Question: I'm a geology master's student working on my dissertation with a focus on the Sulfur Dioxide output of a number of volcanoes in the South Pacific. I have a little experience with R but my supervisor recommended python (JupyterLab specifically) for generating figures and data manipulation so I'm pretty new to programming and essentially teaching myself as I go. I'm trying to use earthquake data to generate some scatterplots using seaborn but I can't seem to get a color bar to show up in the legend for the earthquake magnitude. The code I'm using is below and I'll do my best to format it in a clear way. import pandas as pd import numpy as np import seaborn as sns import matplotlib.pyplot as plt import matplotlib as mpl from scipy import stats import cartopy.crs as ccrs import cartopy.io.img_tiles as cimgt  then the data sets I'm working with. These are the sets for Earthquake data. df = pd.read_csv('Vanuatu Earthquakes May18-May19.csv') df = pd.read_csv('Vanuatu Earthquakes May17-May18.csv') df = pd.read_csv('Vanuatu Earthquakes May19-Jul20.csv')  and locations of the volcanoes, purely there for spatial reference. dg = pd.read_csv('Volcano coordinates.csv')  Here's the main plot I'm trying to work with as it stands at the moment. So far I've been able to classify the earthquakes' magnitudes using the hue function but I don't like how it looks in the legend and want to convert it to a colorbar (or use a colorbar instead of hue, either/or), except I can't quite figure out how to do that. Alternatively, if there's a different function that would give me the results I'm looking for, I'm definitely open to that instead of a scatterplot. Also the black triangles are the volcanoes so those can be ignored for now. plt.figure(figsize=(5.5,9)) sns.scatterplot(x='longitude', y='latitude', data=df, marker='D', hue='mag', palette='colorblind', cmap='RdBu') sns.scatterplot(x='longitude', y='latitude', data=dg, marker='^', legend='brief', color='k', s=100) plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0., title='Magnitude (Mw)') plt.xlabel('Longitude (degrees)') plt.ylabel('Latitude (degrees)') plt.title('Earthquake and Volcano Locations', size=15) plt.show()   Hopefully that's clear enough but let me know if more info is needed!

Answer: The same method employed in this answer regarding Seaborn barplots can be applied to a scatterplot as well. With your code that would look something like this: # ... norm = plt.Normalize(df['mag'].min(), df['mag'].max()) sm = plt.cm.ScalarMappable(cmap="RdBu", norm=norm) sm.set_array([]) ax = sns.scatterplot(x='longitude', y='latitude', data=df, marker='D', palette='RdBu', hue='mag') sns.scatterplot(x='longitude', y='latitude', data=dg, marker='^', legend='brief', color='k', s=100, ax=ax) # Remove the legend and add a colorbar (optional) # ax.get_legend().remove() # ax.figure.colorbar(sm) # ...  See this question and its answers for information on manipulating the labels and ticks of the color bar. For a complete example using the tips dataset: import seaborn as sns import matplotlib.pyplot as plt sns.set() tips = sns.load_dataset("tips") ax = sns.scatterplot(x="total_bill", y="tip", hue="size", palette='RdBu', data=tips) norm = plt.Normalize(tips['size'].min(), tips['size'].max()) sm = plt.cm.ScalarMappable(cmap="RdBu", norm=norm) sm.set_array([]) # Remove the legend and add a colorbar ax.get_legend().remove() ax.figure.colorbar(sm) plt.show()