"Python data Analytics Combat" 3 NumPy Library

• Initializing an array

arr = Np.array ([[1,2],[3,4]])

arr = Np.array ([[1,2],[3,4]], Dtype=complex)

arr = Np.zeros ((3,4))

arr = Np.ones (())

arr = Np.arange (4,10). Reshape (2,3)

arr = Np.linspace (0,1,6) #[0.0,0.2,0.4,0.6,0.8,1.0]

arr = Np.random.random (3)

arr = Np.random.random ((5,2))

• Array built-in functions

Type (arr): Numpy.ndarray

Arr.dtype:int32, float64 ...

Arr.ndim:2

Arr.size:4

Arr.shape: (2l,2l)

Arr.itemsize:4

• Array calculation function

Arr+1: Elements and

Arr*2: Elemental Product

ARR1+ARR2: Elements and

ARR1*ARR2: Elemental Product

Np.dot (arr1, ARR2): Matrix product

Sin (arr): element trigonometric Functions

sqrt (arr): Elemental root

Arr.sum (): All elements and

Arr.min (): Minimum value for all elements

Arr.max (): all element Maximum value

Arr.mean (): Average of all elements

• Array iteration function

Np.apply_along_axis (Np.mean, Axis=0, Arr=arr): Iteration by column

Np.apply_along_axis (Lambda x:x*2, Axis=1, Arr=arr): Iteration by row

• Boolean array:

arr<0.5

• Stitching arrays:

Np.vstack ((arr1, ARR2)): Vertically into the stack

Np.hstack ((arr1, ARR2)): Horizontal into the stack

• Cut fraction Group:

Np.vsplit (arr, n): Vertically split N part

Np.hsplit (arr, n): Horizontal split N part

Np.split (arr, [1,3,5], Axis=1): Vertical split

• Array Save/Read

Np.save (' Data.npy ', arr)

arr = np.load (' data.npy ')

"Python data Analytics Combat" 3 NumPy Library