Python data Analysis NumPy (ii)

Numpy (numerical Python)

• Foundation package for high performance scientific computing and data analysis;
• Ndarray, multi-dimensional Array (matrix), with vector computing ability, fast, save space;
• Matrix operations, without loops, can be done similar to MATLAB in the vector operation;
• Linear algebra, random send generation;

Ndarray, n-dimensional array objects (matrices)

• All elements must be of the same type
• Ndim attribute, number of dimensions
• Shape attribute, size of each dimension
• Dtype property, data type

code example:

Import numpy# generate random multidimensional data for the specified dimension (two rows and three columns) data = Numpy.random.rand (2, 3) Print Dataprint type (data)

• Execution Result:

[[0.49458614  0.14245674  0.26883084] [0.87402248  0.71089966  0.29023523]]<type ' Numpy.ndarray ' >

print ' Number of dimensions ', Data.ndimprint ' Dimensions: ', Data.shapeprint ' data type: ', Data.dtype

• Execution Result:

Number of dimensions 2 dimension size:  (2L, 3L) data type:  float64

1. Create Ndarray

Nd.array (collection), collection is a Sequence object (list), nested sequence (list of lists)

# The list is converted to Ndarray (one-dimensional array) L = Range (Ten) data = Np.array (l) Print Dataprint Data.shapeprint Data.ndim

• Execution results

[0 1 2 3 4 5 6 7 8 9] (10L, 1)

# The nested sequence is converted to NDARRAYL2 = [Range], range (Ten)]data = Np.array (L2) Print Dataprint Data.shape

• Execution results

[[0 1 2 3 4 5 6 7 8 9] [0 1 2 3 4 5 6 7 8 9]] (2L, 10L)

Np.zeros,np.ones,np.empty full 0 or all 1 arrays of the specified size

• The first parameter is Ganso, which is used to specify the size, such as (3,4)
• Empty does not always return a total of 0, something returned is an uninitialized random value

Python data Analysis NumPy (ii)