Type Conversion

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What is Type Conversion in Python?

Type Conversion is the process of converting the data from one data type to another.

There are two types of type conversion:

1. Implicit Type Conversion – Python automatically converts.

2. Explicit Type Conversion – You manually convert.

Why It’s Important in Machine Learning?

In machine learning, you:

• Read data from files (like CSVs) – everything might be a string.

• Need to feed numbers (not strings) into models.

• Often convert data types to fit into numpy arrays, pandas DataFrames, or scikit-learn models.

1. Implicit Type Conversion (Automatic by Python)

Python automatically converts smaller data types to larger data types during operations.

Example:

x = 5      # int
y = 2.0    # float

result = x + y
print(result)       # Output: 7.0
print(type(result)) # Output: <class 'float'>

Notes:

• int + float = float

• Useful in calculations – Python promotes to the higher precision type.

2. Explicit Type Conversion (Manual by You)

You manually convert using functions like:

Function	Converts To
int()	Integer
float()	Float
str()	String
bool()	Boolean
list()	List

Real-world Example in Machine Learning:

You read CSV data, and you get this:

age = "25"          # str from CSV
height = "1.75"     # str from CSV

But ML models need numeric types:

age = int(age)
height = float(height)

3. Practical Examples of int(), float(), str(), bool()

Convert String to Integer

s = "123"
num = int(s)
print(num, type(num))  # 123 <class 'int'>

But this will fail:

s = "123.45"
num = int(s)  # ValueError!

Solution:

s = "123.45"
num = int(float(s))  # First to float, then int
print(num)  # 123

Convert String to Float

s = "3.14"
f = float(s)
print(f, type(f))  # 3.14 <class 'float'>

Convert Number to String

n = 100
s = str(n)
print(s, type(s))  # '100' <class 'str'>

Convert Boolean

print(bool(1))     # True
print(bool(0))     # False
print(bool("hi"))  # True
print(bool(""))    # False

Note: Empty string or zero = False. Everything else = True.

Use Case in Machine Learning: pandas

import pandas as pd

df = pd.read_csv("data.csv")

# Suppose 'salary' is stored as string
df['salary'] = df['salary'].astype(float)

Important: Use .astype() to convert entire pandas column.

Type Conversion in NumPy

import numpy as np

arr = np.array(['1.2', '3.4', '5.6'])  # dtype = <U3 (Unicode string)
arr_float = arr.astype(float)
print(arr_float)   # [1.2 3.4 5.6]

Example Project: Cleaning Data for ML

data = [
    {'age': '25', 'height': '1.80', 'gender': 'male'},
    {'age': '30', 'height': '1.75', 'gender': 'female'}
]

# Cleaned data
clean_data = []

for row in data:
    clean_data.append({
        'age': int(row['age']),
        'height': float(row['height']),
        'gender': 1 if row['gender'] == 'male' else 0  # boolean encoding
    })

print(clean_data)
# [{'age': 25, 'height': 1.8, 'gender': 1}, {'age': 30, 'height': 1.75, 'gender': 0}]

Tip: Check Before Converting

Always check the type before and after conversion:

print(type(value))  # Before
value = float(value)
print(type(value))  # After

Summary Cheat Sheet

int("123")           # 123
float("3.14")        # 3.14
str(123)             # '123'
bool(0)              # False
bool("text")         # True

list("hello")        # ['h', 'e', 'l', 'l', 'o']
tuple([1, 2, 3])     # (1, 2, 3)

ML Case You’ll Use Often

Scenario	Conversion Required
Data from CSV as strings	int(), float()
Encode categorical features	str to int (LabelEncoder)
Convert labels to booleans	str → bool()
Pandas columns conversion	.astype(dtype)
NumPy array conversion	astype(dtype)

Keywords

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