Delve into Python data analysis and visualization using NumPy, SciPy, Pandas, and Seaborn. Gain insights into reading, cleaning, and visualizing data to generate actionable insights.

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visualization

This course assumes no prior experience and can teach anyone how to use Python to analyze and visualize data. Stop using Excel and start harnessing the power of Python.

Data analysis is one of the hottest careers of the 21st century. As an analyst, your goal is to peel back layers of data in order to answer questions of interest; that is the power of analytics. It allows you to take raw data and create meaningful, actionable insights.

In this course, you’ll learn how to use Python, NumPy, SciPy, Pandas, and Seaborn to perform data analysis and visualization. You’ll explore the four crucial steps for any data analysis project: reading, describing, cleaning, and visualizing data. In each step, you will work with the most common and popular tools that data analysts use every day. By the end of the course, you will be able to confidently extract knowledge and answers from data.

Python Data Analysis and Visualization

# Introduction to multi-plot grids

Seaborn also has some multi-plot grid capabilities.

I don't think I could explain it any better than the Seaborn documentation:

When exploring medium-dimensional data, a useful approach is to draw multiple instances of the same plot on different subsets of your dataset. This technique is sometimes called either “lattice” or “trellis” plotting, and it is related to the idea of “small multiples”. It allows a viewer to quickly extract a large amount of information about complex data. Matplotlib offers good support for making figures with multiple axes; seaborn builds on top of this to directly link the structure of the plot to the structure of your dataset.

# Multi-plot grids in Python

## FacetGrid and map

I think the easiest way to understand, is to use an example in Python from the flight's dataset.

First, we will look at the `FacetGrid()` and `map()` combination in Seaborn.

The parameters we will use for `FacetGrid` are:

* `data` which is the first parameter and specifies the dataframe to use.
* `row` which is the column name from your dataframe you want to use as the rows of your grid.
* `col` which is the column name from your dataframe you want to use as the columns of your grid.
* `margin_titles` which if `True`, the titles for the row variable are drawn to the right of the last column.

The parameters we will use for `map` are:

* `func` which is the first argument and is the plotting function you wish to use.
* `args` is the second parameter and is the column name for the variable you wish to plot.
* `color` which allows you to specify the plot color.

Let's take a look:


In this lesson, we will discuss how to use Seaborn to create multi-plot grids.

What is Analytics

Python Basics for Analytics

Reading Data

Describing Data

Cleaning Data

Visualizing Data

Visualization with Multi-Plot Grids

Introduction to multi-plot grids #