Category : vectorization

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 ..

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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, ..

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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 ..

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