numpy 学习笔记
导入 numpy 包
import numpy as np声明 ndarray 的几种方法
方法一，从list中创建
l = [[1,2,3], [4,5,6], [7,8,9]]
matrix = np.array(l)
print(matrix)[[1 2 3]
[4 5 6]
[7 8 9]]方法二，指定维度，不赋值
matrix = np.ndarray(shape=(3,4))
print(matrix)[[9.66308774e-312 2.47032823e-322 0.00000000e+000 0.00000000e+000]
[1.89146896e-307 2.42336543e-057 5.88854416e-091 9.41706373e-047]
[5.44949034e-067 1.46609735e-075 3.99910963e+252 3.43567991e+179]]由上述的输出可见，矩阵内部的值未初始化，其实这都是原来对应内存地址中的数值
方法三，指定维度，初始化成全零的矩阵
matrix = np.zeros(shape=[3,4])
print(matrix)[[0. 0. 0. 0.]
[0. 0. 0. 0.]
[0. 0. 0. 0.]]方法四，使用默认参数（arange），生成从0至arange的一组数据
matrix = np.arange(12).reshape(3,4)
print(matrix)[[ 0 1 2 3]
[ 4 5 6 7]
[ 8 9 10 11]]数值计算
操作全部元素
print(matrix)
print("after times 10 on every elements:")
print(matrix * 10)[[ 0 1 2 3]
[ 4 5 6 7]
[ 8 9 10 11]]
after times 10 on every elements:
[[ 0 10 20 30]
[ 40 50 60 70]
[ 80 90 100 110]]print(matrix)
print("after plus 10 on every elements:")
print(matrix + 10)[[ 0 1 2 3]
[ 4 5 6 7]
[ 8 9 10 11]]
after plus 10 on every elements:
[[10 11 12 13]
[14 15 16 17]
[18 19 20 21]]操作部分元素
print(matrix)
print("after times 10 on every elements:")
print(matrix[1] * 10)[[ 0 1 2 3]
[ 4 5 6 7]
[ 8 9 10 11]]
after times 10 on every elements:
[40 50 60 70]索引部分元素
取一行数据
print(matrix)
print("a line of a matrix:")
print(matrix[1])[[ 0 1 2 3]
[ 4 5 6 7]
[ 8 9 10 11]]
a line of a matrix:
[4 5 6 7]取一列数据
以行的形式返回，得到一个行向量
print(matrix)
print("a column of a matrix:")
print(matrix[:,1])[[ 0 1 2 3]
[ 4 5 6 7]
[ 8 9 10 11]]
a column of a matrix:
[1 5 9]以列的形式返回，得到一个列向量
print(matrix)
print("a column of a matrix:")
print(matrix[:,1:2])[[ 0 1 2 3]
[ 4 5 6 7]
[ 8 9 10 11]]
a column of a matrix:
[[1]
[5]
[9]]参考资料
《利用python进行数据分析》. https://book.douban.com/subject/25779298/
Numpy. Quickstart tutorial. https://docs.scipy.org/doc/numpy/user/quickstart.html