importnumpyasnpa=np.array([[1357911][24681012]])# horizontal splittingprint('Splitting along horizontal axis into 2 parts:n'np.hsplit(a2))# vertical splittingprint('nSplitting along vertical axis into 2 parts:n'np.vsplit(a2))
Izraz emitiranje opisuje kako NumPy tretira nizove različitih oblika tijekom aritmetičkih operacija. Podložno određenim ograničenjima, manji niz se 'emituje' preko većeg niza tako da imaju kompatibilne oblike. Broadcasting pruža način vektorizacije operacija polja tako da se petlje pojavljuju u C-u umjesto u Pythonu. To čini bez izrade nepotrebnih kopija podataka i obično dovodi do učinkovitih implementacija algoritama. Također postoje slučajevi u kojima je emitiranje loša ideja jer dovodi do neučinkovite upotrebe memorije koja usporava računanje. NumPy operacije se obično izvode element po element što zahtijeva da dva niza imaju potpuno isti oblik. Numpyjevo pravilo emitiranja ublažava ovo ograničenje kada oblici nizova zadovoljavaju određena ograničenja. Pravilo emitiranja: Kako bi se emitirala veličina pratećih osi za oba niza u operaciji mora biti iste veličine ili jedna od njih mora biti jedan . Let us see some examples:
A(2-D array): 4 x 3 B(1-D array): 3 Result : 4 x 3
A(4-D array): 7 x 1 x 6 x 1 B(3-D array): 3 x 1 x 5 Result : 7 x 3 x 6 x 5
But this would be a mismatch:
A: 4 x 3 B: 4
The simplest broadcasting example occurs when an array and a scalar value are combined in an operation. Consider the example given below: Python
importnumpyasnpa=np.array([1.02.03.0])# Example 1b=2.0print(a*b)# Example 2c=[2.02.02.0]print(a*c)
Output:
[ 2. 4. 6.] [ 2. 4. 6.]
We can think of the scalar b being stretched during the arithmetic operation into an array with the same shape as a. The new elements in b as shown in above figure are simply copies of the original scalar. Although the stretching analogy is only conceptual. Numpy is smart enough to use the original scalar value without actually making copies so that broadcasting operations are as memory and computationally efficient as possible. Because Example 1 moves less memory (b is a scalar not an array) around during the multiplication it is about 10% faster than Example 2 using the standard numpy on Windows 2000 with one million element arrays! The figure below makes the concept more clear: In above example the scalar b is stretched to become an array of with the same shape as a so the shapes are compatible for element-by-element multiplication. Now let us see an example where both arrays get stretched. Python
U nekim slučajevima emitiranje rasteže oba niza kako bi se formirao izlazni niz veći od bilo kojeg od početnih nizova.
Rad s datumom i vremenom:
Numpy has core array data types which natively support datetime functionality. The data type is called datetime64 so named because datetime is already taken by the datetime library included in Python. Consider the example below for some examples: Python
importnumpyasnp# creating a datetoday=np.datetime64('2017-02-12')print('Date is:'today)print('Year is:'np.datetime64(today'Y'))# creating array of dates in a monthdates=np.arange('2017-02''2017-03'dtype='datetime64[D]')print('nDates of February 2017:n'dates)print('Today is February:'todayindates)# arithmetic operation on datesdur=np.datetime64('2017-05-22')-np.datetime64('2016-05-22')print('nNo. of days:'dur)print('No. of weeks:'np.timedelta64(dur'W'))# sorting datesa=np.array(['2017-02-12''2016-10-13''2019-05-22']dtype='datetime64')print('nDates in sorted order:'np.sort(a))
Output:
Date is: 2017-02-12 Year is: 2017 Dates of February 2017: ['2017-02-01' '2017-02-02' '2017-02-03' '2017-02-04' '2017-02-05' '2017-02-06' '2017-02-07' '2017-02-08' '2017-02-09' '2017-02-10' '2017-02-11' '2017-02-12' '2017-02-13' '2017-02-14' '2017-02-15' '2017-02-16' '2017-02-17' '2017-02-18' '2017-02-19' '2017-02-20' '2017-02-21' '2017-02-22' '2017-02-23' '2017-02-24' '2017-02-25' '2017-02-26' '2017-02-27' '2017-02-28'] Today is February: True No. of days: 365 days No. of weeks: 52 weeks Dates in sorted order: ['2016-10-13' '2017-02-12' '2019-05-22']
Linearna algebra u NumPyju:
Modul linearne algebre NumPy nudi različite metode za primjenu linearne algebre na bilo koji numpy niz. Možete pronaći:
trag determinante ranga itd. niza.
vlastite vrijednosti ili matrice
matrični i vektorski produkti (dot inner outeret. produkt) matrično potenciranje
rješavanje linearnih ili tenzorskih jednadžbi i još mnogo toga!
Consider the example below which explains how we can use NumPy to do some matrix operations. Python
importnumpyasnpA=np.array([[611][4-25][287]])print('Rank of A:'np.linalg.matrix_rank(A))print('nTrace of A:'np.trace(A))print('nDeterminant of A:'np.linalg.det(A))print('nInverse of A:n'np.linalg.inv(A))print('nMatrix A raised to power 3:n'np.linalg.matrix_power(A3))
Output:
Rank of A: 3 Trace of A: 11 Determinant of A: -306.0 Inverse of A: [[ 0.17647059 -0.00326797 -0.02287582] [ 0.05882353 -0.13071895 0.08496732] [-0.11764706 0.1503268 0.05228758]] Matrix A raised to power 3: [[336 162 228] [406 162 469] [698 702 905]]
Let us assume that we want to solve this linear equation set:
x + 2*y = 8 3*x + 4*y = 18
This problem can be solved using linalg.riješiti method as shown in example below: Python
importnumpyasnp# coefficientsa=np.array([[12][34]])# constantsb=np.array([818])print('Solution of linear equations:'np.linalg.solve(ab))
Output:
Solution of linear equations: [ 2. 3.]
Finally we see an example which shows how one can perform linear regression using least squares method. A linear regression line is of the form w1 x + w 2 = y i linija je ta koja minimizira zbroj kvadrata udaljenosti od svake podatkovne točke do linije. Dakle, s obzirom na n parova podataka (xi yi), parametri koje tražimo su w1 i w2 koji minimiziraju pogrešku: Let us have a look at the example below: Python
importnumpyasnpimportmatplotlib.pyplotasplt# x co-ordinatesx=np.arange(09)A=np.array([xnp.ones(9)])# linearly generated sequencey=[192020.521.522232325.524]# obtaining the parameters of regression linew=np.linalg.lstsq(A.Ty)[0]# plotting the lineline=w[0]*x+w[1]# regression lineplt.plot(xline'r-')plt.plot(xy'o')plt.show()
Output: Dakle, ovo vodi do zaključka ove serije NumPy vodiča. NumPy je široko korištena biblioteka opće namjene koja je srž mnogih drugih računalnih biblioteka kao što je scipy scikit-learn tensorflow matplotlib opencv itd. Posjedovanje osnovnog razumijevanja NumPy pomaže u učinkovitom radu s drugim bibliotekama više razine! Reference:
In above example the scalar b is stretched to become an array of with the same shape as a so the shapes are compatible for element-by-element multiplication. Now let us see an example where both arrays get stretched. Python 
U nekim slučajevima emitiranje rasteže oba niza kako bi se formirao izlazni niz veći od bilo kojeg od početnih nizova. 
Let us have a look at the example below: Python 
Dakle, ovo vodi do zaključka ove serije NumPy vodiča. NumPy je široko korištena biblioteka opće namjene koja je srž mnogih drugih računalnih biblioteka kao što je scipy scikit-learn tensorflow matplotlib opencv itd. Posjedovanje osnovnog razumijevanja NumPy pomaže u učinkovitom radu s drugim bibliotekama više razine! Reference: