New NaT (Not-a-Time) type to represent NA in timestamp arrays
#Wes mckinney klib series#
Optimized support for datetime64-dtype data in Series and DataFrame DatetimeIndex will parse array or list of strings to
To_datetime function efficiently parses array of strings toĭatetimeIndex. Property of DatetimeIndex, with option to infer frequency on construction of
Robust frequency inference function infer_freq and inferred_freq New date_range, bdate_range, and period_range factory Time series string indexing conveniences / shortcuts: slice years, yearĮnhanced time series plotting adaptation of scikits.timeseries Zone-aware time series with different time zones will result in a UTC-indexed Time zone-aware timestamps areĮqual if and only if their UTC timestamps match. Time zone names as as strings are required. User needs to know very little about pytz library now only Time zone conversions are thereforeĮssentially free. Zone set will be localized to local time. Timestamps are stored as UTC Timestamps from DatetimeIndex objects with time Tz_convert and tz_localize methods to TimeSeries and DataFrame. Interface while enabling working with nanosecond-resolution data. New Timestamp data type subclasses datetime.datetime, providing the same
#Wes mckinney klib code#
This is a partial port of, and a substantial enhancement to,Įlements of the scikits.timeseries code base. Including the 12 fiscal quarterly frequencies. Time spans and performing calendar logic, New PeriodIndex and Period classes for representing New DatetimeIndex class supports both fixed Revamp of frequency aliases and support forįrequency shortcuts like ‘15min’, or ‘1h30min’
(including Open-High-Low-Close) have also been implemented. A suite of high performance Cython/C-based resampling functions Supports interpolation, user-definedĪggregation functions, and control over how the intervals and result labelingĪre defined. High performance and flexible resample method for converting from New datetime64 representation speeds up join operations and dataĪlignment, reduces memory usage, and improve serialization /ĭeserialization performance significantly over datetime.datetime See documentation for overview of pandas timeseries API.
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