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.

Data Visualization and Analysis With Seaborn Library (1).png

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

**Count plots** are used to count the number of instances each category has. We use it for categorical data to represent the number of instances of each unique category in the form of bars. 

 

Let's start by importing the required libraries and the `tips` dataset from seaborn library using the `sns.load_dataset()` function. Next, we can view the first five data records to get an overview of the data using the pandas `head()` function.

import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
sns.set_theme()
tips_df = sns.load_dataset('tips')
print(tips_df.head())

Let's check the ratio of male and female customers in the `tips` dataset  by passing `x='sex'` to the `sns.countplot()` function. The `sns.countplot()` function returns a bar container with all "artists" describing the bars in a count plot. Next, we call the `bar_label()` function to add labels to the bar. The function takes a container as input to add labels to the bars of the count plot.



cplot = sns.countplot( data = tips_df , x ='sex')   #returns bar container
plt.bar_label(cplot.containers[0])
plt.savefig('output/graph.png')    # save figure

In the example above, we saw that different colors represent two categories. However, we can also set it to one color by specifying the required color in the `color` parameter of the `sns.countplot()` function. We can also customize the bar labels by selecting `padding=2` (the distance between the bars and labels), `color='red'`, `fontsize=15`, and `label_type ='edge'` (displays the labels on top of the bars). Moreover, adding the labels on top of the bars clutters them, so we increase the range of the y-axis using the `ylim()` function. 
 

Learn how to style and customize count plots according to the needs of the data.

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 Count Plots

Overview

Plotting count plots