This is a draft cheat sheet. It is a work in progress and is not finished yet.
TO START
import numpy as np
# optional: to shorten the writing
# avoid to type np.random
from numpy.random import randint
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ARRAYS
np.array(my_list) |
create array from list |
np.array(my_matrix) |
create array from matrix |
METHODS (to create arrays)
np.arange(n1, n2) |
create array |
np.arange(n1, n2, n3) |
create array with step |
np.zeros() * |
zeroes array |
np.ones() * |
ones array |
np.linespace(n1,n2,n3) * |
evenly spaced numbers in an interval |
np.eye() |
identity matrix |
np.random.rand() * |
random from uniform distribution |
np.random.randn() |
random from normal distribution |
np.random.randint() |
random integers
from low (inclusive)
to high (exclusive) |
np.zeros/ones can take two numbers (rows and columns)
linespace(): n3 numbers from n1 to n2.
np.random.rand() can take 2 numbers (rows and columns).
ARRAY ATTRIBUTES AND METHODS
arr.reshape(r,c)* |
change shape of array |
arr.max() |
find max value |
arr.argmax() |
find index of maxim value |
arr.min() |
find min value |
arr.argmin() |
find index of minim value |
arr.shape |
return array shape |
arr.reshape().shape |
reshape array and
return array shape |
arr.dtype |
return array type |
(r,c) mean "row, column"
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ARRAY INDEXING AND SELECTION
arr[] |
select item in array |
arr[x:x] |
select items in range |
arr[:x] |
select items up to x |
arr[x:] |
select items from x and beyond |
arr[0:5]=100 * |
broadcast value in range |
arr.copy() |
copy the array |
arr_2d[r,c]* |
select in 2D arrays |
arr_2d[r][c] |
select in 2D arrays (opt.2) |
arr_2d[:2,1:]* |
slicing (2 is not included) |
arr_2d[2] |
show third row |
arr_2d[2, :] |
show third row (opt.2) |
arr_2d[[2,4,6,8]] |
show row 2,4,6,8 |
arr[arr>x] |
conditional selection |
note: changes will happen also into the original array
[r,c] mean "row, column"
When slicing in this example, 2, is not included. Generally, the term after the column (:) is not included when slicing.
OPERATIONS
arr + arr |
sum two arrays |
arr * arr |
multiply two arrays |
arr - arr |
subtract two arrays |
arr / arr |
divide two arrays |
arr ** x |
array to exponential |
np.sqrt(arr) |
sqaure root of array |
np.exp(arr) |
exponential of array (e^) |
np.max(arr) |
find max value |
np.sin(arr) |
sin of array |
np.log(arr) |
lof of an array |
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