Type Conversion in Python
Python has two main numeric types: int (integers) and float (floating-point numbers).
Two Ways to Represent Numbers
Python has two main numeric types: int for integers and float for floating-point numbers. Understanding the difference is the foundation for reasoning about numeric type conversion. An integer represents a whole number, while a float represents a number with a decimal part or a value written in floating-point notation.
The important question is not whether one type is universally better. Choose the type that matches what the number represents: int for whole, discrete quantities and float for measurements, averages, and other values with fractional parts.
Integer and Float Values
| Type | Represents | Typical uses | Example notation |
|---|---|---|---|
| int | Whole numbers | Counting items, indexing positions, discrete quantities | 2 |
| float | Numbers with decimal parts | Measuring distances, calculating averages, scientific data | 3.23 |
A number such as 2 is recognized as an integer. A number such as 3.23 is a float. The distinction is about the numeric type Python uses for the value, not merely about how many characters appear in the literal. Use int when the data is naturally whole and discrete. Use float when fractional values are part of the data.
Reading E Notation
Floats can be written in ordinary decimal notation, such as 3.23, or in scientific notation called E notation. In E notation, E or e means times 10 to the power of. The part before E supplies the starting number, and the signed number after E supplies the power of 10.
Expanding a negative exponent
Interpret the floating-point literal 52.3E-4.
Read E: E means times 10 to the power of.
Apply the exponent: The literal means 52.3 multiplied by 10 to the power of negative 4.
Write the decimal form: 52.3E-4 equals 52.3 × 10^-4, which is 0.00523.
52.3E-4 and 0.00523 are two valid written forms of the same float value.
A negative exponent makes the resulting value smaller than the number before E. E notation is useful for very large or very small numbers, including examples such as 1.5E10 and 2.5E-6.
Converting the Numeric Representation
Type conversion changes which numeric type represents a value. The central distinction is that an integer representation is whole-number based, while a floating-point representation supports decimal values. When thinking about a conversion, track both the numerical quantity and its representation: ask whether the value remains the same and whether the type has changed.
Integers Without a Separate Long Type
Python's int type can represent integers of any size. Python does not use one type for ordinary integers and a separate long type for very large integers. A small integer and a very large integer are both handled with the same int type.
Do not carry the small-integer versus large-integer model from languages that use separate int and long types into Python. Python uses int for both.
Choosing the Appropriate Type
| Task | Appropriate type | Reason |
|---|---|---|
| Count items | int | A count is a whole, discrete quantity. |
| Index a position | int | An index represents a whole-number position. |
| Measure a distance | float | A measurement may contain a fractional part. |
| Calculate an average | float | An average may contain a decimal part. |
| Represent scientific data | float | Scientific values may be very large, very small, or fractional. |
Choose the numeric type that matches the meaning of the data.
For each task, decide whether int or float is the more appropriate type: counting books on a shelf, recording a measured distance, storing an index position, and representing a very small scientific value written with E notation.
Hints
- Ask whether the value is a whole, discrete quantity.
- A decimal measurement or scientific value points toward float.
What do you think happens?
Which type best matches each task: counting books, recording a measured distance, storing an index, and representing a very small scientific value?
Reveal answer
Answer: int, float, int, float
Counts and index positions are whole, discrete quantities. Measurements and scientific values can contain fractional parts or use floating-point notation.
Mistakes Beginners Make
Assuming Python needs a separate type for very large integers.
Python's int type represents integers of any size, and Python has no separate long type.
Fix:
Use the same int type for both ordinary and very large integers.Treating every numeric literal as an integer.
Decimal notation and E notation are forms of floating-point notation.
Fix:
Recognize values such as 3.23 and 52.3E-4 as float values.Reading E as part of a variable name or ignoring the exponent.
E means times 10 to the power of, so the exponent changes the scale of the value.
Fix:
Expand 52.3E-4 as 52.3 × 10^-4 = 0.00523.Choosing a numeric type without considering what the data represents.
The type should match whether the quantity is whole and discrete or fractional and measurable.
Fix:
Choose int for counts and indexes, and float for measurements, averages, and scientific data.
Key Takeaways
- Python has two main numeric types: int and float.
- Use int for whole numbers, counts, indexes, and other discrete quantities.
- Use float for decimal values, measurements, averages, and scientific data.
- E notation means times 10 to the power of; for example, 52.3E-4 equals 0.00523.
- Python's int type can represent integers of any size, so there is no separate long type.
Key Takeaways
- Distinguish int values from float values by asking whether the data is whole and discrete or fractional and measurable.
- Interpret E notation as multiplication by a power of 10.
- Remember that Python uses one int type for integers of any size; it has no separate long type.
- Choose the numeric type according to the meaning and precision needs of the data.