NumPy（九）：类型转换【ndarray.astype(type)】【ndarray.tostring([order])】【ndarray.tobytes([order]) 】

拼搏现实的明天。 2023-09-29 08:40 10阅读 0赞

## 一、ndarray.astype(type) ##

返回修改了类型之后的数组

stock_change.astype(np.int32)

## 二、ndarray.tostring(\[order\])或者ndarray.tobytes(\[order\]) ##

构造包含数组中原始数据字节的Python字节

arr = np.array([[[1, 2, 3], [4, 5, 6]], [[12, 3, 34], [5, 6, 7]]])
    arr.tostring()

jupyter输出太大可能导致崩溃问题

如果遇到

IOPub data rate exceeded.
        The notebook server will temporarily stop sending output
        to the client in order to avoid crashing it.
        To change this limit, set the config variable
        `--NotebookApp.iopub_data_rate_limit`.

这个问题是在jupyer当中对输出的字节数有限制，需要去修改配置文件

创建配置文件

jupyter notebook --generate-config
    vi ~/.jupyter/jupyter_notebook_config.py

取消注释,多增加

## (bytes/sec) Maximum rate at which messages can be sent on iopub before they
    #  are limited.
    c.NotebookApp.iopub_data_rate_limit = 10000000

**但是不建议这样去修改，jupyter输出太大会崩溃**

## 案例 ##

import numpy as np
    
    # 数组类型转换：.astype()
    
    ar1 = np.arange(10, dtype=float)
    print('ar1 = {0}, ar1.dtype = {1}'.format(ar1, ar1.dtype))
    print('-' * 100)
    
    # a.astype()：转换数组类型
    # 注意：养成好习惯，数组类型用np.int32，而不是直接int32
    ar2 = ar1.astype(np.int32)  # 可以在参数位置设置数组类型
    print('ar2 = {0}, ar2.dtype = {1}'.format(ar2, ar2.dtype))

打印结果：

ar1 = [0. 1. 2. 3. 4. 5. 6. 7. 8. 9.], ar1.dtype = float64
    ----------------------------------------------------------------------------------------------------
    ar2 = [0 1 2 3 4 5 6 7 8 9], ar2.dtype = int32
    
    Process finished with exit code 0