group by engine for aggregating and transforming data sets • Date range generation (DateRange) and custom date offsets enabling the implementation of customized fre- quencies • Input/Output tools: loading continued to provide resources for development through the end of 2011, and continues to contribute bug reports today. Since January 2012, Lambda Foundry, has been providing development resources, as well as commercial the head and tail methods. The default number of elements to display is five, but you may pass a custom number. In [5]: long_series = Series(randn(1000)) In [6]: long_series.head() Out[6]: 0 0.031293

group by engine for aggregating and transforming data sets • Date range generation (DateRange) and custom date offsets enabling the implementation of customized fre- quencies • Input/Output tools: loading continued to provide resources for development through the end of 2011, and continues to contribute bug reports today. Since January 2012, Lambda Foundry, has been providing development resources, as well as commercial the head and tail methods. The default number of elements to display is five, but you may pass a custom number. In [5]: long_series = Series(randn(1000)) In [6]: long_series.head() Out[6]: 0 -1.130476

group by engine for aggregating and transforming data sets • Date range generation (DateRange) and custom date offsets enabling the implementation of customized fre- quencies • Input/Output tools: loading continued to provide resources for development through the end of 2011, and continues to contribute bug reports today. Since January 2012, Lambda Foundry, has been providing development resources, as well as commercial the head and tail methods. The default number of elements to display is five, but you may pass a custom number. In [5]: long_series = Series(randn(1000)) In [6]: long_series.head() Out[6]: 0 -0.395255

See here. • Timedelta arithmetic returns NotImplemented in unknown cases, allowing extensions by custom classes (GH8813). • Timedelta now supports arithemtic with numpy.ndarray objects of the appropriate behaves in a similar manner, but allows compatibility with np.timedelta64 types as well as a host of custom representation, parsing, and attributes. This type is very similar to how Timestamp works for datetimes axes (GH7528, GH5517). • Bug in read_csv where line comments were not handled correctly given a custom line terminator or delim_whitespace=True (GH8122). • Bug in read_html where empty tables caused

’.’ with NaN. • pd.melt() now accepts the optional parameters var_name and value_name to specify custom column names of the returned DataFrame. • pd.set_option() now allows N option, value pairs (GH3667) Features • Added experimental CustomBusinessDay class to support DateOffsets with custom holiday calendars and custom weekmasks. (GH2301) Note: This uses the numpy.busdaycalendar API introduced in Numpy 23:59:59.999999999’, tz=None) • File parsers no longer coerce to float or bool for columns that have custom converters specified (GH2184) In [25]: data = ’A,B,C\n00001,001,5\n00002,002,6’ In [26]: from cStringIO

features . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93 1.7.1.1 Custom Business Hour . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93 1.7.1.2 .groupby( Mixed Dtypes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 516 9.6.3.4 Custom describe . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 517 xi 9.6.4 Transform Using offsets with Series / DatetimeIndex . . . . . . . . . . . . . . . . . . . . . . . 831 19.8.3 Custom Business Days . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 832 19.8

features . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91 1.6.1.1 Custom Business Hour . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91 1.6.1.2 .groupby( Mixed Dtypes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 514 9.6.3.4 Custom describe . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 515 9.6.4 Transform Using offsets with Series / DatetimeIndex . . . . . . . . . . . . . . . . . . . . . . . 827 19.8.3 Custom Business Days . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 828 19.8

See here. • Timedelta arithmetic returns NotImplemented in unknown cases, allowing extensions by custom classes (GH8813). • Timedelta now supports arithemtic with numpy.ndarray objects of the appropriate behaves in a similar manner, but allows compatibility with np.timedelta64 types as well as a host of custom representation, parsing, and attributes. This type is very similar to how Timestamp works for datetimes axes (GH7528, GH5517). • Bug in read_csv where line comments were not handled correctly given a custom line terminator or delim_whitespace=True (GH8122). • Bug in read_html where empty tables caused

behaves in a similar manner, but allows compatibility with np.timedelta64 types as well as a host of custom representation, parsing, and attributes. This type is very similar to how Timestamp works for datetimes axes (GH7528, GH5517). • Bug in read_csv where line comments were not handled correctly given a custom line terminator or delim_whitespace=True (GH8122). • Bug in read_html where empty tables caused an evenly divisible frequency (GH4076) • Bug in consistency of groupby aggregation when passing a custom function (GH6715) • Bug in resample when how=None resample freq is the same as the axis frequency

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2840 4.10.1 Registering custom accessors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2840 4.10.2 Extension tutorial... Basic statistics (mean, median, min, max, counts...) are easily calculable. These or custom aggregations can be applied on the entire data set, a sliding window of the data, or grouped by categories columns to melt together • value_name provides a custom column name for the values column instead of the default column name value • var_name provides a custom column name for the column collecting the column