If you’re new to programming, you might consider using python. Python is a high-level programming language. It’s extremely useful for beginner level coders and the most advanced coders. One beneficial part of python is the numerous libraries, like NumPy. Continue reading to get a better understanding of this coding language and its reshape function.
You might be wondering why programming is so important. If you work in the tech and data field you probably already understand the usefulness of programming languages like python. Python allows you to break down data sets into different shapes. This can in turn create a wide range of possibilities, like making it easier to manipulate files to view or sort data. If you’re an engineer, data scientist, or amateur coder, programming needs to be a part of your everyday work because it’s so beneficial.
Experts agree that python is arguably the best and most used programming language. It’s used in the academic world of research and education, but also in the workplace across industries. One great reason to use python is because of the libraries available on it. These libraries allow scientists, researchers, or programming hobbyists to work with complex data and develop algorithms that do computation on large datasets of numbers.
The most common library to use for numerical data computation is the NumPy library. This library allows you to work with multidimensional arrays for computation or data storage. What’s even better is that NumPy is easily installable. What sets it apart from its competitors is the variation in libraries in python. Another factor is its widespread usage. When using python, the options seem endless as you can plot data, make simple video games, or even write sophisticated scientific algorithms.
What’s an Array?
An array is a set of data with a specific structure. It can hold multiple values at the same time. Often you’ll find these arrays need to be reshaped into different dimensions for various applications. NumPy reshape specifically allows you to perform computations on any dimensional arrays. Basically, this function allows you to change the original shape of the array to a different shaped array.
Let’s say that there is an array with 10 elements in one column (10×1) array, but you need your array to be shaped as a 2×5. You can use the reshape method to change the shape of the array to the 2×5. Maybe you are thinking, why would I need to reshape an array anyways?
One very applicable area in which this is necessary is anything dealing with image processing. Images are just pixel values holding the data of what colors are in each pixel. Images are stored in an array with 3 dimensions. The resolution is the shape of the array or size of the image. You can use the reshape function to take that high-resolution image and make it to another size without compromising the quality of the image.
How to Reshape an Array
Within NumPy, as discussed earlier, there is reshape. Reshape is a function offered within the library. To use it, you must first import the reshape function (np.rshape). This function is what allows you to change the size, shape, and dimension of the original array. When using the function, you provide the original array and the desired new shape, or dimension as the input values. Once computed, the result is the new array with a different shape that still consists of the same data and number of elements. To put it simply, you’re changing the shape of the array without altering the data within it. For more explanation and tutorials to further extend your understanding, consider visiting Nick McCullum’s website as he is an expert providing essential tools for those learning NumPy and other Python elements.
Now that you have a more detailed understanding of python and how to manipulate arrays, you may want to continue to learn more. In fact, you should keep learning the various functions so that you can further improve your skills. There’s no doubt that if you utilize the NumPy library and specific tools like its reshape function, you will benefit in all aspects of coding and data analysis.