numpy
start
import numpy as np
array --> 将XX转成ndarray
nd = np.array([1,4,6,5,3,5,7,8,9,0,2])
nd,type(nd)
- array(object, dtype=None, copy=True, order=‘K’, subok=False, ndmin=0)
- out: (array([1, 4, 6, 5, 3, 5, 7, 8, 9, 0, 2]), numpy.ndarray)
索引
nd[::3] # 差3取一个
- out: array([1, 5, 7, 0])
常用函数
np.ones(shape=(3,4))
- out:array([[1., 1., 1., 1.],
[1., 1., 1., 1.],
[1., 1., 1., 1.]])
np.linspace(0,99,num=60) # 均分60段
np.arange(0,100,5) # 差5分
np.random.randint(0,150,size=(4,5))
np.random.normal(175,scale=10,size = 50) # 模拟正太分布
import matplotlib.pyplot as plt
img = np.random.random(size=(300,400,3))
plt.imshow(img)
example
data 是读取的图片
# 转置矩阵
cat10 = data.transpose([1,0,2]) # 转换三维顺序
plt.imshow(cat10)
- <matplotlib.image.AxesImage at 0xe34f860>
cat11 = np.concatenate([data,data]) # axis = 0 高度级联 轴---->控制方向
plt.figure(figsize=(12,9))
plt.imshow(cat11)
cat12 = np.hstack([cat10,cat10])
plt.imshow(cat12)
- np.hstack与np.vstack
水平级联与垂直级联,处理自己，进行维度的变更
切分example
a = np.random.randint(0, 10, size = (3,5))
a
- out:array([[8, 1, 9, 5, 0],
[7, 4, 6, 3, 4],
[8, 6, 4, 5, 9]])
a.T # 逆矩阵 a.transpose([1,0]) 一个库流行起来，大多因为容易记，见名知意
- array([[8, 7, 8],
[1, 4, 6],
[9, 6, 4],
[5, 3, 5],
[0, 4, 9]])
np.split(a,indices_or_sections=[2,3],axis=1) # shift + Tab 查看方法的使用
- [array([[8, 1],
[7, 4],
[8, 6]]), array([[9],
[6],
[4]]), array([[5, 0],
[3, 4],
[5, 9]])]
# cat 是图片数据
cat11,cat12,cat13 = np.split(cat,indices_or_sections=[300,500],axis=1)
plt.imshow(cat11)
其他聚合操作
-
Function Name NaN-safe Version Description np.sum np.nansum Compute sum of elements np.prod np.nanprod Compute product of elements np.mean np.nanmean Compute mean of elements np.std np.nanstd Compute standard deviation np.var np.nanvar Compute variance np.min np.nanmin Find minimum value np.max np.nanmax Find maximum value np.argmin np.nanargmin Find index of minimum value np.argmax np.nanargmax Find index of maximum value np.median np.nanmedian Compute median of elements np.percentile np.nanpercentile Compute rank-based statistics of elements np.any N/A Evaluate whether any elements are true np.all N/A Evaluate whether all elements are true np.power 幂运算
快速排序
m = np.random.normal(50,scale=5,size = 20)
n = np.sort(m)
display(n)
m
- out:array([39.40887649, 41.88521003, 44.76491466, 45.4822017 , 46.17289219,46.70453702, 47.91256991, 48.20295803, 48.7437394 , 49.26693877,51.04438549, 51.28749952,51.39541613,51.68891328,52.96908429,54.02395625, 54.37854927, 55.57017976, 57.83520591, 66.92652775])
array([55.57017976, 54.37854927, 47.91256991, 51.04438549, 66.92652775,44.76491466, 48.20295803, 51.28749952, 57.83520591, 46.70453702,46.17289219, 51.68891328, 52.96908429, 54.02395625, 48.7437394 ,45.4822017 , 39.40887649, 41.88521003, 49.26693877, 51.39541613])
m.sort()
m
- out:array([39.40887649, 41.88521003, 44.76491466, 45.4822017 , 46.17289219, 46.70453702, 47.91256991, 48.20295803, 48.7437394 , 49.26693877,51.04438549, 51.28749952,51.39541613,51.68891328,52.96908429,54.02395625, 54.37854927, 55.57017976, 57.83520591, 66.92652775])
- np.sort()与ndarray.sort()都可以，但有区别：
np.sort()不改变输入
ndarray.sort()本地处理，不占用空间，但改变输入