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DataFrame.sample(n=None, frac=None, replace=False, weights=None, random_state=None, axis=None)
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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 iffrac
= 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
andDataFrame
:>>> 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
DataFrame.sample()
2017-01-12 04:46:29
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