulate) xarray 0.8.2 pandas-like API for N-dimensional data xclip Clipboard I/O on linux xlrd

Indexing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 1.1.5.3 I/O . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 1.1.5.4 Plotting 1.2.2.10 MultiIndex Constructor with a Single Level . . . . . . . . . . . . . . . . . . . . . 22 i 1.2.2.11 UTC Localization with Series . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 Indexing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28 1.2.7.3 I/O . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29 1.2.7.4 Plotting

data analysis toolkit Release 0.25.1 Wes McKinney& PyData Development Team Aug 22, 2019 CONTENTS i ii pandas: powerful Python data analysis toolkit, Release 0.25.1 Date: Aug 22, 2019 Version: 0.25 Series of DataFrame with a length above max_rows, only min_rows number of rows is shown (default: 10, i.e. the first and last 5 rows). This dual option allows to still see the full content of relatively levels) 348 raise ValueError( 349 "On level {level}, code value ({code})" --> 350 " < -1".format(level=i, code=level_codes.min()) 351 ) 352 if not level.is_unique: ValueError: On level 0, code value (-2)

Indexing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 1.1.1.3 I/O . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 1.1.1.4 Plotting Indexing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 1.2.3.3 I/O . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 1.2.3.4 Plotting support for compressed URLs in read_csv . . . . . . . . . . . . . . . . . 13 1.3.1.6 Pickle file I/O now supports compression . . . . . . . . . . . . . . . . . . . . . . . 13 1.3.1.7 UInt64 Support Improved

data analysis toolkit Release 0.25.0 Wes McKinney& PyData Development Team Jul 18, 2019 CONTENTS i ii pandas: powerful Python data analysis toolkit, Release 0.25.0 Date: Jul 18, 2019 Version: 0.25 Series of DataFrame with a length above max_rows, only min_rows number of rows is shown (default: 10, i.e. the first and last 5 rows). This dual option allows to still see the full content of relatively levels) 348 raise ValueError( 349 "On level {level}, code value ({code})" --> 350 " < -1".format(level=i, code=level_codes.min()) 351 ) 352 if not level.is_unique: ValueError: On level 0, code value (-2)