Libraries & Dataset
Let's start by import a few libraries and create a dataset:
# libraries
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
from scipy.stats import gaussian_kde
# create data
df = pd.DataFrame({
'x': np.random.normal(size=10000),
'y': np.random.normal(size=10000)
})
df.head()| x | y | |
|---|---|---|
| 0 | 0.310256 | 0.523202 |
| 1 | 1.689054 | 0.211584 |
| 2 | 0.822795 | 0.328821 |
| 3 | 0.137791 | 0.988868 |
| 4 | -1.469767 | 0.216877 |
Smoothing
Displaying a kde plot requires to smooth the data. For this we use the gaussian_kde() function from scipy.
# Init values and parameters
nbins = 300
x = df['x'] # change 'x' with your column name
y = df['y'] # change 'y' with your column name
k = gaussian_kde([x,y])
xi, yi = np.mgrid[
x.min():x.max():nbins*1j,
y.min():y.max():nbins*1j
]
zi = k(np.vstack([
xi.flatten(),
yi.flatten()
])).reshape(xi.shape)2d density plot
2d density plot requires to use the pcolormesh() function:
fig, ax = plt.subplots(figsize=(8,8))
ax.pcolormesh(xi, yi, zi)
plt.show()
Colors
Thanks to the cmap argument we can super easily change the colors used the chart. Here you can find different examples:
fig, axs = plt.subplots(nrows=2, ncols=2, figsize=(8,8))
# green colormap
axs[0,0].pcolormesh(xi, yi, zi, cmap=plt.cm.Greens_r)
axs[0,0].set_title('cmap=plt.cm.Greens_r')
# red colormap
axs[0,1].pcolormesh(xi, yi, zi, cmap=plt.cm.Reds_r)
axs[0,1].set_title('cmap=plt.cm.Reds_r')
# blue colormap
axs[1,0].pcolormesh(xi, yi, zi, cmap=plt.cm.Blues_r)
axs[1,0].set_title('cmap=plt.cm.Blues_r')
# grey colormap
axs[1,1].pcolormesh(xi, yi, zi, cmap=plt.cm.Greys_r)
axs[1,1].set_title('cmap=plt.cm.Greys_r')
plt.show()
Colorbar and legend
You can add a color bar easily using colorbar() function.
plt.pcolormesh(xi, yi, zi.reshape(xi.shape), cmap=plt.cm.Greens_r)
plt.colorbar()
plt.show()
Going further
You might be interested:
- how to create a contour plot with seaborn
- how to combine a 2d density/histogram plot with marginal plot
# Libraries
import numpy as np
import matplotlib.pyplot as plt
from scipy.stats import gaussian_kde
# Create data: 200 points
data = np.random.multivariate_normal([0, 0], [[1, 0.5], [0.5, 3]], 200)
x, y = data.T
# Create a figure with 6 plot areas
fig, axes = plt.subplots(ncols=7, nrows=1, figsize=(25, 3))
# Everything starts with a Scatterplot
axes[0].set_title('Scatterplot')
axes[0].plot(x, y, 'ko')
# As you can see there is a lot of overlapping here!
# Everything starts with a Scatterplot
axes[1].set_title('Scatterplot')
axes[1].scatter(x, y, color='green', edgecolor='black')
# Thus we can cut the plotting window in several hexbins
nbins = 20
axes[2].set_title('Hexbin')
axes[2].hexbin(x, y, gridsize=nbins, cmap=plt.cm.BuGn_r)
# 2D Histogram
axes[3].set_title('2D Histogram')
axes[3].hist2d(x, y, bins=nbins, cmap=plt.cm.BuGn_r)
# Evaluate a gaussian kde on a regular grid of nbins x nbins over data extents
k = gaussian_kde(data.T)
xi, yi = np.mgrid[x.min():x.max():nbins*1j, y.min():y.max():nbins*1j]
zi = k(np.vstack([xi.flatten(), yi.flatten()]))
# plot a density
axes[4].set_title('Calculate Gaussian KDE')
axes[4].pcolormesh(xi, yi, zi.reshape(xi.shape), shading='auto', cmap=plt.cm.BuGn_r)
# add shading
axes[5].set_title('2D Density with shading')
axes[5].pcolormesh(xi, yi, zi.reshape(xi.shape), shading='gouraud', cmap=plt.cm.BuGn_r)
# contour
axes[6].set_title('Contour')
axes[6].pcolormesh(xi, yi, zi.reshape(xi.shape), shading='gouraud', cmap=plt.cm.BuGn_r)
axes[6].contour(xi, yi, zi.reshape(xi.shape) )
fig.savefig('../../static/graph/what-is-density-chart.png', dpi=300)
