|NO.Z.00031|——————————|BigDataEnd|——|Hadoop&Python.v09|——|Arithmetic.v09|NumPy科学计算库：NumPy形状操作|

一、形状操作

### --- 数组变形

import numpy as np
arr1 = np.random.randint(0,10,size = (3,4,5))
arr2 = arr1.reshape(12,5)                                   # 形状改变，返回新数组
arr3 = arr1.reshape(-1,5)                                   # ⾃动“整形”，⾃动计算

### --- 数组转置

import numpy as np
arr1 = np.random.randint(0,10,size = (3,5)) # shape(3,5)
arr1.T                                                      # shape(5,3) 转置
arr2 = np.random.randint(0,10,size = (3,6,4)) # shape(3,6,4)
np.transpose(arr2,axes=(2,0,1))                             # transpose改变数组维度 shape(4,3,6)

### --- 数组堆叠

import numpy as np
arr1 = np.array([[1,2,3]])
arr2 = np.array([[4,5,6]])
np.concatenate([arr1,arr2],axis = 0)
# 串联合并shape(2,3) axis = 0表示第⼀维串联 输出为
# array([[1, 2, 3],
#       [4, 5, 6]])
np.concatenate([arr1,arr2],axis = 1)
# shape(1,6) axis = 1表示第⼆维串联 输出为：array([[1, 2, 3, 4, 5, 6]])

np.hstack((arr1,arr2))                                      # ⽔平⽅向堆叠 输出为：array([[1, 2, 3, 4, 5, 6]])
np.vstack((arr1,arr2))
# 竖直⽅向堆叠，输出为：
# array([[1, 2, 3],
#       [4, 5, 6]])

### --- split数组拆分

import numpy as np
arr = np.random.randint(0,10,size = (6,5))                  # shape(6,5)
np.split(arr,indices_or_sections=2,axis = 0)                # 在第⼀维（6）平均分成两份
np.split(arr,indices_or_sections=[2,3],axis = 1)            # 在第⼆维（5）以索引2，3为断点分割成3份
np.vsplit(arr,indices_or_sections=3)                        # 在竖直⽅向平均分割成3份
np.hsplit(arr,indices_or_sections=[1,4])                    # 在⽔平⽅向，以索引1，4为断点分割成3份

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Walter Savage Landor:strove with none,for none was worth my strife.Nature I loved and, next to Nature, Art:I warm'd both hands before the fire of life.It sinks, and I am ready to depart

                                                                                                                                                   ——W.S.Landor

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