Create Error Bars in Plotly – Python

Last Updated : 21 Oct, 2021

Plotly is a Python library that 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.

Error Bars in Plotly

For functions representing 2D data points such as px.scatter, px.line, px.bar, etc., error bars are given as a column name which is the value of the error_x (for the error on x position) and error_y (for the error on y position). Error bars are the graphical presentation alternation of data and used on graphs to imply the error or uncertainty in a reported capacity.

Example 1: In this example, we will plot a simple error plot using tips() data set.

Python3

import plotly.express as px 
  
  
df = px.data.tips() 
df["error"] = df["total_bill"]/100
  
fig = px.scatter(df, x="total_bill", y="day", color="sex", 
                 error_x="error", error_y="error") 
fig.show() 

Output:

Example 2:

Python3

import plotly.express as px 
  
df = px.data.tips() 
df["e"] = df["total_bill"]/100
  
fig = px.bar(df, x="total_bill", y="day", color="sex", 
             error_x="e", error_y="e") 
fig.show() 

Output:

The above example my seems something meshed up, but once you zoom it you’ll understand the graph more accurately.

Example 3: In this example, we will see Asymmetric Error Bars, Asymmetric errors arise when there is a non-linear dependence of a result.

Python3

import plotly.express as px 
  
  
df = px.data.tips() 
df["error"] = df["total_bill"]/100
df["W_error"] = df["total_bill"] - df["tip"]  
  
fig = px.scatter(df, x="total_bill", y="day", color="sex", 
                 error_x="error", error_y="W_error") 
fig.show()

Output:

Example 4: In this example, we will see Symmetric Error Bars, Symmetric mean absolute percentage error is an accuracy measure based on percentage errors.

Python3

import plotly.graph_objects as go 
x_data = [1, 2, 3, 4] 
y_data = [3, 5, 2, 6] 
  
fig = go.Figure(data = go.Scatter( 
        x= x_data, 
        y= y_data, 
        error_y = dict( 
              
              # value of error bar given in data coordinates 
            type ='data', 
            array = [1, 2, 3,4], 
            visible = True) 
    )) 
fig.show()

Output:

Example 5: In this example, we will see how to coloring and styling the error bar using their attributes.

Python3

import plotly.express as px 
import plotly.graph_objects as go 
import numpy as np 
  
X = np.linspace(-1, 1, 100) 
Y = np.sinc(X) 
  
x = [-0.89, -0.24, -0.0, 0.41, 0.89, ] 
y = [0.36, 0.75, 1.03, 0.65, 0.28, ] 
  
fig = go.Figure() 
fig.add_trace(go.Scatter( 
    x=X, y=Y, 
    name='error bar'
)) 
  
fig.add_trace(go.Scatter( 
    x=x, y=y, 
    mode='markers', 
    name='measured', 
    error_y=dict( 
        type='constant', 
        value=0.1, 
        color='green', 
        thickness=1.5, 
        width=3, 
    ), 
    error_x=dict( 
        type='constant', 
        value=0.2, 
        color='blue', 
        thickness=1.5, 
        width=3, 
    ), 
    marker=dict(color='green', size=8) 
)) 
  
fig.show() 