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NumPy Part 3

Representing missing and infinite values

  • Missing values can be represented using np.nan

  • Infinite values can be represented using np.inf

Example 1


np.nan

Example 2


np.inf



Checking whether an array contains and nan value or an infinite value

  • To check if nan value is present in the array we will use isnan() function

  • isnan() function returns True if an array contains nan or it will return False

  • To check if an infinite value is present in an array we will use isinf() function

  • isinf() function returns True if an array contains infinite or it will return False

Example 3


isnan()

Example 4


isinf()


Comparison operators on arrays

  • We can compare the arrays by using comparison operators like (==,<,>,>=,<=)

  • Comparison operators compare the arrays element wise

== ( equal )

  • To compare whether the elements are equal in the arrays

Example 5


!= ( not equal )

  • To compare whether the elements are not equal in the arrays

Example 6


> ( greater than )

  • To compare whether the elements are greater than in the arrays

Example 7


< ( lesser than )

  • To compare whether the elements are lesser than in the arrays

Example 8


>= ( greater than or equal to)

  • To compare whether the elements are greater than or equal to in the arrays

Example 9


<= ( lesser than or equal to)

  • To compare whether the elements are lesser than or equal to in the arrays

Example 10



array-wise comparisons

  • In array - wise comparisons we compare whether the arrays are same or not

  • For array-wise comparisons we will use array_equal(array1,array2) function

Example 11

Logical Operations

  • We can also apply logical operation on arrays

  • logical_or(array1,array2) and logical_and(array1,array2) functions are used to perform logical operations on arrays

logical_or(array1,array2)

  • It will compare two arrays elementwise and returns true if any one of the element is not a 0 and returns false if both the elements are 0

Example 12

logical_and(array1,array2)

  • It will compare two arrays elementwise and returns true if both the element is not a 0 and returns false if both the elements are 0

Example 13

Extras:-


np.all(array)

  • np.all() function returns True if all the elements in an arrays is not 0's and returns False if any element in an array is 0's



np.any(array)

  • np.any() function returns True if any elements in an arrays is not 0's and returns False if all the elements in an array is 0's



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