## NumPy Data Types - Errors related to numpy array data types

Specifically for numerical and array-based computations, the Python module NumPy is utilised in scientific computing. It offers an effective N-dimensional array object for storing multidimensional and homogenous (of the same kind) data.

Numerous data types are supported by NumPy, which can be broadly divided into two groups: numeric and non-numeric.

Numeric Data Types:

1. bool_ : Boolean (True or False) stored as a byte
2. int_ : Default integer type (same as C long; typically 32 bits)
3. intc : Identical to C int (typically 32 bits)
4. intp : Integer used for indexing (same as C ssize_t; typically the same as intc)
5. int8 : Byte (-128 to 127)
6. int16 : Integer (-32768 to 32767)
7. int32 : Integer (-2147483648 to 2147483647)
8. int64 : Integer (-9223372036854775808 to 9223372036854775807)
9. uint8 : Unsigned byte (0 to 255)
10. uint16 : Unsigned integer (0 to 65535)
11. uint32 : Unsigned integer (0 to 4294967295)
12. uint64 : Unsigned integer (0 to 18446744073709551615)
13. float_ : Shorthand for float64
14. float16 : Half precision float: sign bit, 5 bits exponent, 10 bits mantissa
15. float32 : Single precision float: sign bit, 8 bits exponent, 23 bits mantissa
16. float64 : Double precision float: sign bit, 11 bits exponent, 52 bits mantissa
17. complex_ : Shorthand for complex128
18. complex64 : Complex number, represented by two 32-bit floats
19. complex128 : Complex number, represented by two 64-bit floats

Non-Numeric Data Types:

1. bytes_ : Raw bytes of fixed size
2. unicode_ : Unicode string of fixed size
3. void : Record, which is a fixed size and a structure containing array fields of different types. It is used to define data types of structured arrays.

Note that the above data types are NumPy specific data types and are not the same as the built-in Python data types.

Python has several built-in data types that are commonly used in programming. Some of the most frequently used built-in data types in Python are:

1. Numeric types: These include integers, floating-point numbers, and complex numbers. Integers are represented using the `int` data type, floating-point numbers are represented using the `float` data type, and complex numbers are represented using the `complex` data type.

2. Boolean type: This represents a logical value and has only two possible values, `True` and `False`.

3. Sequence types: These include lists, tuples, and range objects. Lists and tuples are ordered collections of objects, but lists are mutable (can be changed), while tuples are immutable (cannot be changed). Range objects represent an immutable sequence of numbers.

4. String type: This represents a sequence of characters and is represented using the `str` data type.

5. Set types: These include sets and frozensets. Sets are unordered collections of unique elements, while frozensets are immutable sets.

6. Mapping types: These include dictionaries. Dictionaries are unordered collections of key-value pairs.

7. NoneType: This is a special type that represents the absence of a value.

It is important to note that Python is dynamically typed, meaning that variables can be assigned values of any type, and the type of a variable can change during runtime.

### How to know the types of Array?

In Python, you can check the data type of an array using the `dtype` attribute of the NumPy array.

### Constructing Arrays of a Particular Data Type

In NumPy, you can construct arrays of a particular data type using the `dtype` parameter of the array constructor functions. The `dtype` parameter allows you to specify the data type of the elements in the array.

For example, to create an array of 64-bit floating-point numbers, you can use the `dtype` parameter with the `float64` type:

For example, to create an array of 64-bit floating-point numbers, you can use the `dtype` parameter with the `float64` type:

# Errors related to numpy array data types

Errors related to NumPy array data types are common when working with arrays, especially when doing operations that involve multiple arrays. Here are some common errors related to NumPy array data types and how to fix them:

TypeErrors: These errors occur when you try to perform operations on arrays with incompatible data types. For example, if you try to add two arrays of different data types, you will get a TypeError:

To fix this error, you can convert one of the arrays to the data type of the other array using the `astype()` method.

ValueErrors: These errors occur when you try to create an array with invalid values or dimensions. For example, if you try to create an array with invalid data types, you will get a ValueError.

To fix this error, you can ensure that the input values are valid for the data type you are using, or you can use a different data type that is appropriate for the values you have.

OverflowErrors: These errors occur when you try to perform operations that result in values that are too large or too small to be represented by the data type of the array. For example, if you try to add two large numbers that exceed the maximum value of the data type, you will get an OverflowError.

To fix this error, you can use a data type with a larger range or precision, such as `np.int64` or `np.float64`.

By understanding the common errors related to NumPy array data types and how to fix them, you can write more robust and error-free code when working with arrays.

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