MingMath

MingMath is a Python package for undergraduate mathematics and mathematical computing.

Installation

pip install mingmath

import mingmath

1. String Processing

print(mingmath.reverse_string("MingMath"))
print(mingmath.count_vowels("MingMath"))

Output:

htaMgniM
2

2. List

numbers = [1, 2, 3, 4, 5]
print(mingmath.list_sum(numbers))

Output:

15

3. Tuple

data = (1, 2, 3)
print(mingmath.tuple_to_list(data))

Output:

[1, 2, 3]

4. Dictionary

student = {"name": "Ming", "year": 2}
print(mingmath.dictionary_keys(student))

Output:

['name', 'year']

5. Set

a = {1, 2, 3}
b = {3, 4, 5}

print(mingmath.set_union(a, b))
print(mingmath.set_intersection(a, b))

Output:

{1, 2, 3, 4, 5}
{3}

6. Positional Arguments

print(mingmath.positional_arguments(10, 20))

Output:

30

7. Keyword Arguments

print(mingmath.keyword_arguments(name="Ming", age=20))

Output:

Ming is 20 years old.

8. Default Arguments

print(mingmath.default_arguments(5))
print(mingmath.default_arguments(5, 3))

Output:

25
125

9. Variable-length Arguments

print(mingmath.variable_length_args(1, 2, 3, 4, 5))

Output:

15

10. Recursive Function

print(mingmath.factorial_recursive(5))
print(mingmath.fibonacci_recursive(6))

Output:

120
8

11. Memoization

print(mingmath.fibonacci_memo(10))

Output:

55

12. Algorithm Comparison

edges = [
    ("A", "B"),
    ("B", "C"),
    ("A", "C")
]

print(mingmath.shortest_path_bfs(edges, "A", "C"))

weighted_edges = [
    ("A", "B", 4),
    ("B", "C", 2),
    ("A", "C", 10)
]

print(mingmath.shortest_path_dijkstra(
    weighted_edges, "A", "C"
))

BFS is used for unweighted graphs, while Dijkstra considers edge weights.

13. Class

number = mingmath.Number(10)

print(number.value)
print(number)

Output:

10
Number(10)

14. Magic Methods

a = mingmath.Number(10)
b = mingmath.Number(5)

print(a + b)

Output:

Number(15)

15. Inheritance

number = mingmath.PositiveNumber(10)

print(number.value)
print(number.is_positive())

Output:

10
True

MingMath also demonstrates multiple, multilevel, hierarchical, and hybrid inheritance.

16. File I/O

mingmath.write_text_file(
    "example.txt",
    "Hello MingMath!"
)

print(mingmath.read_text_file("example.txt"))

Output:

Hello MingMath!

17. Exception Handling

try:
    mingmath.factorial_recursive(-1)
except ValueError as error:
    print(error)

Output:

n must be non-negative

18. NumPy

numbers = [1, 2, 3, 4, 5]
print(mingmath.numpy_mean(numbers))

Output:

3.0

19. Pandas

data = {
    "score": [80, 90, 70],
    "age": [19, 20, 19]
}

df = mingmath.create_dataframe(data)
print(df)

Output:

   score  age
0     80   19
1     90   20
2     70   19

20. SciPy

print(mingmath.scipy_mean([1, 2, 3, 4, 5]))

Output:

3.0

21. SymPy

print(mingmath.sympy_derivative("x**3 + 2*x"))

Output:

3*x**2 + 2

22. SageMath

print(mingmath.sage_available())

Output:

True

SageMath must be installed separately.

23. PARI/GP

print(mingmath.pari_factorial(5))
print(mingmath.pari_gcd(24, 18))

Output:

120
6

24. GMPY2

print(mingmath.high_precision_factorial(10))

Output:

3628800

25. Matplotlib

x = [1, 2, 3, 4]
y = [1, 4, 9, 16]

mingmath.plot_line(x, y)

Creates a line graph.

26. Seaborn

x = [1, 2, 3, 4]
y = [2, 4, 3, 5]

mingmath.plot_scatter(x, y)

Creates a scatter plot.

27. Plotly

x = ["A", "B", "C"]
y = [10, 20, 15]

mingmath.plot_bar(x, y)

Creates an interactive bar chart.

28. NetworkX

edges = [
    ("A", "B"),
    ("B", "C")
]

graph = mingmath.create_graph(edges)

print(mingmath.graph_degrees(edges))

Output:

{'A': 1, 'B': 2, 'C': 1}

29. PyVista

import numpy as np

x = np.linspace(-2, 2, 10)
y = np.linspace(-2, 2, 10)

X, Y = np.meshgrid(x, y)
Z = X**2 + Y**2

mingmath.show_3d_surface(X, Y, Z)

Creates a 3D mathematical surface.

Version

0.4.0

License

MIT License