import numpy as np
data1 = np.arange(9)
.reshape((3
,3))
print
('\ndata1:\n'
,data1)
data1:[[
012]
[345
][67
8]]
data3 = data1 +
1print
(data3)[[
123]
[456
][78
9]]data4 = data1 -
1print
(data4)[[
-101
][23
4][5
67]]
data5 = data1 *
2print
(data5)[[
024]
[6810
][121416]
]data6 = data1 /
2print
(data6)[[
0.0.51.]
[1.52.
2.5][3
.3.54.
]]
import numpy as np
data1 = np.arange(9)
.reshape((3
,3))
data2 = np.array([[
1,2,
3],[
1,2,
3],[
1,2,
3]])
print
('\ndata1:\n'
,data1)
print
('\ndata2:\n'
,data2)
data1:[[
012]
[345
][67
8]]data2:[[
123]
[123
][12
3]]
data3 = data1 + data2
data4 = data1 * data2 #對應座標相乘
data5 = data1 / data2
print(data3)
[[ 1 3 5]
[ 4 6 8]
[ 7 9 11]]
print(data4)
[[ 0 2 6]
[ 3 8 15]
[ 6 14 24]]
print(data5)
[[0. 0.5 0.66666667]
[3. 2. 1.66666667]
[6. 3.5 2.66666667]]
np.mat()
data3 = np.mat(data2)
print
(data3)
matrix([[
1,2,
3],[
1,2,
3],[
1,2,
3]])
np.matmul()
np.dot()
import numpy as np
data1 = np.arange(9)
.reshape((3
,3))
data2 = np.array([[
1,2,
3],[
1,2,
3],[
1,2,
3]])
print
('\ndata1:\n'
,data1)
print
('\ndata2:\n'
,data2)
data1:[[
012]
[345
][67
8]]data2:[[
123]
[123
][12
3]]
data3 = np.matmul(data1,data2)
data4 = np.dot(data1,data2)
print
(data3)
array([[
3,6,
9],[
12,24,
36],[
21,42,
63]])
print
(data4)
array([[
3,6,
9],[
12,24,
36],[
21,42,
63]])
說明求和
t.sum(axis = none)
均值t.mean(a,axis = none )
中值np.median(t,axis = none)
最大值t.max(axis = none)
最小值t.min(axis = none)
極值np.ptp(t,axis = none)
標準差t.std(axis = none)
方差t.var(axis = none)
import numpy as np
data1 = np.arange(9)
.reshape((3
,3))
print
(data1)[[
012]
[345
][67
8]]
print
(np.
sum(data1))36
print
(np.
sum(data1,axis =0)
)#縱向求和 data1.sum(axis=0)[9
1215
]print
(np.
sum(data1,axis =1)
)#橫向求和[3
1221
]
print
(np.mean(data1)
)4.0
print
(np.mean(data1,axis =0)
)#縱向求均值[3
.4.5
.]print
(np.mean(data1,axis =1)
)#橫向求均值[1
.4.7
.]
print
(np.
max(data1))8
print
(data1.
max(axis=0)
)[67
8]print
(data1.
max(axis=1)
)[25
8]
print
(np.std(data2)
)0.816496580927726
print
(data2.std(axis=1)
)[0.81649658
0.81649658
0.81649658
]print
(data2.std(axis=0)
)[0.
0.0.
]
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