If you do not specify a path for the file, the file will be created in your current working directory. Which will save the latest figure rendered. You can easily save a figure to an image file such as. Save a Matplotlib Figure As An Image File show () You can define an overall title for your figure using the fig.suptitle() method. set ( title = "Scatter Plot", xlabel = "Month", ylabel = "Precipitation \n (inches)" ) plt.
scatter ( months, boulder_monthly_precip, c = boulder_monthly_precip, cmap = 'YlGnBu' ) # Set plot title and axes labelsĪx2. set ( title = "Bar Plot", xlabel = "Month", ylabel = "Precipitation \n (inches)" ) # Define x and y axesĪx2. bar ( months, boulder_monthly_precip, color = 'cyan', edgecolor = 'darkblue' ) # Set plot title and axes labelsĪx1. suptitle ( "Average Monthly Precipitation for Boulder, CO", fontsize = 16 ) # Define x and y axesĪx1. In the example below, ax1.bar creates a customized bar plot in the first plot, and ax2.scatter creates a customized scatter in the second plot.įig, ( ax1, ax2 ) = plt. Once you have your fig and two axis objects defined, you can add data to each axis and define the plot with unique characteristics. Recall that matplotlib’s object oriented approach makes it easy to include more than one plot in a figure by creating additional axis objects:įig, (ax1, ax2) = plt.subplots(num_rows, num_columns) show () You can adjust the colors of the points in a scatter plot using color maps (cmap argument), which allows you to specify a range of colors that will be applied to the data points depending on their value. set ( title = "Average Monthly Precipitation \n Boulder, CO", xlabel = "Month", ylabel = "Precipitation \n (inches)" ) plt. scatter ( months, boulder_monthly_precip, c = boulder_monthly_precip, cmap = 'YlGnBu' ) # Set plot title and axes labelsĪx. The example below uses the YlGnBu colormap, in which lower values are filled in with yellow to green shades, while higher values are filled in with increasingly darker shades of blue.ĭata Tip: To see a list of color map options, visit the matplotlib documentation on colormaps.Īx. boulder_monthly_precip), while cmap allows you to specify the color map to use for the sequence. The c argument allows you to specify the sequence of values that will be color-mapped (e.g. When using scatter plots, you can also assign each point a color based upon its data value using the c and cmap arguments. show () You can adjust the bar fill and edge colors of a bar plot using the arguments color and edgecolor. bar ( months, boulder_monthly_precip, color = 'cyan', edgecolor = 'darkblue' ) # Set plot title and axes labelsĪx. Visit the Matplotlib documentation for a list of marker types.Īx. You can change the point marker type in your line or scatter plot using the argument marker = and setting it equal to the symbol that you want to use to identify the points in the plot.įor example, "," will display the point markers as a pixel or box, and “o” will display point markers as a circle. show () You can use plt.setp(ax.get_xticklabels(), rotation 45) to rotate the tick marks along the x axis by 45 degrees.
plot ( months, boulder_monthly_precip ) # Set plot title and axes labelsĪx. subplots ( figsize = ( 10, 6 )) # Define x and y axesĪx.
#Scatter plot matplotlib markers how to#
The following code shows how to create a scatterplot using the variable z to color the markers based on category: import matplotlib.pyplot as plt Suppose we have the following pandas DataFrame: import pandas as pdĭf = pd.DataFrame() Example 1: Color Scatterplot Points by Value This tutorial explains several examples of how to use this function in practice. You can use c to specify a variable to use for the color values and you can use cmap to specify the actual colors to use for the markers in the scatterplot.
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