Python Program to Compute Life Path Number

Given a String of date of format YYYYMMDD, our task is to compute the life path number.  Life Path Number is the number obtained by summation of individual digits of each element repeatedly till single digit, of datestring. Used in Numerology Predictions.

Examples:

Input : test_str = “19970314”

Output : 7

Explanation : 1 + 9 + 9 + 7 = 26 , 2 + 6 = 8 [ year ] ; 0 + 3 = 3 [ month ] ; 1 + 4 = 5 [ day ]. 5 + 3 + 8 = 16 ; 1 + 6 = 7.

Input : test_str = “19970104”

Output : 4

Explanation : 1 + 9 + 9 + 7 = 26 , 2 + 6 = 8 [ year ] ; 0 + 1 = 1 [ month ] ; 0 + 4 = 4 [ day ]. 4 + 1 + 8 = 13 ; 1 + 3 = 4.

Method 1: Using loop

The logic behind computing this is getting a summation of each digit and perform %10 at each step. This way result curlers to single digit if it goes to double-digit. 

Python3

# Python3 code to demonstrate working of
# Life Path Number
# Using loop

# initializing string
test_str = "19970314"

# printing original string
print("The original string is : " + str(test_str))

res = 0
for num in test_str:
    res += int(num)

    # modulation in case of 2 digit number
    if res > 9:
        res = res % 10 + res // 10

# printing result
print("Life Path Number  : " + str(res))

Output:

The original string is : 19970314
Life Path Number  : 7

Time Complexity: O(n)

Space Complexity: O(n)

Method 2: Using recursion

Similar way as above, the difference being the recursive function is used for repeated modulation in case of digit count greater than 1.

Python3

# Python3 code to demonstrate working of
# Life Path Number
# Using recursion

# initializing string
test_str = "19970314"

# printing original string
print("The original string is : " + str(test_str))

# recursion function definition
def lpn(num): return num if num < 10 else lpn(num // 10 + num % 10)

# recursive function initial call
res = lpn(int(test_str))

# printing result
print("Life Path Number  : " + str(res))

Output:

The original string is : 19970314
Life Path Number  : 7

Time Complexity: O(n)

Auxiliary Space: O(n)