Numpy: what has changed and what is going to change?
#Numpy #python #data-science #machine-learning
Go announced Go 1.15 version on 11 Aug 2020. Highlighted updates and features include Substantial improvements to the Go linker, Improved allocation for small objects at high core counts, X.509 CommonName deprecation, GOPROXY supports skipping proxies that return errors, New embedded tzdata package, Several Core Library improvements and more.
As Go promise for maintaining backward compatibility. After upgrading to the latest Go 1.15 version, almost all existing Golang applications or programs continue to compile and run as older Golang version.
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Welcome to DataFlair!!! In this tutorial, we will learn Numpy Features and its importance.
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
NumPy (Numerical Python) is an open-source core Python library for scientific computations. It is a general-purpose array and matrices processing package. Python is slower as compared to Fortran and other languages to perform looping. To overcome this we use NumPy that converts monotonous code into the compiled form.
These are the important features of NumPy:
This is the most important feature of the NumPy library. It is the homogeneous array object. We perform all the operations on the array elements. The arrays in NumPy can be one dimensional or multidimensional.
The one-dimensional array is an array consisting of a single row or column. The elements of the array are of homogeneous nature.
In this case, we have various rows and columns. We consider each column as a dimension. The structure is similar to an excel sheet. The elements are homogenous.
We can use the functions in NumPy to work with code written in other languages. We can hence integrate the functionalities available in various programming languages. This helps implement inter-platform functions.
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In this Numpy tutorial, we will learn Numpy applications.
NumPy is a basic level external library in Python used for complex mathematical operations. NumPy overcomes slower executions with the use of multi-dimensional array objects. It has built-in functions for manipulating arrays. We can convert different algorithms to can into functions for applying on arrays.NumPy has applications that are not only limited to itself. It is a very diverse library and has a wide range of applications in other sectors. Numpy can be put to use along with Data Science, Data Analysis and Machine Learning. It is also a base for other python libraries. These libraries use the functionalities in NumPy to increase their capabilities.
Arrays in Numpy are equivalent to lists in python. Like lists in python, the Numpy arrays are homogenous sets of elements. The most important feature of NumPy arrays is they are homogenous in nature. This differentiates them from python arrays. It maintains uniformity for mathematical operations that would not be possible with heterogeneous elements. Another benefit of using NumPy arrays is there are a large number of functions that are applicable to these arrays. These functions could not be performed when applied to python arrays due to their heterogeneous nature.
Arrays in NumPy are objects. Python deletes and creates these objects continually, as per the requirements. Hence, the memory allocation is less as compared to Python lists. NumPy has features to avoid memory wastage in the data buffer. It consists of functions like copies, view, and indexing that helps in saving a lot of memory. Indexing helps to return the view of the original array, that implements reuse of the data. It also specifies the data type of the elements which leads to code optimization.
We can also create multi-dimensional arrays in NumPy.These arrays have multiple rows and columns. These arrays have more than one column that makes these multi-dimensional. Multi-dimensional array implements the creation of matrices. These matrices are easy to work with. With the use of matrices the code also becomes memory efficient. We have a matrix module to perform various operations on these matrices.
Working with NumPy also includes easy to use functions for mathematical computations on the array data set. We have many modules for performing basic and special mathematical functions in NumPy. There are functions for Linear Algebra, bitwise operations, Fourier transform, arithmetic operations, string operations, etc.
#numpy tutorials #applications of numpy #numpy applications #uses of numpy #numpy
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NumPy is an essential Python library to perform mathematical and scientific computations. NumPy offers Python’s array-like data structures with exclusive operations and methods. Many data science libraries and frameworks, including Pandas, Scikit-Learn, Statsmodels, Matplotlib and SciPy, are built on top of NumPy with Numpy arrays in their building blocks. Some frameworks, including TensorFlow and PyTorch, introduce NumPy arrays or NumPy-alike arrays as their fundamental data structure in the name of tensors.
How NumPy becomes the base of Data Science computing system (source)
Data Science relies heavily on Linear Algebra. NumPy is famous for its Linear Algebra operations. This article discusses methods available in the NumPy library to perform various Linear Algebra operations with examples. These examples assume that the readers have a basic understanding of NumPy arrays. Check out the following articles to have a better understanding of NumPy fundamentals:
#developers corner #linear algebra #matrices #numpy #numpy array #numpy dot product #numpy matrix multiplication #numpy tutorial #svd #vectors
NumPy consists of different methods to duplicate an original array. The two main functions for this duplication are copy and view. The duplication of the array means an array assignment. When we duplicate the original array, the changes made in the new array may or may not reflect. The duplicate array may use the same location or may be at a new memory location.
It returns a copy of the original array stored at a new location. The copy doesn’t share data or memory with the original array. The modifications are not reflected. The copy function is also known as deep copy.
import numpy as np arr = np.array([20,30,50,70]) a= arr.copy() #changing a value in original array arr = 100 print(arr) print(a)
[100 30 50 70]
[20 30 50 70]
Changes made in the original array are not reflected in the copy.
import numpy as np arr = np.array([20,30,50,70]) a= arr.copy() #changing a value in copy array a = 5 print(arr) print(a)
[20 30 50 70]
[ 5 30 50 70]
Changes made in copy are not reflected in the original array
#numpy tutorials #numpy copy #numpy views #numpy