In this session, we'll delve into more advanced Python features, including modules, libraries, and an introduction to some popular Python libraries for data manipulation and visualization.
Modules and Packages
Modules are files containing Python code that can be imported into other Python scripts. Packages are collections of modules.
Creating a Module
Create a file named mymodule.py
:
# mymodule.py
def greet(name):
return f"Hello, {name}!"
Importing a Module
You can import your custom module into another script:
import mymodule
print(mymodule.greet("Alice")) # Output: Hello, Alice!
Standard Libraries
Python comes with a rich standard library. Here are a few useful modules:
math
: Provides mathematical functions.datetime
: Supplies classes for manipulating dates and times.os
: Provides functions for interacting with the operating system.
Example Using math
import math
print(math.sqrt(16)) # Output: 4.0
Popular Python Libraries
Python has a vast ecosystem of third-party libraries. Here are some popular ones:
NumPy
NumPy is used for numerical computations.
import numpy as np
array = np.array([1, 2, 3])
print(array * 2) # Output: [2 4 6]
Pandas
Pandas is used for data manipulation and analysis.
import pandas as pd
data = {'Name': ['Alice', 'Bob'], 'Age': [25, 30]}
df = pd.DataFrame(data)
print(df)
Matplotlib
Matplotlib is used for creating static, interactive, and animated visualizations.
import matplotlib.pyplot as plt
x = [1, 2, 3]
y = [4, 5, 6]
plt.plot(x, y)
plt.show()
Introduction to Virtual Environments
Virtual environments allow you to manage dependencies for different projects separately.
Creating a Virtual Environment
python -m venv myenv
Activating a Virtual Environment
On Windows:
myenv\Scripts\activate
On macOS/Linux:
source myenv/bin/activate
Installing Packages
Once the virtual environment is activated, you can install packages using pip:
pip install numpy pandas matplotlib
These topics will help you build more complex applications and manage your projects efficiently.