Managing data in Numpy- Part 2

This post is the second part for the data management in numpy. We are going to continue with different set of functions and its syntax, code snippets and description in this post.
Hello Readers,

This article is the part 2 of the 2 part series for the data management functions in Numpy.
If you have not read the first part of this post, you can directly head to this link and check it out: Managing data in Numpy- Part 1

Following are the functions or attributes of the numpy array which we will study:


5.concatenate()
6.vstack()
7. hstack()
8. flatten()
9. ravel()

5. concatenate()
np.concatenate() is used to concatenate two numpy arrays by passing them as a parameter to the function call.Other parameters that can be passed to the function call are axis, which specifies along which axis (1 or 0) the arrays should be joined.Another one is out which specifies the destination to place the result.

Syntax: np.concatenate((a1,a2,..),axis,out)
(a1,a2,..) : sequence of array to be flattened
axis: Specifies along which axis the array should be concatenated
out: specifies the destination at which the result should be written

Following is the code snippet for np.concatenate():

6. vstack()
Like concatenate vstack() is also used for joining the arrays but only vertically.This function call has only one parameter which is the tuple of arrays to be joined.

Syntax: np.vstack(tup)
tup: sequence of ndarrays

Following is the code snippet for np.vstack():  

7.hstack()
Similar to vstack() we have hstack() which is used for stacking arrays horizontally. The function call has only one parameter which is the tuple of arrays too be joined.
Syntax: np.hstack(tup)
tup: sequence of arrays

Following is the code snippet for np.hstack():

8.flatten()
ndarray.flatten() is used to flatten the array along the horizontal axis.So if the dimension of the array is 3x3 then after using flatten function on that array the dimension will be 1x9.

Syntax: ndarray.flatten(order='C') 

order: {‘C’, ‘F’, ‘A’, ‘K’}, optional 

‘C’ means to flatten in row-major (C-style) order. ‘F’ means to flatten in column-major (Fortran- style) order. ‘A’ means to flatten in column-major order if a is Fortran contiguous in memory, row-major order otherwise. ‘K’ means to flatten a in the order the elements occur in memory. The default is ‘C’. 

 

Following is the code snippet for ndarray.flatten():

9.ravel()

np.ravel() returns a flattened array of the same type which is passed in the function call to ravel.Ravel is similar to flatten but there is a subtle difference between them which is covered in the post further.

 

Syntax: np.ravel(a,order='C')

a: Input array

Order: same as the order parameter in ndarray.flatten()

 

Following is the code snippet for np.ravel():

 

Before closing the post I want to point you attention towards something important.

We have seen how to use flatten and ravel but both the functions give the same output.

Why do we need 2 different function calls to get the same output?????

This difference is explained succinctly in the following post : ndarray.flatten vs np.ravel

Thank you.

That's all for this post!!
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References:
1.https://docs.scipy.org/doc/numpy/reference/generated/numpy.concatenate.html
2.https://docs.scipy.org/doc/numpy/reference/generated/numpy.vstack.html
3.https://docs.scipy.org/doc/numpy/reference/generated/numpy.hstack.html
4.https://docs.scipy.org/doc/numpy/reference/generated/numpy.ndarray.flatten.html
5.https://docs.scipy.org/doc/numpy/reference/generated/numpy.ravel.html
6.https://stackoverflow.com/questions/28930465/what-is-the-difference-between-flatten-and-ravel-functions-in-numpy