Tariffin

Koyaranci Python

• Koyaranci Python
• Introduction da Python
• Koyaranci Python Beginner
• Syntax da Python
• Koyaranci Python Comment
• Variables da Python
• Data Types da Python
• Number Python
• Casting da Python
• Strings da Python
• Boolean da Python
• Operators da Python
• Lists da Python
• Tuples da Python
• Sets da Python
• Dictionaries da Python
• If Else Python
• While Loop da Python
• For Loop da Python
• Functions da Python
• Lambda da Python
• Arrays da Python
• Klassi da kwayan Python
• Inheritance da Python
• Iteration da Python
• Scope da Python
• Modules da Python
• Dates da Python
• JSON da Python
• RegEx da Python
• PIP da Python
• Try Except da Python
• Kiriyya amfani aiki Python
• String Formatting da Python

Kirkirin fayil

• File Open da Python
• File Read da Python
• Rarraba/fabawa Python
• File Delete da Python

Python NumPy

• Introduction da NumPy
• Beginner da NumPy
• Creation of NumPy Arrays
• Indexing da NumPy Arrays
• Slicing da NumPy Arrays
• Data Types da NumPy
• Copy/View da NumPy Arrays
• Shape da NumPy Arrays
• Reshaping da NumPy Arrays
• Iteration da NumPy Arrays
• Concatenation da NumPy Arrays
• Splitting da NumPy Arrays
• Searching da NumPy Arrays
• Sorting da NumPy Arrays
• Filtering da NumPy Arrays
• Random da NumPy
• ufuncs da NumPy

Iyaliyya masinan

• Koyarwa
• Kwarin yana kai kai
• Kwarin
• Kwamfure
• Yanar dandamawo
• Yanar yanci
• Gidan dandamawo
• Iyaliyya biki
• Iyaliyya gudunmu
• Iyaliyya muli
• Sakamawo
• Tunawa/cikin
• Dabiru

Python MySQL

• MySQL入门
• MySQL Create Database
• MySQL Create Table
• MySQL Insert
• MySQL Select
• MySQL Where
• MySQL Order By
• MySQL Delete
• MySQL Drop Table
• MySQL Update
• MySQL Limit
• MySQL Join

Python MongoDB

• MongoDB入门
• MongoDB Create Database
• MongoDB Create Collection
• MongoDB Insert
• MongoDB Find
• MongoDB Query
• MongoDB Sort
• MongoDB Delete
• MongoDB Delete Collection
• MongoDB Update
• MongoDB Limit

Python Magana

• Python kaiyanci
• Python nafta
• Python nafta
• Python nafta
• Python nafta
• Python nafta
• Python nafta
• Python girmi nafta
• Python kaiyanci

Nafta kaiyanci

• Random nafta
• Request nafta

Python How To

• Rantici girmi nafta
• Rantici girmi

Python Macegi

• Python Macegi
• Python Ilimin Koyan

Kurukutu kaiyanci

Kurukutu:

• CodeW3C.com Batawari

A girmi kaiyanci nafta.

Index nafta nafta nafta a zai kaiyanci nafta.

Anu a girmi kaiyanci kaiyanci ndima nafta, a zai kaiyanci ndima nafta.

Index nafta NumPy nafta a zai 0 kaiyanci, anu a girmi kaiyanci a zai 0, a girmi kaiyanci a zai 1, kaiyanci.

Macegi

A girmi kaiyanci 1st element from the following array:

import numpy as np
arr = np.array([1, 2, 3, 4])
print(arr[0])

Kaiwa Macegi

Macegi

A girmi kaiyanci 2nd element from the following array:

import numpy as np
arr = np.array([1, 2, 3, 4])
print(arr[1])

Kaiwa Macegi

Macegi

A girmi kaiyanci 3rd and 4th elements from the following array and add them:

import numpy as np
arr = np.array([1, 2, 3, 4])
print(arr[2] + arr[3])

Kaiwa Macegi

A girmi kaiyanci 2-D nafta

A girmi kaiyanci kaiyanci nafta, anu a girmi kaiyanci kai ndima nafta, a zai kaiyanci ndima kai ndima.

Macegi

A girmi kaiyanci 2nd element on 1st dim:

import numpy as np
arr = np.array([[1,2,3,4,5], [6,7,8,9,10]])
print('2nd element on 1st dim: ', arr[0, 1])

Kaiwa Macegi

Macegi

A girmi kaiyanci 5th element on 2nd dim:

import numpy as np
arr = np.array([[1,2,3,4,5], [6,7,8,9,10]])
print('5th element on 2nd dim: ', arr[1, 4])

Kaiwa Macegi

A girmi kaiyanci 3-D nafta

A girmi kaiyanci 3-D nafta, anu a girmi kaiyanci kai ndima nafta, a zai kaiyanci ndima kai ndima.

Macegi

Gudanar gida na farko na ikon biyu na ikon guda:

import numpy as np
arr = np.array([[[1, 2, 3], [4, 5, 6]], [[7, 8, 9], [10, 11, 12]]])
print(arr[0, 1, 2])

Kaiwa Macegi

Tatawurin Shafi

arr[0, 1, 2] Ina cikata Gida: 6.

Kurmi:

gida na farko wanda ke da gudun gida na:

[[1, 2, 3], [4, 5, 6]]

sai:

[[7, 8, 9], [10, 11, 12]]

wannan saboda a nuna: 0kuma a gana gida na farko:

[[1, 2, 3], [4, 5, 6]]

gida na biyu wanda ke da gudun gida na:

[1, 2, 3]

sai:

[4, 5, 6]

wannan saboda a nuna: 1kuma a gana gida na biyu:

[4, 5, 6]

gida na uku wanda ke da gudun gida na:

4
5
6

wannan saboda a nuna: 2kuma a gana ta gaba da na samun guda na:

6

Ikon Dutsi

A samu ikon daga farko daga farkon kowane

Macegi

Ina cikata akirin guda daga ikon daga 2nd dim:

import numpy as np
arr = np.array([[1,2,3,4,5], [6,7,8,9,10]])
print('Akirin guda daga 2nd dim: ', arr[1, -1])

Kaiwa Macegi

Tasarsu

Python Magana

Python Macegi

Python Ilimin Koyan

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