Python-用Numpy数据分析

目录

目录

1.切片和索引

2.赋值

 3.布尔索引

4.np.where()

5.裁剪

6.转置

7.求极值

---

1.切片和索引

# -*- coding : utf-8 -*-
"""
@Time: 2021/7/29 21:51
@Author : zhanxuejie
@FileName: part01.py
@Software:PyCharm
"""
import numpy as np
a = [[1,2,3],
     [4,5,6],
     [7,8,9]]
# a = [1,2,3,4,5,6]
t = np.array(a)
# 选择行
print(t)
print(t[1:,:]) #第一行到最后一行
print(t[1])  #只取第一行
print(type(t[1:,:]))
print(type(t[1]))

#output:
#       [[4 5 6]
#        [7 8 9]]
#        [4 5 6]
#       <class 'numpy.ndarray'>
#       <class 'numpy.ndarray'>

# 选择列
print(t[:,2:])
print(type(t[:,2:]))
print(t[:,2])
print(type(t[:,2]))

#output:
#        [[3]
#         [6]
#         [9]]
#        <class 'numpy.ndarray'>
#        [3 6 9]
#        <class 'numpy.ndarray'>

import numpy as np
t = np.array([[1,2,3,7],
              [4,5,6,8],
              [7,8,9,10],
              [6,3,1,11]])
# 选择行列
print(t[1:,:3]) #选择连续的多行多列
# 不连续
print(t[[2,2],[3,2]]) #选择的是（2,3），（2,2）两个位置
# 索引
print(t[2,3])

#output:  [[4 5 6]
 #        [7 8 9]
 #        [6 3 1]]

 #        [10  9]
 #        10

2.赋值

# 赋值
t[1:,3]=3
print(t)

#output:[[1 2 3 7]
#        [4 5 6 3]
#        [7 8 9 3]
#        [6 3 1 3]]

 3.布尔索引

# 布尔索引
t[t>6] = 6    #大于6的赋值为6
print(t)

# output:
#          [[1 2 3 6]
#           [4 5 6 3]
#           [6 6 6 3]
#           [6 3 1 3]]

4.np.where()

# 三元运算符
print(np.where(t>6, 2, 3)) #大于6的赋值为2，其余赋值为3

# output:
#         [[3 3 3 2]
#          [3 3 3 2]
#          [2 2 2 2]
#          [3 3 3 2]]

5.裁剪

# 裁剪
a = t.clip(5,8)  #大于5的替换为5，大于8 的替换为8
print(a)

# output:
#         [[5 5 5 7]
#          [5 5 6 8]
#          [7 8 8 8]
#          [6 5 5 8]]

6.转置

# 转置，三种方法效果一样
a = t.T
b = t.transpose()
c = t.swapaxes(1,0)
print(t)
print(a)
print(b)
print(c)

# output:
#         [[ 1  2  3  7]
#          [ 4  5  6  8]
#          [ 7  8  9 10]
#          [ 6  3  1 11]]
# 
#         [[ 1  4  7  6]
#          [ 2  5  8  3]
#          [ 3  6  9  1]
#          [ 7  8 10 11]]

#         [[ 1  4  7  6]
#          [ 2  5  8  3]
#          [ 3  6  9  1]
#          [ 7  8 10 11]]

#         [[ 1  4  7  6]
#          [ 2  5  8  3]
#          [ 3  6  9  1]
#          [ 7  8 10 11]]

7.求极值

# 求极值,np.ptp()
print(t)
print(np.ptp(t))  #输出max-min
print(np.ptp(t,axis=0)) #输出y轴上max-min
print(np.ptp(t,axis=1)) #输出x轴上max-min

# output:
#       [[ 1  2  3  7]
#       [ 4  5  6  8]
#       [ 7  8  9 10]
#       [ 6  3  1 11]]
# 
#       10
#       [6 6 8 4]
#       [ 6  4  3 10]