1603465200

This tutorial will show you how to speed up the processing of NumPy arrays using Cython. By explicitly specifying the data types of variables in Python, Cython can give drastic speed increases at runtime.

**The sections covered in this tutorial are as follows:**

- Looping through NumPy arrays
- The Cython type for NumPy arrays
- Data type of NumPy array elements
- NumPy array as a function argument
- Indexing, not iterating, over a NumPy Array
- Disabling bounds checking and negative indices
- Summary

For an introduction to Cython and how to use it, check out my post on using Cython to boost Python scripts. Otherwise, let’s get started!

We’ll start with the same code as in the previous tutorial, except here we’ll iterate through a NumPy array rather than a list. The NumPy array is created in the *arr* variable using the arrange() function, which returns one billion numbers starting from 0 with a step of 1.

```
import time
import numpy
total = 0
arr = numpy.arange(1000000000)
t1 = time.time()
for k in arr:
total = total + k
t2 = time.time()
print("Total = ", total)
t = t2 - t1
print("%.20f" % t)
```

I’m running this on a machine with Core i7–6500U CPU @ 2.5 GHz, and 16 GB DDR3 RAM. The Python code completed in 458 seconds (7.63 minutes). It’s too long.

Let’s see how much time it takes to complete after editing the Cython script created in the previous tutorial, as given below. The only change is the inclusion of the NumPy array in the *for* loop. Note that you have to rebuild the Cython script using the command below before using it.

```
python setup.py build_ext --inplace
```

The Cython script in its current form completed in 128 seconds (2.13 minutes). Still long, but it’s a start. Let’s see how we can make it even faster.

#paperspace #numpy #c #cython #python

1595467140

The most important feature of NumPy is the homogeneous high-performance n-dimensional array object. Data manipulation in Python is nearly equivalent to the manipulation of NumPy arrays. NumPy array manipulation is basically related to accessing data and sub-arrays. It also includes array splitting, reshaping, and joining of arrays. Even the other external libraries in Python relate to NumPy arrays.

**_Keeping you updated with latest technology trends, _***Join DataFlair on Telegram*

Arrays in NumPy are synonymous with lists in Python with a homogenous nature. The homogeneity helps to perform smoother mathematical operations. These arrays are mutable. NumPy is useful to perform basic operations like finding the dimensions, the bite-size, and also the data types of elements of the array.

NumPy has a variety of built-in functions to create an array.

For 1-D arrays the most common function is np.arange(…), passing any value create an array from 0 to that number.

- import numpy as np
- array=np.
**arange**(20) - array

Output

array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11,12, 13, 14, 15, 16, 17, 18, 19])

We can check the dimensions by using array.shape.

#numpy tutorials #array in numpy #numpy array #python numpy array

1603465200

This tutorial will show you how to speed up the processing of NumPy arrays using Cython. By explicitly specifying the data types of variables in Python, Cython can give drastic speed increases at runtime.

**The sections covered in this tutorial are as follows:**

- Looping through NumPy arrays
- The Cython type for NumPy arrays
- Data type of NumPy array elements
- NumPy array as a function argument
- Indexing, not iterating, over a NumPy Array
- Disabling bounds checking and negative indices
- Summary

For an introduction to Cython and how to use it, check out my post on using Cython to boost Python scripts. Otherwise, let’s get started!

We’ll start with the same code as in the previous tutorial, except here we’ll iterate through a NumPy array rather than a list. The NumPy array is created in the *arr* variable using the arrange() function, which returns one billion numbers starting from 0 with a step of 1.

```
import time
import numpy
total = 0
arr = numpy.arange(1000000000)
t1 = time.time()
for k in arr:
total = total + k
t2 = time.time()
print("Total = ", total)
t = t2 - t1
print("%.20f" % t)
```

I’m running this on a machine with Core i7–6500U CPU @ 2.5 GHz, and 16 GB DDR3 RAM. The Python code completed in 458 seconds (7.63 minutes). It’s too long.

Let’s see how much time it takes to complete after editing the Cython script created in the previous tutorial, as given below. The only change is the inclusion of the NumPy array in the *for* loop. Note that you have to rebuild the Cython script using the command below before using it.

```
python setup.py build_ext --inplace
```

The Cython script in its current form completed in 128 seconds (2.13 minutes). Still long, but it’s a start. Let’s see how we can make it even faster.

#paperspace #numpy #c #cython #python

1619660285

A geek in Machine Learning with a Master’s degree in…

####### READ NEXT

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

1595235180

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.

#numpy tutorials #features of numpy #numpy features #why use numpy #numpy

1595235240

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