Скачать или смотреть Set Datatype in Python - Day 10 Data Science Course

In Python, data scientists often work with various data structures and data types to manipulate and analyze data efficiently. Some of the commonly used data types in data science include:

1. Numeric Types:
int: Integers (e.g., 1, 42, -10)
float: Floating-point numbers (e.g., 3.14, -2.5)

2. Text Type:
str: Strings (e.g., "hello", 'Data Science')

3. Boolean Type:
bool: Represents truth values, either True or False.

4. Sequence Types:
list: Ordered, mutable collections of elements (e.g., [1, 2, 3])
tuple: Ordered, immutable collections of elements (e.g., (1, 2, 3))

5. Set Types:
set: Unordered, mutable collections of unique elements (e.g., {1, 2, 3})
frozenset: Unordered, immutable collections of unique elements (e.g., frozenset({1, 2, 3}))

6. Mapping Type:
dict: Unordered, mutable collections of key-value pairs (e.g., {'name': 'John', 'age': 30})

7. Date and Time Types:
datetime: Represents dates and times (e.g., `datetime.datetime(2023, 7, 29)`)

8. NumPy Data Types (used extensively in numerical computing):
numpy.ndarray: Multidimensional arrays (e.g., `np.array([1, 2, 3])`)

When working with data science libraries like NumPy, Pandas, or TensorFlow, you will often encounter specialized data types and structures suited for scientific computing, data manipulation, and machine learning.

Python is dynamically typed, meaning you don't have to explicitly specify the data type when declaring variables. The interpreter infers the data type based on the assigned value. For example:

```python
Integers
age = 30
Floating-point number
height = 1.75
String
name = "John"
Boolean
is_student = True

List
grades = [90, 85, 95]
Tuple
coordinates = (10, 20)

Set
unique_numbers = {1, 2, 3}
Dictionary
person = {'name': 'Alice', 'age': 25}
```

However, Python is also a dynamically typed language, which means that the type of a variable can change during runtime. While this flexibility can be convenient, it can also lead to bugs if not managed carefully. As a best practice, it's essential to be aware of the data types you're working with and to ensure that your data is consistent and correctly handled throughout your data science workflows.

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