What is NumPy in Python?- Types & Function

What is NumPy? 

NumPy, short for “Numerical Python,” is a fundamental library in Python for scientific computing. It provides support for large, multi-dimensional arrays and matrices, along with an extensive collection of high-level mathematical functions to operate on these arrays efficiently. NumPy module in Python is an open-source library and is widely used in various fields, such as Data Analysis, Machine Learning, scientific research, and more.

Key features of NumPy include:

1. N-dimensional Array (ndarray): The ndarray is the core Data Structure of NumPy, which allows you to create and manipulate arrays of any dimension. These arrays are more efficient and flexible than regular Python lists.
2. Universal Functions (ufuncs): NumPy provides a collection of fast and vectorized mathematical functions that can operate element-wise on arrays. These functions greatly improve computational efficiency compared to traditional loop-based operations.
3. Broadcasting: NumPy allows operations on arrays with different shapes and dimensions through broadcasting. Broadcasting enables element-wise operations without the need for explicit loop operations, making code concise and efficient.
4. Integration with Low-Level Languages: NumPy is designed to integrate seamlessly with low-level languages like C and Fortran, allowing you to write efficient code for numerical computations.
5. Mathematical Operations: NumPy supports a wide range of mathematical operations such as linear algebra, random number generation, Fourier transforms, statistical functions, and more.

Example of NumPy

Following is an example of NumPy:

Example 1- Creating NumPy Arrays:

Example 2- Broadcasting:

Types of NumPy in Python

In NumPy, there is a single fundamental Data Structure: the N-dimensional array, often referred to as ndarray. This array is the cornerstone of NumPy and enables efficient manipulation of large datasets in Python. NumPy arrays are more powerful and flexible than regular Python lists, as they allow for efficient element-wise operations and broadcasting across arrays with different shapes.

NumPy arrays come in different flavors based on their dimensions. Let’s explore the types of NumPy arrays in detail:

1. One-dimensional Array (1D Array):

• A 1D array is similar to a Python list, but it has the advantages of better memory usage and faster execution for numerical operations.
• It is created using np.array() or by converting a Python list using np.asarray().

Example: 

2. Two-dimensional Array (2D Array):

• A 2D array represents a matrix-like structure with rows and columns.
• It is created using nested lists or arrays.

Example:

3. Multi-dimensional Arrays (ND Array):

• NumPy allows you to create arrays with more than two dimensions, often referred to as N-dimensional arrays or ND arrays.
• These arrays are useful in representing complex data structures such as images, videos, or higher-dimensional mathematical entities.

Example:

4. Special Arrays:

• NumPy provides several functions to create special types of arrays quickly.
• Some of these functions include:

1. np.zeros(shape): Creates an array filled with zeros.
2. np.ones(shape): Creates an array filled with ones.
3. np.eye(N): Creates an identity matrix of size N x N.
4. np.arange(start, stop, step): Creates an array with values in a specified range with a given step size.
5. np.linspace(start, stop, num): Creates an array with a specified number of evenly spaced values between start and stop.

5. Structured Arrays:

• NumPy supports structured arrays, which allow you to create arrays with elements of different data types.
• This is useful when dealing with datasets containing heterogeneous data.
• Structured arrays are created using np.array() with a dtype parameter specifying the data type of each element.

Example:

6. Views and Copies:

• NumPy arrays can be sliced and indexed to create views or copies of the original array.
• A view refers to the same underlying data as the original array, while a copy creates a new array with separate data.
• Understanding the distinction between views and copies is important to avoid unexpected behavior when modifying arrays.

These are the main types of NumPy arrays in Python. The ndarray provides a solid foundation for performing efficient numerical computations and is at the core of many Data Analysis and scientific computing libraries in the Python ecosystem.

NumPy Functions in Python

NumPy provides a wide range of functions that are essential for scientific computing, numerical analysis, and Data Manipulation in Python. These uses of numpy in Python can be broadly categorized into the following groups:

1. Array Creation:

• np.array(): Creates an array from a Python list or tuple.
• np.zeros(): Creates an array filled with zeros.
• np.ones(): Creates an array filled with ones.
• np.empty(): Creates an array without initializing its elements to any specific value.
• np.arange(): Creates an array with values in a specified range with a given step size.
• np.linspace(): Creates an array with a specified number of evenly spaced values between start and stop.
• np.eye(): Creates an identity matrix of a given size.

2. Array Manipulation:

• ndarray.shape: Returns the dimensions of the array as a tuple.
• ndarray.reshape(): Changes the shape of the array.
• ndarray.ravel(): Flattens the array to a 1D array.
• np.transpose(): Transposes the array (rows become columns and vice versa).
• np.concatenate(): Joins arrays along a specified axis.
• np.split(): Splits an array into multiple sub-arrays along a specified axis.
• np.vstack(): Stacks arrays vertically (row-wise).
• np.hstack(): Stacks arrays horizontally (column-wise).

3. Mathematical Operations:

• NumPy provides element-wise mathematical operations for arrays, including addition, subtraction, multiplication, division, exponentiation, etc.
• np.add(), np.subtract(), np.multiply(), np.divide(), np.exp(), np.log(), np.sin(), np.cos(), and many more.

4. Reduction Operations:

• ndarray.sum(): Computes the sum of array elements.
• ndarray.mean(): Computes the mean (average) of array elements.
• ndarray.min(), ndarray.max(): Finds the minimum and maximum values in an array.
• ndarray.argmax(), ndarray.argmin(): Returns the indices of the maximum and minimum values, respectively.
• ndarray.prod(): Computes the product of array elements.

5. Array Broadcasting:

• NumPy allows broadcasting, which enables element-wise operations on arrays with different shapes and dimensions.
• Broadcasting automatically adjusts the shape of smaller arrays to match the shape of larger arrays, eliminating the need for explicit loops.

6. Linear Algebra:

• np.dot(): Computes the dot product of two arrays.
• np.linalg.inv(): Computes the inverse of a square matrix.
• np.linalg.det(): Computes the determinant of a matrix.
• np.linalg.eig(): Computes the eigenvalues and eigenvectors of a square matrix.
• np.linalg.solve(): Solves a system of linear equations.

7. Random Number Generation:

• NumPy provides various functions to generate random numbers from different distributions.
• np.random.rand(): Generates random numbers from a uniform distribution between 0 and 1.
• np.random.randn(): Generates random numbers from a standard normal distribution (mean=0, variance=1).
• np.random.randint(): Generates random integers within a specified range.
• np.random.choice(): Generates random samples from a given 1D array.

8. Statistical Functions:

• np.mean(), np.median(), np.var(), np.std(): Compute various statistical measures for the array.

These are just some of the many functions provided by NumPy. The library’s extensive functionality makes it an indispensable tool for scientific computing, Data Analysis, and Machine Learning in Python. 

How to Import NumPy Library in Python?

Follow these easy steps to import the NumPy library in Python:

Install NumPy (if not already installed):

NumPy is not a built-in library, so you may need to do so first. You can use pip, the Python package manager, to install NumPy. Open your terminal or command prompt and enter the following command:

In your Python script or interactive environment, import NumPy:

Using the import keyword, you can import NumPy into a Python script or interactive environment after installation:

The as np clause in the import statement gives NumPy an alias of “np,” which is a customary practise to condense code by shortening the module name.

Start utilising NumPy’s arrays and functions:

Since NumPy has been imported, you may now use its functions to build NumPy arrays, which are the language’s core Data Structure. For instance:

To efficiently handle complex numerical operations, NumPy offers a wide range of mathematical and array manipulation capabilities.

You may successfully import the NumPy library into your Python environment and take advantage of its robust capabilities for numerical computing and array operations by following the procedures below.

NumPy vs Pandas:

1. Two well-known Python libraries for Data Manipulation and analysis are NumPy and Pandas.
2. The acronym NumPy refers to a programming language that is primarily used to manage numerical data using n-dimensional arrays (ndarrays).
3. In contrast, Pandas, which stands for Panel Data and is based on NumPy, offers data structures like Series (1D labelled arrays) and DataFrames (2D labelled arrays) that are more suited for manipulating and analysing data.
4. Mathematical workloads are best served by NumPy since it focuses more on numerical computations and offers high-performance array operations.
5. Pandas offers practical ways for addressing missing data, reshaping data, and working with time series data. Pandas is developed for data manipulation, cleaning, and analysis.
6. While the functions of the two libraries overlap, Pandas is more adaptable and better suited to processing real-world data in tabular form, whereas NumPy shines when it comes to numerical calculations and basic array operations.
7. In conclusion, Pandas and NumPy are complementing libraries, with Pandas offering higher-level data manipulation tools designed primarily for working with structured data while NumPy serves as the foundation for numerical computations.

FAQ:

How to add in a list in Python NumPy library?

The np.append() and np.insert() functions of the NumPy module can be used to add elements to a NumPy array. Using a copy of the original array arr, the np.append() function appends values or an array to the end of the copy. Similar to that, you may insert elements into the array at certain locations by using the np.insert() function. Both functions let you add new elements to the array without changing the existing array’s contents.

How to import NumPy library in Python?

You can follow the steps below;

1. Check the version of Python
2. Install Pip
3. Install NumPy
4. Verify NumPy Installation
5. Import the NumPy Package

What are the most basic functions in Python?

The most basic functions in Python include print(), abs(), round(), min(), max(), sorted(), sum(), and len().

Conclusion

From the above blog, you have come to learn about NumPy in Python and the different features and types it includes. with the help of the various instances, you can understand how NumPy works within an organisation and can excel your skills after learning Python for Data Science by Pickl.AI. Additionally, you will be able to learn classes of numpy library in Python for Data Science short-term course.