Numpy is one of the libraries available for Python programming language. This library or module provides numerical and mathematical functions which are pre-compiled.
Numpy is designed to used for multidimensional arrays and for scientific computing which are memory efficient.
Here we have 2 packages
1. Numpy – This provides basic calculations with multi-dimensional arrays and matrices of numeric data.
2. Scipy – This package provides functionality of Numpy with added algorithms like , regression, minimization, Fourier transforms, statistical operations, random simulation and applied mathematical techniques.
Let’s not waste time, Start learning Numpy tutorial!!!
It is best to use the pre-built packages to install the Numpy. Otherwise, you can install the python distributions like Anaconda, python(x,y), Pyzo for installing all necessary packages which ever needed. Learn more..
In this tutorial, we will learn how to import and use Numpy. You have to use the keyword “import” to import the numpy moduleIt is better to give an alias name and try using the same alias name for every call to numpy. Otherwise it will become write numpy.X file every time. By giving the alias name, it will use the np.X instead of using numpy.X. Learn more..
Nympy provides the below dataypes more than what exactly python holds.Let us see some example below:
How identify the datatype ?Below is the command. we will use the “dtype” method to identify the datatype. Learn more..
In NumPy, Array is an Object class little similar to lists in Python. But, here the array holds the elements which are same numeric data type like int or float.This array object can process the large numeric data efficiently when compared to lists. Learn more..
The arange function which almost like a Range function in Python. The arange function will return an array as a result. Learn more..
In NumPy, very efficient and optimized indexing can be done by using various functions which are provided in the package.Let us understand indexing with Numpy by creating an array of one-dimensional and accessing all the values. Learn more..
Let us say x[obj] is an array which holds obj as the selection object. When the object is a non-tuple sequence object or a tuple with atleast one sequence object which is ndarray of type integer or Boolean. Learn more..
Operations on Numpy arrays are generally happened on element wise. It means the arrays of same size works better for operations.But it is also possible to do the operations on the those arrays which are different in size.How to do this ?Nothing to do anything by us. Numpy can transform the arrays of different sizes into the same sizes. Learn more..
In this tutorial, we will learn about array iteration in Numpy.In python, we have used iteration through lists. In the same way, we can iterate over arrays in Numpy.Let us see an example by giving an array of integer elements. Learn more..
We can change the array shape using the Array manipulation routines like reshape and ravel.reshape: This will give us the new shape for an array without changing its dataravel: This will return the contiguous flattened array. Learn more..
In Numpy, we can handle the string operations with provided functions. some of which we can discuss here.add: This will return element-wise string concatenation for two arrays of str. Learn more..
n Numpy, we can perform various Mathematical calculations using the various functions that are provided in the library.Advanced Mathematical operations those can be performed are Trigonometric functions, Hyperbolic functions, Rounding, Sums, Products, differences, exponents, logarithms etc. Learn more..
In Numpy, we can perform various Arithmetic calculations using the various functions like add, reciprocal, negative, multiply, divide, power, substract, remainder etc. Learn more..
In Numpy, we can perform various statistical calculations using the various functions that are provided in the library like Order statistics, Averages and variances, correlating, Histograms.Let us work on some of those statistical functions. Learn more..
In Numpy, we can perform various Sorting, Searching, Counting operations using the various functions that are provided in the library like sort, argmax, argmin, count_nonzero etc. Learn more..
Matrix module contains the functions which return matrices instead of arrays.matrix – This will return a matrix from an array kind of an object or from the string of data. Learn more..
Performing Linear Algebra on several matrices which are stacked as an array.Below are the some of Linear Algebra functions we are going work on. Learn more..
This will return the computed histogram of a set of data.This function mainly works with bins and set of data given as input. Numpy histogram function will give the computed result as the occurances of input data which fall in each of the particular range of bins. Learn more..