["python","pandas","matplotlib","seaborn","scatter-plot"]
plot different color for different categorical levels

I have this data frame diamonds which is composed of variables like (carat, price, color), and I want to draw a scatter plot of price to carat for each color, which means different color has different color in the plot. This is easy in R with ggplot: ggplot(aes(x=carat, y=price, color=color), #by setting color=color, ggplot automatically draw in different colors data=diamonds) + geom_point(stat='summary', fun.y=median) I wonder how could this be done in Python using matplotlib ? PS: I know about auxiliary plotting packages, such as seaborn and ggplot for python, and I don't prefer them, just want to find out if it is possible to do the job using matplotlib alone, ;P
Imports and Sample DataFrame import matplotlib.pyplot as plt import pandas as pd import seaborn as sns # for sample data from matplotlib.lines import Line2D # for legend handle # DataFrame used for all options df = sns.load_dataset('diamonds') carat cut color clarity depth table price x y z 0 0.23 Ideal E SI2 61.5 55.0 326 3.95 3.98 2.43 1 0.21 Premium E SI1 59.8 61.0 326 3.89 3.84 2.31 2 0.23 Good E VS1 56.9 65.0 327 4.05 4.07 2.31 With matplotlib You can pass plt.scatter a c argument, which allows you to select the colors. The following code defines a colors dictionary to map the diamond colors to the plotting colors. fig, ax = plt.subplots(figsize=(6, 6)) colors = {'D':'tab:blue', 'E':'tab:orange', 'F':'tab:green', 'G':'tab:red', 'H':'tab:purple', 'I':'tab:brown', 'J':'tab:pink'} ax.scatter(df['carat'], df['price'], c=df['color'].map(colors)) # add a legend handles = [Line2D([0], [0], marker='o', color='w', markerfacecolor=v, label=k, markersize=8) for k, v in colors.items()] ax.legend(title='color', handles=handles, bbox_to_anchor=(1.05, 1), loc='upper left') plt.show() df['color'].map(colors) effectively maps the colors from "diamond" to "plotting". (Forgive me for not putting another example image up, I think 2 is enough :P) With seaborn 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. For this you could use seaborn.lmplot with fit_reg=False (which prevents it from automatically doing some regression). sns.scatterplot(x='carat', y='price', data=df, hue='color', ec=None) also does the same thing. Selecting hue='color' tells seaborn to split and plot the data based on the unique values in the 'color' column. sns.lmplot(x='carat', y='price', data=df, hue='color', fit_reg=False) With pandas.DataFrame.groupby & pandas.DataFrame.plot If you don't want to use seaborn, use pandas.groupby 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)) grouped = df.groupby('color') for key, group in grouped: group.plot(ax=ax, kind='scatter', x='carat', y='price', label=key, color=colors[key]) plt.show() This code assumes the same DataFrame as above, and then groups it based on color. It then iterates over these groups, plotting for each one. 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).