Data Types

Nerd Cafe | نرد کافه

Introduction

In Python, everything is an object, and every object has a data type. A data type defines:

• What kind of value it stores.

• What operations can be performed on it.

Example:

• A str stores text.

• An int stores whole numbers.

• A float stores decimals.

• A list stores collections of items.

Understanding data types is critical in machine learning because ML models expect specific data formats — usually numbers for training, strings for labels, or structured collections like lists/arrays.

1. Numeric Types

int (Integer)

Whole numbers (positive, negative, or zero).

a = 10
b = -3
c = 0
print(type(a))  # <class 'int'>

float (Floating Point)

Decimal numbers.

x = 3.14
y = -2.5
z = 0.0
print(type(x))  # <class 'float'>

complex (Complex Numbers)

Numbers with real and imaginary parts.

c1 = 2 + 3j
c2 = complex(4, -5)
print(c1 + c2)  # (6-2j)
print(type(c1))  # <class 'complex'>

ML Note:

• Use int and float for training data and features.

• complex is rarely used in ML, except in advanced fields like signal processing.

2. Text Type

str (String)

Stores text data.

name = "Nerd Cafe"
category = "spam"
file_path = "data/train.csv"

print(name.upper())     # NERD CAFE
print(len(category))    # 4

ML Use Cases:

• Labels ("cat", "dog")

• File paths ("dataset/images/cat.jpg")

• Column names in pandas

⚠️ ML models cannot directly use strings. Convert them with Label Encoding or One-Hot Encoding.

3. Sequence Types

list

Ordered, changeable, allows duplicates.

features = [5.1, 3.5, 1.4, 0.2]
mixed = [1, "dog", 3.5]  # Possible, but not recommended in ML

# Access
print(features[0])     # 5.1
print(features[-1])    # 0.2

# Modify
features.append(9.9)
print(features)        # [5.1, 3.5, 1.4, 0.2, 9.9]

ML Use Case:

Store a row of features before converting to NumPy arrays or pandas DataFrames.

tuple

Ordered, immutable (unchangeable).

point = (3.5, 7.2)

print(point[0])  # 3.5
# point[0] = 5   # ❌ Error: tuples cannot be changed

ML Use Case:

Represent fixed data such as image shape:

image_shape = (224, 224, 3)  # (height, width, channels)

range

Generates a sequence of numbers.

nums = range(5)  
print(list(nums))  # [0, 1, 2, 3, 4]

ML Use Case:

Iterating over epochs, batches, or samples.

for epoch in range(3):
    print(f"Training epoch {epoch}")

4. Mapping Type

dict (Dictionary)

Stores key-value pairs.

student = {"name": "Nerd Cafe", "age": 25, "score": 98.5}
print(student["name"])   # Nerd Cafe

ML Use Cases:

• Model configurations:

config = {"learning_rate": 0.01, "epochs": 10}

Label mapping:

labels = {"cat": 0, "dog": 1}
print(labels["dog"])  # 1

5. Boolean Type

bool

Represents True or False.

is_training = True
accuracy = 0.96

if accuracy > 0.95:
    print("Excellent model!")

ML Use Case:

Used in conditions (e.g., stopping training if accuracy reaches a threshold).

6. Binary Types

Used for handling raw binary data (e.g., images, serialized models).

data = b"Hello"
print(type(data))  # <class 'bytes'>

• bytes → immutable

• bytearray → mutable

• memoryview → view of binary data

7. Type Checking

x = 42
print(type(x))            # <class 'int'>
print(isinstance(x, int)) # True

ML Tip:

Validate data types before feeding them into ML models.

8. Type Casting (Conversion)

x = "42"
x = int(x)     # "42" → 42
y = float("3.14")

ML Example:

When reading CSV files, numbers are often loaded as strings. Convert them to int or float.

9. Summary Table

Data Type	Example	ML Usage
int	5	ID, count, label
float	3.14	Feature value, weight
str	"cat"	Label, file path, text
bool	True	Training flag, condition
list	[1, 2]	Features, dataset samples
tuple	(224, 224, 3)	Image shape, fixed data
dict	{"lr": 0.01}	Configurations, label mappings

10. Video Tutorial

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Keywords

int, float, complex, str, list, tuple, range, dict, set, bool, bytes, bytearray, memoryview, type, isinstance, type casting, data conversion, immutable, mutable, nerd cafe , نرد کافه

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