- Alias
- Booleans
- Code block
- Conditions
- Console
- Equality operator
- False
- Float
- For loop
- Formatted strings
- Functions
- Greater than operator
- Greater than or equal to operator
- If statement
- Index
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- Integer
- Less than operator
- Less than or equal to operator
- Lists
- Parameter
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- String
- The not operator
- True
- Variables
- While loop
- append()
- insert()
- pop()

PYTHON

# Python **Floats**: Syntax, Usage, and Examples

A float, or floating-point number, is a data type in Python that represents real numbers with a decimal component.

## How to Use Python Floats

A float in Python is created by including a decimal point in a number.

Python

```
# Numbers with decimal points create floats
quarter = 0.25
```

Dividing two numbers results in a float, even if both numbers are integers.

Python

```
# Generating a float by dividing two integers
third = 1 / 3 # Results in a float, approximately 0.3333
```

## **When to Use Python Floats**

Floats are essential for working with floating-point numbers in Python. From simple arithmetic operations to more complex scientific calculations, floats are essential in almost every Python program.

## **Practical Examples of Working with Python Floats**

Python programs of any kind use the float data type for improved precision over integers. Here are some simple examples:

### Working with Decimals

The most basic use of floats is representing numbers with a decimal component as values or within variables.

Python

```
inch_in_cm = 2.54
```

### Calculating with Precision

Floats are essential for making calculations that require decimal precision. As an example, consider calculating the compound interest for investments or loans:

Python

```
principal = 10000.0 # Initial amount
rate = 0.05 # Annual interest rate
time = 5 # Years
compound_interest = principal * ((1 + rate) ** time) - principal
```

### Analyzing Data

Data science often involves handling floats for statistical calculations, data transformations, and visualizations.

Python

```
import numpy as np
data = np.array([1.5, 2.3, 4.4, 5.9])
mean = np.mean(data)
print(f"Mean value: {mean:.2f}")
```

## Learn More About **Python Floats**

### Formatting Floats in Python

When displaying floats, showing a smaller number of decimal places might make a float easier to read. Using f-string formatting, you can control how many decimal places of a float you want to include. You can print floats in various formats, including percentages and scientific notation. Here are some common examples:

Python

```
temperature = 23.56789
print(f"Temperature: {temperature:.1f}°C") # Outputs: Temperature: 23.6°C
```

You can display a separator for thousands and two decimal places, for example when displaying financial information:

Python

```
value = 2762.815625000003
print(f"Value: ${value:,.2f}") # Outputs: Value: $2,762.82
```

You can also format floats as percentages, which is handy for displaying ratios or proportions.

Python

```
progress = 0.853
print(f"Loading... {progress:.1%}") # Outputs: Loading... 85.3%
```

For particularly large or small numbers, scientific notation provides a concise way to represent floats.

Python

```
avogadros_number = 6.02214076e23
print(f"Avogadro's Number: {avogadros_number:.2e}") # Outputs: Avogadro's Number: 6.02e+
```

When formatting floats for display, consider your context and audience. Financial data might require two decimal places. Scientific data, on the other hand, might need scientific notation or a specific number of significant figures.

### Converting String to Float in Python

When processing user input or data from external sources, you can use the `float()`

function to convert a Python string to a float. Once converted, the numbers are safe to use for calculations.

Python

```
input_str = "123.45"
input_float = float(input_str)
print(input_float * 2) # Outputs: 246.90
```

### Converting Float to Int in Python

In some scenarios, you might need to convert floats to integers, either by truncation or rounding. In Python, float-to-int conversion is possible with the built-in `int()`

function.

Python

```
my_float = 7.75
# Truncating float to int
int_value = int(my_float)
print(int_value) # Outputs: 7
# Rounding float and converting to int
rounded_int = round(my_float)
print(rounded_int) # Outputs: 8
```

### **Rounding Floats**

Sometimes, you might want to round float numbers to a certain number of decimal places. For this purpose, Python's built-in function `round()`

is ideal:

Python

```
import math
pi_approx = round(math.pi, 2)
print(f"Pi approximation: {pi_approx}") # Outputs: Pi approximation: 3.14
```

### Floating-Point Precision Issues

Unlike programming languages, computers represent floating-point numbers in binary. This can lead to precision errors because some decimal numbers are impossible to represent as binary fractions. For example, the decimal number `0.1`

is not representable in a finite binary fraction. This leads to a small error when represented in binary.

Therefore, floating-point arithmetic can lead to precision issues and unexpected behavior. For example, checking for equality or summing up large amounts of data can be dangerous.

Python

```
sum = 0.1 + 0.2
print(sum) # Often outputs: 0.30000000000000004
print(sum == 0.3) # Often outputs: False
```

For high-precision needs, particularly in financial applications, Python's `decimal`

module provides operations for decimal arithmetic. This can prevent common floating-point issues, ensuring accuracy in calculations:

Python

```
from decimal import Decimal
total = Decimal('0.1') + Decimal('0.2')
print(total == Decimal('0.3')) # Outputs: True
```

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