Variables and Data Types

Data Type Conversions

Python offers various ways to convert data from one type to another. This is crucial for flexibility and performing operations requiring specific data types. Incorrect conversions can lead to errors.

Common Conversions:

• int(): Converts a value to an integer. Handles strings representing integers and floats (truncating the decimal part).

int("10")  # Output: 10
int(10.7)  # Output: 10
int("10a") # Output: ValueError

• float(): Converts a value to a floating-point number. Handles integers and strings representing numbers (including decimals).

float("10")  # Output: 10.0
float(10)   # Output: 10.0
float("10.5") # Output: 10.5

• str(): Converts a value to a string representation. This works for almost any data type.

str(10)    # Output: "10"
str(10.5)  # Output: "10.5"
str(1,2,3) # Output: "1, 2, 3"

• bool(): Converts a value to a boolean (True or False). Many values evaluate to False (e.g., 0, 0.0, “”, 1, {}, None); most others are True.

bool(1)    # Output: True
bool(0)    # Output: False
bool("")   # Output: False
bool("hello") # Output: True
bool(1)   # Output: False

• list(): Converts an iterable (like a tuple or string) into a list.

list((1,2,3)) # Output: 1, 2, 3
list("abc")   # Output: ['a', 'b', 'c']]

Type Errors:

Attempting to convert incompatible types will raise a TypeError. For example:

int("ten")  # Raises TypeError

Explicit vs. Implicit Conversions:

Python can sometimes perform implicit type conversions (e.g., adding an integer and a float automatically results in a float). However, explicit conversions using the functions above are generally preferred for clarity and to avoid unexpected behavior.

Error Handling (Type Hinting)

Advanced Conversions:

• For more complex conversions (e.g., between custom data structures or using external libraries), you might need to write your own conversion functions.

• Libraries like json handle conversion to and from JSON strings.

JSON Conversion