The above code means that we are setting the color of the scatter plot as red. For example, the small dot up around 1.0 on the y-axis has the color value 0.47368421 in the first plot and 0.92515847 in the second. c can be a 2-D array in which the rows are RGB or RGBA, however. Note that c should not be a single numeric RGB or RGBA sequence because that is indistinguishable from an array of values to be colormapped. CurrentsArray and rf85CurrentsArray have different values at the same x and y coordinate). plt.scatter(x, y, ccolor) Quoting the documentation of matplotlib: c : color or sequence of color, optional, default. To set the colors of a scatter plot, we need to set the argument color or simply c to the pyplot.scatter() function.įor example, take a look at the code below: plt.scatter(x, y, color = 'red') This is because your color values are different in each plot (i.e. Let's show this by creating a random scatter plot with points of many colors and sizes. Setting colors to the multiple scatter plot The primary difference of plt.scatter from plt.plot is that it can be used to create scatter plots where the properties of each individual point (size, face color, edge color, etc.) can be individually controlled or mapped to data. By default, pyplot returned orange and blue. This kind of plot is useful to see complex correlations between two variables. The coordinates of each point are defined by two dataframe columns and filled circles are used to represent each point. Note: Notice that the two plots in the figure above gave two different colors. Create a scatter plot with varying marker point size and color. Line 16: The pyplot.show() function is used, which tells pyplot to display both the scatter plots. pyplot.scatter(x,y2) is used to create a scatter plot of x and y2.

Lines 12 to 13: The array y2 is created, which contains the y-coordinates for the second scatter plot. pyplot.scatter(x,y1) is used to create a scatter plot of x and y1. Lines 8 to 9: The array y1 is created, which contains the y-coordinates for the first scatter plot. plt.scatter (x, y, sarea, c'w', alpha0.5, edgecolorscolors) Additionally, you can't use a single float to define the colour for edgecolors as they are doing for the 'c' argument in the demo script so you either need to supply an array of length N that are all specified colours, e.g. normstr or Normalize, optional The normalization method used to scale scalar data to the 0, 1 range before mapping to colors using cmap. This parameter is ignored if c is RGB (A). Line 5: The array x is created, containing the x-coordinates common to both plots. You only need to change the internal colour to white, i.e. The Colormap instance or registered colormap name used to map scalar data to colors.

Line 2: The numpy module is imported, which will be used to create arrays. Often you may want to shade the color of points within a matplotlib scatterplot based on some third variable. Line 1: In matplotlib, the pyplot module is imported, which will be used to create plots.

Scatter plot python color