Error Plot in Matplotlib
Error plots display the variability or uncertainty associated with each data point in a dataset. They are commonly used in scientific research, engineering, and statistical analysis to visualize measurement errors, confidence intervals, standard deviations, or other statistical properties of the data. By incorporating error bars into plots, we can convey not only the central tendency of the data but also the range of possible values around each point.
# importing matplotlib
import matplotlib.pyplot as plt
# making a simple plot
x =[1, 2, 3, 4, 5, 6, 7]
y =[1, 2, 1, 2, 1, 2, 1]
# creating error
y_error = 0.2
# plotting graph
plt.plot(x, y)
plt.errorbar(x, y,
yerr = y_error,
fmt ='o')
Output
Matplotlib Tutorial
Matplotlib is easy to use and an amazing visualizing library in Python. It is built on NumPy arrays and designed to work with the broader SciPy stack and consists of several plots like line, bar, scatter, histogram, etc.
In this article, you’ll gain a comprehensive understanding of the diverse range of plots and charts supported by Matplotlib, empowering you to create compelling and informative visualizations for your data analysis tasks.
Table of Content
- Matplotlib Getting Started
- Creating Different Types of Plot
- Line Graph in Matplotlib
- Stem Plot in Matplotlib
- Bar chart in Matplotlib
- Plotting Histogram in Matplotlib
- Scatter Plot in Matplotlib
- Stack Plot in Matplotlib
- Box Plot in Matplotlib
- Pie Chart in Matplotlib
- Error Plot in Matplotlib
- Violin Plot in Matplotlib
- 3D Plots in Matplotlib