Today's topic is how we can choose either interactive or non-interactive visualization components in jupyter especially in Python since quite a lot of people enjoy programming in Python environment. My journey begun from installing BeakerX plugin in jupyter because this extension provides a lot of different languages for learners or piloting code development.
Non-interactive
Seaborn (https://seaborn.pydata.org/)
This package is quite useful to handle Panda dataframe mostly and gives fancy charts to analyze and take some insights a specific dataset. Seaborn is based on matplotlib by improving usability except interaction. When we have to check patterns on the dataset fast, seaborn would be a great choice.
from beakerx import *
from beakerx.object import beakerx
beakerx.pandas_display_default()
from matplotlib import pyplot as plt
%matplotlib inline
sns.pairplot(df, hue="species")
The above script produces a pairplot which is shown as the cover image.
Personally, static charts are easier to handle and do not require external javascript library loading. Non-interactive chart method is very useful for piloting, testing and checking datasets as web browser requires less memory where we can focus on more data intensive workflows.
Interactive
Plotly (https://plot.ly/)
Plotly supports wide range of languages such as Javascript, python and R. Therefore, we can get used to Plotly chart in any languages.
import plotly.express as px
# df = px.data.iris()
fig = px.scatter_matrix(df,
dimensions=["sepal_width", "sepal_length", "petal_width", "petal_length"],
color="species", symbol="species",
title="Scatter matrix of iris data set",
labels={col:col.replace('_', ' ') for col in df.columns})
fig.update_traces(diagonal_visible=False)
fig.show()
Implicit interactive plot conversion in Plotly
There is a way to convert implicitly to plotly plot from the pyplot. It requires a function named, iplot_mpl. Here are brief source codes of doing the conversion.
%matplotlib inline
import matplotlib.pyplot as plt # side-stepping mpl backend
import matplotlib.gridspec as gridspec # subplots
import numpy as np
fig1 = plt.figure()
# Make a legend for specific lines.
import matplotlib.pyplot as plt
import numpy as np
t1 = np.arange(0.0, 2.0, 0.1)
t2 = np.arange(0.0, 2.0, 0.01)
# note that plot returns a list of lines. The "l1, = plot" usage
# extracts the first element of the list into l1 using tuple
# unpacking. So l1 is a Line2D instance, not a sequence of lines
l1, = plt.plot(t2, np.exp(-t2))
l2, l3 = plt.plot(t2, np.sin(2 * np.pi * t2), '--go', t1, np.log(1 + t1), '.')
l4, = plt.plot(t2, np.exp(-t2) * np.sin(2 * np.pi * t2), 'rs-.')
plt.xlabel('time')
plt.ylabel('volts')
plt.title('Damped oscillation')
plt.show()
import plotly as py
import plotly.tools as tls
import plotly.graph_objects as go
py.offline.iplot_mpl(fig1)
If we want to remove the given styles, we need to create a Figure by using mpl_to_plotly, then override the layouts like below
fig = tls.mpl_to_plotly(fig1)
fig['layout'] = {'title': 'Damped oscillation',
'xaxis': {'anchor': 'y', 'domain': [0.0, 1.0], 'title': 'time'},
'yaxis': {'anchor': 'x', 'domain': [0.0, 1.0], 'title': 'volts'}}
fig['layout'].update(showlegend=True, autosize=True, template= None)
py.offline.iplot(fig)
The whole source codes are in gist.
In addition, we can show plotly charts here with runkit.
This code block is no longer available. The original code is shown below.
// hidden setup JavaScript code goes in this preamble area
const hiddenVar = 42
const wrapPlotly = require('runkit-plotly');
var makeChart = wrapPlotly((data, layout, Plotly) => {
Plotly.newPlot(layout, data);
});
var trace1 = {
x: [1, 2, 3, 4, 5],
y: [1, 3, 2, 3, 1],
mode: "lines+markers",
name: "'linear'",
line: {shape: "linear"},
type: "scatter"
};
var trace2 = {
x: [1, 2, 3, 4, 5],
y: [6, 8, 7, 8, 6],
mode: "lines+markers",
name: "'spline'",
text: ["tweak line smoothness<br>with 'smoothing' in line object", "tweak line smoothness<br>with 'smoothing' in line object", "tweak line smoothness<br>with 'smoothing' in line object", "tweak line smoothness<br>with 'smoothing' in line object", "tweak line smoothness<br>with 'smoothing' in line object", "tweak line smoothness<br>with 'smoothing' in line object"],
line: {shape: "spline"},
type: "scatter"
};
var trace3 = {
x: [1, 2, 3, 4, 5],
y: [11, 13, 12, 13, 11],
mode: "lines+markers",
name: "'vhv'",
line: {shape: "vhv"},
type: "scatter"
};
var trace4 = {
x: [1, 2, 3, 4, 5],
y: [16, 18, 17, 18, 16],
mode: "lines+markers",
name: "'hvh'",
line: {shape: "hvh"},
type: "scatter"
};
var trace5 = {
x: [1, 2, 3, 4, 5],
y: [21, 23, 22, 23, 21],
mode: "lines+markers",
name: "'vh'",
line: {shape: "vh"},
type: "scatter"
};
var trace6 = {
x: [1, 2, 3, 4, 5],
y: [26, 28, 27, 28, 26],
mode: "lines+markers",
name: "'hv'",
line: {shape: "hv"},
type: "scatter"
};
var data = [trace1, trace2, trace3, trace4, trace5, trace6];
var layout = {legend: {
y: 0.5,
traceorder: "reversed",
font: {size: 16},
yref: "paper"
}};
makeChart(data, layout);
This code block is no longer available. The original code is shown below.
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6.3,
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7.1,
6.3,
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6.4,
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6.4,
6.5,
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6,
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6.3,
6.7,
7.2,
6.2,
6.1,
6.4,
7.2,
7.4,
7.9,
6.4,
6.3,
6.1,
7.7,
6.3,
6.4,
6,
6.9,
6.7,
6.9,
5.8,
6.8,
6.7,
6.7,
6.3,
6.5,
6.2,
5.9
],
"visible": true,
"meanline": {
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},
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{
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"y": [
3.3,
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3,
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3,
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3,
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6,
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5.3,
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5,
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5.6,
5.1,
5.6,
6.1,
5.6,
5.5,
4.8,
5.4,
5.6,
5.1,
5.1,
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5,
5.2,
5.4,
5.1
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{
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],
"y": [
2.5,
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2.2,
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2,
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],
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"visible": true
},
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"legendgroup": "petal_width"
}
];
let layout = {
"title": {
"text": "Iris Dataset Distribution"
}
};
const wrapPlotly = require('runkit-plotly');
var makeChart = wrapPlotly((data, layout, Plotly) => {
Plotly.newPlot(layout, data);
});
makeChart(data, layout);
Happy coding!
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