create account

Python numpy arrays ( diziler ) by hakancelik

View this thread on: hive.blog |  peakd.com |  ecency.com
coogger · @hakancelik · (edited)
$0.42
Python numpy arrays ( diziler )
<center>

<a href="https://www.coogger.com/@hakancelik/python-numpy-arrays-diziler">


  <img alt="" src="https://www.coogger.com/media/images/numpy_fzRp3P5.png">

</a>

<a href="https://www.coogger.com/@hakancelik/python-numpy-arrays-diziler">Read this content on coogger</a>

</center>
👍  , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , and 289 others
properties (23)
authorhakancelik
permlinkpython-numpy-arrays-diziler
categorycoogger
json_metadata"{"format": "markdown", "tags": ["coogger", "tr", "python", "numpy"], "app": "coogger/1.4.1", "ecosystem": {"version": "1.4.1", "body": "<img gnrl=\"center br-2\" src=\"https://www.coogger.com/media/images/numpy_fzRp3P5.png\">\r\n\r\n## Arrays\r\nBir **Numpy** dizisi ayn\u0131 t\u00fcrden bir de\u011ferler tablosudur ve negatif olmayan tamsay\u0131 dizisi ile indexlenir.\r\n\r\nBoyut say\u0131s\u0131, dizi s\u0131ralamas\u0131d\u0131r; bir dizinin \u015fekli, her boyut boyunca dizinin boyutunu veren bir tamsay\u0131 dizisidir. Numpy dizilerini i\u00e7 i\u00e7e ge\u00e7mi\u015f Python listelerinden ba\u015flatabilir ve k\u00f6\u015feli ayra\u00e7lar\u0131 **[ ]** kullanarak \u00f6\u011felere eri\u015febiliriz:\r\n\r\n```python\r\nimport numpy as np # numpy k\u00fct\u00fcphanemizi np takma isim ile projemize dahil ettik\r\n\r\na = np.array([1, 2, 3]) # Derecesi ( rank ) bir olan bir dizi yaratt\u0131k\r\n```\r\nyani numpy k\u00fct\u00fcphanesinde dizi yaratmak i\u00e7in **array()** fonksiyonunu kullan\u0131yoruz.\r\n```python\r\nimport numpy as np # numpy k\u00fct\u00fcphanemizi np takma isim ile projemize dahil ettik\r\n\r\na = np.array([1, 2, 3]) # Derecesi ( rank ) bir olan bir dizi yaratt\u0131k\r\nprint(type(a)) # \u00e7\u0131kt\u0131 olarak \"<class 'numpy.ndarray'>\" python'da her\u015fey bir nesneydi olay\u0131n\u0131 hat\u0131rlad\u0131m \r\nprint(a.shape) # \u00e7\u0131kt\u0131 \"(3,)\" verir peki neden nedir bu shape ?\r\n```\r\n**shape** dizi i\u00e7indeki sat\u0131r ve s\u00fct\u00fcnlarda ka\u00e7 adet nesne varsa onun say\u0131s\u0131n\u0131 verir, verdi\u011fi \u00e7\u0131kt\u0131 bir tuble'd\u0131r ve (a,b) \u015feklinde vb olan tuble' da birincisi yani a sat\u0131rd\u0131r\u00a0 ikincisi ise yani b ise s\u00fct\u00fcnd\u00fcr.\r\n\r\nbizim olu\u015fturdu\u011fumuz\u00a0**np.array([1, 2, 3])** dizisinde sat\u0131rda 3 nesne oldu\u011fu i\u00e7in \u00e7\u0131kt\u0131 olarak (3,) verdir. bir ba\u015fka \u00f6rnek mesela ;\r\n\r\n```python\r\n>>> import numpy as np\r\n>>> a = np.array([1, 2, 3,4]) # 4 sat\u0131r 0 s\u00fctun\r\n>>> a.shape\r\n(4,)\r\n>>> a = np.array([[1, 2],[3,4]]) # 2 sat\u0131r 2 s\u00fctun\r\n>>> a.shape # shape \u00e7\u0131kt\u0131s\u0131 \r\n(2, 2)\r\n>>> a\r\narray([[1, 2], # a nesne g\u00f6r\u00fcn\u00fc\u015f\u00fc iki sat\u0131r ve iki s\u00fctun var\r\n [3, 4]])\r\n# \u00e7ok boyutlu dizi olu\u015fturma birazdan anlataca\u011f\u0131m buray\u0131 \u015fimdilik b\u00f6yle bilin yeter\r\n\r\n>>> np.indices((3,5)).shape\r\n(2, 3, 5)\r\n\r\n# shape 3 \u00e7\u0131kt\u0131 verirse (z,x,y)\r\n# yani y\u00fckseklik ( boyutu ) sat\u0131r ve s\u00fctun \u015feklinde s\u0131ral\u0131d\u0131r.\r\n```\r\nbence anla\u015f\u0131lm\u0131\u015ft\u0131r.\r\n\r\ndizi \u00f6\u011felerine eri\u015fmek i\u00e7in [ ] parantezleri kullan\u0131yorduk t\u0131pk\u0131 liste \u00f6\u011felerine eri\u015firken kulland\u0131\u011f\u0131m\u0131z gibi.\r\n```python\r\nimport numpy as np # numpy k\u00fct\u00fcphanemizi np takma isim ile projemize dahil ettik\r\n\r\na = np.array([1, 2, 3]) # Derecesi ( rank ) bir olan bir dizi yaratt\u0131k\r\nprint(type(a)) # \u00e7\u0131kt\u0131 olarak \"<class 'numpy.ndarray'>\" python'da her\u015fey bir nesneydi olay\u0131n\u0131 hat\u0131rlad\u0131m \r\nprint(a.shape) # \u00e7\u0131kt\u0131 \"(3,)\" \r\n# \u00f6\u011felerine eri\u015fiyoruz\r\nprint(a[0], a[1], a[2]) # \u00e7\u0131kt\u0131 \"1 2 3\"\r\na[0] = 5 # dizinin ilk nesnesini 5 ile de\u011fi\u015ftiriyoruz listelerde yapt\u0131\u011f\u0131m\u0131z gibi \r\nprint(a) # \u00e7\u0131kt\u0131 \"[5, 2, 3]\"\r\n\r\nb = np.array([[1,2,3],[4,5,6]]) # derecesi ( rank )'\u0131 2 olan bir dizi yarat\u0131yoruz 2 sat\u0131r,3 s\u00fctun var\r\nprint(b.shape) # bu y\u00fczden \u00e7\u0131kt\u0131m\u0131z \"(2, 3)\"\r\nprint(b[0, 0], b[0, 1], b[1, 0]) \r\n# listelerdeki gibi \u00f6nce ilk listeye eri\u015fiyoruz daha sonra\r\n# ilk liste i\u00e7indeki ilk nesneye sonra ikinci sonrada ikinci \r\n# listenin ilk nesnesine eri\u015fiyoruz ve \u00e7\u0131kt\u0131m\u0131z \"1 2 4\"\r\n```\r\n----\r\n## Numpy'nin dizi olu\u015fturmak i\u00e7in sundu\u011fu bir ka\u00e7 fonksiyon\r\n### **zeros()**\u00a0\u00a0\r\n\r\nzeros fonksiyonu ald\u0131\u011f\u0131 sat\u0131r ve s\u00fctun bilgisini kullanarak b\u00fct\u00fcn de\u011ferleri 0 olan bir array ( dizi ) yarat\u0131r. \u00f6rnek olarak\r\n```python\r\nimport numpy as np\r\n\r\na = np.zeros((2,2)) # 2 sat\u0131r l\u0131 2 s\u00fctun'l\u00fc bir dizi yaratt\u0131k b\u00fct\u00fcn de\u011ferleri 0 olan\r\nprint(a) \r\n\r\n# \u00e7\u0131kt\u0131m\u0131z\r\n\r\n# \"[[ 0. 0.]\r\n# [ 0. 0.]]\"\r\n```\r\n---\r\n#### ones()\r\nt\u0131pk\u0131 zeros fonksiyonu gibi ald\u0131\u011f\u0131 parametreler ile b\u00fct\u00fcn de\u011ferleri 1 olan bir dizi yarat\u0131r.\r\n```python\r\nb = np.ones((1,2)) # 1 sat\u0131r 2 s\u00fctun de\u011ferleri 1 olan bir dizi\r\nprint(b) # \u00e7\u0131kt\u0131 \"[[ 1. 1.]]\"\r\n\r\n# ba\u015fka \u00f6rnek\r\n \r\n>>> b = np.ones((3,2))\r\n>>> b\r\narray([[ 1., 1.],\r\n [ 1., 1.],\r\n [ 1., 1.]])\r\n>>>\r\n```\r\n---\r\n#### full()\r\nfull fonksiyonunda zeros ve ones'dan pek fark\u0131 yok iki parametre al\u0131r birinci 'de olu\u015furmak istedi\u011fimiz sat\u0131r s\u00fctun bilgisini gireriz ikincisinde b\u00fct\u00fcn de\u011ferlerinin ne olaca\u011f\u0131n\u0131 0 yazarsan\u0131z zeros fonksiyonu olur 1 yazarsan\u0131z ones fonksiyonu nu elde etmi\u015f olursunuz\u00a0\r\n\r\n```python\r\n>>> b = np.full((2,3),0) # zeros\r\n>>> b\r\narray([[0, 0, 0],\r\n [0, 0, 0]])\r\n>>> b = np.full((3,4),1) # ones\r\n>>> b\r\narray([[1, 1, 1, 1],\r\n [1, 1, 1, 1],\r\n [1, 1, 1, 1]])\r\n>>> b = np.full((3,4),6) # ve di\u011ferleri\r\n>>> b\r\narray([[6, 6, 6, 6],\r\n [6, 6, 6, 6],\r\n [6, 6, 6, 6]])\r\n>>>\r\n```\r\n-----\r\n\r\n#### eye()\r\nBu fonksiyon birim matrix ( matris ) olu\u015fturmak i\u00e7in kullan\u0131l\u0131r ,birim matris kare matris tir bunu unutmamak gerek.\r\n```python\r\n>>> np.eye(2, dtype=int)\r\narray([[1, 0],\r\n [0, 1]])\r\n>>> np.eye(4, dtype=int)\r\narray([[1, 0, 0, 0],\r\n [0, 1, 0, 0],\r\n [0, 0, 1, 0],\r\n [0, 0, 0, 1]])\r\n>>> np.eye(6, dtype=int) \r\narray([[1, 0, 0, 0, 0, 0],\r\n [0, 1, 0, 0, 0, 0],\r\n [0, 0, 1, 0, 0, 0],\r\n [0, 0, 0, 1, 0, 0],\r\n [0, 0, 0, 0, 1, 0],\r\n [0, 0, 0, 0, 0, 1]])\r\n>>>\r\n```\r\ndtype k\u0131sm\u0131 na float vs yazabilirsiniz size ba\u011fl\u0131\r\nrandom.random()\u200b\u200b\u200b\u200b\u200b\u200b\r\ngirilen sat\u0131r ve s\u00fctun bilgisine g\u00f6re de\u011ferleri rastgele \u00fcretir\r\n```python\r\n>>> np.random.random((2,2))\r\narray([[ 0.36722804, 0.42820984],\r\n [ 0.71009591, 0.756548 ]])\r\n>>> np.random.random((2,4))\r\narray([[ 0.70346788, 0.60435722, 0.74678671, 0.60162181],\r\n [ 0.16416124, 0.42160131, 0.93412007, 0.38546619]])\r\n>>> np.random.random((3,4))\r\narray([[ 0.96196352, 0.96182994, 0.95024854, 0.57300293],\r\n [ 0.68440183, 0.42133541, 0.72824596, 0.83025332],\r\n [ 0.52649033, 0.52013271, 0.54852089, 0.54985063]])\r\n>>>\r\n```\r\n---\r\n#### arange()\r\nBunu direk \u00f6rnek le a\u00e7\u0131klayal\u0131m\u00a0\r\nSadece 1 de\u011fer girerseniz arange(10) gibi 0 dan 10 'a kadar 1'er 1'er artarak bir dizi olu\u015fturur\r\n2 de\u011fer girerseniz ilk girdi\u011finiz de\u011ferden ba\u015flar ve ikinci de\u011fere kadar 1 'er 1'er artarak bir dizi olu\u015fturur\r\n3 de\u011fer yazarsan\u0131z ilk ikisi bir yukardaki gibi i\u015fler 3.yazd\u0131g\u0131n\u0131z de\u011fer art\u0131\u015f miktar\u0131n\u0131 belirtir yani 1 yazarsan\u0131z 1'er 1'er\u00a0 0.2 yazarsan\u0131z da bu \u015fekilde artacakt\u0131r\r\n\u00f6rnekler\u200b\u200b\u200b\u200b\u200b\u200b\u200b\u200b\u200b\u200b\u200b\u200b\u200b\u200b\u200b\u200b\u200b\u200b\u200b\u200b\u200b\r\n```python\r\n>>> np.arange(10)\r\narray([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])\r\n>>> np.arange(4)\r\narray([0, 1, 2, 3])\r\n>>> np.arange(4,4)\r\narray([], dtype=int32)\r\n>>> np.arange(2,6)\r\narray([2, 3, 4, 5])\r\n>>> np.arange(10)\r\narray([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])\r\n>>> np.arange(4)\r\narray([0, 1, 2, 3])\r\n>>> np.arange(2,6)\r\narray([2, 3, 4, 5])\r\n>>> np.arange(4,12,dtype=np.float)\r\narray([ 4., 5., 6., 7., 8., 9., 10., 11.])\r\n>>> np.arange(4,12,0.1)\r\narray([ 4. , 4.1, 4.2, 4.3, 4.4, 4.5, 4.6, 4.7, 4.8,\r\n 4.9, 5. , 5.1, 5.2, 5.3, 5.4, 5.5, 5.6, 5.7,\r\n 5.8, 5.9, 6. , 6.1, 6.2, 6.3, 6.4, 6.5, 6.6,\r\n 6.7, 6.8, 6.9, 7. , 7.1, 7.2, 7.3, 7.4, 7.5,\r\n 7.6, 7.7, 7.8, 7.9, 8. , 8.1, 8.2, 8.3, 8.4,\r\n 8.5, 8.6, 8.7, 8.8, 8.9, 9. , 9.1, 9.2, 9.3,\r\n 9.4, 9.5, 9.6, 9.7, 9.8, 9.9, 10. , 10.1, 10.2,\r\n 10.3, 10.4, 10.5, 10.6, 10.7, 10.8, 10.9, 11. , 11.1,\r\n 11.2, 11.3, 11.4, 11.5, 11.6, 11.7, 11.8, 11.9])\r\n>>> np.arange(4,12,1)\r\narray([ 4, 5, 6, 7, 8, 9, 10, 11])\r\n```\r\n-----\r\n\r\n#### linspace()\r\n- \u200b\u200b\u200b\u200b\u200b\u200b\u200b1. parametre ba\u015flayaca\u011f\u0131 de\u011fer\r\n- 2.parametre bitece\u011fi de\u011fer\r\n- 3.parametre ka\u00e7 sat\u0131r olaca\u011f\u0131d\u0131r.\r\nnp.linspace(1., 4., 6)\r\narray([ 1. , 1.6, 2.2, 2.8, 3.4, 4. ])\r\n\r\n```python\r\n>>> np.linspace(1,4,0)\r\narray([], dtype=float64)\r\n>>> np.linspace(1,4,1)\r\narray([ 1.])\r\n>>> np.linspace(1,4,3)\r\narray([ 1. , 2.5, 4. ])\r\n>>> np.linspace(1,4,5)\r\narray([ 1. , 1.75, 2.5 , 3.25, 4. ])\r\n>>> np.linspace(1,6,12)\r\narray([ 1. , 1.45454545, 1.90909091, 2.36363636, 2.81818182,\r\n 3.27272727, 3.72727273, 4.18181818, 4.63636364, 5.09090909,\r\n 5.54545455, 6. ])\r\n```\r\n-----\r\n#### indices()\r\n\u200b\u200b\u200b\u200b\u200b\u200b\u200bindices (), her boyut i\u00e7in varyasyon g\u00f6stererek boyut ba\u015f\u0131na bir tane olmak \u00fczere (daha y\u00fcksek boyutlu bir dizi olarak y\u0131\u011f\u0131lm\u0131\u015f) bir dizi dizisi olu\u015fturacakt\u0131r. Bir \u00f6rnek, s\u00f6zl\u00fc bir tan\u0131mlamadan \u00e7ok daha iyi oldu\u011funu g\u00f6sterir:\r\n```python\r\n>>> np.indices((1,0))\r\narray([], shape=(2, 1, 0), dtype=int32)\r\n>>> np.indices((1,1)) # >>> np.indices((1,1)).shape --> (2, 1, 1) (z,x,y) y\u00fckseklik ( boyut ) sat\u0131r,s\u00fctun\r\narray([[[0]],\r\n\r\n [[0]]])\r\n>>> np.indices((1,2))\r\narray([[[0, 0]],\r\n\r\n [[0, 1]]])\r\n>>> np.indices((1,3))\r\narray([[[0, 0, 0]],\r\n\r\n [[0, 1, 2]]])\r\n>>> np.indices((1,4))\r\narray([[[0, 0, 0, 0]],\r\n\r\n [[0, 1, 2, 3]]])\r\n>>> np.indices((1,5))\r\narray([[[0, 0, 0, 0, 0]],\r\n\r\n [[0, 1, 2, 3, 4]]])\r\n>>> np.indices((1,6))\r\narray([[[0, 0, 0, 0, 0, 0]],\r\n\r\n [[0, 1, 2, 3, 4, 5]]])\r\n>>> np.indices((2,2))\r\narray([[[0, 0],\r\n [1, 1]],\r\n\r\n [[0, 1],\r\n [0, 1]]])\r\n>>> np.indices((3,2)) # np.indices((3,2)).shape --> (2, 3, 2)\r\narray([[[0, 0],\r\n [1, 1],\r\n [2, 2]],\r\n\r\n [[0, 1],\r\n [0, 1],\r\n [0, 1]]])\r\n>>> np.indices((3,5)) # >>> np.indices((3,5)).shape -- > (2, 3, 5) y\u00fckseklik ,sat\u0131r,s\u00fctun\r\narray([[[0, 0, 0, 0, 0],\r\n [1, 1, 1, 1, 1],\r\n [2, 2, 2, 2, 2]],\r\n\r\n [[0, 1, 2, 3, 4],\r\n [0, 1, 2, 3, 4],\r\n [0, 1, 2, 3, 4]]])\r\n>>>\r\n```\r\n<center>Hepsi bu kadar bir di\u011fer konuda g\u00f6r\u00fc\u015fmek \u00fczere\u00a0</center>"}}"
created2018-03-29 12:27:45
last_update2019-05-20 01:40:15
depth0
children3
last_payout2018-04-05 12:27:45
cashout_time1969-12-31 23:59:59
total_payout_value0.340 HBD
curator_payout_value0.078 HBD
pending_payout_value0.000 HBD
promoted0.000 HBD
body_length288
author_reputation15,102,487,166,852
root_title"Python numpy arrays ( diziler )"
beneficiaries[]
max_accepted_payout1,000,000.000 HBD
percent_hbd10,000
post_id47,268,428
net_rshares138,801,576,083
author_curate_reward""
vote details (353)
@cicek · 
Ooo Python. Alırım bi dal :)

Python'u neden bu kadar sevdiğimi bilmiyorum.
properties (22)
authorcicek
permlinkre-hakancelik-python-numpy-arrays-diziler-20180404t092532516z
categorycoogger
json_metadata{"tags":["coogger"],"app":"steemit/0.1"}
created2018-04-04 09:25:33
last_update2018-04-04 09:25:33
depth1
children1
last_payout2018-04-11 09:25:33
cashout_time1969-12-31 23:59:59
total_payout_value0.000 HBD
curator_payout_value0.000 HBD
pending_payout_value0.000 HBD
promoted0.000 HBD
body_length75
author_reputation13,052,718,307
root_title"Python numpy arrays ( diziler )"
beneficiaries[]
max_accepted_payout1,000,000.000 HBD
percent_hbd10,000
post_id48,267,129
net_rshares0
@hakancelik · 
Sevmek için çok neden var bence 😊
properties (22)
authorhakancelik
permlinkre-cicek-re-hakancelik-python-numpy-arrays-diziler-20180404t113527590z
categorycoogger
json_metadata{"tags":["coogger"],"app":"steemit/0.1"}
created2018-04-04 11:35:27
last_update2018-04-04 11:35:27
depth2
children0
last_payout2018-04-11 11:35:27
cashout_time1969-12-31 23:59:59
total_payout_value0.000 HBD
curator_payout_value0.000 HBD
pending_payout_value0.000 HBD
promoted0.000 HBD
body_length33
author_reputation15,102,487,166,852
root_title"Python numpy arrays ( diziler )"
beneficiaries[]
max_accepted_payout1,000,000.000 HBD
percent_hbd10,000
post_id48,283,845
net_rshares0
@minnowsupport · 
<p>Congratulations!  This post has been upvoted from the communal account, @minnowsupport, by hakancelik from the Minnow Support Project. It's a witness project run by aggroed, ausbitbank, teamsteem, theprophet0, someguy123, neoxian, followbtcnews, and netuoso. The goal is to help Steemit grow by supporting Minnows.  Please find us at the <a href="https://discord.gg/HYj4yvw"> Peace, Abundance, and Liberty Network (PALnet) Discord Channel</a>.  It's a completely public and open space to all members of the Steemit community who voluntarily choose to be there.</p> <p>If you would like to delegate to the Minnow Support Project you can do so by clicking on the following links: <a href="https://v2.steemconnect.com/sign/delegateVestingShares?delegator=&amp;delegatee=minnowsupport&amp;vesting_shares=102530.639667%20VESTS">50SP</a>, <a href="https://v2.steemconnect.com/sign/delegateVestingShares?delegator=&amp;delegatee=minnowsupport&amp;vesting_shares=205303.639667%20VESTS">100SP</a>, <a href="https://v2.steemconnect.com/sign/delegateVestingShares?delegator=&amp;delegatee=minnowsupport&amp;vesting_shares=514303.639667%20VESTS">250SP</a>, <a href="https://v2.steemconnect.com/sign/delegateVestingShares?delegator=&amp;delegatee=minnowsupport&amp;vesting_shares=1025303.639667%20VESTS">500SP</a>, <a href="https://v2.steemconnect.com/sign/delegateVestingShares?delegator=&amp;delegatee=minnowsupport&amp;vesting_shares=2053030.639667%20VESTS">1000SP</a>, <a href="https://v2.steemconnect.com/sign/delegateVestingShares?delegator=&amp;delegatee=minnowsupport&amp;vesting_shares=10253030.639667%20VESTS">5000SP</a>. <br><strong>Be sure to leave at least 50SP undelegated on your account.</strong></p>
properties (22)
authorminnowsupport
permlinkre-python-numpy-arrays-diziler-20180402t025949
categorycoogger
json_metadata""
created2018-04-02 02:59:51
last_update2018-04-02 02:59:51
depth1
children0
last_payout2018-04-09 02:59:51
cashout_time1969-12-31 23:59:59
total_payout_value0.000 HBD
curator_payout_value0.000 HBD
pending_payout_value0.000 HBD
promoted0.000 HBD
body_length1,706
author_reputation148,902,805,319,183
root_title"Python numpy arrays ( diziler )"
beneficiaries[]
max_accepted_payout1,000,000.000 HBD
percent_hbd10,000
post_id47,857,575
net_rshares0
Help/Feedback