An Introduction to NUMPY: Numerical Python for Computation

NUMPY abbreviation of Numerical python is one of the most important library for data scientists and machine learning engineers and scientists for computation and numerical processes. Probably when we studied of data science, machine learning or artificial intelligence, we come to know that we have to learn following libraries necessarily



These libraries are consider compulsory for any data science projects but in my view, as far as calculation of data or training of data is concerned, we just need Numerical Python (NUMPY) for the purpose. The other libraries are mostly used for data plotting data and analyzing at the end. As matplotlib is for plotting data and has amazingly useful features but now a days, Seaborn is ruling with more advanced and comprehensive plotting techniques. We often come to know about pandas in data science for computation in data science and machine learning but why we should we choose NUMPY?? That’s our target ?Therefore, in this article, we will see that why NUMPY is so powerful and which features of NUMPY makes it of great importance to data scientists .Following are some advanced features of numpy

  • Vector programming
  • Array data processing
  • Linear Algebra manipulation
  • Fast element wise universal functions

So if you want to become data science engineer, you must have to download numpy on your machine for faster and efficient computation. There are several sources available on youtube where you can learn NUMPY. You can also learn it from Numpy documentation available on


When it comes to data science and artificial intelligence, we have to proceed large amount of data to train our models. This data is usually in the form of tables. So in order to manipulate that data we have to convert them into arrays and here NUMPY plays a key role. NUMPY provides a number of useful features for manipulation of array, which you can check at Suppose you don’t have NUMPY and you want to operate on big data, you would probably run a loop to access each entry step by step which is very tedious and time consuming task as one loop takes 1 milli second to run and if each loop contains 2 or 3 steps and you have data of several million entries, then it will take several hours to manipulate. Surely, you do not want that. Therefore, as mentioned earlier, we have to convert this data into arrays and the method where we use array instead of loops is called Vector Programming. In almost your whole data science career, you have to deal with vector programming and numpy is the best tool/library for vector programming.

As in the above section, in vector programming we have to deal with large arrays. So in order to process and manipulate that arrays, NUMPY provides with many built in functions such as sum, multiply or divide many arrays, taking absolute of arrays and many more as shown in table

                Function                                  Description
                abs, fabs.These functions compute the absolute value of any element( integer, floating point, or complex values ). You should use fabsas which is a faster alternative for non complex valued data
                    sqrtThis function compute the square root of any element. It is equivalent to arr ** 0.5
                 squareThis function compute the square of any element. It is equivalent to arr ** 2
          log, log10, log2, log1pNatural logarithm (base e), log base 10, log base 2, and log(1 + x), respectively

sign Compute the sign of each element: 1 (positive), 0 (zero), or -1 (negative)

                     floorThis function compute the floor of any element, i.e. the largest integer less than or equal to each element
                    f addThis function will add corresponding/alternative elements in arrays
                 subtractThis function subtract enteries in second array from first array
                multiplyThis function multiply array elements divide, floor divide Divide or floor divide (truncating the remainder)
                  powerThis function raises enteries in first array to the powers indicated in second array
         maximum, fmaxMaximum function computes the maximum of all enteries, Fmax performs same function but ignores NaN (NaN stands for not a Number)
         minimum, fminMinimum function computes the minimum of all enteries. fmin performs same function but ignores NaN
                  modMod function will compute the element wise modulus of entry (remainder of division)
                 isnanNaN stands for not a Number. Isnan will return boolean array recognizing whether each value is NaN(Not a Number)
            isfinite, isinfThese functions will return the boolean recognizing whether each element is finite (non-inf, non-NaN) or infinite, respectively
     cos, cosh, sin, sinh,

tan, tanh

Hyperbolic and regular trigonometric functions
   arccos, arccosh, arcsin,

arcsinh, arctan, arctanh

These are inverse trigonometric functions
            logical_notThese functions will compute truth value of not x element wise. Equivalent to -arr.


These are the few operations which are used to manipulate large arrays in a very short time so these are a bite of cake for the data scientist to manipulate such a large amount of data. Morever when it comes to engineers, they also have data in arrays (which comes mostly under the scope of linear Algebra) and they need some sort of library to manipulate data and computation to get desired results so numpy is equally important to engineers


Finally we can say that as far as fast computation of very large data is required, we would have to rely upon NUMPY as it is faster and contains sufficient and powerful tools. So in order to train your model and get desired, you would probably switch to NUMPY for better results

What do you think?

Written by Ghost

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