# 11 Python Magic Strategies Each Programmer Ought to Know

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In Python, magic strategies aid you emulate the habits of built-in features in your Python courses. These strategies have main and trailing double underscores (__), and therefore are additionally referred to as **dunder strategies**.

These magic strategies additionally aid you implement operator overloading in Python. You’ve most likely seen examples of this. Like utilizing the multiplication operator * with two integers provides the product. Whereas utilizing it with a string and an integer `okay`

provides the string repeated `okay`

occasions:

```
>>> 3 * 4
12
>>> 'code' * 3
'codecodecode'
```

On this article, we’ll discover magic strategies in Python by making a easy two-dimensional vector `Vector2D`

class.

We’ll begin with strategies you’re seemingly acquainted with and progressively construct as much as extra useful magic strategies.

Let’s begin writing some magic strategies!

Think about the next `Vector2D`

class:

When you create a category and instantiate an object, you may add attributes like so: `obj_name.attribute_name = worth`

.

Nevertheless, as an alternative of manually including attributes to each occasion that you simply create (not fascinating in any respect, after all!), you want a solution to initialize these attributes if you instantiate an object.

To take action you may outline the `__init__`

methodology. Let’s outline the outline the `__init__`

methodology for our `Vector2D`

class:

```
class Vector2D:
def __init__(self, x, y):
self.x = x
self.y = y
v = Vector2D(3, 5)
```

While you attempt to examine or print out the article you instantiated, you’ll see that you do not get any useful data.

```
v = Vector2D(3, 5)
print(v)
```

`Output >>> <__main__.Vector2D object at 0x7d2fcfaf0ac0>`

Because of this you need to add a illustration string, a string illustration of the article. To take action, add a `__repr__`

methodology like so:

```
class Vector2D:
def __init__(self, x, y):
self.x = x
self.y = y
def __repr__(self):
return f"Vector2D(x={self.x}, y={self.y})"
v = Vector2D(3, 5)
print(v)
```

`Output >>> Vector2D(x=3, y=5)`

The `__repr__`

ought to embrace all of the attributes and knowledge wanted to create an occasion of the category. The `__repr__`

methodology is usually used for the aim of debugging.

The `__str__`

can be used so as to add a string illustration of the article. Basically, the `__str__`

methodology is used to supply data to the top customers of the category.

Let’s add a `__str__`

methodology to our class:

```
class Vector2D:
def __init__(self, x, y):
self.x = x
self.y = y
def __str__(self):
return f"Vector2D(x={self.x}, y={self.y})"
v = Vector2D(3, 5)
print(v)
```

`Output >>> Vector2D(x=3, y=5)`

If there isn’t a implementation of `__str__`

, it falls again to `__repr__`

. So for each class that you simply create, you need to—on the minimal—add a `__repr__`

methodology.

Subsequent, let’s add a technique to examine for equality of any two objects of the `Vector2D`

class. Two vector objects are equal if they’ve an identical x and y coordinates.

Now create two `Vector2D`

objects with equal values for each x and y and examine them for equality:

```
v1 = Vector2D(3, 5)
v2 = Vector2D(3, 5)
print(v1 == v2)
```

The result’s False. As a result of by default the comparability checks for equality of the article IDs in reminiscence.

Let’s add the `__eq__`

methodology to examine for equality:

```
class Vector2D:
def __init__(self, x, y):
self.x = x
self.y = y
def __repr__(self):
return f"Vector2D(x={self.x}, y={self.y})"
def __eq__(self, different):
return self.x == different.x and self.y == different.y
```

The equality checks ought to now work as anticipated:

```
v1 = Vector2D(3, 5)
v2 = Vector2D(3, 5)
print(v1 == v2)
```

Python’s built-in `len()`

perform helps you compute the size of built-in iterables. Let’s say, for a vector, size ought to return the variety of components that the vector accommodates.

So let’s add a `__len__`

methodology for the `Vector2D`

class:

```
class Vector2D:
def __init__(self, x, y):
self.x = x
self.y = y
def __repr__(self):
return f"Vector2D(x={self.x}, y={self.y})"
def __len__(self):
return 2
v = Vector2D(3, 5)
print(len(v))
```

All objects of the `Vector2D`

class are of size 2:

Now let’s consider frequent operations we’d carry out on vectors. Let’s add magic strategies so as to add and subtract any two vectors.

When you straight attempt to add two vector objects, you’ll run into errors. So you need to add an `__add__`

methodology:

```
class Vector2D:
def __init__(self, x, y):
self.x = x
self.y = y
def __repr__(self):
return f"Vector2D(x={self.x}, y={self.y})"
def __add__(self, different):
return Vector2D(self.x + different.x, self.y + different.y)
```

Now you can add any two vectors like so:

```
v1 = Vector2D(3, 5)
v2 = Vector2D(1, 2)
consequence = v1 + v2
print(consequence)
```

`Output >>> Vector2D(x=4, y=7)`

Subsequent, let’s add a `__sub__`

methodology to calculate the distinction between any two objects of the `Vector2D`

class:

```
class Vector2D:
def __init__(self, x, y):
self.x = x
self.y = y
def __repr__(self):
return f"Vector2D(x={self.x}, y={self.y})"
def __sub__(self, different):
return Vector2D(self.x - different.x, self.y - different.y)
```

```
v1 = Vector2D(3, 5)
v2 = Vector2D(1, 2)
consequence = v1 - v2
print(consequence)
```

`Output >>> Vector2D(x=2, y=3)`

We will additionally outline a `__mul__`

methodology to outline multiplication between objects.

Let’s implement let’s deal with

- Scalar multiplication: the multiplication of a vector by scalar and
- Interior product: the dot product of two vectors

```
class Vector2D:
def __init__(self, x, y):
self.x = x
self.y = y
def __repr__(self):
return f"Vector2D(x={self.x}, y={self.y})"
def __mul__(self, different):
# Scalar multiplication
if isinstance(different, (int, float)):
return Vector2D(self.x * different, self.y * different)
# Dot product
elif isinstance(different, Vector2D):
return self.x * different.x + self.y * different.y
else:
increase TypeError("Unsupported operand kind for *")
```

Now we’ll take a few examples to see the `__mul__`

methodology in motion.

```
v1 = Vector2D(3, 5)
v2 = Vector2D(1, 2)
# Scalar multiplication
result1 = v1 * 2
print(result1)
# Dot product
result2 = v1 * v2
print(result2)
```

```
Output >>>
Vector2D(x=6, y=10)
13
```

The `__getitem__`

magic methodology lets you index into the objects and entry attributes or slice of attributes utilizing the acquainted square-bracket [ ] syntax.

For an object `v`

of the `Vector2D`

class:

`v[0]`

: x coordinate`v[1]`

: y coordinate

When you strive accessing by index, you’ll run into errors:

```
v = Vector2D(3, 5)
print(v[0],v[1])
```

```
---------------------------------------------------------------------------
TypeError Traceback (most up-to-date name final)
``` in ()
----> 1 print(v[0],v[1])
TypeError: 'Vector2D' object is just not subscriptable |

Let’s implement the `__getitem__`

methodology:

```
class Vector2D:
def __init__(self, x, y):
self.x = x
self.y = y
def __repr__(self):
return f"Vector2D(x={self.x}, y={self.y})"
def __getitem__(self, key):
if key == 0:
return self.x
elif key == 1:
return self.y
else:
increase IndexError("Index out of vary")
```

Now you can entry the weather utilizing their indexes as proven:

```
v = Vector2D(3, 5)
print(v[0])
print(v[1])
```

With an implementation of the `__call__`

methodology, you may name objects as in the event that they have been features.

Within the `Vector2D`

class, we are able to implement a `__call__`

to scale a vector by a given issue:

```
class Vector2D:
def __init__(self, x, y):
self.x = x
self.y = y
def __repr__(self):
return f"Vector2D(x={self.x}, y={self.y})"
def __call__(self, scalar):
return Vector2D(self.x * scalar, self.y * scalar)
```

So when you now name 3, you’ll get the vector scaled by issue of three:

```
v = Vector2D(3, 5)
consequence = v(3)
print(consequence)
```

`Output >>> Vector2D(x=9, y=15)`

The `__getattr__`

methodology is used to get the values of particular attributes of the objects.

For this instance, we are able to add a `__getattr__`

dunder methodology that will get referred to as to compute the magnitude (L2-norm) of the vector:

```
class Vector2D:
def __init__(self, x, y):
self.x = x
self.y = y
def __repr__(self):
return f"Vector2D(x={self.x}, y={self.y})"
def __getattr__(self, identify):
if identify == "magnitude":
return (self.x ** 2 + self.y ** 2) ** 0.5
else:
increase AttributeError(f"'Vector2D' object has no attribute '{identify}'")
```

Let’s confirm if this works as anticipated:

```
v = Vector2D(3, 4)
print(v.magnitude)
```

That is all for this tutorial! I hope you realized the right way to add magic strategies to your class to emulate the habits of built-in features.

We’ve coated among the most helpful magic strategies. However this isn’t this isn’t an exhaustive checklist. To additional your understanding, create a Python class of your alternative and add magic strategies relying on the performance required. Preserve coding!

** Bala Priya C** is a developer and technical author from India. She likes working on the intersection of math, programming, information science, and content material creation. Her areas of curiosity and experience embrace DevOps, information science, and pure language processing. She enjoys studying, writing, coding, and occasional! At present, she’s engaged on studying and sharing her information with the developer neighborhood by authoring tutorials, how-to guides, opinion items, and extra.