（一）檢視numpy版本

# 檢視numpy版本
import numpy as np
print np.version.version
# 1.11.3

（二）numpy多維陣列為numpy.ndarray

# numpy多維陣列為numpy.ndarray
import numpy as np
# 引數可以為list或tuple
print np.array([1,2,3,4])
# [1 2 3 4]
print np.array([[1,2,3],[4,5,6]])
# [[1 2 3]
#  [4 5 6]]
print np.array((1.5,2,3,4))
# [ 1.5  2.   3.   4. ]
 

print np.array(((1,2,3),(4,5,6)))
# [[1 2 3]
#  [4 5 6]]
print type(np.array([1,2,3,4]))
# <type 'numpy.ndarray'>

(三)numpy資料型別設定dtype和轉換astype

# numpy資料型別設定dtype和轉換astype
import numpy as np
array_str = np.array(['1.2','1.3','1.456'],dtype=np.string_)
print array_str
# ['1.2' '1.3' '1.456']
 

# 從string型別轉換為float型別
print array_str.astype(float)
# [ 1.2    1.3    1.456]

（四）numpy深拷貝和淺拷貝問題

# 關於numpy引用問題,因為numpy本身是拿來處理大資料的，所以x,y同時指向同一塊記憶體空間
import numpy as np
x = np.array([1,2])
y = x
y[1] = 3
print x
# [1 3]

# 如果想獨立開來
import numpy as np
x = np.array([1,2])
y = x.copy()
y[1] = 3
print x
# [1 2]
 

（五）numpy多維陣列轉置，索引，切片

# numpy取唯一元素
# np.unique(x)
# numpy轉置.T
import numpy as np
x = np.array([1,2]).T
# numpy切片
import numpy as np
arr = np.arange(10)
print arr
# [0 1 2 3 4 5 6 7 8 9]
arr[5:8] = 12
print arr
# [ 0  1  2  3  4 12 12 12  8  9]
arr2 = arr[1:6]
print arr2
# [ 1  2  3  4 12]
arr = np.arange(1,10).reshape(3,3)
print arr
# [[1 2 3]
#  [4 5 6]
#  [7 8 9]]
print arr[1,2]
# 6
arr = arr.reshape(9,1)
print arr
# [[1]
#  [2]
#  [3]
#  [4]
#  [5]
#  [6]
#  [7]
#  [8]
#  [9]]
print arr[5,0]
# 6
arr = arr.reshape(3,3)
# 對陣列內的各個元素進行比較
print arr == 3
# [[False False  True]
#  [False False False]
#  [False False False]]
# 如果陣列中的值小於6則都為0
arr[arr<6] = 0
print arr
# [[0 0 0]
#  [0 0 6]
#  [7 8 9]]

（六）numpy陣列檔案輸出和輸入,儲存格式為.npy

import numpy as np
arr = np.arange(10)
np.save('D:\\machinetest\\array',arr)
# 從檔案載入陣列
arr = np.load('D:\\machinetest\\array.npy')
print arr
# [0 1 2 3 4 5 6 7 8 9]
# 從txt讀取資料loadtxt,savetxt為寫入txt,預設讀取資料為float型，需要指定資料型別dtype
# 例如data1.txt:
# aa bb cc
# dd ee rr
# 例如data2.txt
# aa,bb,cc
# dd,ee,rr
arr1 = np.loadtxt('D:\\machinetest\\data1.txt',delimiter=' ',dtype=np.string_)
print arr1
arr2 = np.loadtxt('D:\\machinetest\\data2.txt',delimiter=',',dtype=np.string_)
print arr2
# [['aa' 'bb' 'cc']
#  ['dd' 'ee' 'rr']]

(七)使用numpy初始化資料

# 使用numpy初始化資料
# numpy.arange
import numpy as np
arr = np.arange(10)
print arr
# [0 1 2 3 4 5 6 7 8 9]
# 從1-5,平均生成9個值
print np.linspace(1,5,9)
# [ 1.   1.5  2.   2.5  3.   3.5  4.   4.5  5. ]
# 初始化0
np.zeros((3,4))
# [[ 0.  0.  0.  0.]
#  [ 0.  0.  0.  0.]
#  [ 0.  0.  0.  0.]]
# 初始化1
np.ones((3,4))
# [[ 1.  1.  1.  1.]
#  [ 1.  1.  1.  1.]
#  [ 1.  1.  1.  1.]]
# 生成對角矩陣
print np.eye(3)
# [[ 1.  0.  0.]
#  [ 0.  1.  0.]
#  [ 0.  0.  1.]]
# 生成網格矩陣
arr1 = np.arange(1,5,1)
# [1 2 3 4]
arr2 = np.arange(5,10,1)
# [5 6 7 8 9]
arr3,arr4 = np.meshgrid(arr1,arr2)
print arr3
# [[1 2 3 4]
#  [1 2 3 4]
#  [1 2 3 4]
#  [1 2 3 4]
#  [1 2 3 4]]
print arr4
# [[5 5 5 5]
#  [6 6 6 6]
#  [7 7 7 7]
#  [8 8 8 8]
#  [9 9 9 9]]
# 用於畫圖，填充座標系,可使用以下方法
# from matplotlib.colors import ListedColormap
# m,n = arr4.shape
# Z = np.eye(m)
# print Z
# cmap_light = ListedColormap(['#FFAAAA', '#AAFFAA'])
# plt.pcolormesh(arr3,arr4,Z,cmap=cmap_light)

(八)獲取陣列的屬性

# 獲取陣列的屬性
import numpy as np
arr = np.zeros((2,3,4))
print arr
# [[[ 0.  0.  0.  0.]
#   [ 0.  0.  0.  0.]
#   [ 0.  0.  0.  0.]]
# 
#  [[ 0.  0.  0.  0.]
#   [ 0.  0.  0.  0.]
#   [ 0.  0.  0.  0.]]]
# 陣列的維度
print arr.ndim
# 3
# 陣列每一維的大小
print arr.shape
# (2, 3, 4)
# 陣列的元素數
print arr.size
# 24
# 數組裡面元素的型別
print arr.dtype
# float64
# 每個元素所佔的位元組數
print arr.itemsize
# 8

(九)numpy矩陣的計算

# numpy矩陣的計算
import numpy as np
# 全累加
sum = np.sum([[0,1,2],[2,1,3]])
print sum
# 9
# 往x軸壓縮
sum = np.sum([[0,1,2],[2,1,3]],axis=0)
print sum
# [2 2 5]
# 往y軸壓縮
sum = np.sum([[0,1,2],[2,1,3]],axis=1)
print sum
# [3 6]
# 往某個平面進行壓縮
arr = np.arange(27).reshape(3,3,3)
print arr
# [[[ 0  1  2]
#   [ 3  4  5]
#   [ 6  7  8]]
# 
#  [[ 9 10 11]
#   [12 13 14]
#   [15 16 17]]
# 
#  [[18 19 20]
#   [21 22 23]
#   [24 25 26]]]
# 往xy平面進行壓縮
print np.sum(arr,axis=0)
# [[27 30 33]
#  [36 39 42]
#  [45 48 51]]
# 往xz平面進行壓縮
print np.sum(arr,axis=1)
# [[ 9 12 15]
#  [36 39 42]
#  [63 66 69]]
# 往yz平面進行壓縮
print np.sum(arr,axis=2)
# [[ 9 12 15]
#  [36 39 42]
#  [63 66 69]]
# 矩陣的轉置arr.t==arr.transpose()
arr = np.array([[1,0],[2,3]])
print arr
print arr.transpose()
print arr.T

#求均值
import numpy as np
a = np.array([[1, 2], [3, 4]])
print(np.mean(a))
# 2.5 全部相加後求平均
print(np.mean(a, axis=0))
# [ 2.  3.] 按維度來相加後求平均
print(np.mean(a, axis=1))
# [ 1.5  3.5]

（十）numpy陣列的合併

# numpy陣列的合併,進行的是深拷貝，即arr1和arr2合併生成的arr3，arr3和arr1,arr2互不影響
import numpy as np
arr1 = np.ones((2,2))
arr2 = np.eye(2)
# 垂直新增
print np.vstack((arr1,arr2))
# [[ 1.  1.]
#  [ 1.  1.]
#  [ 1.  0.]
#  [ 0.  1.]]
# 水平新增
print np.hstack((arr1,arr2))
# [[ 1.  1.  1.  0.]
#  [ 1.  1.  0.  1.]]
print np.c_[np.array([1,2,3]), np.array([4,5,6])]
# [[1 4]
#  [2 5]
#  [3 6]]
print np.c_[np.array([[1,2,3]]), 0, 0, np.array([[4,5,6]])]
# [[1 2 3 0 0 4 5 6]]
# 以上都需要陣列知道是2列的，如果陣列只有一列，則合併使用np.append
print np.append([[1],[2],[3]],[[4],[5],[6]])
# [1 2 3 4 5 6]