NumPy is a library for the Python programming language, adding support for large, multi-dimensional arrays and matrices, along with a large collection of high-level mathematical functions to operate on these arrays

Top 9 Python Libraries for Data Science: Pandas, Matplotlib, Numpy, Scipy, Sci-kit Learn, Theano, PyTorch, TensorFlow, Keras. Python is a programming language that lets you work quickly and integrate systems more effectively. Also, Python is a general-purpose language, which means you can build a wide variety of applications, from web developing using Django or Flask, to data science using awesome libraries like Scipy, Scikit-Learn, Tensorflow and much more

Numpy log1p() is a math function that helps the user to calculate the natural logarithmic value of x+1 where x belongs to all the input array elements.

In this article we will understand np.where, np.select with some examples.

In this article, I’ll introduce all of the excellent built-in functions in NumPy for us to generate n-dimensional arrays with certain rules. Please be advised that random arrays can be a separated topic so that they will not be included in this article.

Numpy linalg matrix_rank() calculates the Matrix rank of a given matrix using the SVD method. It returns the matrix rank of an array using the SVD method.

Numpy tensordot() Function Example in Python The tensordot() is a numpy function that sums the product of a’s elements and b’s elements over the axes specified by a_axes and b_axes.

Numpy linalg cond() function computes the condition number of a matrix. The cond() function is capable of returning the condition number.

Numpy linalg norm() method is used to get one of eight different matrix norms or one of the vector norms. It depends on the value of the given parameter.

understanding: numpy.random.choice, numpy.random.rand, numpy.random.randint,numpy.random.shuffle,numpy.random.permutation

Python numpy.trace() method is used to find the sum of diagonals of the array. The trace() method returns the sum along diagonals of the array.

Stats for Data Science, you will be working on an end-to-end case study to understand different stages in the data science life cycle. This will deal with 'data manipulation' with pandas and Numpy, 'data visualization' with Matplotlib, and the basic statistics which are required. After Data manipulation, Data visualization, and the basic statistics an ML model will be built on the dataset to get predictions. You will learn about the basics of the Scikit-learn library to implement the machine learning algorithm.

I think that the best way to really understand how a neural network works is to implement one from scratch. That is exactly what I going to do through this article. I will create a neural network class, and I want to design it in such a way to be more flexible.

How to Create Numpy Arrays? A quick overview about different ways of creating numpy arrays

Learn NumPy Copy and View - Deep Copy, shallow copy and No copy in NumPy, NumPy view creation and types with examples, NumPy View vs Copy

Beginner’s Guide to Data Analysis using numpy and pandas. Oftentimes, we tend to forget that the pandas library is built on top of the numpy package.

Get image RGB using PIL(Pillow) and modify using NumPy. In this article, I will demonstrate how to use two libraries in Python — PIL and NumPy — to achieve most of the basic photo editing features in only 2–3 lines of code.

The content present in the NumPy arrays can be made accessible, and also we can make changes thorough indexing as we got to know in the previous module. Another way of data manipulation in arrays in NumPy is though slicing through the arrays. We can also try changing the position of the elements in the array with the help of their index number. Slicing is the extension of python’s basic concept of changing position in the arrays of N-d dimensions.

NumPy along with Matplotlib is a fundamental feature of Python. Learn Numpy Matplotlib Tutorial to learn basics of Matplotlib. Learn various types of matplotlib charts like histogram, bar chart, scatter plot, box plot etc

Learn about stacking and joining in Numpy. These are important functions for array in NumPy. Learn about dstack, hstack and vstack functions in NumPy.

In our previous article we have seen how to create an array using numpy. Once the creation is done, we must be able to access them. In this article we’ll see how to access an array by indexing, slicing of an array and some other functions that are involved in the creation of array.