pandas.Series.sample

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

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

New in version 0.16.1.

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 : boolean, optional

Sample with or without replacement. Default = False.

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. inf and -inf values not allowed.

random_state : int or numpy.random.RandomState, optional

Seed for the random number generator (if int), or numpy RandomState object.

axis : int or string, optional

Axis to sample. Accepts axis number or name. Default is stat axis for given data type (0 for Series and DataFrames, 1 for Panels).

Returns:

A new object of same type as caller.

Examples

Generate an example Series and DataFrame:

>>> s = pd.Series(np.random.randn(50))
>>> s.head()
0   -0.038497
1    1.820773
2   -0.972766
3   -1.598270
4   -1.095526
dtype: float64
>>> df = pd.DataFrame(np.random.randn(50, 4), columns=list('ABCD'))
>>> df.head()
          A         B         C         D
0  0.016443 -2.318952 -0.566372 -1.028078
1 -1.051921  0.438836  0.658280 -0.175797
2 -1.243569 -0.364626 -0.215065  0.057736
3  1.768216  0.404512 -0.385604 -1.457834
4  1.072446 -1.137172  0.314194 -0.046661

Next extract a random sample from both of these objects...

3 random elements from the Series:

>>> s.sample(n=3)
27   -0.994689
55   -1.049016
67   -0.224565
dtype: float64

And a random 10% of the DataFrame with replacement:

>>> df.sample(frac=0.1, replace=True)
           A         B         C         D
35  1.981780  0.142106  1.817165 -0.290805
49 -1.336199 -0.448634 -0.789640  0.217116
40  0.823173 -0.078816  1.009536  1.015108
15  1.421154 -0.055301 -1.922594 -0.019696
6  -0.148339  0.832938  1.787600 -1.383767