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