A Scatterplot displays the value of 2 sets of data on 2 dimensions. Each dot represents an observation. The position on the X (horizontal) and Y (vertical) axis represents the values of the 2
variables. It is really useful to study the relationship between both variables. It is common to provide even more information using colors or shapes (to show groups, or a third variable). It is also
possible to map another variable to the size of each dot, what makes a bubble plot. If you have many dots and struggle with overplotting, consider using 2D density plot.
Sponsors
Input format
Format 1: 2 numerical variables

Format 1: 2 numerical variables AND 1 categorical.
Seaborn
- #40 Basic scatterplot with seaborn
- #40 Scatterplot with regression | seaborn
- #41 Change marker color
- #41 Change marker shape
- #42 Custom regression fit | seaborn
- #43 Use categorical variable for color
- #44 Control X and Y axis limits
- #45 Control color of each marker
- #46 Text annotation
- #46 Use loop to add text annotation
- #82 Default Marginal plot
- #82 Custom marginal plot
- #111 Represent groups on correlogram
- #101 Discrete color palette
Matplotlib
You can learn more about Matplotlib and scatterplots in this online course.
- #130 Basic Matplotlib scatterplot
- #131 Marker shape possibilities
- #131 Control marker size
- #131 Control marker color
- #131 Control marker edges
- #193 Add math equation
- #193 Horizontal and Vertical lines
- #193 Add a circle
- #193 Add a rectangle
Bad Chart
- #134 How to avoid overplotting
related
- Line plot
- 2D density plot
- Connected Scatter plot
- Bubble plot
- Area plot