Table of Contents

• Imports
• Our first plots
  • Basic Functions
    • Graph Objects
    • FINALLY Plotting
  • Lines and Scatters
    • The usual process
    • Lines or points
  • Multiple plots on a single figure
    • Legends
• Exercise 1
• Exercise 2

Imports

This is for offline usage and presumes plotly for python has been installed

# Import base library
import plotly

# Import helper functions for making different kinds of plots
from plotly import graph_objs as go

# import offline plotting functions
from plotly.offline import iplot, plot, init_notebook_mode

# prepare the notebook to embed plotly graphs
init_notebook_mode()

# import numpy for convenience
import numpy as np

Our first plots

Basic Functions

Graph Objects

plotly.graph_objs

... imported here as go, gives us functions that provide predefined plot types.

There are many of these. The most common is go.Scatter(). Other common ones are go.Bar(), go.Histogram and go.Heatmap().

These functions require only the bare information for your plot, usually just the data, and take care of the styling themselves.

Importantly though, they do provide a number of options for customising your plot, should you desire to do so.

They accept key word arguments for all of their options.

First, we'll focus on the most fundamental kind of plots ... using go.Scatter() to make Line and Scatter Plots.

FINALLY Plotting

iplot will embed plots into the notebook. The 'i' in 'iplot' stands for the 'i' in 'iPython Notebook', which is what the tool used to be called before it becaume the Jupyter Notebook.

plot will save the plot to an independent html file in the local directory. This file can be opened by any browser, and will be opened automatically. There is the option for giving the file your own filename.

Lines and Scatters

The usual process

• Use a graph object from graph_objs to put your data into a plot
• Each single plot is called a trace in plotly. Usually you give it its own variable name.
• Put your trace, or multiple traces if you have more than one, into a list.
• Pass your list to the iplot or plot function.

# data points along the x axis
xdata = np.linspace(0, 5, 50)

# data points along the y axis
ydata = x**2

# define a single plot
trace = go.Scatter(x=xdata, y=ydata)

# graph all plots in the IPython notebook
iplot([trace]) # NOTE ... traces go inside a list

Lines or points

Most common stylistic choice — whether to plot your data points as lines or points.

Despite its name, go.Scatter does both. It is really the 'go-to' plotting function.

To go between lines and dots, you must use our first attribute in a graph object.

The attribute is mode. It has the following options:

• 'markers' ... renders data points as points or symbol; the symbol can be customised
• 'lines' ... renders as simple lines
• 'text' ... renders only the text associated with the data point. We don't have any yet, but will later.

your trace code will now look like this:

...
# define a single plot ... now with a mode option
trace1 = go.Scatter(x=xdata, y=ydata, mode='markers')
...

To combine either of the modes simultaneously, connect them into a single string with a '+' symbol: markers+lines or lines+text or lines+markers+text.

Multiple plots on a single figure

When we define our plots using go.Scatter() or another graph object, which plotly calls 'traces', we put them into a list and pass that list to iplot or plot. Eg iplot([trace])

To make multiple plots on a single figure, create multiple traces using the graph object functions, and put each one into the list that you give to iplot. Each one will be plotted independently.

Legends

To give each trace a unique name for a legend, use the option name for each trace, providing a string. That name will appear automatically in a legend formatted by plotly.

Note The automatically generated legend is also interactive. Clicking each trace name toggles whether that trace appears in the plot or not.

x = np.linspace(0, 5, 50)

# Three sets of y-axis data
y1 = x**2
y2 = 15*np.sin(x)
y3 = 10*np.log(x)

# Three traces, each with a unique data set, mode and name
trace1 = go.Scatter(x=x, y=x**2, mode='markers', name='Markers')
trace2 = go.Scatter(x=x, y=y2, mode='lines', name='Lines')
trace3 = go.Scatter(x=x, y=y3, mode='lines+markers', name='Lines and Markers')

# put each trace into a single list, and plot all at once.
iplot([trace1, trace2, trace3])

Exercise 1

• In the section Exercise Data below, there is a data set broken up into x and y components. Copy and paste this data into your notebook or environment.
• Generate a plot, using either iplot or plot, of this data.
• Change the mode of your plot. Which is best?

x = np.array(x) # x is now an array
y = np.array(y) # y is now an array

x = [ 0.        ,  0.12244898,  0.24489796,  0.36734694,  0.48979592,
        0.6122449 ,  0.73469388,  0.85714286,  0.97959184,  1.10204082,
        1.2244898 ,  1.34693878,  1.46938776,  1.59183673,  1.71428571,
        1.83673469,  1.95918367,  2.08163265,  2.20408163,  2.32653061,
        2.44897959,  2.57142857,  2.69387755,  2.81632653,  2.93877551,
        3.06122449,  3.18367347,  3.30612245,  3.42857143,  3.55102041,
        3.67346939,  3.79591837,  3.91836735,  4.04081633,  4.16326531,
        4.28571429,  4.40816327,  4.53061224,  4.65306122,  4.7755102 ,
        4.89795918,  5.02040816,  5.14285714,  5.26530612,  5.3877551 ,
        5.51020408,  5.63265306,  5.75510204,  5.87755102,  6.        ]

y = [ -4.77230983,  -5.22659968,  -6.10912627,   2.62439273,
         3.17193212,   3.92985444,   4.32653827,  -0.47683265,
         0.17638539,   4.88802425,  -0.29274781,  -4.0633262 ,
        -0.05369636,   2.42199466,   7.87335613,   2.92485951,
         3.83656283,   3.03218709,   2.47220117,   5.01351618,
        13.45426071,   5.59004046,  11.15600962,   2.41768145,
        12.68859787,  12.64836321,  12.48045846,   3.90270861,
        11.34526527,   4.63140355,  11.49365356,  13.59514573,
        19.88436659,   9.18282506,  19.14753706,  19.60772532,
        20.88308577,  20.05346221,  26.35313549,  18.68721231,
        24.04982145,  28.52843377,  22.62437669,  35.89023336,
        29.58917845,  37.07338193,  31.38718976,  28.33350353,
        42.82223893,  30.69027333]

Exercise 2

• In the section Exercise Data below, two more bits of data have been provided — the results of trying to fit two curves to the raw data given above.
• The first curve fit was for a linear curve, and the second for a quadratic or parabola curve.
• Add these two data sets to your previous plot
• Give each of the traces appropriate names for the legend.
• Select an appropriate 'mode' (ie markers or lines) for each of the traces.

y_linear = [ -6.76139873,  -5.99082022,  -5.22024171,  -4.44966321,
        -3.6790847 ,  -2.90850619,  -2.13792768,  -1.36734918,
        -0.59677067,   0.17380784,   0.94438635,   1.71496486,
         2.48554336,   3.25612187,   4.02670038,   4.79727889,
         5.56785739,   6.3384359 ,   7.10901441,   7.87959292,
         8.65017143,   9.42074993,  10.19132844,  10.96190695,
        11.73248546,  12.50306396,  13.27364247,  14.04422098,
        14.81479949,  15.58537799,  16.3559565 ,  17.12653501,
        17.89711352,  18.66769203,  19.43827053,  20.20884904,
        20.97942755,  21.75000606,  22.52058456,  23.29116307,
        24.06174158,  24.83232009,  25.6028986 ,  26.3734771 ,
        27.14405561,  27.91463412,  28.68521263,  29.45579113,
        30.22636964,  30.99694815]

y_quadratic = [ -1.02315297,  -0.9552168 ,  -0.85800387,  -0.73151417,
        -0.57574771,  -0.39070448,  -0.17638449,   0.06721226,
         0.34008578,   0.64223607,   0.97366311,   1.33436692,
         1.7243475 ,   2.14360484,   2.59213894,   3.06994981,
         3.57703744,   4.11340183,   4.67904299,   5.27396091,
         5.8981556 ,   6.55162705,   7.23437527,   7.94640025,
         8.68770199,   9.4582805 ,  10.25813577,  11.08726781,
        11.94567661,  12.83336217,  13.7503245 ,  14.69656359,
        15.67207945,  16.67687207,  17.71094145,  18.7742876 ,
        19.86691051,  20.98881019,  22.13998663,  23.32043984,
        24.5301698 ,  25.76917654,  27.03746004,  28.3350203 ,
        29.66185732,  31.01797111,  32.40336166,  33.81802898,
        35.26197306,  36.73519391]