开发者学堂课程【Python 科学计算库 NumPy 快速入门:类型修改与数组去重】学习笔记,与课程紧密联系,让用户快速学习知识。
课程地址:https://developer.aliyun.com/learning/course/605/detail/8820
类型修改与数组去重
内容简介:
一、类型修改
二、数组的去重
一、类型修改
●ndarray.astype(type)
stock_ change. astype (np. int32)
●ndarray.tostring([order]) 或者 ndarray.tobytes([order]) Construct Python bytes containing the raw data bytes in the array.
。转换成 bytes
arr = np.array([[[1, 2, 3],[4, 5, 6]],[[12, 3,34], [5,6, 7]]])
arr. tostring()
拓展:如果遇到
I0Pub 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
(一)ndarray . astype (type )
In[84]:stock_change.astype(“int32”)
Out[84]:array([[ 0, 1, 0, 2, 0, 0, -1, 0, 1,-1],
[-1, -1, 0, 1, -1, 0, 0, -1, 0, 0] ,
[ 0, 0, 1, 0, 1,2, 0, 0, 0, 0],
[ 0, 0, -1, 0, 0, -1, 0, -1, 0, 0],
[0, -1, -2, -1, 0, 0, 0, 1, 1, 0],
[0, 1, 0, -2, -2, -1, 1, -2, 1, 1],
[-2, 0, 0, 0, 0, -1, 0, 0, 1, 0],
[0, 0, 0, 0, -1, -1, 0, 1, 1, 0]],
dtype=int32)
(二)ndarray 序列化到本地
ndarray. tostring ( )
二、数组的去重
· ndarray.unique
temp = np.array([[l, 2, 3, 4],[3, 4, 5, 6]])
>>> np.unique(temp)
array([1, 2, 3, 4, 5, 6])
例子:
1、In [88]:temp . np.array([[l, 2, 3, 4],[3, 4, 5, 6]])
In [89]:temp
Out(89):array([[l, 2, 3, 4],
l3, 4, 5, 6J)
In [91]:np· unique( temp)
Out(91):array([l, 2, 3, 4, 5, 6])
2、In [94] :set (temp. flatten()
Out(94):(1,2,3,4,5,6)