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Numpy reference sheet

Numpy - Single dimens­ional arrays

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)
 

Useful Numpy Functions

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

Combining Arrays

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
 

Universal Functions (ufuncts)

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)

Iterating

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
           
 

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