modin.pandas.DataFrame.sample¶

DataFrame.sample(n: int | None = None, frac: float | None = None, replace: bool = False, weights: str | np.ndarray | None = None, random_state: RandomState | None = None, axis: Axis | None = None, ignore_index: bool = False)[source]¶

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, np.random.Generator, optional) –

    If int, array-like, or BitGenerator, seed for random number generator. If np.random.RandomState or np.random.Generator, use as given.

    Changed in version 1.1.0: array-like and BitGenerator object now passed to np.random.RandomState() as seed

    Changed in version 1.4.0: np.random.Generator objects now accepted

  • 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. For Series this parameter is unused and defaults to None.

  • ignore_index (bool, default False) –

    If True, the resulting index will be labeled 0, 1, …, n - 1.

    New in version 1.3.0.

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.

Snowpark pandas sample does not support the following cases: weights, random_state, or replace = True when axis = 0. Also, native pandas will raise error if n is larger than the length of the DataFrame while Snowpark pandas will return all rows from the DataFrame.

Examples

>>> df = pd.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
        num_legs  num_wings  num_specimen_seen
falcon         2          2                 10
dog            4          0                  2
spider         8          0                  1
fish           0          0                  8
Copy

Extract 3 random elements from the Series df['num_legs']:

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

A random 50% sample of the DataFrame:

>>> df.sample(frac=0.5)  
      num_legs  num_wings  num_specimen_seen
dog          4          0                  2
fish         0          0                  8
Copy

The exact number of specified rows is returned unless the DataFrame contains fewer rows:

>>> df.sample(n=20)
        num_legs  num_wings  num_specimen_seen
falcon         2          2                 10
dog            4          0                  2
spider         8          0                  1
fish           0          0                  8
Copy
Language: English