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 oddajanje opisuje, kako NumPy med aritmetičnimi operacijami obravnava nize različnih oblik. Ob upoštevanju določenih omejitev se manjši niz "oddaja" čez večji niz, tako da imata združljive oblike. Oddajanje zagotavlja sredstvo za vektorizacijo matričnih operacij, tako da se zanke pojavljajo v C namesto v Pythonu. To počne brez izdelovanja nepotrebnih kopij podatkov in običajno vodi do učinkovitih implementacij algoritmov. Obstajajo tudi primeri, ko je oddajanje slaba ideja, ker vodi do neučinkovite uporabe pomnilnika, ki upočasnjuje računanje. Operacije NumPy se običajno izvajajo element za elementom, kar zahteva, da imata dve matriki popolnoma enako obliko. Numpyjevo pravilo oddajanja sprosti to omejitev, ko oblike nizov ustrezajo določenim omejitvam. Pravilo oddajanja: Za oddajanje velikosti zadnjih osi za obe nizi v operaciji mora biti enaka velikost ali pa mora biti ena od njiju eno . 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
V nekaterih primerih oddajanje raztegne obe matriki, da tvori izhodno matriko, ki je večja od katere koli od začetnih matrik.
Delo z datumom in uro:
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 v NumPy:
Modul linearne algebre NumPy ponuja različne metode za uporabo linearne algebre na kateri koli matriki numpy. Najdete lahko:
determinanta ranga sled itd. matrike.
lastne vrednosti ali matrice
matrični in vektorski produkti (točkovni notranji zunanji itd. produkt) matrično potenciranje
rešite linearne ali tenzorske enačbe in še veliko več!
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.solve 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 in črta je tista, ki minimizira vsoto kvadratov razdalje od vsake podatkovne točke do črte. Torej glede na n parov podatkov (xi yi) sta parametra, ki ju iščemo, w1 in w2, ki minimizirata napako: 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: To vodi do zaključka te serije vadnic NumPy. NumPy je splošno uporabljena knjižnica za splošne namene, ki je jedro številnih drugih računalniških knjižnic, kot je scipy scikit-learn tensorflow matplotlib opencv itd. Osnovno razumevanje NumPy pomaga pri učinkovitem ravnanju z drugimi knjižnicami višje ravni! 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 
V nekaterih primerih oddajanje raztegne obe matriki, da tvori izhodno matriko, ki je večja od katere koli od začetnih matrik. 
Let us have a look at the example below: Python 
To vodi do zaključka te serije vadnic NumPy. NumPy je splošno uporabljena knjižnica za splošne namene, ki je jedro številnih drugih računalniških knjižnic, kot je scipy scikit-learn tensorflow matplotlib opencv itd. Osnovno razumevanje NumPy pomaga pri učinkovitem ravnanju z drugimi knjižnicami višje ravni! Reference: