Learn to use the seaborn library for statistical analysis and data visualization, covering pair plots, scatter plots, line plots, heatmaps, bar plots, box plots and violin plots.

Become a Data Analyst

In this module, you’ll explore the seaborn library for statistical analysis and data visualization. You'll learn to create various plots for numerical and categorical data, style figures for improved aesthetics, and use multiplot grids to visualize complex relationships. By the end, you'll be proficient in using seaborn to create insightful and visually appealing visualizations.

Data Visualization with Seaborn

 **(True or False)** By default, the pandas `fillna()` method modifies the original DataFrame.

(True or False) By default, the pandas <code>fillna()</code> method modifies the original DataFrame.

We can't use categorical data in the `pairplot()` function because it only plots numerical variables.

We can use categorical variables by specifying them in any of the `x_vars` and `y_vars` arguments.

We can use the `hue` property to represent categories.

**(Select all that apply.)** How can we represent categorical data in seaborn's `pairplot()` function?

(Select all that apply.) How can we represent categorical data in seaborn’s <code>pairplot()</code> function?

**(Select all that apply.)** Which plots can we create using seaborn's  `relplot()`?



(Select all that apply.) Which plots can we create using seaborn’s <code>relplot()</code>?

We can use seaborn's `heatmap()` function to represent the correlation between different variables. Which part of the heatmap always represents the highest correlation?

We can use seaborn’s <code>heatmap()</code> function to represent the correlation between different variables. Which part of the heatmap always represents the highest correlation?

We have a dataset containing the accuracies of different machine learning models at different threshold values (0.1–0.9). We're interested in visualizing how these accuracies change at the different threshold values. Which is the appropriate plot to help us do that? 

We have a dataset containing the accuracies of different machine learning models at different threshold values (0.1–0.9). We’re interested in visualizing how these accuracies change at the different threshold values. Which is the appropriate plot to help us do that?

Quiz on plotting numerical data

Quiz on plotting numerical data

Test your knowledge of using seaborn with pandas and numerical data.

Introduction to Seaborn and Statistical Analysis

Plotting Numerical Data

Assessment on Plotting Numerical Data

Plotting the Categorical Data

Assessment on Categorical Plots

Styling and Figure Aesthetics

Assessment on Styling and Figure Aesthetics

Multiplot Grids

Assessment on Multi-Plot Grids

Conclusion

Quiz on Plotting Numerical Data