Numpy Cheat Sheet
Useful NumPy functions
The following table is taken from the Numpy chapter of the Python Basics course.
| Command | Description |
|---|---|
| Creating and manipulating arrays: | |
np.arange(number) |
creates an array of evenly spaced values, e.g. np.arange(3) creates an array of the numbers 1, 2, 3 |
np.array([1,2,3]) |
creates an array, e.g., of the numbers 1, 2, 3 |
np.shape(array) |
get the shape (dimensions and lengths) of array |
array.dtype |
get the type of array (integer?, float?, …) |
array.reshape |
reshapes array, e.g. from shape (6) to (2,3) |
np.transpose or array.T |
transpose array |
np.append(array1, array2) |
append array1 to array2 |
np.concatenate((array1, array2)) |
concatenate array1 and array2 |
np.insert(array, index, value) |
insert value at index-position of array |
np.delete(array, index) |
delete entry at index-position of array |
| Useful statistical functions: | |
np.mean(array) |
calculate the average of array |
np.median(array) |
calculate the median of array |
np.std(array) |
calculate the standard deviation of array |
np.sum(array) |
calculate the sum of array |
np.cumsum(array) |
calculate the cumulative sum of array |
np.max(array) |
calculate the maximum of array |
np.min(array) |
calculate the minimum of array |
| Useful mathematical functions: | |
np.sqrt(array) |
calculate the square root of array |
np.square(array) |
calculate the square of array |
np.abs(array) |
calculate the element-wise absolute values of array |
np.exp(array) |
calculate the exponentiation of array |
np.log(array) |
calculate the natural logarithm of array |
np.sin(array) |
calculate the sine of array |
np.cos(array) |
calculate the cosine of array |
np.round(array) |
round array |
np.floor(array) |
floor of the array |
np.ceil(array) |
ceiling of the array |
np.sort(array) |
sort array |
np.nan_to_num(array) |
replace NaN (Not a Number) with zero and infinity with large finite numberization |
