An article that completely introduces all numpy data type conversion methods

numpy is one of the commonly used tools in the Python scientific computing library, which can perform efficient numerical calculations and data processing. In numpy, data type conversion is a very common operation that can help us adapt data to different needs. This article will introduce commonly used data type conversion methods in numpy, and attach specific code examples.

1. Introduction to numpy data types

In numpy, data types are represented by a descriptor. Each descriptor consists of a character (indicating the data type) and a number (indicating the data size).

Common numpy data types include:

• bool: Boolean data, representing true or false;
• int: integer data, which can be signed Or unsigned;
• float: floating point data, representing real numbers;
• complex: complex data, consisting of real numbers and imaginary numbers;
• object: Python object type , can store any type of data;
• string: string type;
• unicode: Unicode type.

2. Numpy data type conversion method

1. astype() method

astype() method is used to convert the data type of numpy array Convert to the specified data type. The following are some common usage examples:

import numpy as np

# 创建一个整型数组
arr = np.array([1, 2, 3, 4, 5])

# 将整型数组转换为浮点型数组
arr_float = arr.astype(float)
print(arr_float)

# 将浮点型数组转换为整型数组
arr_int = arr_float.astype(int)
print(arr_int)

# 创建一个字符串数组
arr_str = np.array(['1', '2', '3', '4', '5'])

# 将字符串数组转换为整型数组
arr_int = arr_str.astype(int)
print(arr_int)

Output results:

[1. 2. 3. 4. 5.]
[1 2 3 4 5]
[1 2 3 4 5]

2. numpy data type objects

numpy provides a series of data type objects , data types can be specified through these objects. The specific usage is as follows:

import numpy as np

# 使用数据类型对象指定数据类型
dt = np.dtype('int32')
arr = np.array([1, 2, 3, 4, 5], dtype=dt)
print(arr.dtype)

# 创建复数型数组
dt = np.dtype('complex128')
arr = np.array([1 + 2j, 2 + 3j, 3 + 4j], dtype=dt)
print(arr.dtype)

Output result:

int32
complex128

3. Data type conversion function

numpy provides some functions for data type conversion, these Functions can directly convert data types as parameters. The specific usage is as follows:

import numpy as np

# 创建一个整型数组
arr = np.array([1, 2, 3, 4, 5])

# 使用数据类型转换函数进行转换
arr_float = np.float64(arr)
print(arr_float)

# 创建一个字符串数组
arr_str = np.array(['1', '2', '3', '4', '5'])

# 使用数据类型转换函数进行转换
arr_int = np.int32(arr_str)
print(arr_int)

Output result:

[1. 2. 3. 4. 5.]
[1 2 3 4 5]

3. Summary

This article introduces the commonly used data type conversion methods in numpy, including the astype() method, data type Objects and data type conversion functions. Through these methods, we can easily perform data type conversion of numpy arrays to adapt to different needs. In practical applications, appropriate methods can be selected for data type conversion based on specific circumstances to improve data processing efficiency.

The above is a complete list of numpy data type conversion methods. I hope it will be helpful to you!

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