# import NumPy module
1 ImportNumPy as NPTenA = Np.random.random ((2,4)) One a AOut[5]: -Array ([[[0.20974732, 0.73822026, 0.82760722, 0.050551 ], -[0.77337155, 0.06521922, 0.55524187, 0.59209907]])
# Calculate the And, Min, max values of all data in the matrix A Np.sum (a) atOUT[7]: 3.812057513268513 - Np.min (a) -OUT[8]: 0.05055099733013646 - Np.max (a) -OUT[9]: 0.8276072194278252 - Print("a=", a) ina= [[0.20974732 0.73822026 0.82760722 0.050551 ] -[0.77337155 0.06521922 0.55524187 0.59209907]]
# axis=0 for columns, Axis=1 for rows
to Print("min", Np.min (a)) +Min 0.05055099733013646
#求每列当中的最小值 - Print("lmin:", Np.min (a,axis=0)) theLmin: [0.20974732 0.06521922 0.55524187 0.050551 ] * Print("lmin:", Np.min (A,axis=1)) $Lmin: [0.050551 0.06521922]Panax Notoginseng Print("sum:", Np.sum (A,axis=1)) -Sum: [1.8261258 1.98593171]
# reshape data, 3 rows, 4 columns theA = Np.arange (2,14). Reshape (3,4) + A AOut[16]: theArray ([[[2, 3, 4, 5], +[6, 7, 8, 9], -[10, 11, 12, 13]])
# Ndarray minimum, maximum number of values $ Print(Np.argmin (A)) $ 0 - Print(Np.argmax (A)) -11 the Print(Np.mean (A)) -7.5Wuyi Print(Np.average (A)) the7.5 - Print(A.mean ()) Wu7.5
# cumsum Iteration Add the A -Out[24]: inArray ([[[2, 3, 4, 5], the[6, 7, 8, 9], the[10, 11, 12, 13]])Bayi Print(A.cumsum ()) the[2 5 9 14 20 27 35 44 54 65 77 90] the A -Out[27]: -Array ([[[2, 3, 4, 5], the[6, 7, 8, 9], the[10, 11, 12, 13]])
# Clip (A, a_min, A_max) will determine the data in the Ndarray, the value of less than A_min is assigned to A_min, is greater than the A_max of the assignment A_max, the value between this is the same. the Print(Np.clip (a,5,8)) the[5 5 5 5] -[6 7 8 8] the[8 8 8 8]]
# Determine Ndarray order, vector of several dimensions
About A.ndim -OUT[30]: 2101 A102Out[31]: 103Array ([[[2, 3, 4, 5],104[6, 7, 8, 9], the[10, 11, 12, 13]])106 A.ndim107OUT[32]: 2108 a109Out[33]: theArray ([[[0.20974732, 0.73822026, 0.82760722, 0.050551 ],111[0.77337155, 0.06521922, 0.55524187, 0.59209907]]) the A.ndim113OUT[34]: 2 the A theOut[35]: theArray ([[[2, 3, 4, 5],117[6, 7, 8, 9],118[10, 11, 12, 13]])
Python data analysis Numpy (numerical python Basic)