numpy之多维数组的创建全过程
目录
numpy多维数组的创建
1.1 随机抽样创建
1.2 序列创建
1.3 数组重新排列
1.4 数据类型的转换
1.5 数组转列表
numpy 多维数组相关问题
创建（多维）数组
数组赋值
np数组保存
读取np数组
总结
numpy多维数组的创建多维数组（矩阵ndarray）
ndarray的基本属性
shape维度的大小
ndim维度的个数
dtype数据类型
1.1.1 rand
生成指定维度的随机多维度浮点型数组，区间范围是[0,1)
Random values in a given shape.
Create an array of the given shape and populate it with
random samples from a uniform distribution
over ``[0, 1)``.
nd1 = np.random.rand(1,1)
print(nd1)
print('维度的个数',nd1.ndim)
print('维度的大小',nd1.shape)
print('数据类型',nd1.dtype) # float 64
1.1.2 uniform
def uniform(low=0.0, high=1.0, size=None): # real signature unknown; restored from __doc__
"""
uniform(low=0.0, high=1.0, size=None)
Draw samples from a uniform distribution.
Samples are uniformly distributed over the half-open interval
``[low, high)`` (includes low, but excludes high). In other words,
any value within the given interval is equally likely to be drawn
by `uniform`.
Parameters
----------
low : float or array_like of floats, optional
Lower boundary of the output interval. All values generated will be
greater than or equal to low. The default value is 0.
high : float or array_like of floats
Upper boundary of the output interval. All values generated will be
less than high. The default value is 1.0.
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. If size is ``None`` (default),
a single value is returned if ``low`` and ``high`` are both scalars.
Otherwise, ``np.broadcast(low, high).size`` samples are drawn.
Returns
-------
out : ndarray or scalar
Drawn samples from the parameterized uniform distribution.
See Also
--------
randint : Discrete uniform distribution, yielding integers.
random_integers : Discrete uniform distribution over the closed
interval ``[low, high]``.
random_sample : Floats uniformly distributed over ``[0, 1)``.
random : Alias for `random_sample`.
rand : Convenience function that accepts dimensions as input, e.g.,
``rand(2,2)`` would generate a 2-by-2 array of floats,
uniformly distributed over ``[0, 1)``.
Notes
-----
The probability density function of the uniform distribution is
.. math:: p(x) = \frac{1}{b - a}
anywhere within the interval ``[a, b)``, and zero elsewhere.
When ``high`` == ``low``, values of ``low`` will be returned.
If ``high`` < ``low``, the results are officially undefined
and may eventually raise an error, i.e. do not rely on this
function to behave when passed arguments satisfying that
inequality condition.
Examples
--------
Draw samples from the distribution:
>>> s = np.random.uniform(-1,0,1000)
All values are within the given interval:
>>> np.all(s >= -1)
True
>>> np.all(s < 0)
True
Display the histogram of the samples, along with the
probability density function:
>>> import matplotlib.pyplot as plt
>>> count, bins, ignored = plt.hist(s, 15, density=True)
>>> plt.plot(bins, np.ones_like(bins), linewidth=2, color='r')
>>> plt.show()
"""
pass
nd2 = np.random.uniform(-1,5,size = (2,3))
print(nd2)
print('维度的个数',nd2.ndim)
print('维度的大小',nd2.shape)
print('数据类型',nd2.dtype)
运行结果：
1.1.3 randint
def randint(low, high=None, size=None, dtype='l'): # real signature unknown; restored from __doc__
"""
randint(low, high=None, size=None, dtype='l')
Return random integers from `low` (inclusive) to `high` (exclusive).
Return random integers from the "discrete uniform" distribution of
the specified dtype in the "half-open" interval [`low`, `high`). If
`high` is None (the default), then results are from [0, `low`).
Parameters
----------
low : int
Lowest (signed) integer to be drawn from the distribution (unless
``high=None``, in which case this parameter is one above the
*highest* such integer).
high : int, optional
If provided, one above the largest (signed) integer to be drawn
from the distribution (see above for behavior if ``high=None``).
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.
dtype : dtype, optional
Desired dtype of the result. All dtypes are determined by their
name, i.e., 'int64', 'int', etc, so byteorder is not available
and a specific precision may have different C types depending
on the platform. The default value is 'np.int'.
.. versionadded:: 1.11.0
Returns
-------
out : int or ndarray of ints
`size`-shaped array of random integers from the appropriate
distribution, or a single such random int if `size` not provided.
See Also
--------
random.random_integers : similar to `randint`, only for the closed
interval [`low`, `high`], and 1 is the lowest value if `high` is
omitted. In particular, this other one is the one to use to generate
uniformly distributed discrete non-integers.
Examples
--------
>>> np.random.randint(2, size=10)
array([1, 0, 0, 0, 1, 1, 0, 0, 1, 0])
>>> np.random.randint(1, size=10)
array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0])
Generate a 2 x 4 array of ints between 0 and 4, inclusive:
>>> np.random.randint(5, size=(2, 4))
array([[4, 0, 2, 1],
[3, 2, 2, 0]])
"""
pass
nd3 = np.random.randint(1,20,size=(3,4))
print(nd3)
print('维度的个数',nd3.ndim)
print('维度的大小',nd3.shape)
print('数据类型',nd3.dtype)
展示：
[[11 17 5 6]
[17 1 12 2]
[13 9 10 16]]
维度的个数 2
维度的大小 (3, 4)
数据类型 int32
注意点：
1、如果没有指定最大值，只是指定了最小值，范围是[0,最小值）
2、如果有最小值，也有最大值，范围为[最小值,最大值)
1.2 序列创建1.2.1 array
通过列表进行创建
nd4 = np.array([1,2,3])
展示：
[1 2 3]
通过列表嵌套列表创建
nd5 = np.array([[1,2,3],[4,5]])
展示：
[list([1, 2, 3]) list([4, 5])]
综合
nd4 = np.array([1,2,3])
print(nd4)
print(nd4.ndim)
print(nd4.shape)
print(nd4.dtype)
nd5 = np.array([[1,2,3],[4,5,6]])
print(nd5)
print(nd5.ndim)
print(nd5.shape)
print(nd5.dtype)
展示：
[1 2 3]
1
(3,)
int32
[[1 2 3]
[4 5 6]]
2
(2, 3)
int32
1.2.2 zeros
nd6 = np.zeros((4,4))
print(nd6)
展示：
[[0. 0. 0. 0.]
[0. 0. 0. 0.]
[0. 0. 0. 0.]
[0. 0. 0. 0.]]
注意点：
1、创建的数里面的数据为0
2、默认的数据类型是float
3、可以指定其他的数据类型
1.2.3 ones
nd7 = np.ones((4,4))
print(nd7)
展示：
[[1. 1. 1. 1.]
[1. 1. 1. 1.]
[1. 1. 1. 1.]
[1. 1. 1. 1.]]
1.2.4 arange
nd8 = np.arange(10)
print(nd8)
nd9 = np.arange(1,10)
print(nd9)
nd10 = np.arange(1,10,2)
print(nd10)
结果：
[0 1 2 3 4 5 6 7 8 9]
[1 2 3 4 5 6 7 8 9]
[1 3 5 7 9]
注意点：
1、只填写一位数，范围：[0,填写的数字)
2、填写两位，范围：[最低位，最高位)
3、填写三位，填写的是（最低位，最高位，步长）
4、创建的是一位数组
5、等同于np.array(range())
1.3 数组重新排列nd11 = np.arange(10)
print(nd11)
nd12 = nd11.reshape(2,5)
print(nd12)
print(nd11)
展示：
[0 1 2 3 4 5 6 7 8 9]
[[0 1 2 3 4]
[5 6 7 8 9]]
[0 1 2 3 4 5 6 7 8 9]
注意点：
1、有返回值，返回新的数组，原始数组不受影响
2、进行维度大小的设置过程中，要注意数据的个数，注意元素的个数
nd13 = np.arange(10)
print(nd13)
nd14 = np.random.shuffle(nd13)
print(nd14)
print(nd13)
展示：
[0 1 2 3 4 5 6 7 8 9]
None
[8 2 6 7 9 3 5 1 0 4]
注意点：
1、在原始数据集上做的操作
2、将原始数组的元素进行重新排列，打乱顺序
3、shuffle这个是没有返回值的
两个可以配合使用，先打乱，在重新排列
1.4 数据类型的转换nd15 = np.arange(10,dtype=np.int64)
print(nd15)
nd16 = nd15.astype(np.float64)
print(nd16)
print(nd15)
展示：
[0 1 2 3 4 5 6 7 8 9]
[0. 1. 2. 3. 4. 5. 6. 7. 8. 9.]
[0 1 2 3 4 5 6 7 8 9]
注意点：
1、astype()不在原始数组做操作，有返回值，返回的是更改数据类型的新数组
2、在创建新数组的过程中，有dtype参数进行指定
1.5 数组转列表
arr1 = np.arange(10)
# 数组转列表
print(list(arr1))
print(arr1.tolist())
展示：
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
numpy 多维数组相关问题
创建（多维）数组
x = np.zeros(shape=[10, 1000, 1000], dtype='int')
得到全零的多维数组。
数组赋值x[*,*,*] = ***
np数组保存
np.save("./**.npy",x)
读取np数组
x = np.load("path")
总结
以上为个人经验，希望能给大家一个参考，也希望大家多多支持软件开发网。
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