Basic Python scientific computing package-Numpy, python-numpy

Basic Python scientific computing package-Numpy, python-numpy
I. Numpy Concept

Numpy (short for Numerical Python) is the basic package for Python scientific computing. It provides the following functions:

In addition to providing fast array processing capabilities for Python, Numpy also plays a major role in data analysis, that is, it serves as a container for transferring data between algorithms. For numeric data, the Numpy array is much more efficient than the built-in Python Data Structure in data storage and processing. In addition, libraries written in low-level languages (such as C and Fortran) can directly operate the data in the Numpy array without any data replication.

Ii. Highlights of Numpy

Compared with the basic data types of Python, it has the following outstanding advantages:

NumPy provides two basic objects: ndarray (N-dimen1_array object) and ufunc (universal function object ). Ndarray is used to store multi-dimensional arrays of a single data type. ufunc is a function for processing arrays.

 

Iii. ndarray object

The core of Numpy is the ndarray object, which encapsulates an n-dimensional array of homogeneous data types. It differs from the python sequence in the following ways:

Ndarray has a fixed size when being created: different from the list in python, changing the ndarray size will create a new array and delete the elements in the original data ndarray with the same data type ndarray for advanced mathematical operations on a large amount of data: generally, it is more efficient and simpler than python built-in sequences. More and more python-based scientific and mathematical software use ndarray: it is not enough to know python's built-in sequence types, you also need to know how to use the ndaray Array

Ndarray Data Type

Numpy supports more numeric types than Python. For more information, see data types.

Numpy Data Type	Python type	Description
Bool _	Bool	Boolean (True or False), stored as a byte
Int _	Int	Default Integer type (same as C long; usually int64 or int32)
Intc		Same as C int (usually int32 or int64)
Intp		Integer used for index (same as C ssize_t; usually int32 or int64)
Int8		Bytes (-128 to 127)
Int16		INTEGER (-32768 to 32767)
Int32		INTEGER (-2147483648 to 2147483647)
Int64		INTEGER (-9223372036854775808 to 9223372036854775807)
Uint8		Unsigned INTEGER (0 to 255)
Uint16		Unsigned INTEGER (0 to 65535)
Uint32		Unsigned INTEGER (0 to 4294967295)
Uint64		Unsigned INTEGER (0 to 18446744073709551615)
Float _	Float	Float64.
Float16		Semi-precision floating point: Symbol bit, 5-digit index, 10-digit ending number
Float32		Single-precision floating point: Symbol bit, 8-digit index, 23-digit ending number
Float64		Double-precision floating point: Symbol bit, 11-bit index, 52-bit ending number
Complex _	Complex	The abbreviation of complex128.
Complex64		The plural value, composed of two 32-bit floating points (real number and imaginary number)
Complex128		Plural, composed of two 64-bit floating points (real number and imaginary number)

 

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# Set the element type in the array as the type name. For backward compatibility, you can also usefloatOr string'float'x = np.array([1,2,3],dtype=np.float)print x # Viewing data typesprint x.dtype # Conversion function as a single value typeprint np.int32(1.3) # Convert the array type to generate a new copyprint x.astype(np.int)

Result:

 

1234	[ 1.  2.  3.]float641[1 2 3]

 

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