# How to convert list to Numpy array

In this tutorial, we’ll explore three distinct approaches for converting Python lists into NumPy arrays. This conversion is a fundamental operation when working with data in the NumPy library, allowing for more efficient data manipulation and analysis.

There are 3 different ways to convert Python list to Numpy array.

## Utilizing the array Method

The most straightforward way to transform a Python list into a Numpy array is by utilizing the array method. Simply pass your list as a single parameter to create the desired Numpy array.

```import numpy as np

my_list = [1, 2, 3, 4, 5, 6]
my_array = np.array(my_list)
print(f"Numpy array converted "
f"from Python list: {my_list}")
```

This method provides a quick and efficient way to convert a list into a Numpy array, perfect for one-dimensional data.

## Leveraging the asarray Method

Another method at your disposal is the asarray method, which achieves the same result by taking your list as a single parameter.

```import numpy as np

my_list = [1, 2, 3, 4, 5, 6]
my_array = np.asarray(my_list)
print(f"Numpy array converted "
f"from Python list: {my_list}")
```

The asarray method is functionally equivalent to the array method, providing you with flexibility in your conversion approach.

## Handling Lists of Lists with the concatenate Method

When dealing with more complex tasks, such as converting a list of lists into a Numpy array, the concatenate method becomes your ally. To execute this conversion successfully, specify axis=0 as the parameter, ensuring proper concatenation.

```import numpy as np

my_list = [[1, 2, 3], [4, 5], [6, 7, 8, 9]]
my_array = np.concatenate(my_list, axis=0)
print(f"Numpy array converted "
f"from Python list of lists: {my_list}")
```

This technique proves invaluable when working with multi-dimensional data structures, offering control over the axis along which concatenation occurs.