You’re using Pandas to analyze data - and now you want to show how something changed over time. That’s exactly what a line chart is for.
Let’s start with the code. Then we’ll talk about how to avoid making the same charts everyone ignores in reports.
📈 How to Create a Simple Line Chart with Pandas
import pandas as pd # Load the Pandas library import matplotlib.pyplot as plt # Load matplotlib for plotting # Create a simple DataFrame with time and value df = pd.DataFrame({ "date": pd.date_range(start="2024-01-01", periods=10, freq="D"), # Generate 10 days of dates "sales": [100, 110, 130, 120, 150, 160, 170, 165, 180, 190] # Simulated sales data }) # Set the date column as the index df.set_index("date", inplace=True) # Plot the line chart df["sales"].plot(kind="line", title="Sales Over Time") # Add labels and show the chart plt.xlabel("Date") # X-axis label plt.ylabel("Sales") # Y-axis label plt.grid(True) # Add a grid for readability plt.tight_layout() # Improve layout spacing plt.show() # Display the chart
1. Why Use a Line Chart?
Line charts are perfect for showing:
- Trends over time
- Patterns and inflection points
- Comparisons across multiple series (if done right)
If you're trying to show that something grew, spiked, or changed steadily - use a line chart.
2. Most Line Charts Are Bad (and Here’s Why)
Ever seen a line chart with:
- No axis labels?
- No clear title?
- No legend?
- Too many squiggly lines with no explanation?
That’s lazy charting. Don’t make people decode your chart like it’s a puzzle.
Make the story jump out at the viewer.
3. Charts Are for Communication, Not Decoration
Don’t use a chart just because it “looks good.”
Use it when it clearly shows something important.
Example:
"When we implemented feature X on March 5th, conversions increased by 30%."
Put that right under your chart so your boss sees the insight in 2 seconds.
4. How Many Lines Is Too Many?
- ✅ 1–5 lines: Great
- ⚠️ 6–10 lines: Risky
- ❌ 10–15+ lines: Usually a mess
Also consider volatility:
- The more a line goes up and down, the more visual noise it adds.
- 3 noisy lines = harder to follow than 5 smooth ones.
Rule of thumb: less is more.
5. Label Your Chart. Seriously.
At the minimum:
- Add a title
- Label your axes
- Include a legend if there’s more than one line
- ✅ Consider labeling the lines directly to avoid eye-hopping between chart and legend
If you want someone to understand your work, don’t skip this step.
6. Add Commentary Beneath the Chart
Include a small caption below the chart that says what the chart shows in plain language:
“After we launched our pricing update, weekly revenue increased by 30%.”
Don’t make readers guess. That one line will do more work than the chart itself.
7. Style = Substance
Don’t just accept the default Excel or Google Sheets chart style. Everyone's seen it. Everyone ignores it.
Instead:
- Use consistent fonts and color palettes
- Make your charts part of your visual brand
- Add thoughtful whitespace and clarity
Your readers should start to recognize your charts over time. That’s brand-building.
8. Don’t Rush It - Check Your Numbers
Double-check your chart:
- Are the numbers correct?
- Are the labels accurate?
- Does the story match the data?
The fastest way to lose trust is to ship a chart with an error in it. Sanity check everything - twice.
Final Thought
A line chart isn’t just a visual - it’s a narrative tool.
Use Pandas to build trust through clarity, simplicity, and accuracy.
And always make the story obvious.
That's vibecoding. ✨