[matplotlib] plot different color for different categorical levels using matplotlib

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 donot prefer them, just want to find out if it is possible to do the job using matplotlib alone, ;P

You can convert the categorical column into a numerical one by using the commands:

#we converting it into categorical data
cat_col = df['column_name'].astype('categorical') 

#we are getting codes for it 
cat_col = cat_col.cat.codes 

# we are using c parameter to change the color.
plt.scatter(df['column1'],df['column2'], c=cat_col) 

I had the same question, and have spent all day trying out different packages.

I had originally used matlibplot: and was not happy with either mapping categories to predefined colors; or grouping/aggregating then iterating through the groups (and still having to map colors). I just felt it was poor package implementation.

Seaborn wouldn't work on my case, and Altair ONLY works inside of a Jupyter Notebook.

The best solution for me was PlotNine, which "is an implementation of a grammar of graphics in Python, and based on ggplot2".

Below is the plotnine code to replicate your R example in Python:

from plotnine import *
from plotnine.data import diamonds

g = ggplot(diamonds, aes(x='carat', y='price', color='color')) + geom_point(stat='summary')
print(g)

So clean and simple :)

I usually do it using Seaborn which is built on top of matplotlib

import seaborn as sns
iris = sns.load_dataset('iris')
sns.scatterplot(x='sepal_length', y='sepal_width',
              hue='species', data=iris); 

Here's a succinct and generic solution to use a seaborn color palette.

First find a color palette you like and optionally visualize it:

sns.palplot(sns.color_palette("Set2", 8))

Then you can use it with matplotlib doing this:

# Unique category labels: 'D', 'F', 'G', ...
color_labels = df['color'].unique()

# List of RGB triplets
rgb_values = sns.color_palette("Set2", 8)

# Map label to RGB
color_map = dict(zip(color_labels, rgb_values))

# Finally use the mapped values
plt.scatter(df['carat'], df['price'], c=df['color'].map(color_map))

With df.plot()

Normally when quickly plotting a DataFrame, I use pd.DataFrame.plot(). This takes the index as the x value, the value as the y value and plots each column separately with a different color. A DataFrame in this form can be achieved by using set_index and unstack.

import matplotlib.pyplot as plt
import pandas as pd

carat = [5, 10, 20, 30, 5, 10, 20, 30, 5, 10, 20, 30]
price = [100, 100, 200, 200, 300, 300, 400, 400, 500, 500, 600, 600]
color =['D', 'D', 'D', 'E', 'E', 'E', 'F', 'F', 'F', 'G', 'G', 'G',]

df = pd.DataFrame(dict(carat=carat, price=price, color=color))

df.set_index(['color', 'carat']).unstack('color')['price'].plot(style='o')
plt.ylabel('price')

With this method you do not have to manually specify the colors.

This procedure may make more sense for other data series. In my case I have timeseries data, so the MultiIndex consists of datetime and categories. It is also possible to use this approach for more than one column to color by, but the legend is getting a mess.

Using Altair.

from altair import *
import pandas as pd

df = datasets.load_dataset('iris')
Chart(df).mark_point().encode(x='petalLength',y='sepalLength', color='species')

Here a combination of markers and colors from a qualitative colormap in matplotlib:

import itertools
import numpy as np
from matplotlib import markers
import matplotlib.pyplot as plt

m_styles = markers.MarkerStyle.markers
N = 60
colormap = plt.cm.Dark2.colors  # Qualitative colormap
for i, (marker, color) in zip(range(N), itertools.product(m_styles, colormap)):
    plt.scatter(*np.random.random(2), color=color, marker=marker, label=i)
plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0., ncol=4);