# Category: Numpy Tutorial

Numpy Stands for Numerial Python. It is special python programming package for data science projects. It is used to operate high level mathematical with arrays and matrices. It was developed by Jim Hugunin.
• It has powerful n dimensional arrays
• It execute very fast.
• It used less memory for variable storage
• It’s Mainly useful for linear algebra, Fourier transform and random numbers

Installation of Numpy Package:
You need to install by using pip command. Execute the below command to install numpy package.
python -m pip install –user numpy

Get started with numpy:
You need to import numpy package in your program to take advantage of numpy.
Import numpy as np
After importing numpy library you can call functions with np object.

## numpy.ogrid function with example in python

numpy.ogrid(): This function returns mesh-grid ndarrays with only one dimension :math:neq 1 numpy.ogrid = <numpy.lib.index_tricks.OGridClass object> nd_grid instance which returns

## numpy.mgrid function with example program in python

numpy.mgrid function(): This funtion returns  mesh-grid ndarrays all of the same dimensions numpy.mgrid = <numpy.lib.index_tricks.MGridClass object>¶ nd_grid instance which returns

## numpy.geomspace() function with example program in python

numpy.geomspace(): This function return numbers spaced evenly on a log scale (a geometric progression). Syntax: numpy.geomspace(start, stop, num=50, endpoint=True, dtype=None,

## numpy.logspace() function with example in python

numpy.logspace(): This  function return numbers spaced evenly on a log scale. Syntax: numpy.logspace(start, stop, num=50, endpoint=True, base=10.0, dtype=None, axis=0) In

## How to use the NumPy linspace function with examples | 2019

numpy.linspace(): This function Return evenly spaced numbers over a specified interval  and num evenly spaced samples, calculated over the interval

## numpy.arange() function with example in python | 2019

numpy.arange() : If you want generate a sequence of numbers, arange() function is very helpful. It has regular range of

## numpy.random.standard_normal() function with example in python

numpy.random.standard_normal(): This function draw samples from a standard Normal distribution (mean=0, stdev=1). Syntax: numpy.random.standard_normal(size=None) Parameters: size : int or tuple

## numpy.random.randint() function with example in python

numpy.random.randint() function: This function return random integers from low (inclusive) to high (exclusive). Return random integers from the “discrete uniform”