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Plotly for Data Visualization in Python

Plotly is an open-source Python library designed to create interactive, visually appealing charts and graphs. It helps users to explore data through features like zooming, additional details and clicking for deeper insights. It handles the interactivity with JavaScript behind the scenes so that we can focus on writing Python code to build the charts. In this article, we will see plotting in Plotly and covers how to create basic charts and enhance them with interactive features.

To get started with Plotly simply install it using the following command:

pip install plotly

Plotly consists of two key modules:

1. plotly.graph_objects: This module is used to define and create plots. It contains objects such as Figure, layout and data which are responsible for plotting.
2. plotly.express: This is a high-level interface for creating a wide variety of plots with minimal code. It simplifies the process of creating complex visualizations and allows users to create figures with just one line of code.

Example:

import plotly.express as px

fig = px.line(x=[1, 2], y=[3, 4])

print(fig)

Output:

Here we will see how to generate basic charts using Plotly and apply various customizations to enhance their appearance and functionality. We will learn how to visualize different graph like line charts, scatter plots, bar charts, histograms and pie charts. We will cover the following customizations:

• Adjusting chart layout
• Adding annotations
• Customizing markers and lines

Plotly line chart is one of the simple plots where a line is drawn to show relation between the X-axis and Y-axis. It can be created using the px.line() method with each data position is represented as a vertex of a polyline mark in 2D space.

Syntax:

plotly.express.line(data_frame=None, x=None, y=None, color=None, title=None)

Parameters:

• data_frame: Dataset to plot.
• x: Column name for the X-axis.
• y: Column name for the Y-axis.
• color: Color the lines based on this column.
• title: Title of the plot.

Return: A plotly.express.Figure object.

Example:

We will be using Iris dataset and it is directly available as part of scikit-learn. df = px.data.iris() from the plotly.express library loads it into a Pandas DataFrame. This dataset contains measurements of sepal length, sepal width, petal length and petal width for 150 iris flowers and categorized into three species: setosa, versicolor and virginica.

import plotly.express as px

df = px.data.iris()

fig = px.line(df, y="sepal_width",)

fig.show()

Output:

In the above example, we can see that:

• Plotly automatically assigns labels to the X and Y axes.
• The data points for both axes are displayed.
• We can zoom in, zoom out or select specific parts of the data.
• It provides interactive tools in the top-right corner for chart manipulation.
• We can also save the chart locally as a static image.

Now let's try to customize our graph a little. 

Example 1: In this example we will use the line dash parameter which is used to group the lines according to the dataframe column passed.

import plotly.express as px

df = px.data.iris()

fig = px.line(df, y="sepal_width", line_group='species')

fig.show()

Output:

Example 2: In this example, we will group and color the data according to the species. We will also change the line format. For this we will use two attributes such line_dash and color.

import plotly.express as px

df = px.data.iris()

fig = px.line(df, y="sepal_width", line_dash='species',
              color='species')

fig.show()

Output:

A bar chart is a pictorial representation of data that presents categorical data with rectangular bars with heights or lengths proportional to the values that they represent. These data sets contain the numerical values of variables that represent the length or height. It can be created using the px.bar() method.

Syntax:

plotly.express.bar(data_frame=None, x=None, y=None, color=None, title=None)

Parameters:

• data_frame: Dataset to plot.
• x: The column name for the X-axis.
• y: The column name for the Y-axis.
• color: Color the bars based on this column.
• title: Title of the plot.

Return: A plotly.express.Figure object.

Example:

We will be using tips dataset and df = px.data.tips() from the plotly.express library loads a sample dataset about tips into a Pandas DataFrame. This dataset contains 244 rows and 7 columns with each row representing a single restaurant bill and associated information.

import plotly.express as px

df = px.data.tips()

fig = px.bar(df, x='day', y="total_bill")

fig.show()

Output:

Let's try to customize this plot.

Example: Customizations that we will use -

• 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

df = px.data.tips()

fig = px.bar(df, x='day', y="total_bill", color='sex',
             facet_row='time', facet_col='sex')

fig.show()

Output:

A scatter plot is a set of dotted points to represent individual pieces of data in the horizontal and vertical axis. A graph in which the values of two variables are plotted along X-axis and Y-axis, the pattern of the resulting points reveals a correlation between them and it can be created using the px.scatter() method.

Syntax:

plotly.express.scatter(data_frame=None, x=None, y=None, color=None, title=None)

Parameters:

• data_frame: Dataset to plot.
• x: The column name for the X-axis.
• y: The column name for the Y-axis.
• color: Color the bars based on this column.
• title: Title of the plot.

Return: A plotly.express.Figure object.

Example:

import plotly.express as px

df = px.data.tips()

fig = px.scatter(df, x='total_bill', y="tip")

fig.show()

Output:

Example: Let's see various customizations available for this chart that we will use - 

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

import plotly.express as px

df = px.data.tips()

fig = px.scatter(df, x='total_bill', y="tip", color='time',
                 symbol='sex', size='size', facet_row='day',
                 facet_col='time')

fig.show()

Output:

A histogram is used to represent data in the form of some groups. It is a type of bar plot where the X-axis represents the bin ranges while the Y-axis gives information about frequency. It can be created using the px.histogram() method.

Syntax: 

plotly.express.histogram(data_frame=None, x=None, y=None, color=None, nbins=None, histnorm=None, title=None, width=None, height=None)

Parameters:

• data_frame: Dataset to plot.
• x: The column name for the X-axis (values to be binned).
• color: Color the bars based on this column.
• nbins: Set the number of bins.
• histnorm: Normalize the histogram (e.g"percent", "density").

Return: A plotly.express.Figure object.

Example:

import plotly.express as px

df = px.data.tips()

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

fig.show()

Output:

Let's customize the above graph.

Example: Customizations that we will be using are - 

• 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.

Here 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

df = px.data.tips()

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

fig.show()

Output:

A pie chart is a circular statistical graphic which is divided into slices to show numerical proportions. It shows a special chart that uses “pie slices” where each sector shows the relative sizes of data. A circular chart cuts in a form of radii into segments to magnitude of different features. It can be created using the px.pie() method.

Syntax:

plotly.express.pie(data_frame=None, names=None, values=None, color=None, hole=None, title=None, width=None, height=None)

Parameters:

• data_frame: Dataset to plot.
• names: Column name for the pie chart labels.
• values: Column name for the size of the slices.
• hole: Creates a donut chart when set between 0 and 1.

Return: A plotly.express.Figure object.

Example:

import plotly.express as px

df = px.data.tips()

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

Output:

Let's customize the above graph.

Example: Customizations that we will be using are:

• color_discrete_sequence: Strings defining valid CSS colors
• opacity: It finds how transparent or solid the markers (such as points on a scatter plot) appear. 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

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()

Output:

A Box Plot is also known as Whisker plot is created to display the summary of the set of data values having properties like minimum, first quartile, median, third quartile and maximum. In the box plot, a box is created from the first quartile to the third quartile, a vertical line is also there which goes through the box at the median. Here x-axis denotes the data to be plotted while the y-axis shows the frequency distribution. It can be created using the px.box() method

Syntax:

plotly.express.box(data_frame=None, x=None, y=None, color=None, facet_row=None, facet_col=None, title=None, width=None, height=None)

Parameters:

• data_frame: Dataset to plot.
• x: The column name for the X-axis.
• y: The column name for the Y-axis.
• color: Color the boxes based on this column.

Return: A plotly.express.Figure object.

Example:

import plotly.express as px

df = px.data.tips()

fig = px.box(df, x="day", y="tip")

fig.show()

Output:

Let's customize the above graph.  

Example: Customizations that we will be using are - 

• color: used to assign color to marks
• facet_row: assign marks to facetted subplots in the vertical direction
• facet_col: assign marks to facetted subplots in the horizontal direction
• boxmode: One of ‘group’ or ‘overlay’ In ‘overlay’ mode, boxes are on drawn top of one another. In ‘group’ mode, boxes are placed beside each other.
• notched: If True, boxes are drawn with notches

import plotly.express as px

df = px.data.tips()

fig = px.box(df, x="day", y="tip", color='sex',
             facet_row='time', boxmode='group',
             notched=True)

fig.show()

Output:

Violin Plot is a method to visualize the distribution of numerical data of different variables. It is similar to Box Plot but with a rotated plot on each side helps in giving more information about the density estimate on the y-axis. The density is mirrored and flipped over and the resulting shape is filled in creating an image resembling a violin. The advantage of a violin plot is that it can show distribution that aren’t shown in a boxplot. On the other hand the boxplot more clearly shows the outliers in the data. It can be created using the px.violin() method.

Syntax:

plotly.express.violin(data_frame=None, x=None, y=None, color=None, facet_row=None, facet_col=None, title=None, width=None, height=None)

Parameters:

• data_frame: Dataset to plot.
• x: The column name for the X-axis.
• y: The column name for the Y-axis.
• color: Color the violins based on this column.

Return: A plotly.express.Figure object.

Example:

import plotly.express as px

df = px.data.tips()

fig = px.violin(df, x="day", y="tip")

fig.show()

Output:

Let's customize the above graph.  

Example: For customizing the violin plot we will use the same customizations available for the box plot except the boxmode and notched which are not available for the violin plot Setting this parameter to True will show a box plot inside the violin plot.

import plotly.express as px

df = px.data.tips()

fig = px.violin(df, x="day", y="tip", color='sex',
                facet_row='time', box=True)

fig.show()

Output:

3D Scatter Plot shows data points in three dimensions, adding extra information by adjusting color, size and style of the points. These adjustments help make the plot clearer and easier to understand. You can create a 3D scatter plot using the scatter_3d function from the plotly.express class.

Syntax:

plotly.express.scatter_3d(data_frame=None, x=None, y=None, z=None, color=None, symbol=None, size=None, title=None, width=None, height=None)

Parameters:

• data_frame: Dataset to plot.
• x: The column name for the X-axis.
• y: The column name for the Y-axis.
• z: The column name for the Z-axis.
• color: Color the points based on this column.
• size: Size of the points.

Return: A plotly.express.Figure object.

Example:

import plotly.express as px

df = px.data.tips()

fig = px.scatter_3d(df, x="total_bill", y="sex", z="tip")

fig.show()

Output:

Customizing the 3D scatter plot.

Example: We will use the following customization - 

• color: Set the color of the markers
• size: Set the size of the marker
• symbol: Set the symbol of the plot

import plotly.express as px

df = px.data.tips()

fig = px.scatter_3d(df, x="total_bill", y="sex", z="tip", color='day', 
                    size='total_bill', symbol='time')

fig.show()

Output:

Plotly provides various interactive features which allows users to zoom, hover and click for deeper insights. Beyond these built-in interactions it allows customization with tools like dropdown menus, buttons and sliders. These can be added using the update menu attribute in the plot layout. Let’s explore these features:

A drop-down menu allows users to select different options to modify the chart. The update method is used to control the chart based on dropdown choices. 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

Example: Here we will be using Tips dataset which contains 244 rows and 7 columns and with each row representing a single restaurant bill and associated information.

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

df = px.data.tips()

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

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()

Output:

In plotly, adding custom Buttons are used to quickly make actions directly from a record. Custom Buttons can be added to page layouts in CRM, Marketing and Custom Apps. There are also 4 possible methods that can be applied in custom buttons:

• restyle: modify data or data attributes
• relayout: modify layout attributes
• update: modify data and layout attributes
• animate: start or pause an animation

Example:

import plotly.graph_objects as go
import plotly.express as px
import pandas as pd

df = px.data.tips()

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

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()

Output:

In Plotly, the range slider is an input control that allows users to select a value range between a specified minimum and maximum. It allows selecting pre-configured ranges and manually inputting minimum and maximum values or dates.

Example:

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()

Output:

Plotly Dash is a framework for building interactive web applications with Python. It allows us to create dynamic and visually appealing dashboards that can handle complex interactions and data visualizations. In this section, we will see the importance of building interactive dashboards using Plotly Dash.

Steps to Create a Basic Dash App:

1. Install the necessary packages:

!pip install dash

!pip install --upgrade plotly

2. Create and run the Dash app:

import dash
from dash import dcc, html
import plotly.express as px
import pandas as pd

df = pd.DataFrame({
    'x': [1, 2, 3, 4, 5],
    'y': [10, 15, 13, 17, 14]
})

app = dash.Dash(__name__)

app.layout = html.Div([
    dcc.Graph(
        id='example-graph',
        figure=px.scatter(df, x='x', y='y', title='Scatter Plot in Dash')
    )
])

if __name__ == '__main__':
    app.run(debug=True, port=8050)

Output:

With Plotly we can easily create interactive dashboards that make exploring and understanding data more engaging and insightful.

You can download source code from here.

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