The following are some of the top reasons why learning about NumPy will help you going forward:
● Operation speed
You might not know about this, but NumPy is written in one of the oldest programming languages, C. One of the properties you benefit from is that it can execute faster than other packages. This makes a lot of sense when you think about Python as a whole being a dynamic language that needs interpretation. Before interpretation, Python code has to be converted to bytes. A compiled C code will definitely perform faster than the average Python code.
There are specific Python versions that are faster than others. For example, programs written in Python 2 are relatively faster than those written in Python 3. The efficiency is between 5 and 14%, so most people will never notice the performance lag, unless you are very keen. NumPy arrays are stored in blocks of the same type and size. Because of this reason, they are easier to access and execute where necessary. On the other hand, Python uses lists for most tasks. A single list could contain different types of objects, and as a result, rendering a Python code is relatively slower than C
loops, hence NumPy is a very fast package.
● Support for other libraries
One of the reasons why NumPy is an important language to learn is because it supports most of the Python libraries. Through NumPy, you can use Pandas, SciPy, SymPy and many others. In fact, SciPy and NumPy pretty much work hand in hand.
In NumPy, you should also be able to perform lots of linear algebra functions. This is an important part of data analysis, which also hinges on SciPy. Most of the time, you will need to install NumPy and SciPy together to enhance your performance in data analysis or scientific computing.
● Matrix computations
Through the ndarray functions, you can perform a lot of computations involving matrices in NumPy. There are so many matrix computations that you can perform through this package, including raising matrices to specific powers and deriving the product of two matrices.
A lot of the work required in data analysis involves algebraic equations and computations. Performing these in NumPy makes your work easier and enhances your ability to deliver the best outcome.
● Functional package
If there is one reason why using NumPy will be a good idea for you, it is the fact that it supports many functions. Most of the functions built to support different packages are already built into NumPy, so you don’t need to download them independently.
From math computations, to linear algebra, indices, random samples, statistics and polynomials, you will never run out of supporting options when working in NumPy. This further enhances your ability to analyze different types of data and draw conclusive remarks from them.
● Universal support
NumPy uses universal functions, referred to as ufuncs . These are functions that apply to each element in an array input. Owing to their universal nature, the outcome in the output array is stored in the same file size as the input.
Beyond this, you will also find the array broadcasting feature coming in handy, especially when working with different arrays. By default, arrays are available in unique sizes and shapes, and they can all be used within the same function.
Because of the universality of NumPy, your system will automatically adjust the shapes to ensure they match the shape and size of the largest array in your code. NumPy is one of the first Python libraries you should master. Knowledge of NumPy will help you advance into other libraries like SciPy which are equally important, and will form a great part of your data analysis journey.
● Operation speed
You might not know about this, but NumPy is written in one of the oldest programming languages, C. One of the properties you benefit from is that it can execute faster than other packages. This makes a lot of sense when you think about Python as a whole being a dynamic language that needs interpretation. Before interpretation, Python code has to be converted to bytes. A compiled C code will definitely perform faster than the average Python code.
There are specific Python versions that are faster than others. For example, programs written in Python 2 are relatively faster than those written in Python 3. The efficiency is between 5 and 14%, so most people will never notice the performance lag, unless you are very keen. NumPy arrays are stored in blocks of the same type and size. Because of this reason, they are easier to access and execute where necessary. On the other hand, Python uses lists for most tasks. A single list could contain different types of objects, and as a result, rendering a Python code is relatively slower than C
loops, hence NumPy is a very fast package.
● Support for other libraries
One of the reasons why NumPy is an important language to learn is because it supports most of the Python libraries. Through NumPy, you can use Pandas, SciPy, SymPy and many others. In fact, SciPy and NumPy pretty much work hand in hand.
In NumPy, you should also be able to perform lots of linear algebra functions. This is an important part of data analysis, which also hinges on SciPy. Most of the time, you will need to install NumPy and SciPy together to enhance your performance in data analysis or scientific computing.
● Matrix computations
Through the ndarray functions, you can perform a lot of computations involving matrices in NumPy. There are so many matrix computations that you can perform through this package, including raising matrices to specific powers and deriving the product of two matrices.
A lot of the work required in data analysis involves algebraic equations and computations. Performing these in NumPy makes your work easier and enhances your ability to deliver the best outcome.
● Functional package
If there is one reason why using NumPy will be a good idea for you, it is the fact that it supports many functions. Most of the functions built to support different packages are already built into NumPy, so you don’t need to download them independently.
From math computations, to linear algebra, indices, random samples, statistics and polynomials, you will never run out of supporting options when working in NumPy. This further enhances your ability to analyze different types of data and draw conclusive remarks from them.
● Universal support
NumPy uses universal functions, referred to as ufuncs . These are functions that apply to each element in an array input. Owing to their universal nature, the outcome in the output array is stored in the same file size as the input.
Beyond this, you will also find the array broadcasting feature coming in handy, especially when working with different arrays. By default, arrays are available in unique sizes and shapes, and they can all be used within the same function.
Because of the universality of NumPy, your system will automatically adjust the shapes to ensure they match the shape and size of the largest array in your code. NumPy is one of the first Python libraries you should master. Knowledge of NumPy will help you advance into other libraries like SciPy which are equally important, and will form a great part of your data analysis journey.
0 comments:
Post a Comment