Are you familiar with R or Matlab and want to learn the Python package numpy?

Looking for an easy reference of useful numpy functions? Check this list out.

Open up a new script and import the numpy package:

Now cast your eye over these functions.

In no particular order:

  1. np.__version__ – return the version of numpy you have loaded.
  2. np.shape(x) – return the shape of an array x ; essentially the number of rows and columns in x.
  3. np.ndim(x) – return the number of dimensions of an array.
  4. np.zeros(shape) – create an array of zeros in the shape you specify.
  5. np.ones(shape) – create an array of ones in the shape you specify.
  6. np.eye(n) – create a identity matrix.
  7. np.arange(start, stop, step) – create evenly spaced values that are step apart between a start and end value.
  8. np.linspace(start, stop, num) – create num evenly spaced values between a start and end value.
  9. np.reshape(x, newshape) – change the shape of x to newshape.
  10. np.random.random(size) – return size random numbers between [0,1).
  11. np.random.rand(d0, d1, …, dn) – random uniformly distributed values in [0,1), in shape (d0, d1, …, dn).
  12. np.random.randn(d0, d1, …, dn) – random normally distributed values from the standard normal distribution, in a shape (d0, d1, …, dn).
  13. np.random.normal(loc, scale, size) – draw size random samples from a N(loc, scale^2)  distribution.
  14. np.random.randint(low, high, size) – draw size random numbers from a U(low, high) distribution.
  15. np.pad(x) – pads an array. Parameters determine what you pad the array with, how large the pad is and the mode of padding (there are lots!).
  16. np.diag(x, k) – construct a diagonal array, with values x down the diagonal k.
  17. np.tile(x, reps) – repeat x a total of reps times, where reps can be of multiple dimensions.
  18. np.unravel_index(indices, dims) – in an array of shape dims, what is the index of the “indices”th element? For example, np.unravel_index( 32, (3,3,5) ) = (2, 0, 2).
  19. np.dtype() – create your own custom data types.
  20. np.dot(A, B) – find the dot product of two matrices A and B.
  21. np.ndarray.astype(dtype) – change the data type of an array while making a copy of it.
  22. np.ceil(x) – rounds decimal numbers up to the nearest integer.
  23. np.floor(x) – rounds decimal numbers down to the nearest integer.
  24. np.copysign(x1, x2) – changes the sign of elements in array x1 to that of elements in array x2, comparing element-wise.
  25. np.intersect1d(x1, x2) – find the intersection of array x1 and array x2, returning an ordered set.
  26. np.union1d(x1, x2) – find the union of array x1 and array x2, returning an ordered set.
  27. np.datetime64(‘s1’) – convert a string s1 to a numpy datetime.
  28. np.timedelta64(‘s1’) – convert a string s1 to a numpy timedelta, with which you can perform date arithmetic.
  29. np.arange(‘s1’, ‘s2′, dtype=’datetime64[D]’) – get a list of days between two dates s1 and s2.
  30. np.add(x1, x2, out) – add two arrays x1 and x2. If out equals x1, then x1 will be overwritten with the result of the addition. Same thing for np.multiply, np.divide, np.negative.
  31. np.trunc(x) – get rid of decimal points in an floating point array x, leaving just the integer components.
  32. np.sort(x) – sort an array x in ascending order.
  33. np.sum(x, axis) – return the sum of an array over a particular axis.
  34. np.add.reduce(x, axis) – a quicker way of finding sum of an array over a particular axis, for small x. This is an example of a ufunc.
  35. np.array_equal(x1, x2) – check to see if two arrays x1 and x2 are equal.
  36. np.meshgrid(x1, x2) – create a 2d rectangular grid of values from array x1 and array x2. See here for further explanation.
  37. np.outer(x, y) – calculate the outer product of two vectors and y.
  38. np.setprintoptions(threshold) – change the number of elements displayed when printing an array to the console.
  39. np.argmax(x) – return the indices of the maximum values along an axis for an array x.
  40. np.argmin(x) – return the indices of the minimum values along an axis for an array x.
  41. np.put(x, ind, v) – put values v into an array x at indices ind, replacing what was there before.
  42. np.argsort(x) – return indices that would sort an array x. See here for further explanation.
  43. np.any(x, axis) – test if any array element of x along a given axis evaluates to True.
  44. np.ndarray.flat() – a flat iterator object to iterate over arrays. Can be indexed with square brackets.

Knowing how to use these functions will give you a good starting base for your numpy adventures. Good luck!

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