numpy download Reviews & Product Details

Easy to sync with other libraries. Well mix of C and fortran makes it better than standard python Review collected by and hosted on G2.com.

Not optimal for multi-threading and need high storage Review collected by and hosted on G2.com.

Numpy is used for complex mathematical and numerical operations. It provides efficient calculations of arrays and matrices. Execution speed is high. Arbitrary data types can be defined in Numpy. Review collected by and hosted on G2.com.

Allocation of memory is contiguous. Insertion and deletion operations are costly due to such memory. Not suitable for larger datasets. Review collected by and hosted on G2.com.

It's a great library for doing advanced math that helps us work with Python's multidimensional arrays and matrices. It is very easy to use. It comes with Anaconda. It can be used efficiently with data science related libraries. It facilitates processing on large datasets. Review collected by and hosted on G2.com.

Every feature is very good. Maybe the parameters can be improved or the number of ready modules can be increased. Review collected by and hosted on G2.com.

The best thing about NumPy is its array implementation. I can implement 1D, 2D and more dimensions of the array. I can also change the data type of array. I can use NumPy for Image Processing. It is a very fast library for mathematical operations. Review collected by and hosted on G2.com.

Because of the rich collection of functions, I have to check for documentation whenever something is required, and I don't remember it. Review collected by and hosted on G2.com.

NumPy has so many functionality. I think the mostly used package in data science is NumPy and Pandas. Review collected by and hosted on G2.com.

I do not see any drawbacks or dislike for NumPy. Review collected by and hosted on G2.com.

I love how it's possible to do anything that comes to mind when someone says "Math" with NumPy library. It contains so many functionality to read, manipulate, calculate, visualize data. It provides a fundamental base, almost a platform to perform everything. One can create a simple logistic regression algorithm from the scratch, or a complex deep neural network with the same tools, train it, optimize it. Nevertheless the tools Data Scientists use are already built on NumPy, for example: Pandas, Sci-kit Learn.

Not to mention it's efficient to the extreme. Since the functions exist in NumPy are half written in C and are vectorized implementations, they are tens of times faster than writing for loops in Python. Linear algebra operations are especially critical in this term since implementation of machine learning algorithms, especially neural networks need vectorized, fast implementations. Review collected by and hosted on G2.com.

There's no downsides really. NumPy can easily be the perfect library for math, therefore machine learning. The only thing I can name is that it does not have GPU support, however this is the secret of its simplicity. GPU Support would require too much compatibility which in the end would destroy NumPy's "beauty in the simplicity". Review collected by and hosted on G2.com.

Numpy is an amazing library, the best thing I like about Numpy is it's performance. Numpy is very very faster as compared to Python lists. They have built in array data structure, which is really easy to work with and faster. In Numpy array the matrix multiplication and vector manipulation is super fast.

Overall it is best library for Machine Learning related stuff, research related work Review collected by and hosted on G2.com.

The Numpy performance is great but if you are not optimising for performance then python list are sufficient to do the work. No extra import and install the Numpy library. Review collected by and hosted on G2.com.

I used this library in an online Python course. We didn't go too deep into NumPy, but we used it to convert images to arrays for computer vision applications. Given that NumPy was designed for scientific computation and deep learning, I'm really impressed at its versatility in other areas such as computer vision. Review collected by and hosted on G2.com.

I'm a relative newby when it comes to Python in general, but I found the documentation for NumPy somewhat opaque in its organization. Review collected by and hosted on G2.com.

- In the Numpy matrix and vector operations are efficiently implemented.

- NumPy array is faster and You get a lot built in with NumPy, FFTs,

convolutions, fast searching, basic statistics, linear algebra,

histograms, etc.

- I used machine learning libraries like sci-kit-learn or tensorflow use numpy arrays as input which makes the computation faster

- It supports vectorized computation

- Efficient descriptive statistics and aggregating/summarizing data

- In general, Numpy processes faster and uses less code compared to lists. Review collected by and hosted on G2.com.

I used Numpy regularly in Machine Learning problems because it is faster and efficient. But if performance is not an issue, normal Python list will do the work. Python list is efficient and easy to program.

Also, to begin with, Numpy there is a learning curve. At the start, you might baffle about it, how to use it.

To use Numpy in Image Processing, I always find it tricky. Becaus there are lot of variables you should take into consideration. Review collected by and hosted on G2.com.