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<!DOCTYPE html>

<head>

<title>Python - Data Visualization with Python</title>

</head>

<body>

<div id="app" data-server-rendered="true"><div class="theme-container"><header class="navbar"><div class="sidebar-button"><svg xmlns="http://www.w3.org/2000/svg" aria-hidden="true" role="img" viewBox="0 0 448 512" class="icon"><path fill="currentColor" d="M436 124H12c-6.627 0-12-5.373-12-12V80c0-6.627 5.373-12 12-12h424c6.627 0 12 5.373 12 12v32c0 6.627-5.373 12-12 12zm0 160H12c-6.627 0-12-5.373-12-12v-32c0-6.627 5.373-12 12-12h424c6.627 0 12 5.373 12 12v32c0 6.627-5.373 12-12 12zm0 160H12c-6.627 0-12-5.373-12-12v-32c0-6.627 5.373-12 12-12h424c6.627 0 12 5.373 12 12v32c0 6.627-5.373 12-12 12z"></path></svg></div> <a href="/" class="home-link router-link-active"> DevTut</a> <div class="links"><form id="search-form" role="search" class="algolia-search-wrapper search-box"><input id="algolia-search-input" class="search-query"></form> <nav class="nav-links can-hide"> <a href="https://github.com/devtut/generate" target="_blank" rel="noopener noreferrer" class="repo-link">

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import seaborn as sns # common form of importing seaborn

# Generate normally distributed data

data = np.random.randn(1000)

# Plot a histogram with both a rugplot and kde graph superimposed

sns.distplot(data, kde=True, rug=True)

</code></pre></div><a href="http://i.imgur.com/CyxeSfj.png" target="_blank" rel="noopener noreferrer"><img src="http://i.imgur.com/CyxeSfj.png" alt="Example distplot"><svg xmlns="http://www.w3.org/2000/svg" aria-hidden="true" focusable="false" x="0px" y="0px" viewBox="0 0 100 100" width="15" height="15" class="icon outbound"><path fill="currentColor" d="M18.8,85.1h56l0,0c2.2,0,4-1.8,4-4v-32h-8v28h-48v-48h28v-8h-32l0,0c-2.2,0-4,1.8-4,4v56C14.8,83.3,16.6,85.1,18.8,85.1z"></path> <polygon fill="currentColor" points="45.7,48.7 51.3,54.3 77.2,28.5 77.2,37.2 85.2,37.2 85.2,14.9 62.8,14.9 62.8,22.9 71.5,22.9"></polygon></svg> (opens new window)</a> The style of the plot can also be controled using a declarative syntax. <div class="language-py extra-class"><pre class="language-py"><code># Using previously created imports and data.

# Use a dark background with no grid.

sns.set_style('dark')

# Create the plot again

</code></pre></div><a href="http://i.imgur.com/JMjHv6C.png" target="_blank" rel="noopener noreferrer"><img src="http://i.imgur.com/JMjHv6C.png" alt="Example styling"><svg xmlns="http://www.w3.org/2000/svg" aria-hidden="true" focusable="false" x="0px" y="0px" viewBox="0 0 100 100" width="15" height="15" class="icon outbound"><path fill="currentColor" d="M18.8,85.1h56l0,0c2.2,0,4-1.8,4-4v-32h-8v28h-48v-48h28v-8h-32l0,0c-2.2,0-4,1.8-4,4v56C14.8,83.3,16.6,85.1,18.8,85.1z"></path> <polygon fill="currentColor" points="45.7,48.7 51.3,54.3 77.2,28.5 77.2,37.2 85.2,37.2 85.2,14.9 62.8,14.9 62.8,22.9 71.5,22.9"></polygon></svg> (opens new window)</a> As an added bonus, normal matplotlib commands can still be applied to Seaborn plots. Here's an example of adding axis titles to our previously created histogram. <div class="language-py extra-class"><pre class="language-py"><code># Using previously created data and style

# Access to matplotlib commands

import matplotlib.pyplot as plt

# Previously created plot.

# Set the axis labels.

plt.xlabel('This is my x-axis')

plt.ylabel('This is my y-axis')

</code></pre></div><a href="http://i.imgur.com/ei2fa17.png" target="_blank" rel="noopener noreferrer"><img src="http://i.imgur.com/ei2fa17.png" alt="Example matplotlib"><svg xmlns="http://www.w3.org/2000/svg" aria-hidden="true" focusable="false" x="0px" y="0px" viewBox="0 0 100 100" width="15" height="15" class="icon outbound"><path fill="currentColor" d="M18.8,85.1h56l0,0c2.2,0,4-1.8,4-4v-32h-8v28h-48v-48h28v-8h-32l0,0c-2.2,0-4,1.8-4,4v56C14.8,83.3,16.6,85.1,18.8,85.1z"></path> <polygon fill="currentColor" points="45.7,48.7 51.3,54.3 77.2,28.5 77.2,37.2 85.2,37.2 85.2,14.9 62.8,14.9 62.8,22.9 71.5,22.9"></polygon></svg> (opens new window)</a> <h2 id="matplotlib"><a href="#matplotlib" class="header-anchor">#</a> Matplotlib</h2> <a href="http://matplotlib.org/index.html" target="_blank" rel="noopener noreferrer">Matplotlib<svg xmlns="http://www.w3.org/2000/svg" aria-hidden="true" focusable="false" x="0px" y="0px" viewBox="0 0 100 100" width="15" height="15" class="icon outbound"><path fill="currentColor" d="M18.8,85.1h56l0,0c2.2,0,4-1.8,4-4v-32h-8v28h-48v-48h28v-8h-32l0,0c-2.2,0-4,1.8-4,4v56C14.8,83.3,16.6,85.1,18.8,85.1z"></path> <polygon fill="currentColor" points="45.7,48.7 51.3,54.3 77.2,28.5 77.2,37.2 85.2,37.2 85.2,14.9 62.8,14.9 62.8,22.9 71.5,22.9"></polygon></svg> (opens new window)</a> is a mathematical plotting library for Python that provides a variety of different plotting functionality. The matplotlib documentation can be found <a href="http://matplotlib.org/contents.html" target="_blank" rel="noopener noreferrer">here<svg xmlns="http://www.w3.org/2000/svg" aria-hidden="true" focusable="false" x="0px" y="0px" viewBox="0 0 100 100" width="15" height="15" class="icon outbound"><path fill="currentColor" d="M18.8,85.1h56l0,0c2.2,0,4-1.8,4-4v-32h-8v28h-48v-48h28v-8h-32l0,0c-2.2,0-4,1.8-4,4v56C14.8,83.3,16.6,85.1,18.8,85.1z"></path> <polygon fill="currentColor" points="45.7,48.7 51.3,54.3 77.2,28.5 77.2,37.2 85.2,37.2 85.2,14.9 62.8,14.9 62.8,22.9 71.5,22.9"></polygon></svg> (opens new window)</a>, with the SO Docs being available <a href="http://stackoverflow.com/documentation/matplotlib/topics" target="_blank" rel="noopener noreferrer">here<svg xmlns="http://www.w3.org/2000/svg" aria-hidden="true" focusable="false" x="0px" y="0px" viewBox="0 0 100 100" width="15" height="15" class="icon outbound"><path fill="currentColor" d="M18.8,85.1h56l0,0c2.2,0,4-1.8,4-4v-32h-8v28h-48v-48h28v-8h-32l0,0c-2.2,0-4,1.8-4,4v56C14.8,83.3,16.6,85.1,18.8,85.1z"></path> <polygon fill="currentColor" points="45.7,48.7 51.3,54.3 77.2,28.5 77.2,37.2 85.2,37.2 85.2,14.9 62.8,14.9 62.8,22.9 71.5,22.9"></polygon></svg> (opens new window)</a>. Matplotlib provides two distinct methods for plotting, though they are interchangable for the most part: <ul><li>Firstly, matplotlib provides the <code>pyplot</code> interface, direct and simple-to-use interface that allows plotting of complex graphs in a MATLAB-like style.</li> <li>Secondly, matplotlib allows the user to control the different aspects (axes, lines, ticks, etc) directly using an object-based system. This is more difficult but allows complete control over the entire plot.</li></ul> Below is an example of using the <code>pyplot</code> interface to plot some generated data: <div class="language-py extra-class"><pre class="language-py"><code>import matplotlib.pyplot as plt

# Generate some data for plotting.

x = [0, 1, 2, 3, 4, 5, 6]

y = [i**2 for i in x]

# Plot the data x, y with some keyword arguments that control the plot style.

# Use two different plot commands to plot both points (scatter) and a line (plot).

plt.scatter(x, y, c='blue', marker='x', s=100) # Create blue markers of shape "x" and size 100

plt.plot(x, y, color='red', linewidth=2) # Create a red line with linewidth 2.

# Add some text to the axes and a title.

plt.xlabel('x data')

plt.ylabel('y data')

plt.title('An example plot')

# Generate the plot and show to the user.

plt.show()

</code></pre></div><a href="http://i.stack.imgur.com/MESUU.png" target="_blank" rel="noopener noreferrer"><img src="http://i.stack.imgur.com/MESUU.png" alt="Example plot"><svg xmlns="http://www.w3.org/2000/svg" aria-hidden="true" focusable="false" x="0px" y="0px" viewBox="0 0 100 100" width="15" height="15" class="icon outbound"><path fill="currentColor" d="M18.8,85.1h56l0,0c2.2,0,4-1.8,4-4v-32h-8v28h-48v-48h28v-8h-32l0,0c-2.2,0-4,1.8-4,4v56C14.8,83.3,16.6,85.1,18.8,85.1z"></path> <polygon fill="currentColor" points="45.7,48.7 51.3,54.3 77.2,28.5 77.2,37.2 85.2,37.2 85.2,14.9 62.8,14.9 62.8,22.9 71.5,22.9"></polygon></svg> (opens new window)</a> Note that <code>plt.show()</code> is known to be <a href="http://stackoverflow.com/questions/38238612/matplot-program-gives-unresponsive-window-when-run/38238681#38238681" target="_blank" rel="noopener noreferrer">problematic<svg xmlns="http://www.w3.org/2000/svg" aria-hidden="true" focusable="false" x="0px" y="0px" viewBox="0 0 100 100" width="15" height="15" class="icon outbound"><path fill="currentColor" d="M18.8,85.1h56l0,0c2.2,0,4-1.8,4-4v-32h-8v28h-48v-48h28v-8h-32l0,0c-2.2,0-4,1.8-4,4v56C14.8,83.3,16.6,85.1,18.8,85.1z"></path> <polygon fill="currentColor" points="45.7,48.7 51.3,54.3 77.2,28.5 77.2,37.2 85.2,37.2 85.2,14.9 62.8,14.9 62.8,22.9 71.5,22.9"></polygon></svg> (opens new window)</a> in some environments due to running <code>matplotlib.pyplot</code> in interactive mode, and if so, the blocking behaviour can be overridden explicitly by passing in an optional argument, <code>plt.show(block=True)</code>, to alleviate the issue. <h2 id="mayavi"><a href="#mayavi" class="header-anchor">#</a> MayaVI</h2> <a href="http://docs.enthought.com/mayavi/mayavi/" target="_blank" rel="noopener noreferrer">MayaVI<svg xmlns="http://www.w3.org/2000/svg" aria-hidden="true" focusable="false" x="0px" y="0px" viewBox="0 0 100 100" width="15" height="15" class="icon outbound"><path fill="currentColor" d="M18.8,85.1h56l0,0c2.2,0,4-1.8,4-4v-32h-8v28h-48v-48h28v-8h-32l0,0c-2.2,0-4,1.8-4,4v56C14.8,83.3,16.6,85.1,18.8,85.1z"></path> <polygon fill="currentColor" points="45.7,48.7 51.3,54.3 77.2,28.5 77.2,37.2 85.2,37.2 85.2,14.9 62.8,14.9 62.8,22.9 71.5,22.9"></polygon></svg> (opens new window)</a> is a 3D visualization tool for scientific data. It uses the Visualization Tool Kit or <a href="http://www.vtk.org/" target="_blank" rel="noopener noreferrer">VTK<svg xmlns="http://www.w3.org/2000/svg" aria-hidden="true" focusable="false" x="0px" y="0px" viewBox="0 0 100 100" width="15" height="15" class="icon outbound"><path fill="currentColor" d="M18.8,85.1h56l0,0c2.2,0,4-1.8,4-4v-32h-8v28h-48v-48h28v-8h-32l0,0c-2.2,0-4,1.8-4,4v56C14.8,83.3,16.6,85.1,18.8,85.1z"></path> <polygon fill="currentColor" points="45.7,48.7 51.3,54.3 77.2,28.5 77.2,37.2 85.2,37.2 85.2,14.9 62.8,14.9 62.8,22.9 71.5,22.9"></polygon></svg> (opens new window)</a> under the hood. Using the power of <a href="http://www.vtk.org/" target="_blank" rel="noopener noreferrer">VTK<svg xmlns="http://www.w3.org/2000/svg" aria-hidden="true" focusable="false" x="0px" y="0px" viewBox="0 0 100 100" width="15" height="15" class="icon outbound"><path fill="currentColor" d="M18.8,85.1h56l0,0c2.2,0,4-1.8,4-4v-32h-8v28h-48v-48h28v-8h-32l0,0c-2.2,0-4,1.8-4,4v56C14.8,83.3,16.6,85.1,18.8,85.1z"></path> <polygon fill="currentColor" points="45.7,48.7 51.3,54.3 77.2,28.5 77.2,37.2 85.2,37.2 85.2,14.9 62.8,14.9 62.8,22.9 71.5,22.9"></polygon></svg> (opens new window)</a>, MayaVI is capable of producing a variety of 3-Dimensional plots and figures. It is available as a separate software application and also as a library. Similar to <a href="http://matplotlib.org/" target="_blank" rel="noopener noreferrer">Matplotlib<svg xmlns="http://www.w3.org/2000/svg" aria-hidden="true" focusable="false" x="0px" y="0px" viewBox="0 0 100 100" width="15" height="15" class="icon outbound"><path fill="currentColor" d="M18.8,85.1h56l0,0c2.2,0,4-1.8,4-4v-32h-8v28h-48v-48h28v-8h-32l0,0c-2.2,0-4,1.8-4,4v56C14.8,83.3,16.6,85.1,18.8,85.1z"></path> <polygon fill="currentColor" points="45.7,48.7 51.3,54.3 77.2,28.5 77.2,37.2 85.2,37.2 85.2,14.9 62.8,14.9 62.8,22.9 71.5,22.9"></polygon></svg> (opens new window)</a>, this library provides an object oriented programming language interface to create plots without having to know about VTK. MayaVI is available only in Python 2.7x series! It is hoped to be available in Python 3-x series soon! (Although some success is noticed when using its dependencies in Python 3) Documentation can be found <a href="http://docs.enthought.com/mayavi/mayavi/" target="_blank" rel="noopener noreferrer">here<svg xmlns="http://www.w3.org/2000/svg" aria-hidden="true" focusable="false" x="0px" y="0px" viewBox="0 0 100 100" width="15" height="15" class="icon outbound"><path fill="currentColor" d="M18.8,85.1h56l0,0c2.2,0,4-1.8,4-4v-32h-8v28h-48v-48h28v-8h-32l0,0c-2.2,0-4,1.8-4,4v56C14.8,83.3,16.6,85.1,18.8,85.1z"></path> <polygon fill="currentColor" points="45.7,48.7 51.3,54.3 77.2,28.5 77.2,37.2 85.2,37.2 85.2,14.9 62.8,14.9 62.8,22.9 71.5,22.9"></polygon></svg> (opens new window)</a>. Some gallery examples are found <a href="http://docs.enthought.com/mayavi/mayavi/auto/examples.html" target="_blank" rel="noopener noreferrer">here<svg xmlns="http://www.w3.org/2000/svg" aria-hidden="true" focusable="false" x="0px" y="0px" viewBox="0 0 100 100" width="15" height="15" class="icon outbound"><path fill="currentColor" d="M18.8,85.1h56l0,0c2.2,0,4-1.8,4-4v-32h-8v28h-48v-48h28v-8h-32l0,0c-2.2,0-4,1.8-4,4v56C14.8,83.3,16.6,85.1,18.8,85.1z"></path> <polygon fill="currentColor" points="45.7,48.7 51.3,54.3 77.2,28.5 77.2,37.2 85.2,37.2 85.2,14.9 62.8,14.9 62.8,22.9 71.5,22.9"></polygon></svg> (opens new window)</a> Here is a sample plot created using MayaVI from the documentation. <div class="language-py extra-class"><pre class="language-py"><code># Author: Gael Varoquaux <[email protected]>

# License: BSD Style.

from numpy import sin, cos, mgrid, pi, sqrt

from mayavi import mlab

mlab.figure(fgcolor=(0, 0, 0), bgcolor=(1, 1, 1))

u, v = mgrid[- 0.035:pi:0.01, - 0.035:pi:0.01]

X = 2 / 3. * (cos(u) * cos(2 * v)

+ sqrt(2) * sin(u) * cos(v)) * cos(u) / (sqrt(2) -

sin(2 * u) * sin(3 * v))

Y = 2 / 3. * (cos(u) * sin(2 * v) -

sqrt(2) * sin(u) * sin(v)) * cos(u) / (sqrt(2)

- sin(2 * u) * sin(3 * v))

Z = -sqrt(2) * cos(u) * cos(u) / (sqrt(2) - sin(2 * u) * sin(3 * v))

S = sin(u)

mlab.mesh(X, Y, Z, scalars=S, colormap='YlGnBu', )

# Nice view from the front

mlab.view(.0, - 5.0, 4)

mlab.show()

</code></pre></div><img src="https://i.stack.imgur.com/eRg0Y.png" alt="image"> <h2 id="plotly"><a href="#plotly" class="header-anchor">#</a> Plotly</h2> <a href="https://plot.ly/" target="_blank" rel="noopener noreferrer">Plotly<svg xmlns="http://www.w3.org/2000/svg" aria-hidden="true" focusable="false" x="0px" y="0px" viewBox="0 0 100 100" width="15" height="15" class="icon outbound"><path fill="currentColor" d="M18.8,85.1h56l0,0c2.2,0,4-1.8,4-4v-32h-8v28h-48v-48h28v-8h-32l0,0c-2.2,0-4,1.8-4,4v56C14.8,83.3,16.6,85.1,18.8,85.1z"></path> <polygon fill="currentColor" points="45.7,48.7 51.3,54.3 77.2,28.5 77.2,37.2 85.2,37.2 85.2,14.9 62.8,14.9 62.8,22.9 71.5,22.9"></polygon></svg> (opens new window)</a> is a modern platform for plotting and data visualization. Useful for producing a variety of plots, especially for data sciences, Plotly is available as a library for Python, R, JavaScript, Julia and, MATLAB. It can also be used as a web application with these languages. Users can install plotly library and use it offline after user authentication. The installation of this library and offline authentication is given <a href="https://plot.ly/python/getting-started/" target="_blank" rel="noopener noreferrer">here<svg xmlns="http://www.w3.org/2000/svg" aria-hidden="true" focusable="false" x="0px" y="0px" viewBox="0 0 100 100" width="15" height="15" class="icon outbound"><path fill="currentColor" d="M18.8,85.1h56l0,0c2.2,0,4-1.8,4-4v-32h-8v28h-48v-48h28v-8h-32l0,0c-2.2,0-4,1.8-4,4v56C14.8,83.3,16.6,85.1,18.8,85.1z"></path> <polygon fill="currentColor" points="45.7,48.7 51.3,54.3 77.2,28.5 77.2,37.2 85.2,37.2 85.2,14.9 62.8,14.9 62.8,22.9 71.5,22.9"></polygon></svg> (opens new window)</a>. Also, the plots can be made in Jupyter Notebooks as well. Usage of this library requires an account with username and password. This gives the workspace to save plots and data on the cloud. The free version of the library has some slightly limited features and designed for making 250 plots per day. The paid version has all the features, unlimited plot downloads and more private data storage. For more details, one can visit the main page <a href="https://plot.ly/" target="_blank" rel="noopener noreferrer">here<svg xmlns="http://www.w3.org/2000/svg" aria-hidden="true" focusable="false" x="0px" y="0px" viewBox="0 0 100 100" width="15" height="15" class="icon outbound"><path fill="currentColor" d="M18.8,85.1h56l0,0c2.2,0,4-1.8,4-4v-32h-8v28h-48v-48h28v-8h-32l0,0c-2.2,0-4,1.8-4,4v56C14.8,83.3,16.6,85.1,18.8,85.1z"></path> <polygon fill="currentColor" points="45.7,48.7 51.3,54.3 77.2,28.5 77.2,37.2 85.2,37.2 85.2,14.9 62.8,14.9 62.8,22.9 71.5,22.9"></polygon></svg> (opens new window)</a>. For documentation and examples, one can go <a href="https://plot.ly/python/#fundamentals" target="_blank" rel="noopener noreferrer">here<svg xmlns="http://www.w3.org/2000/svg" aria-hidden="true" focusable="false" x="0px" y="0px" viewBox="0 0 100 100" width="15" height="15" class="icon outbound"><path fill="currentColor" d="M18.8,85.1h56l0,0c2.2,0,4-1.8,4-4v-32h-8v28h-48v-48h28v-8h-32l0,0c-2.2,0-4,1.8-4,4v56C14.8,83.3,16.6,85.1,18.8,85.1z"></path> <polygon fill="currentColor" points="45.7,48.7 51.3,54.3 77.2,28.5 77.2,37.2 85.2,37.2 85.2,14.9 62.8,14.9 62.8,22.9 71.5,22.9"></polygon></svg> (opens new window)</a> A sample plot from the documentation examples: <div class="language-py extra-class"><pre class="language-py"><code>import plotly.graph_objs as go

import plotly as ply

# Create random data with numpy

import numpy as np

N = 100

random_x = np.linspace(0, 1, N)

random_y0 = np.random.randn(N)+5

random_y1 = np.random.randn(N)

random_y2 = np.random.randn(N)-5

# Create traces

trace0 = go.Scatter(

x = random_x,

y = random_y0,

mode = 'lines',

name = 'lines'

)

trace1 = go.Scatter(

x = random_x,

y = random_y1,

mode = 'lines+markers',

name = 'lines+markers'

)

trace2 = go.Scatter(

x = random_x,

y = random_y2,

mode = 'markers',

name = 'markers'

)

data = [trace0, trace1, trace2]

ply.offline.plot(data, filename='line-mode')

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