slice - How slicing in Python works - Stack Overflow Understanding the difference between indexing and slicing: Wiki Python has this amazing picture which clearly distinguishes indexing and slicing It is a list with six elements in it To understand slicing better, consider that list as a set of six boxes placed together Each box has an alphabet in it Indexing is like dealing with the contents
list - what does [::-1] mean in python - slicing? - Stack Overflow Slicing Negative numbers for start and stop mean "from the end" It's essianlly equivalent of len-value Negative number for step means "in reverse order" Empty start means 0 i e 1st element Empty stop means len Stop parameter is exclusive! So [::-1] means from 1st element to last element in steps of 1 in reverse order
Python: slicing a multi-dimensional array - Stack Overflow Python's slicing also doesn't support 2D multi-dimensional slicing for lists The expected output for slicing a multi-dimensional list can be tricky For example, If you want the third column (equivalent to a[:][2]), you might expect [3, 7, None] or [3, 7] Where it works: We can use the fact that a sliced list's output is a list
python - Slicing arrays in Numpy Scipy - Stack Overflow Now a two dimensional array is a different beast The slicing syntax for that is a[rowrange, columnrange] So if you want all the rows, but just the last two columns, like in your case, you do: a[0:3, 1:3] Here, "[0:3]" means all the rows from 0 to 3 and "[1:3]" means all columns from column 1 to column 3
What is row slicing vs What is column slicing? - Stack Overflow This makes much more sense to me I was thinking that : was the slice, so that's why I was confused with M[:, index] being column slicing So I guess you are saying, : it's not truly slicing as it just means "all", and then I'm just doing indexing of columns -- Further, for my case, I'm not truly slicing columns only indexing (as you said)
Slicing a list in Python without generating a copy Slicing lists does not generate copies of the objects in the list; it just copies the references to them That is the answer to the question as asked The long answer Testing on mutable and immutable values First, let's test the basic claim We can show that even in the case of immutable objects like integers, only the reference is copied
python - Implementing slicing in __getitem__ - Stack Overflow How to define the getitem class to handle both plain indexes and slicing? Slice objects gets automatically created when you use a colon in the subscript notation - and that is what is passed to __getitem__ Use isinstance to check if you have a slice object:
Does a slicing operation give me a deep or shallow copy? The official Python docs say that using the slicing operator and assigning in Python makes a shallow copy of the sliced list But when I write code for example: o = [1, 2, 4, 5] p = o[:] And when I write: id(o) id(p) I get different id's and also appending one one list does not reflect in the other list
How to take column-slices of dataframe in pandas The labels being the values of the index or the columns Slicing with loc includes the last element Let's assume we have a DataFrame with the following columns: foo, bar, quz, ant, cat, sat, dat # selects all rows and all columns beginning at 'foo' up to and including 'sat' df loc[:, 'foo':'sat'] # foo bar quz ant cat sat