Type Conversion

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1. What is Type Conversion?

Type conversion is the process of converting a variable from one data type to another. This is a fundamental concept in Python programming and is particularly important in data science and machine learning, where raw data often comes in formats unsuitable for direct computation.

There are two main categories of type conversion:

1. Implicit Type Conversion

   • Automatically performed by Python when required.

   • Also known as type promotion.

2. Explicit Type Conversion

   • Performed manually using built-in functions such as int(), float(), str(), bool(), etc.

2. Why Is Type Conversion Important in Machine Learning?

When working with machine learning pipelines:

• Data is frequently imported from files such as CSVs or JSON, where values are read as strings.

• Machine learning models require numerical formats (integers, floats, booleans, or encoded categories).

• Libraries like NumPy, Pandas, and Scikit-learn often require strict type consistency to avoid errors.

For example:

• Converting "25" (string) to 25 (integer) for age.

• Converting "1.75" (string) to 1.75 (float) for height.

• Converting "male"/"female" (categorical string) to binary or integer encoding.

3. Implicit Type Conversion (Automatic)

Python automatically promotes smaller data types to larger ones during operations, ensuring precision is not lost.

Example – Integer and Float Addition:

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

• int + complex → complex

This automatic behavior ensures smooth calculations without requiring manual intervention.

4. Explicit Type Conversion

Explicit conversion is performed using Python’s type conversion functions:

Function	Converts To
int()	Integer
float()	Floating point number
str()	String
bool()	Boolean
list()	List
tuple()	Tuple

4.1 Example: String to Integer

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

⚠️ Potential Pitfall:

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

✅ Solution – Convert via Float:

s = "123.45"
num = int(float(s))  
print(num)  # 123

4.2 Example: String to Float

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

4.3 Example: Number to String

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

4.4 Example: Boolean Conversion

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

Rule of Thumb:

• Zero and empty strings → False.

• All other values → True.

5. Type Conversion in Pandas

When reading data with Pandas:

import pandas as pd

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

# Convert 'salary' column from string to float
df['salary'] = df['salary'].astype(float)

Key Method: .astype(dtype) – for converting entire columns.

6. Type Conversion in NumPy

NumPy arrays often require homogeneous data types.

import numpy as np

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

7. Practical Example – Cleaning Data for Machine Learning

Suppose we have raw data as strings:

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

Convert to machine-friendly format:

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
    })

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

8. Best Practice: Check Before Converting

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

This ensures type integrity and prevents runtime errors.

9. 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)

10. Machine Learning Use Cases for Type Conversion

Scenario	Conversion Required
Data from CSV (all strings)	int(), float()
Categorical features (e.g. gender)	String → Integer (LabelEncoder)
Boolean labels	String → Boolean
Pandas DataFrame column casting	.astype(dtype)
NumPy array casting	.astype(dtype)

11. Video Tutorial

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Keywords

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