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"]/100df["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() |
Output:
