Using histogram info in scatter plot-CodePudding

Is there a way to color scatter plot markers based on histogram bins into which given value has been placed? Having following example data:

df = pd.DataFrame({
    'length': [1.5, 0.5, 1.2, 0.9, 3],
    'width': [0.7, 0.2, 0.15, 0.2, 1.1],
    'price': [12, 9, 2, 1, 10]})

I can print histogram with hist = df.hist(bins=3, column='price') as well as scatter plot with df.scatter.plot(x='length', y='width').

But I cannot figure out a way to combine both of those instructions. What I'd expect to see is have 3 colors on scatter plot and colors will be assigned based on bin where give value fits. Any idea how this can be achieved?

CodePudding user response：

The following approach first creates a colormap with 3 colors, and uses the price column to define the color. The scatter plot will add a colorbar indicating the relation between the colors and the prices.

import matplotlib.pyplot as plt
import pandas as pd`
import numpy as np

df = pd.DataFrame({
    'length': [1.5, 0.5, 1.2, 0.9, 3],
    'width': [0.7, 0.2, 0.15, 0.2, 1.1],
    'price': [12, 9, 2, 1, 10]})
num_colors = 3
cmap = plt.get_cmap('rainbow', num_colors)
df.plot.scatter(x='length', y='width', c='price', cmap=cmap)
plt.tight_layout()
plt.show()

PS: If you want to indicate the price borders, you can add these as colorbar ticks:

ax = df.plot.scatter(x='length', y='width', c='price', cmap=cmap)
ax.collections[0].colorbar.set_ticks(np.linspace(df['price'].min(), df['price'].max(), num_colors   1))

CodePudding user response：

The histogram is just taking the range of values, and splitting it into equal size bins.

You can do this by using