A Comparison Of Linux Math Tools For Command Line Calculations
Performing Math on the Linux Command Line
The Linux command line provides access to several powerful built-in tools that can be used for mathematical calculations and analysis. This includes programmable calculator languages like bc, pattern scanning languages like awk, and general purpose programming languages like Python that have extensive math support. Each tool has its own strengths and is suited for different use cases.
Powerful Built-In Tools
bc – Arbitrary precision calculator language
The bc calculator language performs numeric computations with arbitrary precision. It supports interactive execution as well as running from scripts. Some key capabilities include:
- Arbitrary precision numbers
- Interactive programmable calculator
- Scriptable for automation
- Numeric computations and math functions
- Basic programming language constructs
awk – Pattern scanning and processing language
The awk language scans input line by line and performs data extraction and transformations. Some math-related features include:
- Supports numeric variables and arithmetic
- Built-in mathematical functions
- Can aggregate totals, counts, averages etc.
- Conditionals, comparisons and logical operators
- Easy to integrate math operations into data processing pipelines
Python – General purpose programming language with math capabilities
Python is a widely used general purpose programming language well suited for math due to:
- Very readable syntax
- Open source with extensive packages
- Full featured standard library including math module
- Specialized packages like NumPy and SciPy for scientific computing
- Data visualization with Matplotlib
- Integrates well with Linux environment
Calculations with bc
The bc language operates like an interactive calculator that allows storing results in variables and reusing them. It supports common math operations and functions. Some examples include:
Basic arithmetic and variables
/* Assignments */ x = 3 + 5 y = 9 / 2 /* Arithmetic */ x * y
Mathematical and trigonometric functions
/* Exponents and roots */ 10 ^ 3 sqrt(25) /* Trig functions */ s(0.5) c(0.5)
Comparisons and logical operators
/* Comparisons */ x > 10 x <= y + 3 /* Logical ops */ (x > 5) && (y < 10)
Example calculations
Some examples of full math calculations in bc include:
/* Quadratic formula */ a = 1 b = -3 c = 2 root1 = (-b + sqrt(b^2 - 4*a*c)) / (2*a) root2 = (-b - sqrt(b^2 - 4*a*c)) / (2*a) /* Present value of an annuity */ i = 0.05 n = 10 pmt = 500 pv = pmt / i * (1 - (1 + i)^-n) / -1
Advanced Math with awk
The awk language can crunch numbers and perform mathematical analytics directly on data files without needing additional code. Some examples include:
Numerical calculations
/* Assignments and math */ x = 4 * avg /* Random numbers */ rand()
Statistical aggregates
/* On data files */ sum += $2 avg = sum / NR
Conditional expressions
/* Classify by value */
if ($3 >= limit) {
print "Outlier"
}
Example statistical analysis
Putting awk's math capabilities together enables tasks like this example analysis:
# Calculate averages by category
/cat1/ {sum1 += $2; n1++}
/cat2/ {sum2 += $2; n2++}
# Get averages
END {
avg1 = sum1 / n1
avg2 = sum2 / n2
# Compare
print "Difference =" avg1 - avg2
}
Leveraging Python Math Libraries
Python's large collection of third party packages augment its math capabilities. These enable numerically intensive applications.
Standard math library functions
import math math.sin(2) math.log(10)
NumPy for scientific computing
import numpy as np v = np.array([1, 2, 3]) M = np.matrix([[1,2], [3,4]]) v.dot(M)
Matplotlib for visualization
from matplotlib import pyplot as plt plt.hist(data) plt.boxplot(stats)
Example data analysis script
# Load libraries
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
# Import dataset
data = pd.read_csv('data.csv')
# Calculate stats
avg = np.mean(data['value'])
std = np.std(data['value'])
# Plot
plt.hist(data)
plt.title('Average =' + str(avg))
# Show plot
plt.show()
Performance Comparison
The tools have tradeoffs to consider when choosing which to use.
Speed of execution
For intensive computations Python with NumPy is faster than awk or bc.
Memory usage
The bc calculator language has very low memory needs compared to awk and Python.
Ease of use
Awk works well for math integrated directly into data processing scripts while Python requires more coding but has more available functionality.
Recommendations
Determining which Linux math tool to use depends on the specific requirements.
When to use each tool
- Use bc for simple interactive calculations
- Use awk to combine math with data analysis
- Use Python for intensive computations, number crunching and visualization
Integration into scripts and programs
Prefer Python when developing larger scripts and tools that integrate with other systems. Choose awk for writing one-off data processing scripts involving mathematics.
