Xarray for multidimensional gridded data¶

In last week's lecture, we saw how Pandas provided a way to keep track of additional "metadata" surrounding tabular datasets, including "indexes" for each row and labels for each column. These features, together with Pandas' many useful routines for all kinds of data munging and analysis, have made Pandas one of the most popular python packages in the world.

Assignment 7 - Pandas¶

Due Oct 19.

In this assignment we will use pandas to examine earthquake data.

Start by importing pandas, numpy and matplotlib.

I saved you some time by pre-downloading some data in .csv format from the USGS Earthquakes Database. It is located at:

http://www.ldeo.columbia.edu/~rpa/usgs_earthquakes_2014.csv

You don't even need to download it. You can open it directly with Pandas.

Pandas¶

Pandas is a an open source library providing high-performance, easy-to-use data structures and data analysis tools. Pandas is particularly suited to the analysis of tabular data, i.e. data that can can go into a table. In other words, if you can imagine the data in an Excel spreadsheet, then Pandas is the tool for the job.