- 创建ndarray
- array：将输入数据转换为ndarry
- arange：类似于range，返回一个ndarray
- ones、ones_like：根据指定形状和dtype创建一个全1数组。ones_like以另一个数组为参照，根据其形状和dtype创建一个全1数组
- zeros、zeros_like：根据指定形状和dtype创建一个全0数组。zeros_like以另一个数组为参照，根据其形状和dtype创建一个全0数组
- empty、empty_like：创建新数组，只分配内存空间但不填充任何值
- eye、identity：创建一个正方的N*N单位矩阵（对角线为1，其余为0）
import numpy as np
data1 = np.array([1,9,8,8,0,4,1,6])
print(data1)
'''
[1 9 8 8 0 4 1 6]
'''
data2 = np.array([[1,2,3],[4,5,6]])
print(data2)
'''
[[1 2 3]
[4 5 6]]
'''
data3 = np.arange(10)
print(data3)
'''
[0 1 2 3 4 5 6 7 8 9]
'''
data4 = np.ones(3)
print(data4)
'''
[1. 1. 1.]
'''
data_like = [[1,2,3],[4,5,6]]
data5 = np.ones_like(data_like)
print(data5)
'''
[[1 1 1]
[1 1 1]]
'''
data4 = np.ones(3)
print(data4)
'''
[1. 1. 1.]
'''
data6 = np.zeros(3)
print(data6)
'''
[0. 0. 0.]
'''
data_like = [[1,2,3],[4,5,6]]
data7 = np.zeros_like(data_like)
print(data7)
'''
[[0 0 0]
[0 0 0]]
'''
data8 = np.empty(3)
print(data8)
'''
[4.24399158e-314 8.48798317e-314 1.27319747e-313]
'''
data9 = np.eye(3)
print(data9)
'''
[[1. 0. 0.]
[0. 1. 0.]
[0. 0. 1.]]
'''
import numpy as np
# np.array 会尝试为新建的数组推断一个较为合适的数据类型
arr1 = np.array([1,2,3])
print(arr1.dtype)
'''
int32
'''
# 可使用dtype数据类型
arr2 = np.array([1,2,3],dtype=np.float)
print(arr2.dtype)
'''
float64
'''
arr3 = np.array([1.1,2,3])
print(arr3.dtype)
'''
float64
'''
arr4 = np.array([1.1,2,3],dtype=np.int32)
print(arr4.dtype)
'''
int32
'''
# 通过astype方法可转换dtype
# 调用astype会创建出一个新数组（原始数据的一份拷贝）
arr5 = np.array([1,2,3])
float_arr = arr5.astype(np.float64)
print(float_arr.dtype)
'''
float64
'''
# 注意：如果浮点数转换为整数，则小数部分将会被截断
arr6 = np.array([1.2,2,3])
int_arr = arr6.astype(np.int32)
print(int_arr.dtype)
'''
int32
'''
print(int_arr)
'''
[1 2 3]
'''
import numpy as np
arr = np.array([[1,2,3],[4,5,6]])
print(arr+arr)
'''
[[ 2 4 6]
[ 8 10 12]]
'''
print(arr-arr)
'''
[[0 0 0]
[0 0 0]]
'''
print(arr*arr)
'''
[[ 1 4 9]
[16 25 36]]
'''
print(arr/arr)
'''
[[1. 1. 1.]
[1. 1. 1.]]
'''
print(arr**arr)
'''
[[ 1 4 27]
[ 256 3125 46656]]
'''
print(arr+1)
'''
[[2 3 4]
[5 6 7]]
'''
import numpy as np
# 一维数组
arr = np.arange(10)
print(arr)
'''
[0 1 2 3 4 5 6 7 8 9]
'''
print(arr[5])
'''
5
'''
print(arr[5:8])
'''
[5 6 7]
'''
# 当你将一个标量赋值给一个切片时，该值会自动传播到整个选区
arr[5:8] = 12
print(arr)
'''
[ 0 1 2 3 4 12 12 12 8 9]
'''
# 数组切片是原始数据视图。这意味着数据不会被复制，任何修改直接反应到源数组上
arr_slice = arr[5:8]
arr_slice[1] = 12345
print(arr)
'''
[ 0 1 2 3 4 12 12345 12 8 9]
'''
arr_slice[:] = 64
print(arr)
'''
[ 0 1 2 3 4 64 64 64 8 9]
'''
# 二维数组
arr2d = np.array([[1,2,3],[4,5,6],[7,8,9]])
print(arr2d)
'''
[[1 2 3]
[4 5 6]
[7 8 9]]
'''
print(arr2d[2])
'''
[7 8 9]
'''
print(arr2d[0][2])
'''
3
'''
print(arr2d[0,2])
'''
3
'''
# 多维数组
arr3d = np.array([[[1,2,3],[4,5,6]],[[7,8,9],[10,11,12]]])
print(arr3d)
'''
[[[ 1 2 3]
[ 4 5 6]]
[[ 7 8 9]
[10 11 12]]]
'''
print(arr3d[0])
'''
[[1 2 3]
[4 5 6]]
'''
old_values = arr3d[0].copy()
arr3d[0] = 42
print(arr3d)
'''
[[[42 42 42]
[42 42 42]]
[[ 7 8 9]
[10 11 12]]]
'''
arr3d[0] = old_values
print(arr3d)
'''
[[[ 1 2 3]
[ 4 5 6]]
[[ 7 8 9]
[10 11 12]]]
'''
print(arr3d[1,0])
'''
[7 8 9]
'''
import numpy as np
# 一维数组
arr = np.arange(10)
print(arr[1:6])
'''
[1 2 3 4 5]
'''
# 二维数组
arr2d = np.array([[1,2,3],[4,5,6],[7,8,9]])
print(arr2d[:2])
'''
[[1 2 3]
[4 5 6]]
'''
print(arr2d[:2,1:])
'''
[[2 3]
[5 6]]
'''
# 索引与切片混合
print(arr2d[1,:2])
'''
[4 5]
'''
# 只有冒号表示选区整个轴
print(arr2d[:,:2])
'''
[[1 2]
[4 5]
[7 8]]
'''
# 对切片赋值也会扩散到整个选区
arr2d[:,:2]=0
print(arr2d)
'''
[[0 0 3]
[0 0 6]
[0 0 9]]
'''
import numpy as np
from numpy.matlib import randn
names = np.array(['Bob','Joe','Will','Bob','Will','Joe','Joe'])
# 等于
print(names=="Bob")
'''
[ True False False True False False False]
'''
# 不等于
print(names!="Bob")
'''
[False True True False True True True]
'''
# 组合多个条件使用&、|
print((names=="Bob")|(names=="Will"))
'''
[ True False True True True False False]
'''
data = randn(7,3)
print(data)
'''
[[ 0.35234481 0.68539956 0.2206396 ]
[-1.3719165 -0.42694698 1.28509104]
[-0.95479498 -0.65378008 -0.1673056 ]
[-1.79677508 0.18923784 1.67064335]
[-1.24383276 -0.50056086 -0.7917794 ]
[-0.92646918 0.47489349 -0.62463223]
[ 0.0995606 -1.20420049 -1.55692415]]
'''
mask =(names=="Bob") # 0和3为Ture，取0和3行
print(data[mask])
'''
[[ 0.35234481 0.68539956 0.2206396 ]
[-1.79677508 0.18923784 1.67064335]]
'''
#
mask =(names=="Bob")|(names=="Will") # 0、2、3、4为Ture，取这4行
print(data[mask])
'''
[[ 0.35234481 0.68539956 0.2206396 ]
[-0.95479498 -0.65378008 -0.1673056 ]
[-1.79677508 0.18923784 1.67064335]
[-1.24383276 -0.50056086 -0.7917794 ]]
'''
# 将data中负值置为0
data[data<0] = 0
print(data)
'''
[[0.35234481 0.68539956 0.2206396 ]
[0. 0. 1.28509104]
[0. 0. 0. ]
[0. 0.18923784 1.67064335]
[0. 0. 0. ]
[0. 0.47489349 0. ]
[0.0995606 0. 0. ]]
'''
# 通过布尔数组设置整条或整列的值
data[names=="Bob"] = 7
print(data)
'''
[[7. 7. 7. ]
[0. 0.5615304 0. ]
[0. 1.05144009 0.04887547]
[7. 7. 7. ]
[0.5067665 0. 0. ]
[2.0465758 0.78871507 0.68937188]
[1.58986486 0.86841601 1.46533603]]
'''
import numpy as np
# 花式索引：利用整数数组进行索引
'''
arr = np.empty((8,4))
for i in range(8):
arr[i] = i
'''
arr = np.arange(16).reshape((4,4))
print(arr)
'''
[[ 0 1 2 3]
[ 4 5 6 7]
[ 8 9 10 11]
[12 13 14 15]]
'''
# 为了特定顺序选取行子集，需传入一个用于指定顺序的整数列
print(arr[[1,0,2]])
'''
[[ 4 5 6 7]
[ 0 1 2 3]
[ 8 9 10 11]]
'''
# 传入多个索引
print(arr[[1,0,2],[0,1,2]])
'''
[ 4 1 10]
'''
# 使用负数索引将会从末尾开始选行
print(arr[[-3,-2,-1]])
'''
[[ 4 5 6 7]
[ 8 9 10 11]
[12 13 14 15]]
'''
import numpy as np
arr = np.arange(15).reshape((3,5))
print(arr)
'''
[[ 0 1 2 3 4]
[ 5 6 7 8 9]
[10 11 12 13 14]]
'''
print(arr.T)
'''
[[ 0 5 10]
[ 1 6 11]
[ 2 7 12]
[ 3 8 13]
[ 4 9 14]]
'''
# 矩阵内积
print(np.dot(arr.T,arr))
'''
[[125 140 155 170 185]
[140 158 176 194 212]
[155 176 197 218 239]
[170 194 218 242 266]
[185 212 239 266 293]]
'''
arr2 = np.arange(16).reshape((2,2,4))
print(arr2)
'''
[[[ 0 1 2 3]
[ 4 5 6 7]]
[[ 8 9 10 11]
[12 13 14 15]]]
'''
print(arr2.transpose((1,0,2))) #行列索引值对换
'''
[[[ 0 1 2 3]
[ 8 9 10 11]]
[[ 4 5 6 7]
[12 13 14 15]]]
'''
arr3 = np.arange(16).reshape((2,2,4))
print(arr3)
'''
[[[ 0 1 2 3]
[ 4 5 6 7]]
[[ 8 9 10 11]
[12 13 14 15]]]
'''
print(arr3.swapaxes(1,2))
'''
[[[ 0 4]
[ 1 5]
[ 2 6]
[ 3 7]]
[[ 8 12]
[ 9 13]
[10 14]
[11 15]]]
'''