#Significant (Magnitude > 5.5) Earthquake data from 1965 to 2015. This is done by calling upon pd.read_csv() and passing in the file name to the brackets. Once the libraries have been imported we can now begin importing the Earthquake dataset from Kaggle. The default figures are also more visually appealing compared to matplotib import seaborn as sns import pandas as pd Loading Data It provides much easier to use syntax for creating more advanced plots.
Seaborn is a data visualisation library that is built on top of matplotlib and it allows it to create very powerful and insightful graphics with a few lines of code. Pandas is a Python library that is used for data analysis, manipulation and allows us to load in data from a variety of data sources, including. The first step in our tutorial is to import the necessary libraries, which are Pandas and Seaborn. I have also created the following video of this tutorial which may be of interest to you. You can view the original data at the following link. It is a dataset that contains data from the most significant earthquakes in the world with a magnitude of 5.5 or higher and between the years of 19. The data used for this tutorial is usable under CC0: Public Domain and originates from Kaggle. This allows us to create a grid, on which we can map many other types of plots with minimal code, especially compared to using matplotlib. This is achieved using Seaborn’s FacetGrid function. When working with data visualizations in Python, we may want to split the data up by categories or different groups. It allows us to gain insights about our data, and as the famous saying suggests, ‘a picture is worth a thousand words’. Data visualization is an essential part of any data analysis or machine learning workflow.
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