Verbal IQ scores based on the four Wechsler (1981) subtests In here I will combine pandas data loading with scipy.stats module. show ()Īs you may note we are trying out the linear and cubic interpolation and using the 1d function. plot ( interpolation_time, cubic_results, c = 'g', label = 'cubic results' ) ax. plot ( interpolation_time, linear_results, c = 'r', label = 'linear results' ) ax. linspace ( 0, 1, 50 ) linear_interp = interp1d ( measured_time, measures ) linear_results = linear_interp ( interpolation_time ) cubic_interp = interp1d ( measured_time, measures, kind = 'cubic' ) cubic_results = cubic_interp ( interpolation_time ) ax. ![]() scatter ( measured_time, measures ) # Blue dotsįrom scipy.interpolate import interp1d interpolation_time = np. pi * measured_time ) + noise fig, ax = plt. ![]() Import matplotlib.pyplot as plt measured_time = np. ![]() Various utilities that don’t have another home. ![]() There is also a module called scipy.misc for For more dependencies check out the official link SciPy is concise open-source library based on NumPy, Pandas, Matplotlib, SymPy.
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