pandas.Series.sample¶
- Series.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.
- 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.
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_indexbool, default False
If True, the resulting index will be labeled 0, 1, …, n - 1.
New 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.sampleGenerates random samples from each group of a DataFrame object.
SeriesGroupBy.sampleGenerates random samples from each group of a Series object.
numpy.random.choiceGenerates a random sample from a given 1-D numpy array.
Notes
If frac > 1, replacement should be set to True.
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
Seriesdf['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
DataFramewith 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
DataFramewith 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