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matplotlib is the most used to plot. It is the reference.

Every plotting library works the same way:

  • We define axis (they boundaries can be adjusted based on the data)

  • We add curves, points, surfaces (A).

  • We add legendes (B).

  • We convert the plot into an image.

Step (A) and (B) can be repeated in any order. Curves and legends superimposed.

documentation source installation tutorial gallery

%matplotlib inline
from jyquickhelper import add_notebook_menu


Some parts of matplotlib are written in C and needs to be compiled. Thus the instruction pip install matplotlib usually fails on Windows unless Visual Studio 2015 Community Edition is installed. I recommend to use a precompiled version through conda install matplotlib or Unofficial Windows Binaries for Python Extension Packages sous Windows.

import matplotlib.pyplot as plt

First example

import numpy as np
import matplotlib.pyplot as plt

N = 150
r = 2 * np.random.rand(N)
theta = 2 * np.pi * np.random.rand(N)
area = 200 * r**2 * np.random.rand(N)
colors = theta

ax = plt.subplot(111, projection='polar')
c = plt.scatter(theta, r, c=colors, s=area,


To make it work:

  • Install ffmpeg on Windows or avconv on Linux

  • On Windows, open file matplotlibrc and update parameter animation.ffmpeg_path. On Linux, the same must be done for avconv.

import matplotlib
import os
matplotlib.rcParams['animation.ffmpeg_path'].replace(os.environ.get("USERPROFILE", "~"), "<user>")
import matplotlib.animation
['ffmpeg', 'ffmpeg_file']

Let’s take an example from matplotlib documentation bayes_update.

import numpy as np
import matplotlib.pyplot as plt
import matplotlib.animation as animation

def data_gen():
    t = data_gen.t
    cnt = 0
    while cnt < 1000:
        t += 0.05
        yield t, np.sin(2*np.pi*t) * np.exp(-t/10.)
data_gen.t = 0

fig, ax = plt.subplots()
line, = ax.plot([], [], lw=2)
ax.set_ylim(-1.1, 1.1)
ax.set_xlim(0, 5)
xdata, ydata = [], []
def run(data):
    # update the data
    t,y = data
    xmin, xmax = ax.get_xlim()

    if t >= xmax:
        ax.set_xlim(xmin, 2*xmax)
    line.set_data(xdata, ydata)

    return line,

anim = animation.FuncAnimation(fig, run, data_gen, blit=True, interval=10, repeat=False)

# saves the video
from matplotlib.animation import writers
Writer = writers['ffmpeg']
writer = Writer(fps=15, metadata=dict(artist='Me'), bitrate=1800)'lines2.mp4', writer=writer)
import os
[_ for _ in os.listdir(".") if "mp4" in _]

Open question : how to display the video in the notebook?


Interaction can be done with the regular GUI. See

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