Lesson 1

Welcome to our line plot class! Line plots show how something changes over time by connecting points on a graph, just as one connects the stars to form a constellation. Our mission today is to learn how to weave the data stars together into a meaningful pattern using Python. By the end, you’ll be able to craft a narrative with line plots, understand the necessary data preparations, and personalize your plot with **Matplotlib**, a powerful plotting library in Python.

Think of a line plot as tracking your progress in a video game over a week. Each day, you note your score and then connect each day's score with a line. This line plot would show how your scores rose or fell each day — your journey through the game levels.

A well-constructed line plot features:

- An X-axis (horizontal line) representing your time sequence.
- A Y-axis (vertical line) displaying what you're measuring, such as your game score.
- Data points marking each day's score.
- Lines linking these data points to form a visual 'path' through your gaming week.

Line plots are superb for spotting trends at a glance — they're helpful in countless scenarios, from finance to fitness tracking.

Before plotting, we need ready data — think of it as gathering ingredients for baking cookies. We need matched pairs of time and what we’re tracking (like different cookie types and their quantities). For example, if we're plotting the growth of a garden plant:

- Gather the plant heights at regular intervals.
- Check for any mix-ups, such as missing dates or unrealistic growth spurts.
- Ensure dates and heights are in order, like lining up your cookie shapes before baking.

Organized data results in insightful plots that accurately reflect your story.

**Matplotlib** is our toolkit for plotting with Python. Usually, we import the `matplotlib.pyplot`

module as `plt`

and then use `plt.plot`

function to draw the plot. Note that the `plt.plot`

function takes in two arguments: `x`

and `y`

data, which are arrays of the same length.
Let’s set it up and plot the heights of our garden plant over two weeks:

Python`1import matplotlib.pyplot as plt # Importing our plotting toolkit 2import numpy as np 3 4# Our garden plant's height data 5days = range(1, 15) # From Day 1 to Day 14 6plant_heights = np.array([5, 5.5, 6, 6.5, 7, 7.2, 7.5, 8, 8.3, 8.6, 9, 9.2, 9.5, 10]) # Heights in inches 7 8# Plotting the data 9plt.plot(days, plant_heights) # Connects the heights with lines 10plt.show() # This displays a graph showing the plant's growth`

After running this code, a window pops up illustrating our plant's growth — a line ascending over two weeks.

Just as one picks a frame for a picture, a plot needs customization to enhance its story. Adding a title, labels, and style can transform raw data into an understandable and visually appealing narrative.

For example, let's label our plant growth plot with days and heights:

Python`1plt.title('Garden Plant Growth Over Two Weeks') # Adds a meaningful title 2plt.xlabel('Day') # Specifies days of growth along the X-axis 3plt.ylabel('Height (inches)') # Specifies plant height along the Y-axis 4plt.show() # Reveals our labeled plot`

The resulting plot will look like this:

By customizing, you guide viewers through the data story, just as chapter titles guide a reader through a book.

Bravo! You've traversed the basics of line plotting in Python. You now know how to set the stage with the correct data, create a basic line plot with **Matplotlib**, and customize it to tell the entire tale. Remember, as in painting or writing, plotting is refined with practice.

As you embark on the practice exercises, visualize your data's story — a narrative woven with points and lines, waiting to enlighten and inform. Happy plotting on your journey toward becoming a data storyteller!