Numpy recarray.var () function | python



Record arrays allow you to access fields as elements of an array using arr.a and arr.b numpy.recarray.var () returns the variance of array elements along a given axis.

Syntax: numpy.recarray.var (axis = None, dtype = None, out = None, ddof = 0, keepdims = False)

Parameters:
axis: [int or tuples of int] axis along which we want to calculate the variance. Otherwise, it will consider arr to be flattened (works on all the axis). axis = 0 means variance along the column and axis = 1 means variance along the row.
dtype: [data-type, optional] Type we desire while computing variance.
out: [ndarray, optional] A location into which the result is stored.
 – & gt; If provided, it must have a shape that the inputs broadcast to.
 – & gt; If not provided or None, a freshly-allocated array is returned.
ddof: [int, optional] Delta Degrees of Freedom ”: the divisor used in the calculation is N – ddof, where N represents the number of elements. By default ddof is zero.
keepdims: [bool, optional] If this is set to True, the axes which are reduced are left in the result as dimensions with size one. With this option, the result will broadcast correctly against the input array.

Return: [ndarray] If out = None, returns a new array containing the variance; otherwise, a reference to the output array is returned.

Code # 1:

# Python program explaining
# numpy.recarray.var () method

  
# importing numy as a geek

import numpy as geek

 
# create an input array with two different fields

in_arr = geek.array ( [[( 5.0 , 2 ), ( 3.0 , - 4 ), ( 6.0 , 9 )],

[( 9.0 , 1 ), ( 5.0 , 4 ), ( - 12.0 , - 7 )]],

dtype = [( ` a` , float ), ( `b` , in t )])

 

print ( "Input array:" , in_arr )

 
# convert it to array of posts,
# using arr.view (np.recarray)

rec_arr = in_arr.view (geek.recarray)

print ( "Record array of float:" , rec_arr.a)

print ( "Record array of int:" , rec_arr.b)

 
# applying methods recarray.var # place an array of posts along axis 1

out_arr = rec_arr.a.var (axis = 1 )

print ( " Output array containing variance along axis 1: " , out_arr) 

  
# using recarray.var methods
# place an array of records along the 0 axis

out_arr = rec_arr.a.var (axis = 0 )

print ( " Output array containing variance along axis 0: " , out_arr) 

  
# using recarray.var methods
# place an array of records along the default axis

out_arr = rec_arr.a.var ()

print ( "Output array containing variance along default axis:" , out_arr) 

 

  
# applying recarray.var methods
# into an array of int records along axis 1

out_arr = rec_arr.b.var (axis = 1

print ( "Output array containing variance along axis 1: " , out_arr) 

  
# applying recarray.var methods
# to an array of int records along the 0 axis

out_arr = rec_arr.b.var (axis = 0 )

print ( "Output array containing variance along axis 0:" , out_arr) 

 
# applying recarray.var methods
# to an array of int records along the default axis

o ut_arr = rec_arr.b.var ()

print ( " Output array containing variance along default axis: " , out_arr) 

Exit:

 Input array: [[(5., 2) (3., -4) (6., 9)] [(9., 1) (5., 4) (-12., -7)]] Record array of float: [[5. 3. 6.] [9. 5. -12.]] Record array of int: [[2 -4 9] [1 4 -7]] Output array containing variance along axis 1: [ 1.55555556 82.88888889] Output array containing variance along axis 0: [4. 1. 81.] Output array containing variance along default axis: 46.22222222222222 Output array containing variance along axis 1: [28.22222222 21.55555556] Output array containing variance along axis 0: [0.25 16. 64.] Output array containing variance along default axis: 27.138888888888882