0.2,0.3])# create an array of complex numberscomplex_array = np.array([1+2j,2+3j,3+4j])# check the data type of int_arrayprint(int_array.dtype)# prints int64# check the data type of float_arrayprint(float_
Checking the Data Type of an Array The NumPy array object has a property calleddtypethat returns the data type of the array: ExampleGet your own Python Server Get the data type of an array object: importnumpyasnp arr = np.array([1,2,3,4]) ...
1. NumPy 数据类型(Data Types): NumPy 提供了丰富的数据类型,例如整数、浮点数、布尔值、复数等。可以使用 `dtype` 参数指定数组的数据类型。 import numpy as np # 创建一个整数数组 arr_int = np.array([1, 2, 3]) print(arr_int.dtype) # 输出:int64 # 创建一个浮点数数组 arr_float = np.arra...
import numpy as np # 创建不同类型的数组 lengths = np.array([2.5, 3.8, 4.1], dtype=np.float32) times = np.array([20, 35, 55], dtype=np.int64) flags = np.array([True, False, True], dtype=np.bool_) # 检查数组的数据类型 print("Data Types:", lengths.dtype, times.dtype, flags...
Array types can also be referred to by character codes, mostly to retain backward compatibility with older packages such as Numeric. Some documentation may still refer to these, for example: >>> import numpy as np >>> np.array([3, 5, 7], dtype='f') ...
NumPy data types you can use shorthand type code strings to create ndarray arr = np.array([1.1, 2.2, 3.3], dtype='f8') astype You can explicitly convert or cast an array from one dtype to another using ndarray’sastypemethod. Callingastypealways creates a new array (a copy of the data...
Change Array Data Type Write a NumPy program to change an array's data type. Sample Solution: Python Code: # Importing the NumPy library with an alias 'np'importnumpyasnp# Creating a NumPy array 'x' with specified data type 'int32'x=np.array([[2,4,6],[6,8,10]],np.int32)# Pr...
print(newarr.dtype) 自己试试 » 例子 将数据类型从整数更改为布尔值: import numpyas np arr = np.array([1,0,3]) newarr = arr.astype(bool) print(newarr) print(newarr.dtype) 转载于: https://www.w3schools.com/python/numpy/numpy_data_types.asp...
ndarray.data -该缓冲区包含数组的实际元素。 import numpy as np # 创建一个二维数组 arr = np.array([[1, 2, 3], [4, 5, 6]]) # 使用属性和方法获取数组信息 # 数组的维度(轴的个数) print("维度 (ndim):", arr.ndim) # 输出: 2 # 数组的形状(每个维度的大小) print("形状 (shape):"...
array([ 0., 1., 2.]) >>> np.int8(z) array([0, 1, 2], dtype=int8) 1. 2. 3. 4. 注意,上面,我们使用Python float对象作为dtype。NumPy 知道int指代np.int_、bool表示np.bool_、 float为np.float_以及complex为np.complex_。其他数据类型没有Python等效的类型。