Cordiales saludos <center>  </center> Comencemos entrando a nuestro entorno virtual y luego ejecutando ```jupyter lab``` ``` rafael@HP:~$ rafael@HP:~$ cd .gitlab/datascience/ rafael@HP:~/.gitlab/datascience$ rafael@HP:~/.gitlab/datascience$ source env/bin/activate (env) rafael@HP:~/.gitlab/datascience$ (env) rafael@HP:~/.gitlab/datascience$ jupyter lab ``` Para comenzar importamos la librería de **numpy** <center>  </center> ```python import numpy as np ``` ## Generar una matriz con un rango de fecha Podemos crear un rango de fecha en formato de dos dimensiones. ```python np.arange('2024-01', '2024-02', dtype='datetime64[D]') ``` ``` array(['2024-01-01', '2024-01-02', '2024-01-03', '2024-01-04', '2024-01-05', '2024-01-06', '2024-01-07', '2024-01-08', '2024-01-09', '2024-01-10', '2024-01-11', '2024-01-12', '2024-01-13', '2024-01-14', '2024-01-15', '2024-01-16', '2024-01-17', '2024-01-18', '2024-01-19', '2024-01-20', '2024-01-21', '2024-01-22', '2024-01-23', '2024-01-24', '2024-01-25', '2024-01-26', '2024-01-27', '2024-01-28', '2024-01-29', '2024-01-30', '2024-01-31'], dtype='datetime64[D]') ``` ## Matriz identidad Otra forma de generar la matriz identidad es con ```np.eye()``` ```python np.eye(4, dtype=int) ``` ``` array([[1, 0, 0, 0], [0, 1, 0, 0], [0, 0, 1, 0], [0, 0, 0, 1]]) ``` ```python np.eye(4) array([[1., 0., 0., 0.], [0., 1., 0., 0.], [0., 0., 1., 0.], [0., 0., 0., 1.]]) ``` ## Repaso matriz Identidad Esta forma de generar la matriz identidad ya la habíamos visto en este curso ```python np.identity(4) ``` array([[1., 0., 0., 0.], [0., 1., 0., 0.], [0., 0., 1., 0.], [0., 0., 0., 1.]]) ```python np.identity(4, dtype=int) ``` array([[1, 0, 0, 0], [0, 1, 0, 0], [0, 0, 1, 0], [0, 0, 0, 1]]) ## randint Podemos generar una matriz con números aleatorios, en este caso generamos una matriz de 5 por 5 con números aleatorios entre el 1 y 10. ```python np.random.randint(1,10,(5,5)) ``` array([[9, 1, 5, 3, 6], [2, 1, 9, 9, 1], [2, 6, 7, 3, 9], [2, 1, 1, 6, 9], [2, 2, 2, 2, 1]]) ## Suma de matrices Con ```np.random.randint()``` generamos dos matrices para realizar la suma de matrices. ```python matriz = np.random.randint(1,50,(3,3)) matriz ``` array([[17, 8, 37], [12, 2, 27], [41, 21, 25]]) ```python matriz2 = np.random.randint(1,50,(3,3)) matriz2 ``` array([[ 9, 16, 40], [43, 27, 33], [ 2, 39, 33]]) ## Sumando matriz + matriz2 Con el concepto de *suma de matrices* se realiza la operación de sumar cada uno de sus elementos. ```python matriz + matriz2 ``` array([[26, 24, 77], [55, 29, 60], [43, 60, 58]]) ## max(), min(),sum() Podemos encontrar el máximo valor de la matriz; el menor valor de la matriz y la suma de todos sus valores. ```python matriz3 = np.random.randint(1,50,(3,3)) matriz3 ``` array([[ 6, 18, 25], [ 7, 35, 48], [35, 31, 9]]) ```python matriz3.max() ``` 48 ```python matriz3.min() ``` 6 ```python matriz3.sum() ``` 214 ## Ejercicios Varios ```python lista=([ [1,2,3],[4,5,6] ]) m=np.array(lista) m ``` array([[1, 2, 3], [4, 5, 6]]) ```python m2=np.array([ [10,20,30],[40,50,60], [70,80,90] ]) m2 ``` array([[10, 20, 30], [40, 50, 60], [70, 80, 90]]) ###### Matriz diagonal ```python m3=np.diag(np.diag(m2)) m3 ``` array([[10, 0, 0], [ 0, 50, 0], [ 0, 0, 90]]) ```python m4=np.diag([1,2,3,4,5,6,7,8]) m4 ``` array([[1, 0, 0, 0, 0, 0, 0, 0], [0, 2, 0, 0, 0, 0, 0, 0], [0, 0, 3, 0, 0, 0, 0, 0], [0, 0, 0, 4, 0, 0, 0, 0], [0, 0, 0, 0, 5, 0, 0, 0], [0, 0, 0, 0, 0, 6, 0, 0], [0, 0, 0, 0, 0, 0, 7, 0], [0, 0, 0, 0, 0, 0, 0, 8]]) ```python m4.shape ``` (8, 8) ###### Creando matrices con full() ```python m5=np.full((3,3),100) m5 ``` array([[100, 100, 100], [100, 100, 100], [100, 100, 100]]) ###### Creando matrices con ones() ```python m6=np.ones((3,3)) m6 ``` array([[1., 1., 1.], [1., 1., 1.], [1., 1., 1.]]) ```python m7=np.ones((3,3), dtype=int) m7 ``` array([[1, 1, 1], [1, 1, 1], [1, 1, 1]]) ###### Creando matrices con zeros() ```python m8=np.zeros((3,3)) m8 ``` array([[0., 0., 0.], [0., 0., 0.], [0., 0., 0.]]) ```python m9=np.zeros((3,3), dtype=int) m9 ``` array([[0, 0, 0], [0, 0, 0], [0, 0, 0]]) ###### Creando matrices con arange() ```python m10=np.array([np.arange(4),np.arange(4)]) m10 ``` array([[0, 1, 2, 3], [0, 1, 2, 3]]) ```python list1=np.arange(3) list2=np.arange(3) m11=np.array([list1,list2]) m11 ``` array([[0, 1, 2], [0, 1, 2]]) ###### Subarrreglos ```python m12 = np.array([ [9,8,7,6], [5,4,3,2] ]) m12 ``` array([[9, 8, 7, 6], [5, 4, 3, 2]]) ```python m12[1] ``` array([5, 4, 3, 2]) ```python m12[:,0] ``` array([9, 5]) ###### Posiciones (selección de valores) ```python m12[0,0] ``` 9 ```python m12[0,1] ``` 8 ``` ``` --- En esta publicacíón se repasaron temas anteriores que puedes revisar en: ✅ [Arreglos Bidimensionales](https://peakd.com/hive-154226/@rafaelaquino/data-science-n015-numpy-arreglos-bidimensionales) ✅ [Arreglos Bidimensionales - continuación](https://peakd.com/hive-154226/@rafaelaquino/data-science-n016-numpy-arreglos-bidimensionales-continuacion) ## Ampliando conocimientos Todo el código generado en **Markdown** para esta publicación lo realicé de la siguiente manera. <center>  </center> ## Actualizando el repositorio Este apartado te permite practicar git. Poco a poco de darás cuenta de la utilidad y lo importante para trabajar en este mundo de la programación e informática. ~~~ (env) rafael@HP:~/.gitlab/datascience$ (env) rafael@HP:~/.gitlab/datascience$ git status En la rama main Tu rama está actualizada con 'origin/main'. Archivos sin seguimiento: (usa "git add <archivo>..." para incluirlo a lo que se será confirmado) 29_ejercicios.ipynb no hay nada agregado al commit pero hay archivos sin seguimiento presentes (usa "git add" para hacerles seguimiento) (env) rafael@HP:~/.gitlab/datascience$ git add 29_ejercicios.ipynb (env) rafael@HP:~/.gitlab/datascience$ (env) rafael@HP:~/.gitlab/datascience$ git commit -m "add 29_ejercicios" [main 6ceba72] add 29_ejercicios 1 file changed, 870 insertions(+) create mode 100644 29_ejercicios.ipynb (env) rafael@HP:~/.gitlab/datascience$ git push Username for xxxxxxxxxxxxxxxxxxxx Password for xxxxxxxxxxxxxxxxxxxx Enumerando objetos: 4, listo. Contando objetos: 100% (4/4), listo. Compresión delta usando hasta 4 hilos Comprimiendo objetos: 100% (3/3), listo. Escribiendo objetos: 100% (3/3), 3.20 KiB | 1.60 MiB/s, listo. Total 3 (delta 1), reusados 0 (delta 0), pack-reusados 0 To https://gitlab.com/btcsiraquino/datascience.git 74f5cdb..6ceba72 main -> main (env) rafael@HP:~/.gitlab/datascience$ ~~~ Hasta aquí nuestra publicación, hasta la próxima semana. --- Para ver todas la publicaciones del Curso de Data Science, puedes entrar a: 📍 https://siraquino.github.io/pythoncumanes/datascience.html Todos a programar! [Rafael Aquino](https://twitter.com/Rafa_elaquino) Bogotá / Colombia
author | rafaelaquino |
---|---|
permlink | data-science-n029-numpy-matrices |
category | hive-154226 |
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metroair | 0 | 3,966,867,804 | 0.8% | ||
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dna-replication | 0 | 349,152,957 | 10% | ||
privex | 0 | 460,726,874 | 0.8% | ||
dhimmel | 0 | 53,389,428,514 | 2.5% | ||
elevator09 | 0 | 1,006,000,841 | 0.4% | ||
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federacion45 | 0 | 1,204,632,073 | 0.4% | ||
gamersclassified | 0 | 592,781,172 | 0.4% | ||
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mobbs | 0 | 15,104,939,860 | 5% | ||
jerrybanfield | 0 | 3,002,866,435 | 0.8% | ||
rt395 | 0 | 1,963,378,568 | 1.5% | ||
bitrocker2020 | 0 | 1,627,124,279 | 0.16% | ||
sustainablyyours | 0 | 3,554,894,620 | 5% | ||
cryptoknight12 | 0 | 630,836,151 | 1.88% | ||
helo | 0 | 695,166,000 | 5% | ||
arunava | 0 | 2,424,940,188 | 0.32% | ||
samminator | 0 | 6,115,586,447 | 5% | ||
enjar | 0 | 6,630,532,476 | 0.72% | ||
mahdiyari | 0 | 404,947,464,282 | 80% | ||
lorenzor | 0 | 1,356,782,769 | 50% | ||
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alexander.alexis | 0 | 6,101,877,110 | 10% | ||
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ufv | 0 | 3,006,808,420 | 50% | ||
gunthertopp | 0 | 11,360,547,544 | 0.2% | ||
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minnowbooster | 0 | 982,038,054,361 | 20% | ||
felt.buzz | 0 | 1,482,404,964 | 0.2% | ||
howo | 0 | 323,339,560,617 | 10% | ||
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neumannsalva | 0 | 660,597,313 | 0.4% | ||
stayoutoftherz | 0 | 23,346,171,159 | 0.2% | ||
abigail-dantes | 0 | 3,664,291,205 | 10% | ||
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syh7758520 | 0 | 2,619,187,423 | 80% | ||
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investingpennies | 0 | 2,827,925,883 | 0.8% | ||
khalil319 | 0 | 991,641,687 | 10% | ||
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azulear | 0 | 1,394,829,678 | 100% | ||
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thelordsharvest | 0 | 629,584,234 | 0.8% | ||
sumant | 0 | 518,676,951 | 0.4% | ||
aidefr | 0 | 1,056,463,686 | 5% | ||
cloudspyder | 0 | 9,320,602,498 | 100% | ||
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fatman | 0 | 9,017,430,031 | 2% | ||
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dynamicrypto | 0 | 1,884,591,823 | 1% | ||
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cryptictruth | 0 | 2,132,697,311 | 0.5% | ||
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citizendog | 0 | 657,216,984 | 0.8% | ||
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mafufuma | 0 | 5,065,105,767 | 1% | ||
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cryptojiang | 0 | 132,825,291,462 | 100% | ||
nattybongo | 0 | 19,922,577,890 | 10% | ||
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jpbliberty | 0 | 1,075,514,041 | 0.2% | ||
double-negative | 0 | 540,895,412 | 20% | ||
vaultec | 0 | 6,733,547,693 | 12% | ||
steemstorage | 0 | 947,121,691 | 0.8% | ||
crowdwitness | 0 | 27,091,332,028 | 5% | ||
markwannabee | 0 | 472,750,073 | 100% | ||
steemean | 0 | 10,107,116,149 | 5% | ||
deveney | 0 | 473,981,357 | 100% | ||
hashkings | 0 | 30,402,388,369 | 25% | ||
newton666 | 0 | 4,458,786,697 | 100% | ||
aaronkroeblinger | 0 | 118,777,284,724 | 50% | ||
lmvc | 0 | 998,139,657 | 44.8% | ||
rtron86 | 0 | 6,331,754,056 | 50% | ||
walterprofe | 0 | 739,010,236 | 5% | ||
zeruxanime | 0 | 2,345,478,838 | 50% | ||
swayzilla | 0 | 3,606,773,169 | 100% | ||
lfu-radio | 0 | 799,814,951 | 35% | ||
reggaesteem | 0 | 489,087,780 | 5% | ||
capp | 0 | 11,269,534,179 | 50% | ||
dechuck | 0 | 8,362,483,366 | 50% | ||
sbi-tokens | 0 | 2,987,667,213 | 1.88% | ||
lmvc-spaco | 0 | 542,276,492 | 44.8% | ||
precarious | 0 | 478,807,161 | 50% | ||
steemstem-trig | 0 | 166,318,134 | 10% | ||
baltai | 0 | 887,152,924 | 0.4% | ||
atheistrepublic | 0 | 971,563,666 | 0.4% | ||
ibt-survival | 0 | 33,867,311,274 | 10% | ||
lightpaintershub | 0 | 464,078,027 | 1% | ||
yunnie | 0 | 753,281,660 | 100% | ||
radiohive | 0 | 18,405,384,555 | 70% | ||
stemsocial | 0 | 83,954,199,496 | 10% | ||
greenforever | 0 | 1,983,868,833 | 30% | ||
cybercity | 0 | 3,216,805,630 | 0.8% | ||
kvfm | 0 | 1,443,401,201 | 35% | ||
noelyss | 0 | 2,925,418,483 | 5% | ||
laradio | 0 | 3,268,931,808 | 70% | ||
mercmarg | 0 | 5,068,784,145 | 28% | ||
quinnertronics | 0 | 15,910,468,532 | 7% | ||
gohive | 0 | 2,031,722,773 | 100% | ||
r-nyn | 0 | 14,539,656,208 | 11% | ||
cleydimar2000 | 0 | 6,707,097,862 | 35% | ||
radiolovers | 0 | 14,882,468,093 | 70% | ||
alberto0607 | 0 | 1,710,065,793 | 70% | ||
apendix1994 | 0 | 3,886,533,573 | 90% | ||
junydoble | 0 | 1,223,689,443 | 49% | ||
victor816 | 0 | 639,540,502 | 70% | ||
meritocracy | 0 | 9,022,547,617 | 0.08% | ||
jmsansan | 0 | 623,921,816 | 0.4% | ||
bea23 | 0 | 15,954,259,953 | 70% | ||
pepeymeli | 0 | 590,585,981 | 50% | ||
dcrops | 0 | 5,200,282,601 | 0.4% | ||
infernalcoliseum | 0 | 822,069,442 | 25% | ||
rondonshneezy | 0 | 2,595,149,727 | 12.5% | ||
ciudadcreativa | 0 | 2,558,262,036 | 56% | ||
mayorkeys | 0 | 2,345,785,077 | 30% | ||
failingforwards | 0 | 475,486,222 | 0.4% | ||
drricksanchez | 0 | 2,068,645,614 | 0.4% | ||
trouvaille | 0 | 9,756,701,909 | 35% | ||
m0rt0nmattd | 0 | 3,322,744,423 | 100% | ||
high8125theta | 0 | 45,651,255,967 | 60% | ||
edinson001 | 0 | 661,403,338 | 70% | ||
nfttunz | 0 | 1,158,053,700 | 0.08% | ||
holovision.cash | 0 | 4,308,056,891 | 100% | ||
helencct | 0 | 1,129,513,965 | 70% | ||
sarashew | 0 | 539,015,720 | 0.8% | ||
podping | 0 | 1,133,069,894 | 0.2% | ||
yenmendt | 0 | 23,866,169,856 | 70% | ||
pinkfloyd878 | 0 | 4,386,671,099 | 100% | ||
fabianar25 | 0 | 672,610,213 | 35% | ||
mayberlys | 0 | 2,305,184,129 | 50% | ||
laviesm | 0 | 6,263,895,838 | 50% | ||
ronymaffi | 0 | 1,529,227,219 | 70% | ||
sidalim88 | 0 | 487,215,952 | 0.4% | ||
aries90 | 0 | 7,150,295,730 | 0.8% | ||
martinthemass | 0 | 615,005,113 | 100% | ||
migka | 0 | 4,422,537,575 | 90% | ||
blingit | 0 | 479,460,763 | 0.4% | ||
marsupia | 0 | 1,255,095,336 | 25% | ||
iskafan | 0 | 4,834,806,523 | 30% | ||
yixn | 0 | 5,504,479,912 | 0.4% | ||
waivio.curator | 0 | 2,245,850,693 | 4.12% | ||
ledgar | 0 | 6,036,560,438 | 100% | ||
vickoly | 0 | 1,175,876,553 | 0.4% | ||
susurrodmisterio | 0 | 2,745,666,687 | 35% | ||
deadleaf | 0 | 4,325,528,287 | 100% | ||
vindiesel1980 | 0 | 1,865,129,941 | 0.4% | ||
sam9999 | 0 | 704,185,735 | 5% | ||
blackdaisyft | 0 | 17,839,701,766 | 50% | ||
azj26 | 0 | 4,762,057,209 | 16% | ||
prosocialise | 0 | 8,464,564,527 | 5% | ||
archangel21 | 0 | 512,630,191 | 0.8% | ||
mugueto2022 | 0 | 580,767,352 | 20% | ||
fefe99 | 0 | 497,690,218 | 100% | ||
visualblock | 0 | 300,836,709,134 | 70% | ||
cuzimgleb | 0 | 1,693,833,336 | 70% | ||
mr-rent | 0 | 4,147,387,737 | 75% | ||
soyjoselopez | 0 | 477,779,386 | 20% | ||
sbtofficial | 0 | 685,656,607 | 0.4% | ||
hk-curation | 0 | 64,920,565,146 | 50% | ||
smariam | 0 | 2,622,716,355 | 25% | ||
humbe | 0 | 1,585,894,055 | 1% | ||
peniel2010 | 0 | 784,970,282 | 50% | ||
abu78 | 0 | 528,433,323 | 0.4% | ||
ijelady | 0 | 652,614,358 | 35% | ||
paolasinaid | 0 | 1,048,081,396 | 70% | ||
rhemagames | 0 | 714,318,497 | 0.4% | ||
johndeeback | 0 | 16,002,259,105 | 100% | ||
earn.vote | 0 | 9,944,563,134 | 100% | ||
tecnoticias | 0 | 1,993,170,806 | 100% | ||
agileautomation | 0 | 83,366,542 | 100% |
<div class='text-justify'> <div class='pull-left'> <img src='https://stem.openhive.network/images/stemsocialsupport7.png'> </div> Thanks for your contribution to the <a href='/trending/hive-196387'>STEMsocial community</a>. Feel free to join us on <a href='https://discord.gg/9c7pKVD'>discord</a> to get to know the rest of us! Please consider delegating to the @stemsocial account (85% of the curation rewards are returned). You may also include @stemsocial as a beneficiary of the rewards of this post to get a stronger support. <br /> <br /> </div>
author | stemsocial |
---|---|
permlink | re-rafaelaquino-data-science-n029-numpy-matrices-20240114t113751936z |
category | hive-154226 |
json_metadata | {"app":"STEMsocial"} |
created | 2024-01-14 11:37:51 |
last_update | 2024-01-14 11:37:51 |
depth | 1 |
children | 0 |
last_payout | 2024-01-21 11:37:51 |
cashout_time | 1969-12-31 23:59:59 |
total_payout_value | 0.000 HBD |
curator_payout_value | 0.000 HBD |
pending_payout_value | 0.000 HBD |
promoted | 0.000 HBD |
body_length | 565 |
author_reputation | 22,918,491,691,707 |
root_title | "Data Science N029. numpy Matrices" |
beneficiaries | [] |
max_accepted_payout | 1,000,000.000 HBD |
percent_hbd | 10,000 |
post_id | 130,470,607 |
net_rshares | 0 |