Brooke  Giles

Brooke Giles

1565586190

Why is Python used so widely in big data analysis despite of it being slow?

I have noticed that Python is used a lot in big data.

People call C functions from Python, then process it further in Python, then call some other libraries, possibly again in Python that also look at gigantic data arrays.

Isn't this an extremely inefficient way of doing things? Python is much slower than C++. How can it make sense to use Python in situations when large data is processed, performance-wise?

One company asked me the question "How to bind a C-function to Python that computes a 1GB floating-point array, and then to compute a total of all numbers in Python?" They ask this question from the position when they assume that the use of Python is totally normal, and one should do such things as computing a 1GB fp array in C, then copying it into a gigantic Python list, then computing a total of numbers in Python. But this question in itself assumes that things are done extremely inefficiently, isn't it? They are just indoctrinated and think that things that they do are normal when they are far from normal.

So why is Python used so widely, as opposed to using C++, for example? Is this because many people feel that Python is much easier and C++ is too hard?

#python #data-analysis #big-data #data-science #machine-learning

What is GEEK

Buddha Community

One thing that is easy to forget is that the application context of analysis programs is usually very different. The total amount of time required to perform a task is ‘coding time + execution time’. If you have some code that’s computationally expensive and gets reused over and over for an extended period of time, then investing more coding time in optimization or a lower level language can lead to a net benefit because of the large decrease in execution time. On the other hand, for analysis activities (and particularly exploratory analysis) bits of code tend to be run only a handful of times. In that case, the coding time makes up a greater proportion of the total time than the execution time. That’s why high level languages tend to be used for those kinds of tasks, because they tend to provide the lowest total time due to the nature of analysis. It’s just a different use case

Dylan Iqbal

1565594340

Pure Python is slow but you rarely do the heavily lifting with it. Python is great for wrapping libraries, gluing things together, etc. The common “two-language” solutions do a pretty good job of allowing us to quickly write pretty code that is as highly performant.

In your example, copying all data into a Python list isn’t very useful. But operating on that data in C and returning the value to Python is fine.

Adam Rose

1565602531

Data analysis is usually done once. Then you try something else.

Everybody is okay with losing a minute if computer speed to gain an hour if developer time.

I can’t imagine even trying but I would wager that Python is at least an order of magnitude faster to iterate and experiment in vs c++

Callum Slater

1565620779

  1. If you’re using the toolchain properly, the vast majority of your Python code will run on C arrays, using vectorised operations. Numpy, Scipy, Pandas, and related tools don’t revert to native Python objects. They almost entirely operate in C / FORTRAN, with the performance of those languages.
  2. There is extensive built-in support for common (and uncommon) use cases, from machine learning through to image processing. Python is the industry standard, meaning you don’t have to reinvent the wheel and can find help and documentation easily.
  3. Analytics workloads rarely involve building apps in the traditional sense. Speed to output and ease of iteration is critical, and Python beats C/C++ hands down in that regard.
Siphiwe  Nair

Siphiwe Nair

1620466520

Your Data Architecture: Simple Best Practices for Your Data Strategy

If you accumulate data on which you base your decision-making as an organization, you should probably think about your data architecture and possible best practices.

If you accumulate data on which you base your decision-making as an organization, you most probably need to think about your data architecture and consider possible best practices. Gaining a competitive edge, remaining customer-centric to the greatest extent possible, and streamlining processes to get on-the-button outcomes can all be traced back to an organization’s capacity to build a future-ready data architecture.

In what follows, we offer a short overview of the overarching capabilities of data architecture. These include user-centricity, elasticity, robustness, and the capacity to ensure the seamless flow of data at all times. Added to these are automation enablement, plus security and data governance considerations. These points from our checklist for what we perceive to be an anticipatory analytics ecosystem.

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Ray  Patel

Ray Patel

1619518440

top 30 Python Tips and Tricks for Beginners

Welcome to my Blog , In this article, you are going to learn the top 10 python tips and tricks.

1) swap two numbers.

2) Reversing a string in Python.

3) Create a single string from all the elements in list.

4) Chaining Of Comparison Operators.

5) Print The File Path Of Imported Modules.

6) Return Multiple Values From Functions.

7) Find The Most Frequent Value In A List.

8) Check The Memory Usage Of An Object.

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Brooke  Giles

Brooke Giles

1565586190

Why is Python used so widely in big data analysis despite of it being slow?

I have noticed that Python is used a lot in big data.

People call C functions from Python, then process it further in Python, then call some other libraries, possibly again in Python that also look at gigantic data arrays.

Isn't this an extremely inefficient way of doing things? Python is much slower than C++. How can it make sense to use Python in situations when large data is processed, performance-wise?

One company asked me the question "How to bind a C-function to Python that computes a 1GB floating-point array, and then to compute a total of all numbers in Python?" They ask this question from the position when they assume that the use of Python is totally normal, and one should do such things as computing a 1GB fp array in C, then copying it into a gigantic Python list, then computing a total of numbers in Python. But this question in itself assumes that things are done extremely inefficiently, isn't it? They are just indoctrinated and think that things that they do are normal when they are far from normal.

So why is Python used so widely, as opposed to using C++, for example? Is this because many people feel that Python is much easier and C++ is too hard?

#python #data-analysis #big-data #data-science #machine-learning

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Silly mistakes that can cost ‘Big’ in Big Data Analytics

Big Data has played a major role in defining the expansion of businesses of all kinds as it helps the companies to understand their audience and devise their business techniques in accordance with the requirement.

The importance of ‘Data’ has been spoken very highly in the modern-day business. Thus, while using big data analysis, the companies must keep away from these minor mistakes otherwise it could have a major impact on their performances. Big Data analysis can be the silver bullet that can answer your questions and help your business to scale newer heights.

Read More: Silly mistakes that can cost ‘Big’ in Big Data Analytics

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