  # numpy.ndarray.flat () in Python

Arrays | NumPy | Python Methods and Functions

Parameters :

`  index:  [tuple (int)] index of the values ​​to iterate `

Return:

` 1-D iteration of array `

Code 1: Working with a 2D array

 ` # Python program illustrating ` ` # works with ndarray.flat () `   ` import ` ` numpy as geek `   ` # Working on 1D iteration of a 2D array ` ` array ` ` = ` ` geek.arange (` ` 15 ). reshape ( 3 , 5 ) `` print ( " 2D array: " , array)    # Using flat (): 1D range iterator print ( "Using Array:" , array.flat [ 2 : 6 ])   # Using flat () to print 1D of a presented array print ( "1D representation of array: - & gt;" , array .flat [ 0 : 15 ]) `

Output:

` 2D array : [[0 1 2 3 4] [5 6 7 8 9] [10 11 12 13 14]] Using Array: [2 3 4 5] 1D representation of array: - & gt; [0 1 2 ..., 12 13 14] `

Code 2: changing array values ​​

 ` # Python program illustrating ` ` # works with ndarray.flat () `   ` import ` ` numpy as geek ` ` `  ` # Working on 1D iteration of a 2D array ` ` array = geek.arange ( 15 ). reshape ( 3 , 5 ) `` print ( " 2D array: " , array)    # All elements are set to 1 array.flat = 1 print ( "All Values ​​set to 1:" , array)   array.flat [ 3 : 6 ] = 8 array. flat [ 8 : 10 ] = 9 `` print ( "Changing values ​​in a range:" , array) `

Output:

``` 2D array: [[0 1 2 3 4] [5 6 7 8 9] [10 11 12 13 14]] All Values ​​set to 1: [[1 1 1 1 1] [1 1 1 1 1] [1 1 1 1 1]] Changing values ​​in a range: [[1 1 1 8 8] [8 1 1 9 9] [1 1 1 1 1]]    What is numpy.flatiter really?   The smoothing iterator returns x.flat for any array x. It allows you to iterate (in the main row) over N-dimensional arrays, either in a for loop or by calling its next method.    Code 3: numpy.flatitter () role            ` # Python program illustrating `  ` # works with ndarray.flat () `      ` import ` ` numpy as geek `    ` # Working on 1D iteration of a 2D array `   ` array ` ` = ` ` geek.arange (` ` 15 ` `). reshape (` ` 3 ` `, ` ` 5 ` `) `  ` print ` ` (` ` "2D array:" ` `, array) `      ` print ` ` (` ` "ID array:" ` `, array.flat [` ` 0 ` `: ` ` 15 ` `]) `  ` `   ` print ` ` (` ` "Type of array , flat (): "` `, ` ` type ` ` (array.flat)) `     ` for ` ` i ` ` in ` ` array.flat: `  ` ` ` print ` ` (i, end ` < code class = "keyword"> =  ` `` ` `) `          Output:    2D array: [[0 1 2 3 4] [5 6 7 8 9] [10 11 12 13 14]] ID array: [0 1 2 ..., 12 13 14] Type of array, flat (): 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14    Links:    https://docs.scipy.org/doc/numpy/reference/generated/numpy.ndarray.flat.html#numpy.ndarray.flat     Notes:   These codes will not work for online IDs. Please run them on your systems to see how they work.  This article is courtesy of  Mohit Gupta_OMG