ความสัมพันธ์: scatter และ line

Distributions look at a single column. Now you ask how two columns move together, which is the heart of finding a relationship.

A scatter plot places one dot per row, using two columns as x and y. The cloud of dots reveals how the variables connect.

Dots sloping up mean a positive link, sloping down means negative, and a shapeless blob means little correlation between them.

Color the dots by a category using hue. Now one plot shows the relationship split across each group at the same time.

Map a column to dot size to pack in even more. Bigger dots can mean larger sales, population, or any numeric measure.

When x is ordered, like dates or steps, a line plot connects the points to show how a value changes over that sequence.

Use a scatter for unordered pairs and a line when the x axis flows in order. Picking the right one keeps the trend honest.

If several rows share an x value, seaborn averages their y and draws a shaded confidence band around the line for you.

Add hue to a line plot and each category becomes its own colored line. Comparing trends across groups takes just one extra word.

Want the best-fit line drawn through a scatter? regplot overlays a regression line so the direction is impossible to miss.

A clear upward cloud shows two things rise together, not that one causes the other. Always pair a plot with judgment.

Your x axis is ordered by month and you want to show change over time.

You can now reveal relationships with scatter for pairs and lines for ordered trends, and add hue or size to layer in more. 🔗