# Data Visualization in Python

# What is data visualization?

*Is the practice of visualizing data in graphs, icons, presentations and more. It is most commonly used to translate complex data into digestible insights for a non-technical audience.*

# What is Matplotlib?

Matplotlib is one of the most powerful tools for data visualization in Python. It** **tries to make easy things easy and hard things possible. You can generate plots, histograms, power spectra, bar charts, errorcharts, scatterplots, etc., with just a few lines of code.

**Installation**

You can install Matplotlib from your terminal using:

`pip install matplotlib`

**Importing the library**

To get matplotlib up and running in our environment, we need to import it.

`import `**matplotlib.pyplot** as plt

Whenever you plot with matplotlib, the two main code lines should be,

- Type of graph — this is where you define a bar chart, line chart, etc.
- Show the graph — this is to display the graph

# Introduction to Seaborn

Seaborn provides a high-level interface to Matplotlib, a powerful but sometimes unwieldy Python visualization library.

On Seaborn’s official website, they state:

If matplotlib “tries to make easy things easy and hard things possible”, seaborn tries to make a well-defined set of hard things easy too.

We’ve found this to be a pretty good summary of Seaborn’s strengths. In practice, the “well-defined set of hard things” includes:

- Using default themes that are aesthetically pleasing.
- Setting custom color palettes.
- Making attractive statistical plots.
- Easily and flexibly displaying distributions.
- Visualizing information from matrices and DataFrames.

**Installation**

`pip install seaborn`

**Importing the library**

`import seaborn as sns`

## Different categories of plot in Seaborn

Plots are basically used for visualizing the relationship between variables. Those variables can be either be completely numerical or a category like a group, class or division. Seaborn divides plot into the below categories –

**Relational plots:**This plot is used to understand the relation between two variables.**Categorical plots:**This plot deals with categorical variables and how they can be visualized.**Distribution plots:**This plot is used for examining univariate and bivariate distributions**Regression plots:**The regression plots in seaborn are primarily intended to add a visual guide that helps to emphasize patterns in a dataset during exploratory data analyses.**Matrix plots:**A matrix plot is an array of scatterplots.**Multi-plot grids:**It is an useful approach is to draw multiple instances of the same plot on different subsets of the dataset.

Thank you for reading! I would appreciate any comments, notes, corrections, questions or suggestions — if there’s anything you’d like me to write about, please don’t hesitate to let me know.