Prerequisite: Regular expression in Python Given an input, write a Python program to check whether the given Input is Floating point number or not. Examples:
Input: 1.20 Output: Floating point number Input: -2.356 Output: Floating point number Input: 0.2 Output: Floating point number Input: -3 Output: Not a Floating point number
In this program, we are using search() method of re module. re.search() : This method either returns None (if the pattern doesn’t match), or re.MatchObject that contains information about the matching part of the string. This method stops after the first match, so this is best suited for testing a regular expression more than extracting data. Let’s see the Python program for this :
Python3
# Python program to check input is# Floating point number or not# import re module# re module provides support# for regular expressionsimport re# Make a regular expression for# identifying Floating point number regex = '[+-]?[0-9]+\.[0-9]+'# Define a function to# check Floating point number def check(floatnum): # pass the regular expression # and the string in search() method if(re.search(regex, floatnum)): print("Floating point number") else: print("Not a Floating point number") # Driver Code if __name__ == '__main__' : # Enter the floating point number floatnum = "1.20" # calling run function check(floatnum) floatnum = "-2.356" check(floatnum) floatnum = "0.2" check(floatnum) floatnum = "-3" check(floatnum) |
Output:
Floating point number Floating point number Floating point number Not a Floating point number
Time complexity: O(n), where n is the length of the input string (floatnum).
- The re.search() function is used to match the regular expression pattern with the input string. The time complexity of this function is O(n), where n is the length of the input string.
- As we are calling this function once for each input string, the overall time complexity of the code is O(n).
Auxiliary Space: O(1) as we are not using any data structures to store any values.
