Quick Contact

    Python Tutorial
    Python Panda Tutorial
    Python Selenium
    Python Flask Tutorial
    Python Django
    Numpy
    Tensorflow
    Interview Questions & Answers

    NumPy–Functions

    Numpy is a python package used for scientific computing. So certainly, it supports a vast variety of functions used for computation. The various functions supported by numpy are mathematical, financial, universal, windows, and logical functions. Universal functions are used for array broadcasting, typecasting, and several other standard features. While windows functions are used in signal processing. We will be learning mathematical functions in detail in this article.

    Mathematical Functions in NumPy

    Numpy is written purely in C language. Hence, its mathematical functions are closely associated with functions present is math.h library in C.

    1. Arithmetic Functions

      Function Description
      add(arr1, arr2,..) Add arrays element wise
      reciprocal(arr) Returns reciprocal of elements of the argument array
      negative(arr) Returns numerical negative of elements of an array
      multiply(arr1,arr2,…) Multiply arrays element wise
      divide(arr1,arr2) Divide arrays element wise
      power(arr1,arr2) Return the first array with its each of its elements raised to the power of elements in the second array (element wise)
      subtract(arr1,arr2,…) Subtract arrays element wise
      true_divide(arr1,arr2) Returns true_divide of an array element wise
      floor_divide(arr1,arr2) Returns floor after dividing an array element wise
      float_power(arr1,arr2) Return the first array with its each of its elements raised to the power of elements in the second array (elementwise)
      fmod(arr1,arr2) Returns floor of the remainder after division elementwise
      mod(arr1,arr2) Returns remainder after division elementwise
      remainder(arr1,arr2) Returns remainder after division elementwise
      divmod(arr1,arr2) Returns remainder and quotient after division elementwise

      The above-mentioned operations can be performed in the following ways :

      In the given code snippet, we try to do some basic operations on the arguments, array a and array b.

      Code:

      import numpy as np
      a = np.array([10,20,30])
      b= np.array([1,2,3])
      print("addition of a and b :",np.add(a,b))
      print("multiplication of a and b :",np.multiply(a,b))
      print("subtraction of a and b :",np.subtract(a,b))
      print("a raised to b is:",np.power(a,b))
      

      Output:

      In this code snippet, we try to perform division and related operations on the arguments, array a and array b. We can notice the difference between mod, remainder, divmod and simple division.

      Code:

      import numpy as np
      a = np.array([10,20,30])
      b= np.array([2,3,4])
      print("division of a and b :",np.divide(a,b))
      print("true division of a  :",np.true_divide(a,b))
      print("floor_division of a and b :",np.floor_divide(a,b))
      print("float_power of a raised to b :",np.float_power(a,b))
      print("fmod of a and b :",np.fmod(a,b))
      print("mod of a and b :",np.mod(a,b))
      print("quotient and remainder of a and b :",np.divmod(a,b))
      print("remainders when a/b :",np.remainder(a,b))
      

      Output:

    2. Trigonometric Functions

      Function Description
      sin(arr) Returns trigonometric sine element wise
      cos(arr) Returns trigonometric cos element wise
      tan(arr) Returns trigonometric tan element wise
      arcsin(arr) Returns trigonometric inverse sine element wise
      arccos(arr) Returns trigonometric inverse cosine element wise
      arctan(arr) Returns trigonometric inverse tan element wise
      hypot(a,b) Returns hypotenuse of a right triangle with perpendicular and base as arguments
      degrees(arr)

      rad2deg(arr)

      Covert input angles from radians to degrees
      radians(arr)

      deg2rad(arr)

      Covert input angles from degrees to radians

      Here is an example of how to use trigonometric functions.

      Code:

      import numpy as np
      angles = np.array([0,np.pi/2, np.pi])     -----> input array angles
      sin_angles = np.sin(angles)
      cosine_angles = np.cos(angles)
      tan_angles = np.tan(angles)
      rad2degree = np.degrees(angles)
      print("sin of angles:",sin_angles)
      print("cosine of angles:",cosine_angles)
      print("tan of angles:",tan_angles)
      print("angles in radians",rad2degree)
      

      Output:

    3. Logarithmic and Exponential Functions

      Function Description
      exp(arr) Returns exponential of an input array element wise
      expm1(arr) Returns exponential exp(x)-1 of an input array element wise
      exp2(arr) Returns exponential 2**x of all elements in an array
      log(arr) Returns natural log of an input array element wise
      log10(arr) Returns log base 10 of an input array element wise
      log2(arr) Returns log base 2 of an input array element wise
      logaddexp(arr) Returns logarithm of the sum of exponentiations of all inputs
      logaddexp2(arr) Returns logarithm of the sum of exponentiations of the inputs in base 2

      Here is an example of using logarithmic functions:

      Code:

      import numpy as np
      a = np.array([1,2,3,4,5])
      a_log = np.log(a)
      a_exp = np.exp(a)
      print("log of input array a is:",a_log)
      print("exponent of input array a is:",a_exp)
      

      Output:

    4. Rounding Functions

      Function Description
      around(arr,decimal) Rounds the elements of an input array upto given decimal places
      round_(arr,decimal) Rounds the elements of an input array upto given decimal places
      rint(arr) Round the elements of an input array to the nearest integer towards zero
      fix(arr) Round the elements of an input array to the nearest integer towards zero
      floor(arr) Returns floor of input array element wise
      ceil(arr) Returns ceiling of input array element wise
      trunc(arr) Return the truncated value of an input array element wise

      Example of using rounding functions with numpy arrays:

      Code:

      importnumpy as np
      a = np.array([1.23,4.165,3.8245])
      rounded_a = np.round_(a,2)
      print(rounded_a)
      

      Output:

      Code:

      floor_a = np.floor(a)
      print(floor_a)
      

      Output:

    5. Miscellaneous Functions

      Function Description
      sqrt(arr) Returns the square root of an input array element wise
      cbrt(arr) Returns cube root of an input array element wise
      absolute(arr) Returns absolute value each element in an input array
      maximum(arr1,arr2,…) Returns element wise maximum of the input arrays
      minimum(arr1,arr2,…) Returns element wise minimum of the input arrays
      interp(arr, xp, fp) Calculates one-dimensional linear interpolation
      convolve(arr, v) Returns linear convolution of two one-dimensional sequences
      clip(arr, arr_min, arr_max) Limits the values in an input array

      Some examples using the above functions.

      Finding the Maxima:

      Code:

      import numpy as np
      a = [1,2,3] b = [3,1,2] maximum_elementwise = np.maximum(a,b)
      print("maxima are:",maximum_elementwise)
      

      Output:

      Clipping an array between max_limit and min_limit:

      Code:

      import numpy as np
      a = [1,2,3] b = [3,1,2] limiting_a = np.clip(a,0,2)
      print("limiting a between 0 and 2:",limiting_a)
      

      Output:


     

     

    Apply now for Advanced Python Training Course

    Copyright 1999- Ducat Creative, All rights reserved.

    Anda bisa mendapatkan server slot online resmi dan terpercaya tentu saja di sini. Sebagai salah satu provider yang menyediakan banyak pilihan permainan.