# Numpy Notes – Easiest Explanation – 2024

Hey guys, hope all is well. These days, I was constantly thinking that how could I contribute even more to the community rather than just posting projects. Then suddenly this thought came into my mind, that why am I not sharing my self-made notes which have helped me throughout my Data Science journey.

So from now onwards I will also be sharing my self-made handwritten notes for last-minute revisions before an interview. In today’s blog, I will be sharing my Numpy notes. So without any further due.

### Let’s go through the Numpy notes…

• NumPy stands for Numerical Python.
• The reason it is so fast is because it is having bindings to the C library.
• np.array(list) will convert a list to a NumPy array.
• np.arange(start, stop, step) will create an array from start to stop with a step size of step.
• np.zeros(shape) will create an array of provided shape with all elements as zeros. The array can be 1D, 2D, 3D, etc.
• np.linspace(start, stop, n) will give n eually spaced number of points between start and stop.
• np.eye(n) will give a nXn Identity matrix. An identity matrix is having all elements as zeros except diagonals which are 1s.
• np.random.rand(shape) will give an array with random elements from a uniform distribution between 0 and 1.
• np.random.randn(shape) will give an array with random elements from a normal distribution about 0.
• We can use array.size to check whether it is 1D or 2D or 3D, etc.
• np.random.randint(start, stop, n) will give n number of points between the range of start and stop. One thing to note here is that start is inclusive but the stop is exclusive.
• arr.reshape(shape) will reshape the array arr in the new shape provided.
• np.max(arr) gives the maximum element in the array arr.
• np.min(arr) gives the minimum element in the array arr.
• np.argmax(arr) gives the index of the maximum element in the array arr.
• np.argmin(arr) gives the index of the minimum element in the array arr.
• arr.shape gives the shape of the array.
• I have provided some slicing examples also for a better understanding of slicing operation in NumPy.
• While slicing in NumPy, the data is not copied anywhere, it is just a view of the data out of the full array. If we change something in the slice, it will ultimately also get changed in the original array.
• arr.copy() creates a copy of the array arr. Here new memory is allocated to the new copy. Changes done in the copy are not reflected back in the original array.