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Python Data Analysis by Ivan Idris

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NumPy

The following are useful NumPy functions:

  • numpy.arange([start,] stop[, step,], dtype=None): This function creates a NumPy array with evenly spaced values within a specified range.
  • numpy.argsort(a, axis=-1, kind='quicksort', order=None): This function returns the indices that will sort the input array.
  • numpy.array(object, dtype=None, copy=True, order=None, subok=False, ndmin=0): This function creates a NumPy array from an array-like sequence such as a Python list.
  • numpy.dot(a, b, out=None):This function calculates the dot product of two arrays.
  • numpy.eye(N, M=None, k=0, dtype=<type 'float'>): This function returns the identity matrix.
  • numpy.load(file, mmap_mode=None): This function loads NumPy arrays or pickled objects from .npy, .npz, or pickles. ...

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