What are the basic data types of numpy?

The basic data types of numpy are bool, int, uint, float and complex. Detailed introduction: 1. bool, used to represent logical values, the value is True or False; 2. int, used to represent integer values, which can be signed or unsigned integers; 3. uint, used to represent unsigned integer values; 4. float, used to represent floating point values; 5. complex, used to represent complex values.

The operating system for this tutorial: Windows 10 system, Python version 3.11.4, DELL G3 computer.

NumPy is an important library for scientific computing in Python. It provides efficient multi-dimensional array objects (ndarray) and a large number of functions for operating on these array objects. In NumPy In , there are many basic data types that are used to define and manipulate elements in arrays. The following are some basic data types of NumPy:

1. bool (Boolean): used to represent logical values, the value is True or False.

2. int (integer type): used to represent integer values, which can be signed or unsigned integers, which can be int8, int16, int32, int64, etc.

3. uint (unsigned integer type): used to represent unsigned integer values, which can be uint8, uint16, uint32, uint64, etc.

4. float (floating point number type): used to represent floating point values, which can be float16, float32, float64, etc.

5. Complex (plural type): used to represent complex values, which can be complex64, complex128, etc.

These basic data types are the data types of elements in NumPy arrays. Through these data types, users can define and create arrays containing elements of different types.

In NumPy , each data type has a corresponding identifier and memory footprint. For example, the bool type occupies 1 byte, int32 occupies 4 bytes, float64 occupies 8 bytes, etc. These data types are not only used to define the type of elements in the array, but also specify a specific data type for the array through the dtype parameter. When creating an array, you can specify the type of elements in the array by specifying the data type, or you can check the data type used by the array through the dtype attribute.

In addition to these basic data types, NumPy also provides composite data types, which can customize the data structure of the array. It also provides flexible data type conversion and processing functions, which makes NumPy Ideal for handling various complex data types and functional requirements in scientific computing and data analysis.

In short, NumPy provides a rich set of basic data types that can meet various types of data processing and operation needs in scientific computing. By mastering these basic data types, users can efficiently utilize NumPy Manipulate array data and perform various complex scientific calculations and data analysis tasks. For proficiency in NumPy The use and principles of basic data types are very important for developers engaged in scientific computing, data analysis, machine learning and other fields.

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