right value with a nullable integer dtype series not passing through ddof argument (GH29128) • Improved error message when using frac > 1 and replace = False (GH27451) • Bug in numeric indexes resulted Bug in merge_asof() merging on a tz-aware left_index and right_on a tz-aware column (GH29864) • Improved error message and docstring in cut() and qcut() when labels=True (GH13318) • Bug in missing fill_na just built: doc/build/html/index.html And you’ll have the satisfaction of seeing your new and improved documentation! Building master branch documentation When pull requests are merged into the pandas

to_stata() is now faster when outputting data with any string or non-native endian columns (GH25045) • Improved performance of Series.searchsorted(). The speedup is especially large when the dtype is int8/int16/int32 key is within the integer bounds for the dtype (GH22034) • Improved performance of pandas.core.groupby.GroupBy.quantile() (GH20405) • Improved performance of slicing and other selected operation on a RangeIndex hashtable, hence saving memory (GH16685) • Improved performance of read_csv() by faster tokenizing and faster parsing of small float numbers (GH25784) • Improved performance of read_csv() by faster parsing

to_stata() is now faster when outputting data with any string or non-native endian columns (GH25045) • Improved performance of Series.searchsorted(). The speedup is especially large when the dtype is int8/int16/int32 key is within the integer bounds for the dtype (GH22034) • Improved performance of pandas.core.groupby.GroupBy.quantile() (GH20405) • Improved performance of slicing and other selected operation on a RangeIndex hashtable, hence saving memory (GH16685) • Improved performance of read_csv() by faster tokenizing and faster parsing of small float numbers (GH25784) • Improved performance of read_csv() by faster parsing

just built: doc/build/html/index.html And you’ll have the satisfaction of seeing your new and improved documentation! Building master branch documentation When pull requests are merged into the pandas collection of 2-D arrays is managed by the BlockManager. The primary benefit of the BlockManager is improved performance on certain operations (construction from a 2D array, binary operations, reductions across Easier extensibility with new logical types • Better user control over memory use and layout • Improved micro-performance • Option to provide a C / Cython API to pandas’ internals See these design documents

just built: doc/build/html/index.html And you’ll have the satisfaction of seeing your new and improved documentation! Building master branch documentation When pull requests are merged into the pandas collection of 2-D arrays is managed by the BlockManager. The primary benefit of the BlockManager is improved performance on certain operations (construction from a 2D array, binary operations, reductions across Easier extensibility with new logical types • Better user control over memory use and layout • Improved micro-performance • Option to provide a C / Cython API to pandas’ internals See these design documents

just built: doc/build/html/index.html And you’ll have the satisfaction of seeing your new and improved documentation! Building master branch documentation When pull requests are merged into the pandas collection of 2-D arrays is managed by the BlockManager. The primary benefit of the BlockManager is improved performance on certain operations (construction from a 2D array, binary operations, reductions across Easier extensibility with new logical types • Better user control over memory use and layout • Improved micro-performance • Option to provide a C / Cython API to pandas’ internals See these design documents

just built: doc/build/html/index.html And you’ll have the satisfaction of seeing your new and improved documentation! Building master branch documentation When pull requests are merged into the pandas collection of 2-D arrays is managed by the BlockManager. The primary benefit of the BlockManager is improved performance on certain operations (construction from a 2D array, binary operations, reductions across Easier extensibility with new logical types • Better user control over memory use and layout • Improved micro-performance • Option to provide a C / Cython API to pandas’ internals See these design documents

just built: doc/build/html/index.html And you’ll have the satisfaction of seeing your new and improved documentation! Building master branch documentation When pull requests are merged into the pandas collection of 2-D arrays is managed by the BlockManager. The primary benefit of the BlockManager is improved performance on certain operations (construction from a 2D array, binary operations, reductions across Easier extensibility with new logical types • Better user control over memory use and layout • Improved micro-performance • Option to provide a C / Cython API to pandas’ internals See these design documents

just built: doc/build/html/index.html And you’ll have the satisfaction of seeing your new and improved documentation! Building master branch documentation When pull requests are merged into the pandas collection of 2-D arrays is managed by the BlockManager. The primary benefit of the BlockManager is improved performance on certain operations (construction from a 2D array, binary operations, reductions across Easier extensibility with new logical types • Better user control over memory use and layout • Improved micro-performance • Option to provide a C / Cython API to pandas’ internals See these design documents

just built: doc/build/html/index.html And you’ll have the satisfaction of seeing your new and improved documentation! Building master branch documentation When pull requests are merged into the pandas collection of 2-D arrays is managed by the BlockManager. The primary benefit of the BlockManager is improved performance on certain operations (construction from a 2D array, binary operations, reductions across Easier extensibility with new logical types • Better user control over memory use and layout • Improved micro-performance • Option to provide a C / Cython API to pandas’ internals See these design documents