log10() math function to get logarithm of input number with base 10 in Python

math.log10(x) returns log of input number x with base 10

import math
print(math.log10(2))  # 0.3010299956639812
print(math.log10(4))  # 0.6020599913279624
print(math.log10(5))  # 0.6989700043360189

Using negative number
For any value less than or equal to 0 , we will get ValueError

import math
print(math.log10(-2))
print(math.log10(0))

Above code will generate error.

Comparison with Other Log Functions:

Example: Compare base 10, base e, and base 2 logarithms.

import math
num = 1000
print("log10(1000):", math.log10(num))  # Base 10 log :Output 3.0
print("log(1000):", math.log(num))      # Base e log :6.907755278982137
print("log2(1000):", math.log2(num))    # Base 2 log : 9.965784284662087

Scientific Notation:
Example: Logarithms to find magnitude.

richter_scale = math.log10(1000)
print(f"Richter scale magnitude: {richter_scale}")

Output

Richter scale magnitude: 3.0

Example 1: Calculating Base-10 Logarithm

Basic usage of `log10()` with positive numbers.

import math
print(math.log10(100))  # Output: 2.0
print(math.log10(1000)) # Output: 3.0

Example 2: Error Handling for Negative or Zero Values

Demonstrates error handling with `try-except` for values less than or equal to zero.

import math
try:
    print(math.log10(-10))
except ValueError as e:
    print("Error:", e)

Output:

Error: math domain error

Example 3: Calculating Logarithm in Data Normalization

Logarithmic transformation to reduce skew in a dataset.

data = [1, 10, 100, 1000, 10000]
log_data = [math.log10(x) for x in data]
print(log_data)

Output:

[0.0, 1.0, 2.0, 3.0, 4.0]

Applications of log10()

Data Transformation: Used in data preprocessing to normalize or reduce data skew.
Scientific Calculations: Common in calculating pH values, sound intensity (dB), and earthquake magnitude (Richter scale).
Logarithmic Scaling: Helpful in visualizing data across a wide range, such as in histograms or charts.

log2 log1p() log()

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log10(): Base-10 Logarithm Calculation