What are the advantages of NumPy over regular Python lists

Question

What are the advantages of NumPy over regular Python lists   I have approximately 100 financial markets series  and I am going to create a cube array of 100x100x100   1 million cells  I will be regressing  3-variable  each x with each y and z  to fill the array with standard errors   I have heard that for  large matrices  I should use NumPy as opposed to Python lists  for performance and scalability reasons  Thing is  I know Python lists and they seem to work for me    What will the benefits be if I move to NumPy   What if I had 1000 series  that is  1 billion floating point cells in the cube

User · Answer

All have highlighted almost all major differences between numpy array and python list, I will just brief them out here:

Numpy arrays have a fixed size at creation, unlike python lists (which can grow dynamically). Changing the size of ndarray will create a new array and delete the original.
The elements in a Numpy array are all required to be of the same data type (we can have the heterogeneous type as well but that will not gonna permit you mathematical operations) and thus will be the same size in memory
Numpy arrays are facilitated advances mathematical and other types of operations on large numbers of data. Typically such operations are executed more efficiently and with less code than is possible using pythons build in sequences

User · Answer

Here s a nice answer from the FAQ on the scipy org website   What advantages do NumPy arrays offer over  nested  Python lists      Python   s lists are efficient general-purpose containers  They support    fairly  efficient insertion  deletion  appending  and concatenation    and Python   s list comprehensions make them easy to construct and   manipulate  However  they have certain limitations  they don   t support      vectorized    operations like elementwise addition and multiplication    and the fact that they can contain objects of differing types mean   that Python must store type information for every element  and must   execute type dispatching code when operating on each element  This   also means that very few list operations can be carried out by   efficient C loops     each iteration would require type checks and other   Python API bookkeeping

User · Answer

Alex mentioned memory efficiency  and Roberto mentions convenience  and these are both good points  For a few more ideas  I ll mention speed and functionality   Functionality  You get a lot built in with NumPy  FFTs  convolutions  fast searching  basic statistics  linear algebra  histograms  etc  And really  who can live without FFTs   Speed  Here s a test on doing a sum over a list and a NumPy array  showing that the sum on the NumPy array is 10x faster  in this test -- mileage may vary    from numpy import arange from timeit import Timer  Nelements   10000 Ntimeits   10000  x   arange Nelements  y   range Nelements   t numpy   Timer  x sum      from   main   import x   t list   Timer  sum y     from   main   import y   print  numpy    3e     t numpy timeit Ntimeits  Ntimeits    print  list     3e     t list timeit Ntimeits  Ntimeits      which on my systems  while I m running a backup  gives   numpy  3 004e-05 list   5 363e-04

User · Answer

NumPy is not just more efficient  it is also more convenient  You get a lot of vector and matrix operations for free  which sometimes allow one to avoid unnecessary work  And they are also efficiently implemented   For example  you could read your cube directly from a file into an array   x   numpy fromfile file open  data    dtype float  reshape  100  100  100     Sum along the second dimension   s   x sum axis 1    Find which cells are above a threshold    x  gt  0 5  nonzero     Remove every even-indexed slice along the third dimension   x         2    Also  many useful libraries work with NumPy arrays   For example  statistical analysis and visualization libraries   Even if you don t have performance problems  learning NumPy is worth the effort

User · Answer

NumPy s arrays are more compact than Python lists -- a list of lists as you describe  in Python  would take at least 20 MB or so  while a NumPy 3D array with single-precision floats in the cells would fit in 4 MB  Access in reading and writing items is also faster with NumPy   Maybe you don t care that much for just a million cells  but you definitely would for a billion cells -- neither approach would fit in a 32-bit architecture  but with 64-bit builds NumPy would get away with 4 GB or so  Python alone would need at least about 12 GB  lots of pointers which double in size  -- a much costlier piece of hardware   The difference is mostly due to  indirectness  -- a Python list is an array of pointers to Python objects  at least 4 bytes per pointer plus 16 bytes for even the smallest Python object  4 for type pointer  4 for reference count  4 for value -- and the memory allocators rounds up to 16   A NumPy array is an array of uniform values -- single-precision numbers takes 4 bytes each  double-precision ones  8 bytes  Less flexible  but you pay substantially for the flexibility of standard Python lists

[python] What are the advantages of NumPy over regular Python lists?

Examples related to python

Examples related to arrays

Examples related to list

Examples related to numpy

Examples related to numpy-ndarray