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# pandas.Series.sample

Series.sample(n=None, frac=None, replace=False, weights=None, random_state=None, axis=None, ignore_index=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. When replace = False will not allow `(n * max(weights) / sum(weights)) > 1` in order to avoid biased results. See the Notes below for more details.

    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. Default `None` results in sampling with the current state of np.random.

    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.

  Returns:
    Series or DataFrame

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

  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.

  When replace = False will not allow `(n * max(weights) / sum(weights)) > 1`, since that would cause results to be biased. E.g. sampling 2 items without replacement with weights [100, 1, 1] would yield two last items in 1/2 of cases, instead of 1/102. This is similar to specifying n=4 without replacement on a Series with 3 elements.

  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

  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)
      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)
            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)
              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)
              num_legs  num_wings  num_specimen_seen
      falcon         2          2                 10
      fish           0          0                  8