WebDec 26, 2024 · you could use a dictionary comprehension to get all the repeated numbers and their indexes in one go: L = [1, 2, 3, 4, 5, 3, 8, 9, 9, 8, 9] R = { n:rep [n] for rep in [ {}] for i,n in enumerate (L) if rep.setdefault (n, []).append (i) or len (rep [n])==2 } print (R) {3: [2, 5], 9: [7, 8, 10], 8: [6, 9]} The equivalent using a for loop would be: WebApr 11, 2024 · The ICESat-2 mission The retrieval of high resolution ground profiles is of great importance for the analysis of geomorphological processes such as flow processes (Mueting, Bookhagen, and Strecker, 2024) and serves as the basis for research on river flow gradient analysis (Scherer et al., 2024) or aboveground biomass estimation (Atmani, …
python - How to get the indexes of the same values in a list?
WebJul 18, 2012 · How can I (efficiently, Pythonically) find which elements of a are duplicates (i.e., non-unique values)? In this case the result would be array ( [1, 3, 3]) or possibly array ( [1, 3]) if efficient. I've come up with a few methods that appear to work: Masking m = np.zeros_like (a, dtype=bool) m [np.unique (a, return_index=True) [1]] = True a [~m] WebSep 18, 2015 · I'm using Python 2.7. I have two arrays, A and B. To find the indices of the elements in A that are present in B, I can do. A_inds = np.in1d (A,B) I also want to get the indices of the elements in B that are present in A, i.e. the indices in B of the same overlapping elements I found using the above code. Currently I am running the same … bregdan chronicles box set
How can I find matching values in two arrays? - Stack Overflow
WebMar 1, 2016 · numpy.logical_and allows you to element-wise perform a logical AND operation between two numpy arrays. What we're doing here is determining which locations contain both the x values being 1 and the y values being 4 … WebMay 14, 2012 · If you want to check if two arrays have the same shape AND elements you should use np.array_equal as it is the method recommended in the documentation. Performance-wise don't expect that any equality check will beat another, as there is not much room to optimize comparing two elements. Just for the sake, i still did some tests. WebDec 1, 2016 · # [Optional] sort locations and drop duplicates id2.sort_values (by='ID2', inplace=True) id2.drop_duplicates ('ID2', inplace=True) # columns that you are merging must have the same name id2.rename (columns= {'ID2':'ID1'}, inplace=True) # perform the merge df = id1.merge (id2) Without drop_duplicates you get one row for each item: bregdan chronicles by ginny dye .epub