- Trending Categories
- Data Structure
- Operating System
- C Programming
- Social Studies
- Fashion Studies
- Legal Studies
- Selected Reading
- UPSC IAS Exams Notes
- Developer's Best Practices
- Questions and Answers
- Effective Resume Writing
- HR Interview Questions
- Computer Glossary
- Who is Who
How can a linear relationship be visualized using Seaborn in Python?
Seaborn is a library that helps in visualizing data. It comes with customized themes and a high-level interface.
When regression models are being built, multicollinearity is checked for. This is because we need to understand the correlation present between all different combinations of continuous variables. If multicollinearity exists between the variables, we have to make sure that it is removed from the data. This is where functions ‘regpot’ and ‘implot’ come into play. They help visualize a linear relationship between variables in linear regression.
The ‘regplot’ function accepts values for variables ‘x’ and ‘y’ in a variety of formats, and this includes numpy arrays, pandas series objects, references to variables or values from a pandas dataframe.
On the other hand, the function ‘implot’ requires the user to pass a specific parameter for data, and the values for variables ‘x’ and ‘y’ need to be strings. This type of data format is known as long-form data. Here’s the example −
import seaborn as sb from matplotlib import pyplot as plt my_df = sb.load_dataset('tips') sb.regplot(x = "total_bill", y = "tip", data = my_df) sb.lmplot(x = "total_bill", y = "tip", data = my_df) plt.show()
- The required packages are imported.
- The input data is ‘tips’ which is loaded from the seaborn library.
- This data is stored in a dataframe.
- The ‘load_dataset’ function is used to load the iris data.
- This data is visualized using the ‘regplot’ function.
- This data is visualized using the ‘implot’ function.
- Here, the dataframe is supplied as parameter.
- Also, the x and y values are specified.
- This data is displayed on the console.
- How can data that has multiple variables be visualized using Seaborn in Python?
- How can multiple lines be visualized using Bokeh Python?
- How can a vertical bar graph be visualized using Bokeh and Python?
- Explain how a violin plot can be visualized using factorplot function in Python?
- How can a Beizer curve be visualized using Bokeh?
- How can axis aligned rectangles be visualized using Python and Bokeh?
- How can bar graphs be visualized using Bokeh?
- How can data be represented visually using ‘seaborn’ library in Python?
- Explain how Pygal can be used to create interactive visualizations, and show how a bar graph can be visualized using Pygal.
- How can bar plot be used in Seaborn library in Python?
- How can Linear Regression be implemented using TensorFlow?
- How can Seaborn library be used to display a hexbin plot in Python?
- How can Seaborn library be used to display a Scatter Plot in Python?
- How can patch plot with multiple patches be visualized in Bokeh?
- How can every violin in a violin plot be split in Python Seaborn Library?