Python is frequently used for scientific computing and data analysis because of its strong libraries. The NumPy is one such library that sticks out. With its practical arrays and mathematical functions designed specifically for numerical operations, NumPy expands the scope of data processing.

In this blog, "Convert Python Data Structures to Numpy Array," we will discuss converting Python data structures to numpy array using np.array() in Python.

Numpy Array in Python

The Python NumPy library offers a fundamental data structure called a NumPy array, often called a "Numerical Python" array. It is a potent and effective method for handling and storing massive numerical data arrays. It supports multi-dimensional arrays and offers a range of mathematical operations that can be performed on these arrays.

Several advantages of Numpy array in Python are:-

NumPy arrays are implemented in C, making them faster and more memory-efficient for numerical computations than Python lists.

NumPy arrays can have several dimensions, making working with matrices, pictures, and other multi-dimensional data simple.

NumPy enables vectorized operations, allowing you to act on entire arrays without explicitly defining loops.

NumPy offers many mathematical operations optimized for arrays, including mean, sum, standard deviation, etc.

NumPy arrays enable broadcasting, which makes it easier to operate on arrays of various shapes.

np.array() in Numpy

The np.array() method in NumPy is used to create a new array. This method is the fundamental way to create NumPy arrays and is often one of the first functions you'll use when working with NumPy.

Syntax

The syntax of np.array() method in Numpy is:-

numpy.array(object, dtype=None,**kwargs)

You can also try this code with Online Python Compiler

object: Anything that complies with the array interface. It returns a 0-dimensional array with the item within if the object is a scalar.

type: The chosen data type for the array.

**kwags: Other keyword argument.

Conversion of Python Data Structure to Numpy Array

In this section of "Convert Python Data Structures to Numpy Array," we will see the conversion of list, dictionary, nested list, and pandas series to Numpy array.

List to Numpy array

In this example of "Convert Python Data Structures to Numpy Array," we will convert a Python list to a numpy array.

Python

Python

import numpy as np

cn_list = [2,4,6,8,10]

new_array = np.array(cn_list)

print(new_array)

You can also try this code with Online Python Compiler

In the above example, we have converted the Python dictionary values to a numpy array by converting the first dictionary to a list and then using the function np.array() in Numpy.

Another example is to convert Python dictionary keys to Numpy arrays using np.array() in Python.

Python

Python

import numpy as np

my_dict = {'Pen': 1, 'Book': 2, 'Bag': 3}

new_array = np.array(list(my_dict.keys()))

print(new_array)

You can also try this code with Online Python Compiler

In the above example, we have converted the Python dictionary keys to a numpy array by converting the first dictionary to a list and then using the function np.array() in Numpy.

Pandas Series to NumPy Array

In this example of “Convert Python Data Structures to Numpy Array,” we will convert Python Pandas Series to Numpy array.

Python

Python

import numpy as np

import pandas as pd

cn_series = pd.Series([1, 2, 3, 4, 5])

new_array = np.array(cn_series)

print(new_array)

You can also try this code with Online Python Compiler

In the above example, we have converted the Python pandas series to a numpy array using the function np.array() in Numpy.

Frequently Asked Questions

What happens when multiple data types are included in the Python data structure?

NumPy arrays are homogeneous, so if the data structure contains mixed data types, NumPy will force them to be of a single data type. Using the type argument, you may also directly specify the data type.

Can a Pandas Series be converted to a NumPy array?

Yes, you can use the np.array() function to translate a Pandas Series into a NumPy array because Pandas is built on top of NumPy, so the conversions between the two are simple.

Does the performance of Python lists and NumPy arrays differ?

Yes, due to their optimized memory layout and built-in functions, NumPy arrays are often more performant for numerical computations. When dealing with enormous datasets, they are especially helpful.

Does converting Python data structures to NumPy arrays have any restrictions?

Despite being adaptable, NumPy arrays have constraints when working with very irregular or complex data structures, so the elements in NumPy arrays must all be the same data type.

Conclusion

Numpy is one of the most important and powerful libraries of Python. Users convert Python data structures to Numpy arrays to perform data analysis and manipulation using efficient Numpy arrays and mathematical functions.

In the article "Convert Python data structures to Numpy array," we have discussed the np.array() to convert the Python data structure to Numpy arrays. Here are more articles that are recommended to read: