def timeseries(start="2000-01-01",
end="2000-01-31",
freq="1s",
partition_freq="1d",
dtypes={
"name": str,
"id": int,
"x": float,
"y": float
},
seed=None,
**kwargs):
"""Create timeseries dataframe with random data
Parameters
----------
start : datetime (or datetime-like string)
Start of time series
end : datetime (or datetime-like string)
End of time series
dtypes : dict
Mapping of column names to types.
Valid types include {float, int, str, 'category'}
freq : string
String like '2s' or '1H' or '12W' for the time series frequency
partition_freq : string
String like '1M' or '2Y' to divide the dataframe into partitions
seed : int (optional)
Randomstate seed
kwargs:
Keywords to pass down to individual column creation functions.
Keywords should be prefixed by the column name and then an underscore.
Examples
--------
>>> import dask
>>> 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(
... '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
... )
"""
from dask.dataframe.io.demo import make_timeseries
return make_timeseries(start=start,
end=end,
freq=freq,
partition_freq=partition_freq,
seed=seed,
dtypes=dtypes,
**kwargs)
def timeseries(
start='2000-01-01',
end='2000-01-31',
freq='1s',
partition_freq='1d',
dtypes={'name': str, 'id': int, 'x': float, 'y': float},
seed=None,
**kwargs
):
""" Create timeseries dataframe with random data
Parameters
----------
start : datetime (or datetime-like string)
Start of time series
end : datetime (or datetime-like string)
End of time series
dtypes : dict
Mapping of column names to types.
Valid types include {float, int, str, 'category'}
freq : string
String like '2s' or '1H' or '12W' for the time series frequency
partition_freq : string
String like '1M' or '2Y' to divide the dataframe into partitions
seed : int (optional)
Randomstate seed
kwargs:
Keywords to pass down to individual column creation functions.
Keywords should be prefixed by the column name and then an underscore.
Examples
--------
>>> import dask
>>> 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(
... '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
... )
"""
from dask.dataframe.io.demo import make_timeseries
return make_timeseries(start=start, end=end, freq=freq,
partition_freq=partition_freq,
seed=seed, dtypes=dtypes, **kwargs)
def timeseries(
start='2000-01-01',
end='2000-01-31',
freq='1s',
partition_freq='1d',
dtypes={
'name': str,
'id': int,
'x': float,
'y': float
},
seed=None,
):
""" Create timeseries dataframe with random data
Parameters
----------
start : datetime (or datetime-like string)
Start of time series
end : datetime (or datetime-like string)
End of time series
dtypes : dict
Mapping of column names to types.
Valid types include {float, int, str, 'category'}
freq : string
String like '2s' or '1H' or '12W' for the time series frequency
partition_freq : string
String like '1M' or '2Y' to divide the dataframe into partitions
seed : int (optional)
Randomstate seed
Examples
--------
>>> import dask
>>> 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
"""
from dask.dataframe.io.demo import make_timeseries
return make_timeseries(start=start,
end=end,
freq=freq,
partition_freq=partition_freq,
seed=seed,
dtypes=dtypes)
def timeseries(
start='2000-01-01',
end='2000-01-31',
freq='1s',
partition_freq='1d',
dtypes={'name': str, 'id': int, 'x': float, 'y': float},
seed=None,
):
""" Create timeseries dataframe with random data
Parameters
----------
start: datetime (or datetime-like string)
Start of time series
end: datetime (or datetime-like string)
End of time series
dtypes: dict
Mapping of column names to types.
Valid types include {float, int, str, 'category'}
freq: string
String like '2s' or '1H' or '12W' for the time series frequency
partition_freq: string
String like '1M' or '2Y' to divide the dataframe into partitions
seed: int (optional)
Randomstate seed
Examples
--------
>>> import dask
>>> 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
"""
from dask.dataframe.io.demo import make_timeseries
return make_timeseries(start=start, end=end, freq=freq,
partition_freq=partition_freq,
seed=seed, dtypes=dtypes)