Numpy reference sheet
|
Creating an array from a list
|
x = np.array(['a', 'b', '9', '8']
|
|
Array Data Types
|
Consist of integers, floating-point numbers, or strings. Data type must be consistent. Numpy's record array gives mixed DTypes
|
|
Find the length on an array
|
np.len(x)
|
|
Accessing elements from an array
|
x[index_num] x[1]
|
|
Assign elements from index
|
x[1]=c
|
|
Slicing x[start:end])
|
print(x[1:2]) ['b', '9']
|
|
Slicing x[start:end: step])
|
print(x[1:3:2]) ['b', '8']
|
|
Modify a new version of an array without changing the original
|
y = x.copy()
|
|
Negative slices, single value x[-Distance from end]
|
x[-2] b
|
|
Negative slices, reversal x[start:end: -step])
|
x[3:0:-2]) 8, b
|
|
Adding to an array
|
x.append('7')
|
|
Saving to binary file
|
np.save(open('data.npy', 'wb'), data)
|
|
Array Creation:
|
arange, array, copy, empty, empty_like, eye, fromfile, fromfunction, identity, linspace, logspace, mgrid, ogrid, ones, ones_like, r_, zeros, zeros_like
|
|
Conversions
|
ndarray.astype, atleast_1d, atleast_2d, atleast_3d, mat
|
|
Manipulations:
|
array_split, column_stack, concatenate, diagonal, dsplit, dstack, hsplit, hstack, ndarray.item, newaxis, ravel, repeat, reshape, resize, squeeze, swapaxes, take, transpose, vsplit, vstack
|
|
Questions:
|
all, any, nonzero, where
|
|
Ordering:
|
argmax, argmin, argsort, max, min, ptp, searchsorted, sort
|
|
Operations:
|
choose, compress, cumprod, cumsum, inner, ndarray.fill, imag, prod, put, putmask, real, sum
|
|
Basic Statistics:
|
cov, mean, std, var
|
|
Basic Linear Algebra:
|
cross, dot, outer, linalg.svd, vdot
|
|
np.vtack((a1, a2) =
|
np.concatenate((a1, a2, axis = 0)
|
|
np.hstack(a1,a2) =
|
np.concatenate((a1, a2, axis = 1)
|
|
vsplit
|
splits along the vertical axis
|
|
hsplit
|
splits along the horizontal axis
|
|
Implement vectorization (operations applied to whole arrays instead of individual elements) in NumPy which is way faster than iterating over elements.
|
Also provide broadcasting (when smaller array is cast across the larger array so that they have compatible shapes)
|
|
x = [1, 2, 3, 4] y = [4, 5, 6, 7] z = np.add(x, y)
|
[ 5 7 9 11]
|
|
Check if a function is a ufunc:
|
print(type(np.add))
|
|
arr1 = np.array([10, 20, 30, 40, 50, 60])
|
arr2 = np.array([20, 21, 22, 23, 24, 25])
|
|
np.subtract(arr1, arr2)
|
newarr = np.multiply(arr1, arr2)
|
|
newarr = np.divide(arr1, arr2)
|
newarr = np.power(arr1, arr2)
|
|
newarr = np.remainder(arr1, arr2)
|
newarr = np.absolute(arr)
|
|
arr = np.array([[1, 2, 3], [4, 5, 6]])
|
|
|
for x in arr: print(x)
|
[1, 2, 3], [4, 5, 6]
|
|
for x in arr: for y in x: print(y)
|
123456
|
|
for x in np.nditer(arr):
|
123456
|
|
for x in np.nditer(arr, flags=['buffered'], op_dtypes=['S']): print(x)
|
b '1', b'2', b'3'
|
|
for x in np.nditer(arr[:, ::2]): print(x)
|
1, 3, 5, 7
|
|
Enumeration means mentioning sequence number of somethings one by one.
|
|
|
for idx, x in np.ndenumerate(arr): print(idx, x)
|
|
|
for idx, x in np.ndenumerate(arr): print(idx, x) (
|
0,) 1 (1,) 2 (2,) 3
|
|
for idx, x in np.ndenumerate(arr): print(idx, x)
|
Result Size: 1425 x 1251 import numpy as np arr = np.array([[1, 2, 3, 4], [5, 6, 7, 8]]) for idx, x in np.ndenumerate(arr): print(idx, x) (0, 0) 1 (0, 1) 2 (0, 2) 3 (0, 3) 4 (1, 0) 5 (1, 1) 6 (1, 2) 7 (1, 3) 8
|
No comments yet.
Cheatography n. The art of mapping complex subjects into clear, instantly navigable reference guides.
