NumPy Data Types tutorial in Hindi | Part 84
NumPy Data Types tutorial in Hindi @arvindprogramming
NumPy (Numerical Python) provides a set of numerical and complex data types that can be used to represent arrays and matrices. These data types are designed to efficiently store and manipulate numeric data in scientific and numerical computing applications.
Some common NumPy data types include:
1. int8, int16, int32, int64: Signed integer types with different bit sizes (e.g., int8 represents an 8-bit signed integer).
2. uint8, uint16, uint32, uint64: Unsigned integer types with different bit sizes (e.g., uint16 represents a 16-bit unsigned integer).
3. float16, float32, float64: Floating-point types with different precision (e.g., float32 represents a 32-bit floating-point number).
4. complex64, complex128: Complex number types with different precision.
5. bool: Boolean type representing True or False values.
6. object: Generic Python object type, allowing for arrays of different data types.
7. string_: String data type (fixed-length).
8. unicode_: Unicode string data type (fixed-length).
These data types can be used to specify the type of elements in a NumPy array, allowing for efficient memory allocation and optimized mathematical operations. NumPy also provides functions to convert between different data types and to inspect the properties of a given data type.
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