Visualizing Data Trends with Matplotlib

This challenge will guide you through creating basic but informative data visualizations using the matplotlib library in Python. Visualizing data is crucial for understanding patterns, identifying outliers, and communicating insights effectively. You will practice generating a line plot and a bar chart from provided datasets.

Problem Description

Your task is to create two distinct visualizations using Python's matplotlib.pyplot module:

1. A Line Plot: Represent the trend of a dataset over time. This plot should clearly show the progression of values.
2. A Bar Chart: Compare the magnitudes of different categories. This chart should allow for easy comparison between discrete items.

Key Requirements:

• Use the matplotlib.pyplot module for all plotting.
• Label your axes appropriately (e.g., "Time", "Value", "Category", "Count").
• Provide a clear title for each plot.
• Ensure the plots are displayed correctly.

Expected Behavior:

When your Python script is run, two separate plots should appear: a line plot and a bar chart.

Edge Cases/Considerations:

• Ensure your data is in a format that matplotlib can readily use (e.g., lists or NumPy arrays).
• Consider what happens if the input lists are empty. While this specific challenge may not test this, it's good practice to think about.

Examples

Example 1: Line Plot Data

Input:

months = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun']
sales = [150, 180, 220, 200, 250, 280]

Output: A line plot showing the sales values against the months.

• X-axis: Months ('Jan' to 'Jun')
• Y-axis: Sales figures
• Title: "Monthly Sales Trend"
• Plot Type: Line plot

Explanation: The line plot visually connects the sales figures for each month, illustrating the upward trend in sales over the first half of the year.

Example 2: Bar Chart Data

Input:

products = ['Apples', 'Bananas', 'Oranges', 'Grapes']
inventory = [120, 250, 90, 180]

Output: A bar chart showing the inventory for each product.

• X-axis: Product names
• Y-axis: Inventory count
• Title: "Fruit Inventory Levels"
• Plot Type: Bar chart

Explanation: The bar chart displays a distinct bar for each fruit, with the height of the bar representing its inventory count, making it easy to compare which fruits have more stock.

Constraints

• The input data will be provided as Python lists of equal length.
• The numerical data in the lists will consist of integers.
• The total number of data points for each plot will not exceed 20.
• You are expected to import matplotlib.pyplot as plt.

Notes

• You will need to have matplotlib installed. If not, you can install it using pip: pip install matplotlib.
• For the line plot, you'll likely use plt.plot().
• For the bar chart, you'll likely use plt.bar().
• Remember to call plt.xlabel(), plt.ylabel(), and plt.title() to label your plots.
• Finally, plt.show() is necessary to display the generated plots.