mars.dataframe.Series.sample#

Series.sample(n=None, frac=None, replace=False, weights=None, random_state=None, axis=None, always_multinomial=False)#

Return a random sample of items from an axis of object.

You can use random_state for reproducibility.

Parameters

  • n (int, optional) – Number of items from axis to return. Cannot be used with frac. Default = 1 if frac = None.

  • frac (float, optional) – Fraction of axis items to return. Cannot be used with n.

  • replace (bool, default False) – Allow or disallow sampling of the same row more than once.

  • weights (str or ndarray-like, optional) – Default ‘None’ results in equal probability weighting. If passed a Series, will align with target object on index. Index values in weights not found in sampled object will be ignored and index values in sampled object not in weights will be assigned weights of zero. If called on a DataFrame, will accept the name of a column when axis = 0. Unless weights are a Series, weights must be same length as axis being sampled. If weights do not sum to 1, they will be normalized to sum to 1. Missing values in the weights column will be treated as zero. Infinite values not allowed.

  • random_state (int, array-like, BitGenerator, np.random.RandomState, optional) – If int, array-like, or BitGenerator (NumPy>=1.17), seed for random number generator If np.random.RandomState, use as numpy RandomState object.

  • axis ({0 or ‘index’, 1 or ‘columns’, None}, default None) – Axis to sample. Accepts axis number or name. Default is stat axis for given data type (0 for Series and DataFrames).

  • always_multinomial (bool, default False) – If True, always treat distribution of sample counts between data chunks as multinomial distribution. This will accelerate sampling when data is huge, but may affect randomness of samples when number of instances is not very large.

Returns

A new object of same type as caller containing n items randomly sampled from the caller object.

Return type

Series or DataFrame

See also

DataFrameGroupBy.sample

Generates random samples from each group of a DataFrame object.

SeriesGroupBy.sample

Generates random samples from each group of a Series object.

numpy.random.choice

Generates a random sample from a given 1-D numpy array.

Notes

If frac > 1, replacement should be set to True.

Examples

>>> import mars.dataframe as md
>>> df = md.DataFrame({'num_legs': [2, 4, 8, 0],
...                    'num_wings': [2, 0, 0, 0],
...                    'num_specimen_seen': [10, 2, 1, 8]},
...                   index=['falcon', 'dog', 'spider', 'fish'])
>>> df.execute()
        num_legs  num_wings  num_specimen_seen
falcon         2          2                 10
dog            4          0                  2
spider         8          0                  1
fish           0          0                  8

Extract 3 random elements from the Series df['num_legs']: Note that we use random_state to ensure the reproducibility of the examples.

>>> df['num_legs'].sample(n=3, random_state=1).execute()
fish      0
spider    8
falcon    2
Name: num_legs, dtype: int64

A random 50% sample of the DataFrame with replacement:

>>> df.sample(frac=0.5, replace=True, random_state=1).execute()
      num_legs  num_wings  num_specimen_seen
dog          4          0                  2
fish         0          0                  8

An upsample sample of the DataFrame with replacement: Note that replace parameter has to be True for frac parameter > 1.

>>> df.sample(frac=2, replace=True, random_state=1).execute()
        num_legs  num_wings  num_specimen_seen
dog            4          0                  2
fish           0          0                  8
falcon         2          2                 10
falcon         2          2                 10
fish           0          0                  8
dog            4          0                  2
fish           0          0                  8
dog            4          0                  2

Using a DataFrame column as weights. Rows with larger value in the num_specimen_seen column are more likely to be sampled.

>>> df.sample(n=2, weights='num_specimen_seen', random_state=1).execute()
        num_legs  num_wings  num_specimen_seen
falcon         2          2                 10
fish           0          0                  8