Introduction to NumPy Arrays

Hello students, welcome to Kwickprep. Your school wants to add five grace marks for one thousand students. With a list, you need a loop. With NumPy, it is one short line. Today we will learn what NumPy arrays are, how to make them, and how they differ from lists.

First, a new term. A library is a collection of ready-made code that you add to Python. NumPy stands for numerical Python, and it is a library for working with numbers. You install it once with the command pip install numpy. In your program, you write import numpy as n p, so n p becomes its short name. Data science and AI tools use NumPy underneath.

The main tool in NumPy is the array. An array is a grid of values, and all of them have the same type. The values sit side by side in one block of memory. So NumPy can do maths on all values at once, without a Python loop. Its core is written in the C language, which runs much faster than a Python loop.

We create an array by giving a list to n p dot array. Here the list of marks becomes an array called arr. When we print it, there are no commas between the values. That is how you can spot an array in an output question. Its type is n d array, which means an array with any number of dimensions.

A list of lists makes a two dimensional array, like a table with rows and columns. Here each row is one student, and each column is one subject. The shape attribute gives the rows and columns, so two rows and two columns. The n dim attribute gives the number of dimensions, which is two.

An attribute is a fact stored with the array, written after a dot with no brackets. Shape gives the size in each direction, here two by two. N dim gives the number of dimensions. Size gives the total number of values, two into two, so four. D type gives the data type of the values, here int sixty four, a whole number type.

Every value in an array has the same d type. Array a has only whole numbers, so its d type is int sixty four. Older NumPy on Windows may show int thirty two instead. Pause and predict. What happens to array b, which mixes whole numbers with two point five? NumPy turns all of them into floats, which are decimal numbers. So one prints as one point, and three as three point.

NumPy can also build arrays without a list. N p dot zeros of three makes three zeros, as floats. N p dot ones makes ones, and d type equals int makes them whole numbers. N p dot arange works like range. It starts at one and stops before six.

Now the real power. Canteen prices are fifteen for samosa, ten for tea and thirty for a sandwich. Price plus five adds five to every price, with no loop. The star sign means multiply. Price star quantity multiplies matching items, value by value. The sum method adds them up, so the full bill is ninety rupees. Careful, arrays must match in length. Adding an array of three values to an array of two values gives a value error.

Arrays have ready methods for common maths. Mean gives the average, and max gives the highest value. The greater than sign asks, is the value bigger? Marks greater than eighty gives True or False for each value. Putting that inside square brackets keeps only the True values, so we get eighty eight and ninety two.

This output question catches many students. The same signs mean different things for lists and arrays. Plus on two lists joins them into one longer list. Plus on two arrays adds matching values. Star two on a list repeats the list twice. Star two on an array doubles every value.

Let us compare lists and arrays. A list can mix types, like names and numbers, but an array keeps one type. Plus joins lists, but adds array values. Star two repeats a list, but doubles array values. Maths on many values needs a loop for a list, and one line for an array. For big data, arrays are much faster. And lists are built into Python, while arrays need the NumPy library.

Let us revise what we learned today. NumPy is a library for fast work with numbers. N p dot array turns a list into an array. Shape, n dim, size and d type describe an array. Maths on an array works on every value at once. Lists join and repeat, but arrays add and multiply value by value. Try making an array of your own test marks and find the average.