For a head-like function you can just slice the array using dataset[:10].
For a tail-like function you can just slice the array using dataset[-10:].
For a head-like function you can just slice the array using dataset[:10].
For a tail-like function you can just slice the array using dataset[-10:].
You can do this for any python iterable.
PEP-3132 which is in python 3.x (https://www.python.org/dev/peps/pep-3132/) can use the * symbol for the 'rest' of the iterable.
To do what you want:
>>> import numpy as np
>>> np.array((1,2,3))
array([1, 2, 3])
>>> head, *tail = np.array((1,2,3))
>>> head
1
>>> tail
[2, 3]
Is there anything similar I can use to get a view of whatever data I'm looking at? Array slicing doesn't include the row and column labels, which kind of defeats the purpose. My teacher uses the shell command !head, but it seems to not work on windows.
Thank you in advance.
pandas - What does !head do in python and NumPy? - Stack Overflow
Remove head and tail from numpy array PYTHON - Stack Overflow
pandas - Easiest way to print the head of a data in python? - Stack Overflow
df.head(n) - Why does it exist?
It has nothing to do with Python or NumPy specifically; that's a feature of ipython to run external programs, in this case, the head utility from the standard suite of *NIX command line utilities. head -4 just reads the first four lines of the file provided and echoes them to the terminal.
Using "!" means that you want to call a system command. If you are on a Linux/Unix system (Google Colab uses such a system for instance) then you can call Linux/Unix commands directly using "!". Looks like you are using a Windows system and the command "head" does not exist as a command in Windows. Assuming that you are using a locally hosted Jupyter Notebook, then it is running on a Windows system.
The head command itself is a command-line utility for outputting the first part of files given to it via standard input. It writes results to standard output. By default head returns the first ten lines of each file that it is given.
Instead, use
middle = my_array[h:len(my_array)-t]
For completeness, here's the trial run:
my_array = [0,1,2,3,4,5]
h,t = 0,0
middle = my_array[h:len(my_array)-t]
print(middle)
Output: [0, 1, 2, 3, 4, 5]
This example was just for a standard array. Since your ultimate goal is to work with numpy multidimensional arrays, this problem is actually a bit trickier. When you say you want to remove the first h elements and the last t elements, are we guaranteed that h and t satisfy the proper divisibility criteria so that the result will be a well-formed array?
I actually think the cleanest solution is simply to use this solution, but divide out by the appropriate factor first. For example, in two dimensions:
h = 3
t = 6
a = numpy.array([[ 1, 2, 3],
[ 4, 5, 6],
[ 7, 8, 9],
[10, 11, 12]])
d = numpy.prod(numpy.shape(a)[1:])
mid_a = a[int(h/3):int(len(a)-t/3)]
print(mid_a)
Output: array([[4, 5, 6]])
I have included the int casts in the indices because python 3 automatically promotes division to float, even when the numerator evenly divides the denominator.
The i:j can be replaced with a slice object. and ':j' with slice(None,j), etc:
In [55]: alist = [0,1,2,3,4,5]
In [56]: h,t=1,-1; alist[slice(h,t)]
Out[56]: [1, 2, 3, 4]
In [57]: h,t=None,-1; alist[slice(h,t)]
Out[57]: [0, 1, 2, 3, 4]
In [58]: h,t=None,None; alist[slice(h,t)]
Out[58]: [0, 1, 2, 3, 4, 5]
This works for lists and arrays. For multidimensional arrays use a tuple of indices, which can include slice objects
x[i:j, k:l]
x[(slice(i,j), Ellipsis, slice(k,l))]
Can somebody please explain why the head() function in pandas exists. Since it only returns self.iloc[:n], why was it implemented in the first place?
I use df.head(n) all the time. But I just looked into the source code an realized that there is no "extra value" that is being added. Just a bunch of kinda redunant lines in the code. Is there a reason why it is not redundant?
Sorry for the nooby question and thanks to all serious replies :)