#### Category : vectorization

I’m trying to stack an image and I can’t wrap my head around how to make it more efficient. The following function is correct but not fast enough computationally: def func(table, arr): img_sum = np.zeros((1024, 256)) for i in range(1024): for j in range(256): for k in range(3): img_sum[i, j] += arr[int(table[i, j, k]), i, ..

data picture Sorry for inconvenience of picture of the data ! I get this data, I try to calculate EMA_20 a row base on EMA_20 row before Example: calculate EMA_20 at index 1003 base on EMA_20 at index 1004, I try using vectorization for speed up but don’t know how to specify the index at ..

I have three different time based dataframes with 10s of thousands of data points. df1[‘time’] = 1, 2, 3, 4, 5 df1[‘data1’] = 1, 0, 0, 1, 0 df2[‘time’] = 1, 3, 5, 7, 9 df2[‘data2’] = a, b, c, d, e df3[‘time’] = 3, 4, 5, 6, 7 df3[‘data3’] = z, y, x, w, ..

Sorry for confusing title, but not sure how to make it more concise. Here’s my requirements: arr1 = np.array([3,5,9,1]) arr2 = ?(arr1) arr2 would then be: [ [0,1,2,0,0,0,0,0,0], [0,1,2,3,4,0,0,0,0], [0,1,2,3,4,5,6,7,8], [0,0,0,0,0,0,0,0,0] ] It doesn’t need to vary based on the max, the shape is known in advance. So to start I’ve been able to get ..

Summary: I am trying to vectorize calculating statistics for large continuous datasets. I describe my problems and attempts, in words (in the numbered list) and python (in the code block), respectively. Exact questions are towards the end. I make use of pandas and numpy. Code outline: bin_methods = [‘fixed_width’, ‘fixed_freq’] col_names = raw_df.columns.values.tolist() # Initialize ..

I’m looking to do something like cumsum but with subtraction. I need to do this with vectorization because my dataset length is really 100,000 rather than 3. I have two arrays: a = np.array([-7.021, -1.322, 3.07]) b = np.array([[-1.592, -1.495, -1.415, -1.363, -0.408, -0.36, -0.308], [-0.287, -0.249, -0.226, -0.206, -0.197, -0.165, -0.075], [-0.389, -0.237, 0.144, ..

I have 2 DataFrames A_df = pd.DataFrame(data = np.arange(2, 103, 10) + np.random.randn(11), columns = [‘Time(s)’]) B_df = pd.DataFrame(data = zip(range(1, 102), np.random.randn(101)), columns = [‘Time(s)’, ‘Value’]) A_df.head() Time(s) 0 2.751352 1 12.028663 2 20.638388 3 29.821199 4 42.516302 B_df.head() Time(s) Value 0 1 1.075801 1 2 0.890754 2 3 -0.015543 3 4 0.085298 4 ..