3.Data Visualization using Plotty.ipynb

Kernel: Python 3 (ipykernel)

Plotly

• is an open-source module of Python which is used for data visualization

• supports various graphs like line charts, scatter plots, bar charts, histograms, area plot, etc

• Plotly uses javascript behind the scenes and is used to make interactive plots where we can zoom in on the graph or add additional information like data on hover

• Plotly does not come built-in with Python. To install it type the below command in the terminal.

pip install plotly
pip install wordcloud --trusted-host pypi.org --trusted-host files.pythonhosted.org plotly

import plotly.express as px


# Creating the Figure instance
fig = px.line(x=[1, 2], y=[3, 4])

# printing the figure instance
print(fig)

Figure({
    'data': [{'hovertemplate': 'x=%{x}<br>y=%{y}<extra></extra>',
              'legendgroup': '',
              'line': {'color': '#636efa', 'dash': 'solid'},
              'marker': {'symbol': 'circle'},
              'mode': 'lines',
              'name': '',
              'orientation': 'v',
              'showlegend': False,
              'type': 'scatter',
              'x': array([1, 2], dtype=int64),
              'xaxis': 'x',
              'y': array([3, 4], dtype=int64),
              'yaxis': 'y'}],
    'layout': {'legend': {'tracegroupgap': 0},
               'margin': {'t': 60},
               'template': '...',
               'xaxis': {'anchor': 'y', 'domain': [0.0, 1.0], 'title': {'text': 'x'}},
               'yaxis': {'anchor': 'x', 'domain': [0.0, 1.0], 'title': {'text': 'y'}}}
})

Creating Line Chart

# using the iris dataset
iris_data = px.data.iris()
iris_data

iris_data.describe()

import plotly.express as px

# using the iris dataset
iris_data = px.data.iris()

# plotting the line chart
fig = px.line(iris_data, y="sepal_width",)

# showing the plot
fig.show()

line dash parameter which is used to group the lines according to the dataframe column passed.

import plotly.express as px

# using the iris dataset
iris_data = px.data.iris()

# plotting the line chart
fig = px.line(iris_data, y="sepal_width", line_group='species')

# showing the plot
fig.show()

group and color the data according to the species.

import plotly.express as px

# using the iris dataset
iris_data = px.data.iris()

# plotting the line chart
fig = px.line(iris_data, y="sepal_width", line_dash='species',
			color='species')

# showing the plot
fig.show()

Bar Chart

d

import plotly.express as px

# Loading the data
d = px.data.tips()

# Creating the bar chart
fig = px.bar(d, x='day', y="total_bill")

fig.show()

Customize Bars

• color: Used to color the bars.

• facet_row: Divides the graph into rows according to the data passed

• facet_col: Divides the graph into columns according to the data passed

import plotly.express as px

# Loading the data
d = px.data.tips()

# Creating the bar chart
fig = px.bar(d, x='day', y="total_bill", color='sex',
			facet_row='time', facet_col='sex')

fig.show()

Scatter Plot

import plotly.express as px

# using the dataset
d = px.data.tips()

# plotting the scatter chart
fig = px.scatter(d, x='total_bill', y="tip")

# showing the plot
fig.show()

customizations for Scatter Chart

color: Color the points. symbol: Gives a symbol to each point according to the data passed. size: The size for each point.

import plotly.express as px

# using the dataset
d = px.data.tips()

# plotting the scatter chart
fig = px.scatter(d, x='total_bill', y="tip", color='time',
				symbol='sex', size='size', facet_row='day',
				facet_col='time')

# showing the plot
fig.show()

Histogram

import plotly.express as px

# using the dataset
df = px.data.tips()

# plotting the histogram
fig = px.histogram(df, x="total_bill")

# showing the plot
fig.show()

Customizations with Histogram

• color: To color the bars

• nbins: To set the number of bins

• histnorm: Mode through which the bins are represented. Different values that can be passed using this argument are-

• percent or probability: The output of histfunc for a given bin is divided by the sum of the output of histfunc for all bins.

• density: The output of histfunc for a given bin is divided by the size of the bin.

• probability density: The output of histfunc for a given bin is normalized such that it corresponds to the probability that a random

• barmode: Can be either ‘group’, ‘overlay’ or ‘relative’.

• group: Bars are stacked above zero for positive values and below zero for negative values

• overlay: Bars are drawn on the top of each other

• group: Bars are placed beside each other.

import plotly.express as px

# using the dataset
df = px.data.tips()

# plotting the histogram
fig = px.histogram(df, x="total_bill", color='sex',
				nbins=50, histnorm='percent',
				barmode='overlay')

# showing the plot
fig.show()

Pie Chart

import plotly.express as px

# Loading the iris dataset
df = px.data.tips()

fig = px.pie(df, values="total_bill", names="day")
fig.show()

Customizations to Donut Chart–

• color_discrete_sequence: Strings defining valid CSS colors

• opacity: Opacity for markers. The value should be between 0 and 1

• hole: Creates a hole in between to make it a donut chart. The value should be between 0 and 1

import plotly.express as px

# Loading the iris dataset
df = px.data.tips()

fig = px.pie(df, values="total_bill", names="day",
			color_discrete_sequence=px.colors.sequential.RdBu,
			opacity=0.7, hole=0.5)
fig.show()

BoxPlot

import plotly.express as px

# using the dataset
df = px.data.tips()

# plotting the boxplot
fig = px.box(df, x="day", y="tip")

# showing the plot
fig.show()

Adding interaction to the plot

A drop-down menu is a part of the menu button which is displayed on a screen all the time. Every menu button is associated with a Menu widget that can display the choices for that menu button when clicked on it. In plotly, there are 4 possible methods to modify the charts by using update menu method.

• restyle: modify data or data attributes

• relayout: modify layout attributes

• update: modify data and layout attributes

• animate: start or pause an animation

d

import plotly.graph_objects as px
import numpy as np
import pandas as pd


plot = px.Figure(data=[px.Scatter(
	x=d['day'],
	y=d['tip'],
	mode='markers',)
])

# Add dropdown
plot.update_layout(
	updatemenus=[
		dict(buttons=list([
			dict(
				args=["type", "scatter"],
				label="Scatter Plot",
				method="restyle"
			),
			dict(
				args=["type", "bar"],
				label="Bar Chart",
				method="restyle"
			)
		]),
			direction="down",
		),
	]
)

plot.show()

import plotly.graph_objects as px



plot = px.Figure(data=[px.Scatter(
	x=d['day'],
	y=d['tip'],
	mode='markers',)
])

# Add dropdown
plot.update_layout(
	updatemenus=[
		dict(
			type="buttons",
			direction="left",
			buttons=list([
				dict(
					args=["type", "scatter"],
					label="Scatter Plot",
					method="restyle"
				),
				dict(
					args=["type", "bar"],
					label="Bar Chart",
					method="restyle"
				)
			]),
		),
	]
)

plot.show()

import plotly.graph_objects as px
import plotly.express as go
import numpy as np

df = go.data.tips()

x = df['total_bill']
y = df['tip']

plot = px.Figure(data=[px.Scatter(
	x=x,
	y=y,
	mode='markers',)
])

plot.update_layout(
	xaxis=dict(
		rangeselector=dict(
			buttons=list([
				dict(count=1,
					step="day",
					stepmode="backward"),
			])
		),
		rangeslider=dict(
			visible=True
		),
	)
)

plot.show()