pandas随机打乱数据

╰半橙微兮° 2023-06-12 04:27 19阅读 0赞

### 调用接口 ###

df.sample(n,frac,replace,weights,random_state,axis)

### 例如： ###

df=pd.read_csv('xxx.csv')
    
    df.sample(frac=1.0,random_state)

*  frac=1.0 表示保留全部数据
 *  random\_state 随机种子，保证每次打乱的顺序相同

### 源码中的例子： ###

def sample(self, n=None, frac=None, replace=False, weights=None,
                   random_state=None, axis=None):
            """
            Return a random sample of items from an axis of object.
    
            You can use `random_state` for reproducibility.
    
            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 : bool, default False
                Sample with or without replacement.
            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.
                Infinite 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
            -------
            Series or DataFrame
                A new object of same type as caller containing `n` items randomly
                sampled from the caller object.
    
            See Also
            --------
            numpy.random.choice: Generates a random sample from a given 1-D numpy
                array.
    
            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
    
            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

### 注意： ###

**random.shuffle()**没有返回值，通常和迭代器结合使用。