timeseries(start='2000-01-01', end='2000-01-31', freq='1s', partition_freq='1d', dtypes={'name': <class 'str'>, 'id': <class 'int'>, 'x': <class 'float'>, 'y': <class 'float'>}, seed=None, **kwargs)
Start of time series
End of time series
Mapping of column names to types. Valid types include {float, int, str, 'category'}
String like '2s' or '1H' or '12W' for the time series frequency
String like '1M' or '2Y' to divide the dataframe into partitions
Randomstate seed
Keywords to pass down to individual column creation functions. Keywords should be prefixed by the column name and then an underscore.
Create timeseries dataframe with random data
>>> import daskThis example is valid syntax, but we were not able to check execution
... df = dask.datasets.timeseries()
... df.head() # doctest: +SKIP timestamp id name x y 2000-01-01 00:00:00 967 Jerry -0.031348 -0.040633 2000-01-01 00:00:01 1066 Michael -0.262136 0.307107 2000-01-01 00:00:02 988 Wendy -0.526331 0.128641 2000-01-01 00:00:03 1016 Yvonne 0.620456 0.767270 2000-01-01 00:00:04 998 Ursula 0.684902 -0.463278
>>> df = dask.datasets.timeseries(See :
... '2000', '2010',
... freq='2H', partition_freq='1D', seed=1, # data frequency
... dtypes={'value': float, 'name': str, 'id': int}, # data types
... id_lam=1000 # control number of items in id column
... )
The following pages refer to to this document either explicitly or contain code examples using this.
dask.datasets.timeseries
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