myArray = numpy.array(['abc', 'bca', 'cab']) |
Create a new one-dimensional array called ‘myArray’ with string elements. |
my2DArray = numpy.array([[0, 15, 4],[8, 12, 0]]) |
Create a new two-dimensional array called my2DArray with integer elements |
newArr = numpy.array(variable) |
Convert variable into a numpy array |
newEmptyArr = numpy.empty((shape1, shape2), dtype=np.float32) |
Create a new two-dimensional array that is empty but will store floats shape1*shape2 floats where shape1 is number of rows |
newZerosArr = numpy.zeros((shape1, shape2)) |
Create a new two-dimensional array that contnains only zeros, where shape1 is the number of rows |
newFullArr = numpy.full((shape1, shape2), value) |
Create a new two-dimensional array that contains some value value, like numpy.nan or 42; shape1 is the number of rows |
retrieved = myArray[index] |
Retrieve an item from the one-dimensional myArray at the given index. You can change the item at the given index by setting it equal to a new value. An index of -1 will return the last item in the list. Indexing begins at 0 |
|
retrieved = my2DArray[index] |
Retrieve a row of elements at the given index (dtype is another numpy array) from the two-dimensional my2DArray. Indexing begins at 0 |
|
retreived = my2DArray[:, index] |
Retrieve a column of elements at the given index (dtype is another numpy array) from the two-dimensionnal my2DArray. Indexing begins at 0 |
|
retrieved = my2DArray[index1, index2] |
Retrieve a single element at the given indices from the two-dimensional my2DArray. Indexing begins at 0 |
|
slicedArr = myArray[start:stop] |
Create a new array from a slice of myArray. Either start or stop can be ommitted (e.g. myArray[:3] will give you a slice of myArray up to index 3). Indexing again begins at 0 |
myArray.ndim |
Return the number of dimensions of myArray |
myArray.shape |
Return the shape (or number of elements) of each dimension of myArray as a tuple. (e.g. myArray.shape[0] will give you the shape of the first dimension) |
my2DArray += value |
Add an int or float value to every element in the array my2DArray |
min_value = numpy.amin(my2DArray, axis=0) |
Return the minimum value for each column in the 2D array |
min_value2 = numpy.amin(my2DArray, axis=1) |
Return the minimum value for each row in the 2D array |
min_value3 = numpy.amin(my2DArray) |
Return the minimum value in the whole 2D array |
max_value = numpy.amax(my2DArray, axis=0) |
Return the maximum value for each column in the 2D array |
max_value2 = numpy.amax(my2DArray, axis=1) |
Return the maximum value for each row in the 2D array |
max_value3 = numpy.amax(my2DArray) |
Return the maximum value in the whole 2D array |
min_val = numpy.minimum(my2DArray, value) |
Returns the minimum value element-wise, comparing my2DArray and the given value, or another array of the same shape. If any NaNs are present, that is the default minimum |
max_val = numpy.maximum(my2DArray, value) |
Returns the maximum value element-wise, comparing my2DArray and the given value, or another array of the same shape. If any NaNs are present, that is the default maximum |
indices_true = numpy.where(my2DArray > value) or indices_true = numpy.where(my2DArray == value2) |
Returns a tuple with arrays of the indices where the condition (> value, == value2, etc.) is true. If you want the array of row indices, you would need to do indices_true[0]. Likewise, for the columns, indices_true[1]. Will return empty arrays within the tuple if no elements meet the condition. Will also return a tuple, even for 1D arrays. |
indicies_both_true = numpy.where((my2DArray['name1'] == some_value) & (my2DArray['name2'] == some_other_value)) |
Returns array of indices where both conditions (== some value) and (== some some_other_value) are both true |
indices_atleast_one_true = numpy.where((my2DArray['name1'] == some_value) | (my2DArray['name1'] == some_other_value)) |
Returns array of indices where at least one condition (== some value) or (== some some_other_value) is true |