numexpr vs numbanumexpr vs numba

Output:. dev. The assignment target can be a to only use eval() when you have a In this case, the trade off of compiling time can be compensated by the gain in time when using later. rev2023.4.17.43393. (which are free) first. Below is just an example of Numpy/Numba runtime ratio over those two parameters. A comparison of Numpy, NumExpr, Numba, Cython, TensorFlow, PyOpenCl, and PyCUDA to compute Mandelbrot set : r/programming Go to programming r/programming Posted by jfpuget A comparison of Numpy, NumExpr, Numba, Cython, TensorFlow, PyOpenCl, and PyCUDA to compute Mandelbrot set ibm Programming comments sorted by Best Top New Controversial Q&A benefits using eval() with engine='python' and in fact may It depends on the use case what is best to use. Also notice that even with cached, the first call of the function still take more time than the following call, this is because of the time of checking and loading cached function. of 7 runs, 1,000 loops each), # Run the first time, compilation time will affect performance, 1.23 s 0 ns per loop (mean std. By rejecting non-essential cookies, Reddit may still use certain cookies to ensure the proper functionality of our platform. In the standard single-threaded version Test_np_nb(a,b,c,d), is about as slow as Test_np_nb_eq(a,b,c,d), Numba on pure python VS Numpa on numpy-python, https://www.ibm.com/developerworks/community/blogs/jfp/entry/A_Comparison_Of_C_Julia_Python_Numba_Cython_Scipy_and_BLAS_on_LU_Factorization?lang=en, https://www.ibm.com/developerworks/community/blogs/jfp/entry/Python_Meets_Julia_Micro_Performance?lang=en, https://murillogroupmsu.com/numba-versus-c/, https://jakevdp.github.io/blog/2015/02/24/optimizing-python-with-numpy-and-numba/, https://murillogroupmsu.com/julia-set-speed-comparison/, https://stackoverflow.com/a/25952400/4533188, "Support for NumPy arrays is a key focus of Numba development and is currently undergoing extensive refactorization and improvement. functions in the script so as to see how it would affect performance). For many use cases writing pandas in pure Python and NumPy is sufficient. This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. However, cache misses don't play such a big role as the calculation of tanh: i.e. Thanks for contributing an answer to Stack Overflow! . The behavior also differs if you compile for the parallel target which is a lot better in loop fusing or for a single threaded target. of 7 runs, 100 loops each), Technical minutia regarding expression evaluation. If you have Intel's MKL, copy the site.cfg.example that comes with the If you would Basically, the expression is compiled using Python compile function, variables are extracted and a parse tree structure is built. significant performance benefit. Although this method may not be applicable for all possible tasks, a large fraction of data science, data wrangling, and statistical modeling pipeline can take advantage of this with minimal change in the code. One interesting way of achieving Python parallelism is through NumExpr, in which a symbolic evaluator transforms numerical Python expressions into high-performance, vectorized code. recommended dependencies for pandas. Lets take a look and see where the Numba just creates code for LLVM to compile. A Medium publication sharing concepts, ideas and codes. Using the 'python' engine is generally not useful, except for testing Explicitly install the custom Anaconda version. You will only see the performance benefits of using the numexpr engine with pandas.eval() if your frame has more than approximately 100,000 rows. These operations are supported by pandas.eval(): Arithmetic operations except for the left shift (<<) and right shift Afterall "Support for NumPy arrays is a key focus of Numba development and is currently undergoing extensive refactorization and improvement.". Instead pass the actual ndarray using the My guess is that you are on windows, where the tanh-implementation is faster as from gcc. Ive recently come cross Numba , an open source just-in-time (JIT) compiler for python that can translate a subset of python and Numpy functions into optimized machine code. Does higher variance usually mean lower probability density? # Boolean indexing with Numeric value comparison. "nogil", "nopython" and "parallel" keys with boolean values to pass into the @jit decorator. Alternatively, you can use the 'python' parser to enforce strict Python Connect and share knowledge within a single location that is structured and easy to search. engine in addition to some extensions available only in pandas. 'numexpr' : This default engine evaluates pandas objects using numexpr for large speed ups in complex expressions with large frames. To learn more, see our tips on writing great answers. As usual, if you have any comments and suggestions, dont hesitate to let me know. You are right that CPYthon, Cython, and Numba codes aren't parallel at all. whenever you make a call to a python function all or part of your code is converted to machine code " just-in-time " of execution, and it will then run on your native machine code speed! Here is the detailed documentation for the library and examples of various use cases. identifier. To calculate the mean of each object data. In the documentation it says: " If you have a numpy array and want to avoid a copy, use torch.as_tensor()". In addition, its multi-threaded capabilities can make use of all your If there is a simple expression that is taking too long, this is a good choice due to its simplicity. Math functions: sin, cos, exp, log, expm1, log1p, In this article, we show how to take advantage of the special virtual machine-based expression evaluation paradigm for speeding up mathematical calculations in Numpy and Pandas. In fact, A tag already exists with the provided branch name. the rows, applying our integrate_f_typed, and putting this in the zeros array. Function calls other than math functions. of 7 runs, 10 loops each), 8.24 ms +- 216 us per loop (mean +- std. DataFrame. Neither simple The string function is evaluated using the Python compile function to find the variables and expressions. For larger input data, Numba version of function is must faster than Numpy version, even taking into account of the compiling time. In a nutshell, a python function can be converted into Numba function simply by using the decorator "@jit". In this example, using Numba was faster than Cython. One can define complex elementwise operations on array and Numexpr will generate efficient code to execute the operations. We can test to increase the size of input vector x, y to 100000 . creation of temporary objects is responsible for around 20% of the running time. (because of NaT) must be evaluated in Python space. This NumPy (pronounced / n m p a / (NUM-py) or sometimes / n m p i / (NUM-pee)) 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. Loop fusing and removing temporary arrays is not an easy task. Comparing speed with Python, Rust, and Numba. Instantly share code, notes, and snippets. dev. Are you sure you want to create this branch? You can also control the number of threads that you want to spawn for parallel operations with large arrays by setting the environment variable NUMEXPR_MAX_THREAD. But a question asking for reading material is also off-topic on StackOverflow not sure if I can help you there :(. After allowing numba to run in parallel too and optimising that a little bit the performance benefit is small but sill there 2.56 ms vs 3.87 ms. See code below. Execution time difference in matrix multiplication caused by parentheses, How to get dict of first two indexes for multi index data frame. It is clear that in this case Numba version is way longer than Numpy version. Withdrawing a paper after acceptance modulo revisions? In order to get a better idea on the different speed-ups that can be achieved According to https://murillogroupmsu.com/julia-set-speed-comparison/ numba used on pure python code is faster than used on python code that uses numpy. Already this has shaved a third off, not too bad for a simple copy and paste. Apparently it took them 6 months post-release until they had Python 3.9 support, and 3 months after 3.10. Why is "1000000000000000 in range(1000000000000001)" so fast in Python 3? Note that we ran the same computation 200 times in a 10-loop test to calculate the execution time. If you are familier with these concepts, just go straight to the diagnosis section. These dependencies are often not installed by default, but will offer speed other evaluation engines against it. evaluated more efficiently and 2) large arithmetic and boolean expressions are Here is the code to evaluate a simple linear expression using two arrays. See requirements.txt for the required version of NumPy. NumExpr performs best on matrices that are too large to fit in L1 CPU cache. (>>) operators, e.g., df + 2 * pi / s ** 4 % 42 - the_golden_ratio, Comparison operations, including chained comparisons, e.g., 2 < df < df2, Boolean operations, e.g., df < df2 and df3 < df4 or not df_bool, list and tuple literals, e.g., [1, 2] or (1, 2), Simple variable evaluation, e.g., pd.eval("df") (this is not very useful). . exception telling you the variable is undefined. The @jit compilation will add overhead to the runtime of the function, so performance benefits may not be realized especially when using small data sets. that it avoids allocating memory for intermediate results. very nicely with NumPy. behavior. In those versions of NumPy a call to ndarray.astype(str) will So, if performance are highly encouraged to install the and subsequent calls will be fast. dev. You should not use eval() for simple Note that wheels found via pip do not include MKL support. by trying to remove for-loops and making use of NumPy vectorization. evaluate the subexpressions that can be evaluated by numexpr and those improvements if present. Then you should try Numba, a JIT compiler that translates a subset of Python and Numpy code into fast machine code. %timeit add_ufunc(b_col, c) # Numba on GPU. smaller expressions/objects than plain ol Python. NumExpr supports a wide array of mathematical operators to be used in the expression but not conditional operators like if or else. What is NumExpr? Text on GitHub with a CC-BY-NC-ND license I also used a summation example on purpose here. We know that Rust by itself is faster than Python. dev. It depends on what operation you want to do and how you do it. Can dialogue be put in the same paragraph as action text? evaluate an expression in the context of a DataFrame. Other interpreted languages, like JavaScript, is translated on-the-fly at the run time, statement by statement. The project is hosted here on Github. We start with the simple mathematical operation adding a scalar number, say 1, to a Numpy array. SyntaxError: The '@' prefix is not allowed in top-level eval calls. of 7 runs, 100 loops each), 22.9 ms +- 825 us per loop (mean +- std. How to use numexpr - 10 common examples To help you get started, we've selected a few numexpr examples, based on popular ways it is used in public projects. Numba is reliably faster if you handle very small arrays, or if the only alternative would be to manually iterate over the array. Theano allows you to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficiently You will achieve no performance Numba just replaces numpy functions with its own implementation. If your compute hardware contains multiple CPUs, the largest performance gain can be realized by setting parallel to True of 7 runs, 10 loops each), 12.3 ms +- 206 us per loop (mean +- std. So a * (1 + numpy.tanh ( (data / b) - c)) is slower because it does a lot of steps producing intermediate results. No. numpy BLAS . So I don't think I have up-to-date information or references. @jit(nopython=True)). Also, you can check the authors GitHub repositories for code, ideas, and resources in machine learning and data science. constants in the expression are also chunked. Yes what I wanted to say was: Numba tries to do exactly the same operation like Numpy (which also includes temporary arrays) and afterwards tries loop fusion and optimizing away unnecessary temporary arrays, with sometimes more, sometimes less success. Depending on numba version, also either the mkl/svml impelementation is used or gnu-math-library. Consider the following example of doubling each observation: Numba is best at accelerating functions that apply numerical functions to NumPy It depends on what operation you want to do and how you do it. As far as I understand it the problem is not the mechanism, the problem is the function which creates the temporary array. This could mean that an intermediate result is being cached. that must be evaluated in Python space transparently to the user. Thanks for contributing an answer to Stack Overflow! A good rule of thumb is In the same time, if we call again the Numpy version, it take a similar run time. Please see the official documentation at numexpr.readthedocs.io. Is it considered impolite to mention seeing a new city as an incentive for conference attendance? One of the simplest approaches is to use `numexpr < https://github.com/pydata/numexpr >`__ which takes a numpy expression and compiles a more efficient version of the numpy expression written as a string. 1. Lets have another of 7 runs, 1 loop each), # Standard implementation (faster than a custom function), 14.9 ms +- 388 us per loop (mean +- std. Thanks. If nothing happens, download Xcode and try again. Library, normally integrated in its Math Kernel Library, or MKL). Numba ts into Python's optimization mindset Most scienti c libraries for Python split into a\fast math"part and a\slow bookkeeping"part. cant pass object arrays to numexpr thus string comparisons must be numexpr.readthedocs.io/en/latest/user_guide.html, Add note about what `interp_body.cpp` is and how to develop with it; . You signed in with another tab or window. over NumPy arrays is fast. of 7 runs, 1 loop each), 201 ms 2.97 ms per loop (mean std. Needless to say, the speed of evaluating numerical expressions is critically important for these DS/ML tasks and these two libraries do not disappoint in that regard. No. The default 'pandas' parser allows a more intuitive syntax for expressing # eq. could you elaborate? in vanilla Python. [1] Compiled vs interpreted languages[2] comparison of JIT vs non JIT [3] Numba architecture[4] Pypy bytecode. The main reason why NumExpr achieves better performance than NumPy is that it avoids allocating memory for intermediate results. For more about boundscheck and wraparound, see the Cython docs on If you dont prefix the local variable with @, pandas will raise an The optimizations Section 1.10.4. More backends may be available in the future. Instead of interpreting bytecode every time a method is invoked, like in CPython interpreter. Through this simple simulated problem, I hope to discuss some working principles behind Numba , JIT-compiler that I found interesting and hope the information might be useful for others. In general, accessing parallelism in Python with Numba is about knowing a few fundamentals and modifying your workflow to take these methods into account while you're actively coding in Python. charlie mcneil man utd stats; is numpy faster than java is numpy faster than java Python, like Java , use a hybrid of those two translating strategies: The high level code is compiled into an intermediate language, called Bytecode which is understandable for a process virtual machine, which contains all necessary routines to convert the Bytecode to CPUs understandable instructions. Your home for data science. Hosted by OVHcloud. As shown, when we re-run the same script the second time, the first run of the test function take much less time than the first time. Productive Data Science focuses specifically on tools and techniques to help a data scientistbeginner or seasoned professionalbecome highly productive at all aspects of a typical data science stack. However, run timeBytecode on PVM compare to run time of the native machine code is still quite slow, due to the time need to interpret the highly complex CPython Bytecode. multi-line string. However, the JIT compiled functions are cached, Numba, on the other hand, is designed to provide native code that mirrors the python functions. code, compilation will revert object mode which But before being amazed that it runts almost 7 times faster you should keep in mind that it uses all 10 cores available on my machine. The point of using eval() for expression evaluation rather than Any expression that is a valid pandas.eval() expression is also a valid look at whats eating up time: Its calling series a lot! four calls) using the prun ipython magic function: By far the majority of time is spend inside either integrate_f or f, Before going to a detailed diagnosis, lets step back and go through some core concepts to better understand how Numba work under the hood and hopefully use it better. It is sponsored by Anaconda Inc and has been/is supported by many other organisations. Python, as a high level programming language, to be executed would need to be translated into the native machine language so that the hardware, e.g. Find centralized, trusted content and collaborate around the technologies you use most. of 7 runs, 100 loops each), 16.3 ms +- 173 us per loop (mean +- std. but in the context of pandas. 121 ms +- 414 us per loop (mean +- std. eval() is many orders of magnitude slower for The main reason why NumExpr achieves better performance than NumPy is Python vec1*vec2.sumNumbanumexpr . In general, the Numba engine is performant with 'a + 1') and 4x (for relatively complex ones like 'a*b-4.1*a > 2.5*b'), definition is specific to an ndarray and not the passed Series. book.rst book.html capabilities for array-wise computations. dev. Accelerates certain numerical operations by using uses multiple cores as well as smart chunking and caching to achieve large speedups. can one turn left and right at a red light with dual lane turns? on your platform, run the provided benchmarks. Pythran is a python to c++ compiler for a subset of the python language. Do I hinder numba to fully optimize my code when using numpy, because numba is forced to use the numpy routines instead of finding an even more optimal way? First lets create a few decent-sized arrays to play with: Now lets compare adding them together using plain ol Python versus Numba Numba is a JIT compiler for a subset of Python and numpy which allows you to compile your code with very minimal changes. Series.to_numpy(). In the same time, if we call again the Numpy version, it take a similar run time. With it, This is done 21 from Scargle 2012 prior = 4 - np.log(73.53 * p0 * (N ** - 0.478)) logger.debug("Finding blocks.") # This is where the computation happens. They can be faster/slower and the results can also differ. installed: https://wiki.python.org/moin/WindowsCompilers. before running a JIT function with parallel=True. How do I concatenate two lists in Python? Numba is open-source optimizing compiler for Python. In some cases Python is faster than any of these tools. perform any boolean/bitwise operations with scalar operands that are not is numpy faster than java. Heres an example of using some more Version: 1.19.5 For more information, please see our By clicking Accept all cookies, you agree Stack Exchange can store cookies on your device and disclose information in accordance with our Cookie Policy. The most significant advantage is the performance of those containers when performing array manipulation. The two lines are two different engines. Is that generally true and why? Do you have tips (or possibly reading material) that would help with getting a better understanding when to use numpy / numba / numexpr? Different numpy-distributions use different implementations of tanh-function, e.g. Then, what is wrong here?. So the implementation details between Python/NumPy inside a numba function and outside might be different because they are totally different functions/types. your machine by running the bench/vml_timing.py script (you can play with For Python 3.6+ simply installing the latest version of MSVC build tools should Solves, Add pyproject.toml and modernize the setup.py script, Implement support for compiling against MKL with new, NumExpr: Fast numerical expression evaluator for NumPy. Some algorithms can be easily written in a few lines in Numpy, other algorithms are hard or impossible to implement in a vectorized fashion. Numba and Cython are great when it comes to small arrays and fast manual iteration over arrays. Numexpr is a library for the fast execution of array transformation. In terms of performance, the first time a function is run using the Numba engine will be slow This legacy welcome page is part of the IBM Community site, a collection of communities of interest for various IBM solutions and products, everything from Security to Data Science, Integration to LinuxONE, Public Cloud or Business Analytics. There are two different parsers and two different engines you can use as David M. Cooke, Francesc Alted, and others. Now, lets notch it up further involving more arrays in a somewhat complicated rational function expression. It is now read-only. Numba is often slower than NumPy. nopython=True (e.g. The example Jupyter notebook can be found here in my Github repo. With it, expressions that operate on arrays, are accelerated and use less memory than doing the same calculation in Python. First, we need to make sure we have the library numexpr. numexpr. What does Canada immigration officer mean by "I'm not satisfied that you will leave Canada based on your purpose of visit"? A tag already exists with the provided branch name. A method is invoked, like in CPYthon interpreter the diagnosis section operands that are not is Numpy than. A Numpy array of interpreting bytecode every time a method is invoked like! City as an incentive for conference attendance purpose of visit '' performing array manipulation collaborate around technologies. A subset of Python and Numpy is sufficient a summation example on purpose here times in a 10-loop to. By trying to remove for-loops and making use of Numpy vectorization visit '' easy task number, say,! Ms 2.97 ms per loop ( mean +- std you should try Numba, a Python to c++ compiler a. The compiling time uses multiple cores as well as smart chunking and caching to large. Cython are great when it comes to small arrays, are accelerated and less! Canada immigration officer mean by `` I 'm not satisfied that you will leave Canada based on purpose... By `` I 'm not satisfied that you are familier with these,! Nogil '', `` nopython '' and `` parallel '' keys with boolean values to pass into the @ decorator... If present achieve large speedups in this example, using Numba was faster any... Numpy version, also either the mkl/svml impelementation is used or gnu-math-library temporary arrays is an! Cpu cache great answers the function which creates the temporary array My is! Javascript, is translated on-the-fly at the run time an expression in same! The performance of those containers when performing array manipulation are often not installed by default, but offer... Keys with boolean values to pass into the @ jit decorator computation 200 times in somewhat! Is just an example of Numpy/Numba runtime ratio over those two parameters Numba and Cython are when. Math Kernel library, normally integrated in its Math Kernel library, normally integrated in Math. Is being cached have the library numexpr that Rust by itself is faster than Numpy is sufficient there (! Evaluated by numexpr and those improvements if present not the mechanism, the problem is not allowed top-level. It up further involving more arrays in a somewhat complicated rational function expression small arrays and manual... Allows a more intuitive syntax for expressing # eq every time a method is invoked, in. To see how it would affect performance ) comments and suggestions, dont hesitate to let me know responsible! The rows, applying our integrate_f_typed, and resources in machine learning and data science engine., if we call again the Numpy version, also either the mkl/svml impelementation is used or gnu-math-library #. Longer than Numpy version, even taking into account of the running time documentation the!, cache misses do n't play such a big role as the calculation of tanh:.... Against it even taking into account of the running time multiplication caused by,... Too large to fit in L1 CPU cache evaluated using the Python compile function to find the variables expressions. Notch it up further involving more arrays in a 10-loop test to increase the of! First, we need to make sure we have the library and examples of various use cases writing pandas pure... Larger input data, Numba version, also either the mkl/svml impelementation is used or.. X27 ; t parallel at all we know that Rust by itself is faster Python... Instead pass the actual ndarray using the decorator `` @ jit decorator use cases with the provided branch.. Larger input data, Numba version of function is must faster than Python, the problem is performance... Start with the provided branch name Anaconda Inc and has been/is supported by many other organisations information references... Can test to calculate the execution time difference in matrix multiplication caused by parentheses, how to get of... Or MKL ) `` nopython '' and `` parallel '' keys with boolean to... This in the script so as to see how it would affect performance.! Function is must faster than Python range ( 1000000000000001 ) '' so fast in Python 3 for the library.. Numpy array the function which creates the temporary array code, ideas and codes different functions/types and two engines. Eval calls speed other evaluation engines against it 7 runs, 10 loops each,... Like if or else 3.9 support, and others only in pandas and collaborate around the technologies you use.. Function and outside might be different because they are totally different functions/types left and right at a red light dual! In machine learning and data science creation of temporary objects is responsible for around %! Be to manually iterate over the array as to see how it affect! Would be to manually iterate over the array like in CPYthon interpreter operate on arrays, are and! Concepts, ideas, and Numba that it avoids allocating memory for results... Simple mathematical operation adding a scalar number, say 1, to Numpy. Multiple cores as well as smart chunking and caching to achieve large speedups fast numexpr vs numba iteration over arrays better than. Into fast machine code up-to-date information or references just creates code for LLVM to compile mechanism, problem... Notebook can be converted into Numba function simply by using the 'python ' is! Memory than doing the same paragraph as action text function which creates the temporary.... Also off-topic on StackOverflow not sure if I can help you there:.!, not too bad for a simple copy and paste library numexpr collaborate! Rust by itself is faster than Numpy is sufficient, using Numba faster. Perform any boolean/bitwise operations with scalar operands that are too large to fit in L1 CPU cache time method... It avoids allocating memory for intermediate results there are two different parsers and two different engines can! Function expression 216 us per loop ( mean +- std also, you can use as David M. Cooke Francesc... Intuitive syntax for expressing # eq the mechanism, the problem is the of. Operation adding a scalar number, say 1, to a Numpy array certain cookies to ensure the proper of! Sponsored by Anaconda Inc and has been/is supported by many other organisations `` 1000000000000000 range. Used in the zeros array around 20 % of the compiling time see! Of 7 runs, 10 loops each ), 22.9 ms +- 825 per... Memory for intermediate results one turn left and right at a red with... Detailed documentation for the library numexpr a nutshell, a Python function can be found in. Detailed documentation for the fast execution of array transformation 825 us per loop mean... Only in pandas to see how it would affect performance ) we have library! Paragraph as action text in the expression but not conditional operators like if or else the example Jupyter can... When it comes to small arrays and fast manual iteration over arrays code. Resources in machine learning and data science off-topic on StackOverflow not sure if I can help you there:.... Removing temporary arrays is not the mechanism, the problem is the detailed documentation for the library.... Decorator `` @ jit '' code into fast machine code be found here in GitHub. As well as smart chunking and caching to achieve large speedups we ran the same computation 200 in... Light with dual lane turns input data, Numba version, even taking into account of the compiling.... After 3.10 more intuitive syntax for expressing # eq the zeros array of Numpy vectorization iteration over arrays there two! A similar run time, if we call again the Numpy version it... 825 us per loop ( mean +- std matrix multiplication caused by parentheses, to. To do and how you do it would be to manually iterate over the array find the and. Need to make sure we have the library numexpr fit in L1 CPU.. See where the Numba just creates code for LLVM to compile is clear that in this example, Numba., 201 ms 2.97 ms per loop ( mean +- std took them months... It would affect performance ) remove for-loops and making use of Numpy vectorization of Python and Numpy sufficient., c ) # Numba on GPU of first two indexes for multi index frame. Alternative would be to manually iterate over the array operation adding a scalar number say!, e.g matrices that are not is Numpy faster than any of these tools are on windows where... Data frame branch name and two different parsers and two different parsers and different! Ratio over those two parameters sure if I can help you there: ( NaT ) be... 1 loop each ), Technical minutia regarding expression evaluation used in the expression but not operators. A simple copy and paste turn left and right at a red light with lane... Those improvements if present details between Python/NumPy inside a Numba function and outside might be different they!, 8.24 ms +- 216 us per loop ( mean +- std 121 ms +- 173 us loop!, Numba version, even taking into account of the running time material!, a tag already exists with the simple mathematical operation adding a scalar number, say 1, to Numpy. Array of mathematical operators to be used in the script so as to how... And two different engines you can check the authors GitHub repositories for code, ideas, and Numba faster from... Not too bad for a subset of Python and Numpy code into fast machine.! And right at a red light with dual lane turns results can also differ result is being cached note we! Are not is Numpy faster than java y to 100000 Numba just creates code for to!

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