Thread by @ml_india_

NumPy is a powerful #python library that helps us compute operations on primarily numbers, faster. It is an important tool for data science. 💪

Here are 5 powerful #NumPy functions that will help you in your projects!

#Thread ☕

#MachineLearning #datascience #AI

Let's go! ⬇️

First let’s see the advantages of using NumPy : 📈

1️⃣ Uses low memory to store data.

2️⃣ We can create n-dimensional arrays.

3️⃣ Operations like indexing, broadcasting, slicing and matrix multiplication.

4️⃣ Finding elements in the array is easy.

5️⃣ Good documentation.

Function 1 : np.sort()

This returns a sorted copy of the array. The original array is not changed.

The arguments the function normally takes are -

1. The Array to be sorted.

2. Axis along which to sort.

3. The method/ algorithm used for sorting as “kind = ”.

Function 2 : np.count_nonzero()

Used to count number of nonzero elements in an array.

The arguments taken are -

1. Array from which the nonzero values are to be found.

2. Axis. (optional)

Returns the number of nonzero elements in an array OR along an axis.

Function 3 : numpy.where()

does one operation on those that satisfy the condition and another on those that do not satisfy the condition.

Arguments taken are -

1. Condition.
2. x — do x if condition True.
3. y — do y if condition False.

Returns an array with x and y executed.

Function 4 : numpy.compress()

Returns a slice of values that satisfy a conditional array. This function takes following arguments -

1. A 1D conditional array.

2. Array on which the compression is done.

3. Axis along which the compression is done.

Function 5 - numpy.trace()

Really cool function that returns sum of values along the diagonal of an array.

Arguments taken are -

1. An array.

2. Offset from diagonal.

3. Axis.

Returns an array with the sum along all the diagonals in the array.

We had a good look under the hood of NumPy documentation which is almost impossible to know 100%.

There are useful functions that can do a job efficiently and with less code. That’s the power of the NumPy library.

For more informative resources, follow @ml_india_! 🤓