Question 1. What Is Numpy?

Answer :

NumPy is a Python extension module that gives green operation on arrays of homogeneous information. It lets in python to function a high-degree language for manipulating numerical statistics, just like IDL, MATLAB, or Yorick.

Question 2. Why Should I Use Numpy Rather Than Idl, Matlab, Octave, Or Yorick?

Answer :

As always, you have to pick out the programming equipment that in shape your hassle and your surroundings. Advantages many people cite are that it is open-supply, it doesn’t fee something, it uses the overall-reason language (Python) as opposed to a sui generisprogramming language, and it's miles pretty easy to attach current C and Fortran code to the Python interpreter.

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Question three. What Is A Numpy Array?

Answer :

A NumPy array is a multidimensional array of objects all the same type. In reminiscence, it is an item which factors to a block of memory, continues tune of the type of information saved in that reminiscence, maintains music of what number of dimensions there are and how massive each one is, and - importantly - the spacing between factors along each axis.

For example, you would possibly have a NumPy array that represents the numbers from 0 to nine, stored as 32-bit integers, one right after another, in a single block of memory. (for contrast, every Python integer wishes to have a few type statistics saved along it). You might also have the array of even numbers from 0 to eight, saved inside the identical block of reminiscence, but with a gap of four bytes (one 32-bit integer) between elements. This is known as striding, and it way that you can frequently create a brand new array referring to a subset of the elements in an array without copying any statistics. Such subsets are called views. This is an efficiency gain, obviously, however it also lets in change of selected factors of an array in various approaches.

An important constraint on NumPy arrays is that for a given axis, all the factors need to be spaced via the equal variety of bytes in memory. NumPy can't use double-indirection to access array elements, so indexing modes that might require this ought to produce copies. This constraint makes it viable for all the inner loops in NumPy’s internals to be written in efficient C code.

NumPy arrays offer a number of different opportunities, which include using a memory-mapped disk record because the storage area for an array, and record arrays, wherein each element may have a custom, compound information kind.

Question four. What Advantages Do Numpy Arrays Offer Over (nested) Python Lists?

Answer :

Python’s lists are green trendy-purpose containers. They assist (fairly) efficient insertion, deletion, appending, and concatenation, and Python’s listing comprehensions lead them to clean to assemble and control. However, they've certain boundaries: they don’t aid “vectorized” operations like elementwise addition and multiplication, and the reality that they can incorporate objects of differing types imply that Python have to save kind facts for every detail, and should execute type dispatching code whilst operating on every element. This additionally way that very few list operations may be carried out by way of green C loops – each generation would require kind checks and other Python API bookkeeping.

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Question 5. What’s The Story With Numeric, Numarray, And Numpy?

Answer :

The short model is that Numeric became the unique package that supplied green homogeneous numeric arrays for Python, but some developers felt it lacked certain important features, so that they began developing an impartial implementation referred to as numarray. Having incompatible implementations of array changed into clearly a catastrophe in the making, so NumPy was designed to be an improvement on each.

Neither Numeric nor numarray is currently supported. NumPy has been the standard array package for some of years now. If you use Numeric or numarray, you need to upgrade; NumPy is explicitly designed to have all the abilties of each (and already boasts new features observed in neither of its predecessor packages). There are tools to be had to ease the improve manner; only C code ought to require plenty change.

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Question 6. What Is Scipy?

Answer :

SciPy is a set of open source (BSD certified) clinical and numerical tools for Python. It presently helps special features, integration, normal differential equation (ODE) solvers, gradient optimization, parallel programming gear, an expression-to-C++ compiler for immediate execution, and others. A exact rule of thumb is that if it’s blanketed in a popular textbook on numerical computing (as an example, the well-known Numerical Recipes collection), it’s possibly carried out in scipy.

Question 7. How Much Does It Cost?

Answer :

SciPy is freely available. It is shipped as open supply software program, meaning which you have complete get entry to to the source code and might use it in any manner allowed via its liberal BSD license.

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Question eight. What Are Scipy’s Licensing Terms?

Answer :

SciPy’s license is free for each business and non-industrial.

Question nine. How Can Scipy Be Fast If It Is Written In An Interpreted Language Like Python?

Answer :

Actually, the time-vital loops are commonly carried out in C or Fortran. Much of SciPy is a thin layer of code on top of the medical routines. Netlib is a large repository of rather treasured and sturdy clinical algorithms written in C and Fortran. It would be silly to rewrite those algorithms and might take years to debug them. SciPy makes use of a variety of techniques to generate “wrappers” around those algorithms in order that they may be utilized in Python. Some wrappers have been generated by way of hand coding them in C. The rest have been generated using either SWIG or f2py. Some of the more recent contributions to SciPy are either written entirely or wrapped with Cython.

A second answer is that for tough troubles, a higher set of rules could make a terrific distinction within the time it takes to clear up a hassle. So the usage of scipy’s built-in algorithms can be lots quicker than a simple set of rules coded in C.

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Question 10. I’ve Found A Bug. What Do I Do?

Answer :

The SciPy development team works difficult to make SciPy as reliable as feasible, but, as in any software product, insects do arise. If you locate bugs that have an effect on your software program, please inform us by using entering a price tag within the SciPy worm tracker, or NumPy malicious program tracker, as suitable.

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Question 11. How Can I Get Involved In Scipy?

Answer :

Drop us a mail at the mailing lists. We are keen for greater humans to help out writing code, unit exams, documentation (along with translations into other languages), and assisting out with the internet site.

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Question 12. Is There Commercial Support Available?

Answer :

Yes, business aid is obtainable for SciPy with the aid of Enthought.

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Question 13. What Is The Difference Between Numpy And Scipy?

Answer :

In a great global, NumPy could comprise not anything but the array data type and the maximum primary operations: indexing, sorting, reshaping, primary elementwise capabilities, et cetera. All numerical code would reside in SciPy. However, certainly one of NumPy’s vital goals is compatibility, so NumPy tries to hold all features supported by using either of its predecessors. Thus NumPy consists of a few linear algebra capabilities, despite the fact that these more properly belong in SciPy. In any case, SciPy incorporates extra absolutely-featured versions of the linear algebra modules, in addition to many other numerical algorithms. If you are doing scientific computing with python, you have to possibly install both NumPy and SciPy. Most new features belong in SciPy in preference to NumPy.

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Question 14. How Do I Make Plots Using Numpy/scipy?

Answer :

Plotting functionality is past the scope of NumPy and SciPy, which focus on numerical items and algorithms. Several programs exist that integrate closely with NumPy to supply excessive great plots, inclusive of the immensely popular Matplotlib and the extensible, modular toolkit Chaco.

Question 15. How Do I Make 3d Plots/visualizations Using Numpy/scipy?

Answer :

Like 2D plotting, 3-d pics is beyond the scope of NumPy and SciPy, however just as inside the 2D case, applications exist that combine with NumPy. Matplotlib presents fundamental 3-D plotting within the mplot3d subpackage, whereas Mayavi presents a huge variety of brilliant 3-d visualization functions, using the effective VTK engine.

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Question 16. Why Both Numpy.Linalg And Scipy.Linalg? What’s The Difference?

Answer :

One of the layout dreams of NumPy changed into to make it buildable with out a Fortran compiler, and if you don’t have LAPACK to be had NumPy will use its own implementation. SciPy requires a Fortran compiler to be constructed, and heavily relies upon on wrapped Fortran code.

The linalg modules in NumPy and SciPy have a few not unusual capabilities but with specific docstrings, and scipy.Linalgcontains capabilities now not observed in numpy.Linalg, such as features associated with LU decomposition and the Schur decomposition, a couple of methods of calculating the pseudoinverse, and matrix transcendentals like the matrix logarithm. Some functions that exist in both have augmented capability in scipy.Linalg; for example scipy.Linalg.Eig() can take a 2nd matrix argument for solving generalized eigenvalue issues.

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Question 17. Do Numpy And Scipy Support Python three.X?

Answer :

NumPy and SciPy support the Python 2.X collection, (variations 2.6 and 2.7), in addition to Python three.2 and more recent. The first release of NumPy to help Python three was NumPy 1.Five.Zero. Python 3 guide in SciPy begins with model zero.9.0.

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Question 18. Does Numpy/scipy Work With Jython?

Answer :

No. Simply positioned, Jython runs on top of the Java Virtual Machine and has no manner to interface with extensions written in C for the usual Python (CPython) interpreter.

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Question 19. Does Numpy/scipy Work With Ironpython (.Internet)?

Answer :

Some users have suggested success in the use of NumPy with Ironclad on 32-bit Windows. The contemporary reputation of Ironclad aid for SciPy is unknown, but there are numerous complicating elements (specifically the Fortran compiler situation on Windows) that make it much less viable than NumPy.

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Question 20. What Is The Preferred Way To Check For An Empty (zero Element) Array?

Answer :

If you're sure a variable is an array, then use the scale characteristic. If the variable can be a listing or different collection type, use len().

The length characteristic is foremost to len due to the fact:

>>> a = numpy.Zeros((1,zero))

>>> a.Length

0

while

>>> len(a)

1

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Question 21. I Want To Load Data From A Text File. How Do I Make This Code More Efficient?

Answer :

Use numpy.Loadtxt(). Even if your textual content file has header and footer traces or feedback, loadtxt can almost truely study it; it's miles convenient and efficient.

If you find this nevertheless too sluggish, you should recollect changing to a extra efficient file format than plain text. There are a large number of alternatives, depending for your needs (and on which model of NumPy/SciPy you are the use of):

Text documents: gradual, big, portable, human-readable; constructed into NumPy

Raw binary: no metadata, completely unportable, fast; constructed into NumPy

pickle: extremely gradual, quite portable (may be incompatible with exceptional NumPy variations); constructed into NumPy

MATLAB format: transportable; constructed into SciPy (scipy.Io.Loadmat())

HDF5: excessive-powered kitchen-sink layout; each PyTables and h5py offer a NumPy friendly interface on pinnacle of the core HDF5 library written in C.

FITS: wellknown kitchen-sink layout in astronomy; the astropy library gives a convenient Python interface thru its io.Fits bundle.

.Npy: NumPy local binary statistics format, simple, green, transportable; built into NumPy as of 1.Zero.5.

Question 22. What Is The Difference Between Matrices And Arrays?

Answer :

NumPy’s primary records type is the multidimensional array. These can be one-dimensional (that is, one index, like a listing or a vector), -dimensional (two indices, like an picture), three-dimensional, or more (0-dimensional arrays exist and are a barely ordinary nook case). They support various operations, such as addition, subtraction, multiplication, exponentiation, and so forth - however all of these are elementwise operations. If you need matrix multiplication among two -dimensional arrays, the feature numpy.Dot() does this. It additionally works excellent for purchasing the matrix fabricated from a -dimensional array and a one-dimensional array, in either course, or one-dimensional arrays. If you need a few sort of matrix multiplication-like operation on better-dimensional arrays (tensor contraction), you want to think which indices you need to be contracting over. Some aggregate of tensordot() and rollaxis() ought to do what you want.

However, a few users find that they are doing such a lot of matrix multiplications that always having to write dot as a prefix is simply too cumbersome, or they really want to maintain row and column vectors separate. For these customers, there is a matrix magnificence. This is simply a transparent wrapper round arrays that forces arrays to be at least two-dimensional, and that overloads the multiplication and exponentiation operations. Multiplication will become matrix multiplication, and exponentiation turns into matrix exponentiation. If you need elementwise multiplication, use numpy.Multiply().

The function asmatrix() converts an array into a matrix (with out ever copying any data); asarray() converts matrices to arrays.Asanyarray() makes sure that the end result is both a matrix or an array (but not, say, a list). Unfortunately, a few of NumPy’s many capabilities use asarray() after they have to use asanyarray(), so once in a while you may find your matrices by chance get transformed into arrays. Just use asmatrix() on the output of those operations, and keep in mind filing a computer virus.

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Question 23. Why Not Just Have A Separate Operator For Matrix Multiplication?

Answer :

From Python three.5, the @ symbol will be defined as a matrix multiplication operator, and Numpy and Scipy will make use of this. This addition become the subject of PEP 465. The separate matrix and array sorts exist to work round the lack of this operator in earlier variations of Python.

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Question 24. How Do I Find The Indices Of An Array Where Some Condition Is True?

Answer :

The prefered idiom for doing this is to use the characteristic numpy.Nonzero() , or the nonzero() method of an array. Given an array a, the circumstance a > 3 returns a boolean array and seeing that False is interpreted as 0 in Python and NumPy, np.Nonzero(a > 3)yields the indices of a where the condition is real.

>>> import numpy as np

>>> a = np.Array([[1,2,3],[4,5,6],[7,8,9]])

>>> a > three

array([[False, False, False],

[ True, True, True],

[ True, True, True]], dtype=bool)

>>> np.Nonzero(a > three)

(array([1, 1, 1, 2, 2, 2]), array([0, 1, 2, 0, 1, 2]))

The nonzero() method of the boolean array can also be known as.

>>> (a > three).Nonzero()

(array([1, 1, 1, 2, 2, 2]), array([0, 1, 2, 0, 1, 2]))

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Question 25. How Do I Count The Number Of Times Each Value Appears In An Array Of Integers?

Answer :

Use numpy.Bincount(). The ensuing array is

>>> arr = numpy.Array([0, 5, 4, 0, 4, 4, 3, 0, 0, 5, 2, 1, 1, 9])

>>> numpy.Bincount(arr)

The argument to bincount() have to consist of nice integers or booleans. Negative integers are not supported.

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Question 26. Does Numpy Support Nan?

Answer :

nan, short for “now not quite a number”, is a special floating factor price defined by using the IEEE-754 specification at the side of inf (infinity) and different values and behaviours. In principle, IEEE nan changed into mainly designed to address the problem of lacking values, however the reality is that specific systems behave otherwise, making life extra hard. On a few structures, the presence of nanslows calculations 10-100 instances. For integer facts, no nan fee exists. Some platforms, considerably older Crays and VAX machines, don’t guide nan by any means.

Despite these kinds of problems NumPy (and SciPy) endeavor to assist IEEE-754 behaviour (based on NumPy’s predecessor numarray). The maximum substantial undertaking is a loss of go-platform guide within Python itself. Because NumPy is written to take advantage of C99, which helps IEEE-754, it could side-step such problems internally, however users may additionally nonetheless face issues when, as an example, evaluating values inside Python interpreter. In reality, NumPy presently assumes IEEE-754 behavior of the underlying floats, a decision which can must be revisited while the VAX network rises up in rebellion.

Those wishing to avoid ability headaches will be inquisitive about an alternative solution which has a protracted history in NumPy’s predecessors – masked arrays. Masked arrays are general arrays with a second “mask” array of the same shape to suggest whether or not the value is gift or missing. Masked arrays are the area of the numpy.Ma module, and retain the pass-platform Numeric/numarray subculture. See “Cookbook/Matplotlib/Plotting values with masked arrays” (TODO) as an example, to keep away from plotting missing information in Matplotlib. Despite their additional memory requirement, masked arrays are faster than nans on many floating factor devices.

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