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 pximport 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 goimport numpy as np n = 10000plot = 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 pximport 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 pximport plotly.express as goimport numpy as npdf = 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 pximport 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:
