I had three points and a line to fit, and I kept writing normal equations by hand. I expected residuals to always be a number, so the empty array on my first exact system felt like a bug until I…
I asked np.arange for the values between 2.23 and 2.24 in steps of 0.005, and it handed back three of them, the last one printing as the upper limit I had just excluded. A reader on Stack Overflow hit the…
np.exp turns a large input into inf and only warns instead of raising an error. The overflow matters because it turns a softmax into NaNs, and the fix is one subtraction. What NumPy exp computes np.exp is a universal function,…
np.clip limits every value in an array to a range you give it. Values below the lower bound become the lower bound, and values above become the upper. The examples also show reversed bounds and NaN values. What np.clip does…
np.eye builds an identity matrix, and the arguments that matter are its size, its dtype, and an offset that moves the diagonal. The examples go from a basic identity matrix to one-hot encodings. What np.eye actually returns That prints True.…
Call np.where with only a condition and it answers with a tuple of index positions, not the values themselves. That shape catches you on the first line you print, and the trailing comma inside the parentheses is the clue to…
NumPy interp estimates a value between known samples by joining neighboring points with straight line segments. Give it the x-coordinate you need, the sorted sample positions, and the matching sample values. Use numpy.interp for a value between samples The function…
NumPy full creates an array with a chosen shape and the same fill value in every cell. Install NumPy and import it Install the current NumPy release in your environment, then import it with the short name np used in…
I ran into this problem the hard way during a data pipeline project – I needed to duplicate a large NumPy array for processing, and my first attempt with the assignment operator left me with two variables pointing at the…
NumPy is one of the most important Python libraries for numerical computing and data analysis. It is widely used in data science, machine learning, and backend development to handle arrays, matrices, and mathematical operations efficiently. These 100 Python NumPy MCQs…
