NumPy Common Properties, creating arrays

1. Properties of several common numpy

• Ndim: Dimensions
• Shape: Number of rows and columns
• Size: Number of elements

1>>>ImportNumPy as NP#Import the NumPy module, NP is to use the convenient shorthand2>>> array = Np.array ([[1,2,3],[2,3,4]])#list into a matrix3>>>Print(Array)4[[1 2 3]5[2 3 4]]6>>>7>>>Print('Number of Dim:', Array.ndim)#Dimension of8Number of Dim:29>>>Print('Shape:', Array.shape)#Number of rows and columnsTenShape: (2, 3) One>>>Print('Size:', array.size)#Number of elements ASize:6

2. NumPy Create array

2.1 Key Words

• Array: Creating arrays
• Dtype: Developing data types
• Zeros: Create data is all 0
• Ones: Create data is all 1
• Empty: Create data close to 0
• Arrange: Create data by specified range
• Linspace: Creating a segment 2.2 creating an array

1 #Create an array2>>> a = Np.array ([2,23,4])#List 1d3>>>Print(a)4[2 23 4]5 6 #Specify Type7>>> a = Np.array ([2,23,4],dtype=np.int)8>>>Print(A.dtype)9 Int32Ten  One>>> a = Np.array ([2,23,4],dtype=Np.int32) A>>>Print(A.dtype) - Int32 -  the>>> a = Np.array ([2,23,4],dtype=np.float) ->>>Print(A.dtype) - float64 -  +>>> a = Np.array ([2,23,4],dtype=Np.float32) ->>>Print(A.dtype) + float32 A  at #Create specific data ->>> a = Np.array ([[2,23,4],[2,32,4]])#2d Matrix 2 rows 3 columns ->>>Print(a) -[[2 23 4] -[2 32 4]] -  in #Create a full zero group ->>> a = Np.zeros ((3,4))#data is all 0, 3 rows 4 columns to>>>Print(a) + [0.0. 0.0.] - [0.0. 0.0.] the [0.0. 0.0.] *  $ #Create a full 1 arrayPanax Notoginseng>>> a = Np.ones ((3,4), Dtype = Np.int)#data is 1, 3 Rows 4 columns ->>>Print(a) the[1 1 1 1] +[1 1 1 1] A[1 1 1 1]] the  + #create an all-empty array, in fact each value is close to zero number: ->>> a = Np.empty ((3,4))#data is empty,3 row 4 column $>>>Print(a) $ [0.0. 0.0.] - [0.0. 0.0.] - [0.0. 0.0.] the  - #To create a contiguous array with Arange:Wuyi>>> a = Np.arange (10,20,2)#10-19 of data, 2 steps the>>>Print(a) -[10 12 14 16 18] Wu  - #use reshape to change the shape of the data About>>> a = Np.arange (a). Reshape ((3,4))#3 Rows, 4 columns, 0 to one $>>>Print(a) -[0 1 2 3] -[4 5 6 7] -[8 9 10 11]] A  + #To create a segment type data with Linspace: the>>> a = Np.linspace (1,10,20)#start end 1, end 10, and split into 20 data to generate a segment ->>>Print(a) $[1.1.47368421 1.94736842 2.42105263 2.89473684 3.36842105 the3.84210526 4.31578947 4.78947368 5.26315789 5.73684211 6.21052632 the6.68421053 7.15789474 7.63157895 8.10526316 8.57894737 9.05263158 the9.52631579 10. ] the  -  in #the same can be done reshape work: the>>> a = Np.linspace (1,10,20). Reshape ((5,4))#Change Shape the>>>Print(a) About[1.1.47368421 1.94736842 2.42105263] the[2.89473684 3.36842105 3.84210526 4.31578947] the[4.78947368 5.26315789 5.73684211 6.21052632] the[6.68421053 7.15789474 7.63157895 8.10526316] +[8.57894737 9.05263158 9.52631579 10. ]]

NumPy Common Properties, creating arrays