Python Libraries and Virtual Environments

As you progress in Python, you'll use external libraries — ready-made tools and functions that make your life much easier. You’ll also work in virtual environments to keep your projects organized and avoid conflicts.

Why use virtual environments?

A virtual environment is a self-contained folder with its own Python installation and libraries.

Benefits:

• Different projects can use different library versions.
• Keeps your system Python clean.
• Makes sharing and deploying code easier.

Using Pip and Virtual Environments

What is pip?

pip is Python’s package manager. You use it to install and manage libraries.

Prompt

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# Show how to create a virtual environment and install numpy

Example Copilot Suggestion

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# Create a virtual environment
python -m venv myenv

# Activate it (Windows)
myenv\Scripts\activate

# Activate it (Mac/Linux)
source myenv/bin/activate

# Install numpy
pip install numpy

Explanation

• python -m venv myenv: Creates a new virtual environment in a folder called myenv.
• activate: Activates the environment (your terminal now uses this isolated environment).
• pip install numpy: Installs NumPy inside the environment.

Deactivating

When you're done, type:

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deactivate

Overview of Popular Python Libraries

Let’s explore some essential libraries that are widely used in data science and general Python projects.

NumPy

What is NumPy?

NumPy is a library for working with arrays and numerical data. It’s faster and more powerful than basic Python lists for math-heavy tasks.

Prompt

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# Show basic array operations using NumPy

Example Copilot Suggestion

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import numpy as np

# Create an array
arr = np.array([1, 2, 3, 4, 5])

# Basic operations
print(arr + 2)     # [3 4 5 6 7]
print(arr * 3)     # [3 6 9 12 15]
print(np.mean(arr)) # 3.0
print(np.sum(arr))  # 15

Explanation

• np.array: Creates a NumPy array.
• arr + 2: Adds 2 to each element.
• np.mean: Calculates the average.
• np.sum: Calculates the total sum.

Pandas

What is Pandas?

Pandas helps you work with tabular data, similar to spreadsheets or SQL tables. You can store, filter, and analyze data easily.

Prompt

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# Write code to create a DataFrame from a dictionary

Example Copilot Suggestion

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import pandas as pd

# Create a dictionary
data = {
    "Name": ["Alice", "Bob", "Charlie"],
    "Age": [25, 30, 35],
    "City": ["New York", "Paris", "London"]
}

# Convert to DataFrame
df = pd.DataFrame(data)

print(df)

Explanation

• pd.DataFrame(data): Creates a table-like structure (DataFrame).
• You can easily view, filter, and analyze data using Pandas.

Matplotlib

What is Matplotlib?

Matplotlib is a popular plotting library for creating charts and visualizations.

Prompt

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# Plot a simple line graph using Matplotlib

Example Copilot Suggestion

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import matplotlib.pyplot as plt

# Data
x = [1, 2, 3, 4, 5]
y = [2, 4, 6, 8, 10]

# Create line plot
plt.plot(x, y)

# Add title and labels
plt.title("Simple Line Graph")
plt.xlabel("X Axis")
plt.ylabel("Y Axis")

# Show plot
plt.show()

Explanation

• plt.plot(x, y): Draws a line graph.
• plt.title, plt.xlabel, plt.ylabel: Add labels and titles.
• plt.show(): Displays the plot.

Practice Challenge

Prompt:

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# Create a Pandas DataFrame with student names and scores, then plot the scores using Matplotlib

Try using Copilot to generate this code and experiment with different data.

Key Takeaways

• Virtual environments keep your projects isolated and organized.
• Pip allows you to install external Python libraries easily.
• NumPy is for numerical computations and array operations.
• Pandas is for handling tabular data (DataFrames).
• Matplotlib is for data visualization.

Extra Practice

1. Create a virtual environment and install both pandas and matplotlib.
2. Write a script that reads a CSV file using Pandas and plots a column of data using Matplotlib.
3. Create a NumPy array of random numbers and calculate its mean and standard deviation.

Tips for Beginners

• Start with small, simple scripts before moving on to large data files.
• Use Copilot prompts clearly and describe exactly what you want (e.g., "Create a DataFrame with three columns").
• Check your virtual environment is active before installing libraries.