المخططات الخطية والعمودية في matplotlib

A wall of numbers hides trends. A quick chart reveals them instantly, which is why every analysis ends with a plot. 📊

The drawing engine lives in matplotlib.pyplot, almost always imported as plt. That short alias keeps every plot call tidy.

Reach for a line chart when something moves over a continuous axis, like sales rising month after month.

Use a bar chart to compare separate groups side by side, like total sales for each region.

Every Series and DataFrame has a built-in .plot that calls matplotlib for you, so you skip the manual setup.

The pandas plot takes a kind argument. Pass kind="bar" for bars or leave it off for the default line.

A grouped summary plots beautifully because its index becomes the x-axis. Group, aggregate, then chain .plot.

Outside a notebook you must call plt.show to pop the window. In Jupyter the chart appears on its own.

Tiny charts hide detail. Pass figsize as a width and height in inches to give your plot room to breathe.

Call plt.plot several times before showing and matplotlib stacks each line onto the same axes for easy comparison.

Leftover plots can pile up across cells. Start fresh with plt.figure or close old ones so charts do not overlap.

You want to compare total sales across a few regions. Which chart fits best?

You can now import plt, draw lines for trends and bars for categories, and plot straight from pandas. Your data has a face now! 🎨