Delve into data visualization and analysis with Python's Seaborn library. Learn about variable types, data cleaning, creating detailed visualizations, and applying themes for more appealing results.

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jupyter.tar.gz

Adding Hue Parameter for color encoding

Data cleaning with Pandas

Getting Started with Seaborn

Intro to Pandas

Line Plots in Seaborn

Pair Plot

Scatter Plot

libraries

project

solution

This course aims to provide an introduction to data visualization and analysis using Python and the Seaborn library.

The course begins by introducing various variable types and statistical analysis methods. Then, you get to review the foundations of data cleaning and extraction using the pandas library. In the second half of the course, you will go over different plots in Seaborn for numerical, continuous, and categorical data, as well as distribution and regression plots to gain insightful information and identify patterns in the data. Lastly, you get to learn to create complex visualizations that are also aesthetically pleasing and go into great detail about the Seaborn themes, color palettes, styling, and multiplot grids.

By the end of this course, you’ll apply the knowledge you’ve gained with a hands-on project.

Data Visualization and Analysis With Seaborn Library

**Bar plots**, also called categorical estimate plots, are where we compute the mean and confidence interval using bootstrapping techniques for each unique group within the category. The count plots and bar plots have many similarities. Count plots count the number of instances for each category, whereas bar plots compute each group's mean and confidence intervals.

Let's begin by importing the required libraries and the `titanic` dataset stored in the `titanic_df` DataFrame. Typically, in a bar plot, we have one categorical variable and one numerical variable. For example, using the `sns.barplot()` function, we can categorize the passengers based on `pclass` and visualize the distribution of `age` for each category of `pclass`. In addition, the plot contains bars with vertical lines. These vertical lines are error bars that serve as visual indicators of data variability and denote error or uncertainty in reported measurements.

Additionally, we can customize the bar labels using the `bar_label()` function. Because the bar plot shows the mean value (floating point number), we can format the floating point number by specifying `fmt= "%.2f"`, which displays two decimals after the floating point. We also increase the range of the y-axis using the `ylim()` function to fit the labels and bars within the figure.



Learn to plot, interpret, and style bar plots for data analysis.

About the Course

Introduction to Seaborn and Statistical Analysis

Plotting Numerical Data

Assessment on Plotting Numerical Data

Plotting Categorical Data

Plotting the Categorical Data

Assessment on Categorical Plots

Visualizing Distribution of Data

Assessment on Distributions of Data

Visualizing Regression Models

Assessment on Visualization of Regression Models

Styling and Figure Aesthetics

Assessment on Styling and Figure Aesthetics

Multiplot Grids

Assessment on Multi-Plot Grids

Project

Closing Remarks

Visualization with Bar Plots

Overview

Plotting bar plots