This creates a plot without a legend, using the default "viridis" colormap. Plt.gca().set(xlabel='Carat', ylabel='Price', title='Carat vs. The easiest way is to simply pass an array of integer category levels to the plt.scatter() color parameter. To select a color, I've created a colors dictionary, which can map the diamond color (for instance D) to a real color (for instance tab:blue). It then iterates over these groups, plotting for each one. This code assumes the same DataFrame as above, and then groups it based on color. ot(ax=ax, kind='scatter', x='carat', y='price', label=key, color=colors) If you don't want to use seaborn, use oupby to get the colors alone, and then plot them using just matplotlib, but you'll have to manually assign colors as you go, I've added an example below: fig, ax = plt.subplots(figsize=(6, 6)) sns.lmplot(x='carat', y='price', data=df, hue='color', fit_reg=False) Selecting hue='color' tells seaborn to split and plot the data based on the unique values in the 'color' column. sns.scatterplot(x='carat', y='price', data=df, hue='color', ec=None) also does the same thing.You can use seaborn which is a wrapper around matplotlib that makes it look prettier by default (rather opinion-based, I know :P) but also adds some plotting functions.įor this you could use seaborn.lmplot with fit_reg=False (which prevents it from automatically doing some regression). (Forgive me for not putting another example image up, I think 2 is enough :P) With seaborn Handles =, , marker='o', color='w', markerfacecolor=v, label=k, markersize=8) for k, v in ems()]Īx.legend(title='color', handles=handles, bbox_to_anchor=(1.05, 1), loc='upper left')ĭf.map(colors) effectively maps the colors from "diamond" to "plotting". fig, ax = plt.subplots(figsize=(6, 6))Ĭolors = Īx.scatter(df, df, c=df.map(colors)) The following code defines a colors dictionary to map the diamond colors to the plotting colors. You can pass plt.scatter a c argument, which allows you to select the colors.
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