## Can NumPy arrays have mixed data types?

Introduction to Data Types Having a data type (dtype) is one of the key features that distinguishes NumPy arrays from lists. In lists, the types of elements can be mixed. One index of a list can contain an integer, another can contain a string.

## Can we store different data types in array in Python?

No, we cannot store multiple datatype in an Array, we can store similar datatype only in an Array.

**Can NumPy arrays be multidimensional?**

In general numpy arrays can have more than one dimension. One way to create such array is to start with a 1-dimensional array and use the numpy reshape() function that rearranges elements of that array into a new shape.

**Is NumPy array heterogeneous?**

NumPy arrays are made to be created as homogeneous arrays, considering the mathematical operations that can be performed on them. It would not be possible with heterogeneous data sets.

### Can NumPy array store heterogeneous data?

NumPy arrays are typed arrays of fixed size. Python lists are heterogeneous and thus elements of a list may contain any object type, while NumPy arrays are homogenous and can contain object of only one type.

### How do I store different data types in one array?

in single array? Yes we can store different/mixed types in a single array by using following two methods: Method 1: using Object array because all types in . net inherit from object type Ex: object[] array=new object[2];array[0]=102;array[1]=”csharp”;Method 2: Alternatively we can use ArrayList class present in System.

**What is the type of NumPy array?**

NumPy knows that int refers to np. int_ , bool means np. bool_ , that float is np….Array types and conversions between types.

Numpy type | C type | Description |
---|---|---|

numpy.intc | int | Platform-defined |

numpy.uintc | unsigned int | Platform-defined |

numpy.int_ | long | Platform-defined |

numpy.uint | unsigned long | Platform-defined |

**What are the different data types in Python?**

Basic Data Types in Python

- Integers.
- Floating-Point Numbers.
- Complex Numbers.
- Strings. Escape Sequences in Strings. Raw Strings. Triple-Quoted Strings.
- Boolean Type, Boolean Context, and “Truthiness”
- Built-In Functions. Math. Type Conversion. Iterables and Iterators. Composite Data Type.
- Conclusion.

#### What is NDIM in NumPy?

In NumPy the number of dimensions is referred to as rank. The ndim is the same as the number of axes or the length of the output of x.shape. >>> x. ndim 2.

#### Is NumPy arrays are immutable?

Numpy Arrays are mutable, which means that you can change the value of an element in the array after an array has been initialized.