数据结构
网络
关系型数据库管理系统
操作系统
Java
MS Excel
iOS
HTML
CSS
Android
Python
C 语言编程
C++
C#
MongoDB
MySQL
Javascript
PHP
物理学
化学
生物学
数学
英语
经济学
心理学
社会学
服装设计
法律研究在 NumPy 中显示当前掩码
要显示当前掩码，请在 Python NumPy 中使用 **ma.MaskedArray.mask**。掩码数组是标准 numpy.ndarray 和掩码的组合。掩码要么是 nomask，表示关联数组的任何值均有效，要么是布尔数组，用于确定关联数组的每个元素的值是否有效。
NumPy 提供了全面的数学函数、随机数生成器、线性代数例程、傅里叶变换等。它支持各种硬件和计算平台，并且可以很好地与分布式、GPU 和稀疏数组库配合使用。
步骤
首先，导入所需的库：
import numpy as np import numpy.ma as ma
使用 numpy.arange() 方法创建一个包含 int 元素的 4x4 数组：
arr = np.arange(16).reshape((4,4))
print("Array...
", arr)获取数组的维度：
print("
Array Dimensions...
",arr.ndim)
获取数组的形状：
print("
Our Masked Array Shape...
",arr.shape)获取数组的元素数量：
print("
Elements in the Masked Array...
",arr.size)
创建一个掩码数组：
arr = ma.array(arr) arr[0, 1] = ma.masked arr[1, 1] = ma.masked arr[2, 1] = ma.masked arr[2, 2] = ma.masked arr[3, 0] = ma.masked arr[3, 2] = ma.masked arr[3, 3] = ma.masked
沿特定轴计算掩码元素的数量：
print("
The number of masked elements...
",ma.count_masked(arr, axis = 1))
返回掩码数组的掩码：
print("
The mask of a masked array...
",ma.getmask(arr))将掩码数组的数据作为 ndarray 返回：
print("
Data of a masked array as an ndarray...
",ma.getdata(arr))
确定输入是否为掩码数组的实例：
print("
Whether input is an instance of masked array?
",ma.isMaskedArray(arr))要显示当前掩码，请在 Python NumPy 中使用 ma.MaskedArray.mask：
print("
Result...
",arr.mask)
示例
# Python ma.MaskedArray - Display the current mask
import numpy as np
import numpy.ma as ma
# Creating a 4x4 array with int elements using the numpy.arange() method
arr = np.arange(16).reshape((4,4))
print("Array...
", arr)
print("
Array type...
", arr.dtype)
# Get the dimensions of the Array
print("
Array Dimensions...
",arr.ndim)
print("
Our Array type...
", arr.dtype)
# Get the shape of the Array
print("
Our Masked Array Shape...
",arr.shape)
# Get the number of elements of the Array
print("
Elements in the Masked Array...
",arr.size)
# Create a masked array
arr = ma.array(arr)
arr[0, 1] = ma.masked
arr[1, 1] = ma.masked
arr[2, 1] = ma.masked
arr[2, 2] = ma.masked
arr[3, 0] = ma.masked
arr[3, 2] = ma.masked
arr[3, 3] = ma.masked
# Count the number of masked elements along specific axis
print("
The number of masked elements...
",ma.count_masked(arr, axis = 1))
# Return the mask of a masked array
print("
The mask of a masked array...
",ma.getmask(arr))
# Return the data of a masked array as an ndarray
print("
Data of a masked array as an ndarray...
",ma.getdata(arr))
# Determine whether input is an instance of masked array
print("
Whether input is an instance of masked array?
",ma.isMaskedArray(arr))
# To display the current mask, use the ma.MaskedArray.mask in Python Numpy
print("
Result...
",arr.mask)输出
Array... [[ 0 1 2 3] [ 4 5 6 7] [ 8 9 10 11] [12 13 14 15]] Array type... int64 Array Dimensions... 2 Our Array type... int64 Our Masked Array Shape... (4, 4) Elements in the Masked Array... 16 The number of masked elements... [1 1 2 3] The mask of a masked array... [[False True False False] [False True False False] [False True True False] [ True False True True]] Data of a masked array as an ndarray... [[ 0 1 2 3] [ 4 5 6 7] [ 8 9 10 11] [12 13 14 15]] Whether input is an instance of masked array? True Result... [[False True False False] [False True False False] [False True True False] [ True False True True]]
更新于： 2022年2月3日
191 次查看
广告