Python Data analysis

ImportNumPy as Nplist= [[1,3,5,7],[2,4,6,8]]np_list= Np.array (list)#convert l list data to an array typePrint(np_list)" "[ [1 3 5 7] [2 4 6 8]" "Np_list1= Np.array (list,dtype=np.float)#The type of the array is defined by Dtype, and the default auto-recognitionPrint(NP_LIST1)#bool,int,int8,int32,int16,int64,int128,uint8 and so on, float. float16/32/, etc." "[1.3. 5.7.]  [2.4. 6.8.]" "#properties of the dataPrint(Np_list.shape)#(2, 4) Two rows four columnsPrint(Np_list.ndim)#2 Dimensions of the dataPrint(Np_list.dtype)#int32 types of arraysPrint(np_list.itemsize)#4 size of each dataPrint(np_list.size)#8 Number of data#some arrays some arraysPrint(Np.ones (3,5)))" "[1.1.  1.1.  1.] [1.  1.1. 1.1.]  [1.1.  1.1. 1.]" "                 Print(Np.zeros (2,4)))" "[0.0. 0.0.]  [0.0. 0.0.]" " #Random number#RandPrint(Np.random.rand (2,4))#generate a two-line, four-class random number" "[ [0.72024033 0.93403506 0.73121086 0.84075394] [0.98034306 0.6471637 0.77923702 0.44984363]" "Print(Np.random.rand ())#0.702153504735015 a random number#RandintPrint(Np.random.randint (1,10))#You have to fill in the number rangePrint(Np.random.randint (1,10,3))#The first two digits are the range, followed by a random number of outputs#RANDN Standard dynamically generated random numbersPrint(Np.random.randn ())#0.2021606168747088Print(Np.random.randn (2,4))#normal random number of two rows of four columns" "[ [ -0.43522053 0.288716 1.5751424-0.89094638] [ -1.12602864 1.27198812-0.4784293 1.90768013]" "#choice randomly generates a random number within an arrayPrint(Np.random.choice ([10,20,30]))#randomly generate a random numberPrint(Np.random.choice ([10,20,30],2))#randomly generate a random number#distribution of mathematical functionsPrint(Np.random.beta (1,10,100))#distribution of beta functions

Python Data analysis