利用Python进行采样的几种方式

﹏ヽ暗。殇╰゛Y 2022-03-17 13:40 206阅读 0赞

1) random.sample(population,k)：

Chooses k unique random elements from a population sequence or set.
        
        Returns a new list containing elements from the population while
        leaving the original population unchanged.  The resulting list is
        in selection order so that all sub-slices will also be valid random
        samples.  This allows raffle winners (the sample) to be partitioned
        into grand prize and second place winners (the subslices).
        
        Members of the population need not be hashable or unique.  If the
        population contains repeats, then each occurrence is a possible
        selection in the sample.
        
        To choose a sample in a range of integers, use range as an argument.
        This is especially fast and space efficient for sampling from a
        large population:   sample(range(10000000), 60)

2) numpy.random.choice(a,size=None,replace=None,p=None)：

Parameters
        -----------
        a : 1-D array-like or int
            If an ndarray, a random sample is generated from its elements.
            If an int, the random sample is generated as if a were np.arange(a)
        size : int or tuple of ints, optional
            Output shape.  If the given shape is, e.g., ``(m, n, k)``, then
            ``m * n * k`` samples are drawn.  Default is None, in which case a
            single value is returned.
        replace : boolean, optional
            Whether the sample is with or without replacement
        p : 1-D array-like, optional
            The probabilities associated with each entry in a.
            If not given the sample assumes a uniform distribution over all
            entries in a.
        
        Returns
        --------
        samples : single item or ndarray
            The generated random samples
        
        Raises
        -------
        ValueError
            If a is an int and less than zero, if a or p are not 1-dimensional,
            if a is an array-like of size 0, if p is not a vector of
            probabilities, if a and p have different lengths, or if
            replace=False and the sample size is greater than the population
            size

3) pandas.DataFrame.sample(n=None,frac=None,replace=False,

weights=None,random\_state=None,axis=None)：

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
        
        You can use `random state` for reproducibility:
        
        >>> df.sample(random_state=1)
        A         B         C         D
        37 -2.027662  0.103611  0.237496 -0.165867
        43 -0.259323 -0.583426  1.516140 -0.479118
        12 -1.686325 -0.579510  0.985195 -0.460286
        8   1.167946  0.429082  1.215742 -1.636041
        9   1.197475 -0.864188  1.554031 -1.505264