# Density chart with Matplotlib

This post describes how to build a basic density chart with `Python` and the `Matplotlib` library. It uses the `gaussian_kde()` function to compute the density and plot it thanks to the `plot()` function.

Warning: the seaborn library provides a `kdeplot()` function allowing to build a density chart much more easily. Check it out here!

## From dummy data

Justin Peel suggested this nice solution on stackoverflow to build a density chart from a vector of value with `matplotlib`.

``````# Import libraries
import matplotlib.pyplot as plt
import numpy as np
from scipy.stats import gaussian_kde

# Build dummy data
data = [1.5]*7 + [2.5]*2 + [3.5]*8 + [4.5]*3 + [5.5]*1 + [6.5]*8

# Build a "density" function based on the dataset
# When you give a value from the X axis to this function, it returns the according value on the Y axis
density = gaussian_kde(data)
density.covariance_factor = lambda : .25
density._compute_covariance()

# Create a vector of 200 values going from 0 to 8:
xs = np.linspace(0, 8, 200)

# Set the figure size
plt.figure(figsize=(14, 8))

# Make the chart
# We're actually building a line chart where x values are set all along the axis and y value are
# the corresponding values from the density function
plt.plot(xs,density(xs))
plt.show()``````

## From a `Pandas` dataframe

``````# libraries
import matplotlib.pyplot as plt
import pandas as pd

# load dataset from github and convert it to a long format
data = pd.melt(data, var_name='text', value_name='value')

# take only "Almost No Chance", "About Even", "Probable", "Almost Certainly"
data = data.loc[data.text.isin(["We Believe"])]

# Build a "density" function based on the dataset
# When you give a value from the X axis to this function, it returns the according value on the Y axis
density = gaussian_kde(data.value)
density.covariance_factor = lambda : .25
density._compute_covariance()

# Create a vector of 200 values going from 0 to 8:
xs = np.linspace(0, 100, 200)

# Set the figure size
plt.figure(figsize=(14,8))

# plot
plt.fill_between( xs, density(xs), color="#69b3a2", alpha=0.4)

# title
plt.title("How probable something is when someone says 'We believe'", loc='left', fontsize=18)
plt.title("python graph gallery", loc='right', fontsize=13, color='grey', style='italic')

# Axis name
plt.xlabel("probability (%)")

# Remove Y axis
plt.yticks([])

plt.show()
``````

Violin

Density

Histogram

Boxplot

Ridgeline

## Contact & Edit

👋 This document is a work by Yan Holtz. Any feedback is highly encouraged. You can fill an issue on Github, drop me a message onTwitter, or send an email pasting `yan.holtz.data` with `gmail.com`.

Violin

Density

Histogram

Boxplot

Ridgeline

Scatterplot

Heatmap

Correlogram

Bubble

Connected Scatter

2D Density

Barplot

Wordcloud

Parallel

Lollipop

Circular Barplot

Treemap

Venn Diagram

Donut

Pie Chart

Dendrogram

Circular Packing

Line chart

Area chart

Stacked Area

Streamgraph

Timeseries

Map

Choropleth

Hexbin

Cartogram

Connection

Bubble

Chord Diagram

Network

Sankey

Arc Diagram

Edge Bundling

Colors

Interactivity

Animation

Cheat sheets

Caveats

3D