# How Do You Change Data Type in Python?

//

Angela Bailey

Changing data types in Python is a common task when working with variables and data structures. Python provides several built-in functions that allow you to convert one data type to another. In this tutorial, we will explore these conversion functions and how to use them effectively.

## 1. Converting Numeric Data Types

Python provides functions for converting between numeric data types, such as int(), float(), and complex(). These functions allow you to convert numbers from one type to another.

For example, if you have a variable that stores an integer and you want to convert it to a float, you can use the float() function:

```x = 5
y = float(x)
print(y)  # Output: 5.0
```

You can also convert a float to an integer using the int() function:

```x = 3.14
y = int(x)
print(y)  # Output: 3
```

If you want to convert a number into a complex number, you can use the complex() function:

```x = 2
y = complex(x)
print(y)  # Output: (2+0j)
```

## 2. Converting Strings to Numeric Data Types

Oftentimes, it is necessary to convert strings that represent numbers into their corresponding numeric data types. Python provides functions such as int(), float(), and eval() for this purpose.

The int() function converts a string into an integer:

```x = "10"
y = int(x)
print(y)  # Output: 10
```

If the string represents a floating-point number, you can use the float() function:

```x = "3.14"
y = float(x)
print(y)  # Output: 3.14
```

The eval() function is a powerful tool that can evaluate an expression represented as a string and return the result. This function can handle more complex conversions:

```x = "5 + 3"
y = eval(x)
print(y)  # Output: 8
```

## 3. Converting Data Types within Collections

In Python, you can convert data types within collections like lists, tuples, and dictionaries using list comprehension or mapping functions such as map().

### a) Converting Elements in a List

To convert elements in a list to a different data type, you can use list comprehension:

```numbers = ["1", "2", "3", "4"]
int_numbers = [int(x) for x in numbers]
print(int_numbers)  # Output: [1, 2, 3, 4]
```

### b) Converting Values in a Dictionary

To convert values in a dictionary to a different data type, you can use dictionary comprehension:

```prices = {"apple": "0.99", "banana": "0.50", "orange": "0.75"}
float_prices = {key: float(value) for key, value in prices.items()}
print(float_prices)  # Output: {'apple': 0.99, 'banana': 0.5, 'orange': 0.75}
```

## 4. Handling Conversion Errors

When performing data type conversions, it’s important to handle potential errors that may occur. For example, if you attempt to convert a string that cannot be converted to an integer using the int() function, a ValueError will be raised.

To handle such errors, you can use exception handling with try-except blocks:

```x = "abc"
try:
y = int(x)
print(y)
except ValueError:
print("Conversion error: Invalid integer format")
```

In the above example, the code inside the try block attempts to convert the string “abc” into an integer. Since this conversion is not possible, a ValueError is raised and caught in the except ValueError block, which then displays an error message.

## Conclusion

In Python, changing data types is a common task when working with variables and data structures. By utilizing built-in conversion functions like int(), float(), and eval(), you can easily convert between different types.

Remember to handle potential conversion errors using exception handling to ensure smooth execution of your code. With these techniques in your toolkit, you can confidently manipulate and convert data types as needed in your Python programs.