pandas.DataFrame.sample#

DataFrame.sample(n=None, frac=None, replace=False, weights=None, random_state=None, axis=None, ignore_index=False)[source]#

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

You can use random_state for reproducibility.

Parameters:

nint, optional: Number of items from axis to return. Cannot be used with frac. Default = 1 if frac = None.
fracfloat, optional: Fraction of axis items to return. Cannot be used with n.
replacebool, default False: Allow or disallow sampling of the same row more than once.
weightsstr 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_stateint, 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.

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_indexbool, default False: If True, the resulting index will be labeled 0, 1, …, n - 1.

Added in version 1.3.0.

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

pandas.DataFrame.sample#

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