Scatter plot in Plotly using graph_objects class

Plotly is a Python library which is used to design graphs, especially interactive graphs. It can plot various graphs and charts like histogram, barplot, boxplot, spreadplot and many more. It is mainly used in data analysis as well as financial analysis. plotly is an interactive visualization library.

Scatter plot using graph_objects class

Scatter plot are those charts in which data points are represented horizontally and on vertical axis to show that how one variable affect on another variable. The scatter() method of graph_objects class produces a scatter trace. The mode of the property decides the appearance of data points.

Syntax: plotly.graph_objects.Scatter(arg=None, cliponaxis=None, connectgaps=None, customdata=None, customdatasrc=None, dx=None, dy=None, error_x=None, error_y=None, fill=None, fillcolor=None, groupnorm=None, hoverinfo=None, hoverinfosrc=None, hoverlabel=None, hoveron=None, hovertemplate=None, hovertemplatesrc=None, hovertext=None, hovertextsrc=None, ids=None, idssrc=None, legendgroup=None, line=None, marker=None, meta=None, metasrc=None, mode=None, name=None, opacity=None, orientation=None, r=None, rsrc=None, selected=None, selectedpoints=None, showlegend=None, stackgaps=None, stackgroup=None, stream=None, t=None, text=None, textfont=None, textposition=None, textpositionsrc=None, textsrc=None, texttemplate=None, texttemplatesrc=None, tsrc=None, uid=None, uirevision=None, unselected=None, visible=None, x=None, x0=None, xaxis=None, xcalendar=None, xsrc=None, y=None, y0=None, yaxis=None, ycalendar=None, ysrc=None, **kwargs)

Parameters:

Name	Description
dx	Sets the x coordinate step.
dy	Sets the y coordinate step.
x	Sets the x coordinates.
x0	Alternate to x. Builds a linear space of x coordinates. Use with dx where x0 is the starting coordinate and dx the step.
y	Sets the y coordinates.
y0	Alternate to y. Builds a linear space of y coordinates. Use with dy where y0 is the starting coordinate and dy the step.

Example:

Python3

import plotly.graph_objects as px
import numpy as np


# creating random data through randomint
# function of numpy.random
np.random.seed(42)

random_x= np.random.randint(1,101,100)
random_y= np.random.randint(1,101,100)

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

plot.show()

Output:

Presenting Scatter with a Color Dimension

Color scale can be shown using the showscale parameter. This parameter takes a boolean value. If the value is true then the scale is shown otherwise not.

Example:

Python3

import plotly.graph_objects as go
import numpy as np

n = 10000
plot = go.Figure(data=[go.Scatter(
    x = np.random.randn(n),
    mode = 'markers',
    marker=dict(
        color=np.random.randn(n),
        colorscale='Viridis',
        showscale=True
    )
)
])

plot.show()

Output:

Styling Scatter Plots

In scatter plot can be styled using keywords arguments, let’s see the examples given below:

Example 1: Changing the color of the graph

Python3

import plotly.graph_objects as px
import numpy as np


# creating random data through randomint
# function of numpy.random
np.random.seed(42)

random_x= np.random.randint(1,101,100)
random_y= np.random.randint(1,101,100)

plot = px.Figure(data=[px.Scatter(
    x = random_x,
    y = random_y,
    mode = 'markers',
    marker_color='rgba(199, 10, 165, .9)')
])

plot.show()

Output:

Example 2: Using tips dataset

Python3

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['day']

plot = px.Figure(data=[px.Scatter(
    x = x,
    y = y,
    mode = 'markers',
    marker_color='rgba(199, 10, 165, .9)')
])

plot.update_traces(mode='markers', marker_size=10)

plot.show()

Output:

Bubble Scatter Plots

The bubble scatter plot can be created using the marker size. Marker size and color are used to control the overall size of the marker. Marker size helps to maintain the color inside the bubble in the graph.

Example:

Python3

import plotly.graph_objects as px
import numpy as np


# creating random data through randomint
# function of numpy.random
np.random.seed(42)

random_x= np.random.randint(1,101,100)
random_y= np.random.randint(1,101,100)

plot = px.Figure(data=[px.Scatter(
    x = random_x,
    y = random_y,
    mode = 'markers',
    marker_size = [115, 20, 30])
])

plot.show()

Output: