175 lines
5.4 KiB
Markdown
175 lines
5.4 KiB
Markdown
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# NumPy full_like()函数——返回一个与给定数组具有相同形状和类型的完整数组
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> 原文：<https://www.askpython.com/python-modules/numpy/numpy-full_like>
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NumPy 是一个强大的、广泛使用的 Python 科学计算库。它提供了许多处理数字数据和数组的函数和工具，包括`full_like()`函数。这个函数允许你创建一个新的数组，它的形状和数据类型与给定的数组相同，但是填充了一个特定的值。
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这在许多情况下都很有用，例如当您需要创建某种形状和类型的数组来保存占位符或默认值时。在本文中，我们将详细探讨`full_like()`函数的用法和语法，并提供如何在实践中使用它的例子。
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***也读作: [NumPy full()函数](https://www.askpython.com/python-modules/numpy/numpy-full)***
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## NumPy 中的 full_like()是什么？
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`full_like()`是 NumPy 库中的一个函数，它创建一个与给定数组具有相同形状和数据类型的新数组，但用指定的值填充。它接受两个必需的参数:
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1. `a`:用作新数组的形状和数据类型模板的数组。
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2. `fill_value`:填充新数组的值。
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## Numpy full_like()的语法
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```py
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numpy.full_like(a, fill_value, dtype=None, order='K', subok=True, shape=None)
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```
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### 因素
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* **答:数组 _like**
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* 需要
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* 返回数组的这些相同特征是由。
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* **fill_value: array_like**
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* 需要
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* 新数组中用来初始化元素的值。
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* **dtype:数据类型**
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* 可选择的
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* 覆盖结果的数据类型。
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* **顺序:{'C '，' F '，' A '或' K'}**
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* 可选择的
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* 更改结果的内存布局。“C”代表 C 阶，“F”代表 F 阶，“A”代表 C，除非“A”是 Fortran 连续的。“K”表示与 a 非常相似的布局。
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* **subok: bool**
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* 可选择的
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* 如果为 True，新生成的数组将使用的子类类型；否则，将使用基类数组。通常设置为 True。
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* **形状:int 或整数序列**
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* 可选择的
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* 改变结果的形式。如果 order='K '并且维度数量保持不变，将尝试保持顺序；否则，假定 order='C '。
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## Numpy full_like()的实现
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在实现该函数之前，请确保在 IDE 中导入 NumPy 包。为此，请在您的 IDE 中运行以下代码行。
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```py
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import numpy as np
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```
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### 例 1。仅传递必需的参数
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```py
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x = ([1,2,3],[4,4,6])
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print(x)
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print("\n")
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np.full_like(x, 3)
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y = ([1,2,3],
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[4,5,6])
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print(y)
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print("\n")
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np.full_like(y,([9,8,7],[6,5,4]))
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```
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Example 1
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### 例 2。传递其他参数
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```py
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w = ([1,2,3],[4,5,6],[7,8,9])
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print(w)
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print("\n")
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np.full_like(w, 5, dtype=np.float16, order='C')
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z = ([1,2,3],[4,5,6])
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print(z)
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print("\n")
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np.full_like(z, (1+3j), dtype = float, order='F')
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```
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Example 2
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### 示例 3:用 numpy.full_like()将所有内容放在一起
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下面是一个更完整的使用 numpy.full_like()方法的例子，包括所有不同的使用方法。
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```py
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import numpy as np
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# Create a random array of integers from 0 to 9
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a = np.random.randint(10, size=(2, 3, 4))
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print(f"Original array: \n{a}")
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# Create a new array of zeros with the same shape and data type as a
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b = np.full_like(a, 0)
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print(f"Array of zeros with same shape and data type as a: \n{b}")
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# Create a new array of ones with the same shape and data type as a
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c = np.full_like(a, 1, dtype=np.float64)
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print(f"Array of ones with same shape and data type as a, but with data type float64: \n{c}")
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# Create a new array with the same shape and data type as a, but filled with the maximum value of a's data type
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d = np.full_like(a, np.amax(a))
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print(f"Array with same shape and data type as a, filled with the maximum value of a's data type: \n{d}")
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# Create a new array with the same shape and data type as a, but filled with the minimum value of a's data type
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e = np.full_like(a, np.amin(a))
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print(f"Array with same shape and data type as a, filled with the minimum value of a's data type: \n{e}")
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```
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**输出**
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```py
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Original array:
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[[[2 9 7 3]
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[9 8 6 4]
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[1 0 8 9]]
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[[3 9 9 7]
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[0 8 0 3]
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[5 5 4 3]]]
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Array of zeros with same shape and data type as a:
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[[[0 0 0 0]
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[0 0 0 0]
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[0 0 0 0]]
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[[0 0 0 0]
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[0 0 0 0]
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[0 0 0 0]]]
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Array of ones with same shape and data type as a, but with data type float64:
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[[[1\. 1\. 1\. 1.]
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[1\. 1\. 1\. 1.]
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[1\. 1\. 1\. 1.]]
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[[1\. 1\. 1\. 1.]
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[1\. 1\. 1\. 1.]
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[1\. 1\. 1\. 1.]]]
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Array with same shape and data type as a, filled with the maximum value of a's data type:
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[[[9 9 9 9]
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[9 9 9 9]
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[9 9 9 9]]
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[[9 9 9 9]
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[9 9 9 9]
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[9 9 9 9]]]
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Array with same shape and data type as a, filled with the minimum value of a's data type:
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[[[0 0 0 0]
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[0 0 0 0]
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[0 0 0 0]]
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[[0 0 0 0]
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[0 0 0 0]
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[0 0 0 0]]]
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```
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## 结论
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总之，NumPy 中的`full_like()`函数是创建新数组的有用工具，这些新数组具有与给定数组相同的形状和数据类型，但填充了特定的值。这在许多情况下都很有用，例如当您需要创建某种形状和类型的数组来保存占位符或默认值时。无论您是在科学计算中处理数字数据，还是仅仅需要在 Python 脚本中操作数组，`full_like()`函数都是您工具箱中的宝贵补充。
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## 参考
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[https://numpy . org/doc/stable/reference/generated/numpy . full _ like . html](https://numpy.org/doc/stable/reference/generated/numpy.full_like.html)
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