Python Data Visualization with Matplotlib

Move from summary table to chart-ready data

Use a grouped summary table from pandas as the chart input layer. Sort time values before plotting lines and sort categories when plotting bars to improve readability.

For weekly KPI reporting, a line chart can show total revenue trend while a bar chart can compare channels for the latest week.

Add context that supports decisions

A chart should show why a value matters. Add a target line, annotate major outliers, and keep the legend close to plotted elements.

• Use plt.tight_layout to avoid clipped labels.
• Use consistent color meaning across multiple charts.
• Export figures with explicit size and dpi for reports.

Plot weekly revenue and a target line

A DataFrame has weekly revenue totals and a constant weekly target value.

1. Create a figure and plot revenue by week with plt.plot.
2. Plot the target as a second series using a dashed line style.
3. Set title, axis labels, legend, and save the figure to kpi_trend.png.

Common mistakes

• Using unsorted time values, which creates misleading line paths.
• Removing axis labels and forcing readers to guess units.
• Using too many colors without stable meaning across charts.
• Cropping labels because figure size and layout were not set.

Try one

Sources

• Python documentationOfficial reference for Python syntax, standard library modules, and tutorials.
• pandas documentationOfficial reference for pandas DataFrame operations, indexing, and analysis APIs.
• Matplotlib documentationOfficial reference for plotting with Matplotlib and pyplot.