Jenny PalominoJenny Palomino has contributed to the materials listed below. Jenny is an earth data science course developer and instructor with the Earth Analytics Education Initiative at Earth Lab.
Course lessons are developed as a part of a course curriculum. They teach specific learning objectives associated with data and scientific programming. Jenny Palomino has contributed to the following lessons:
This lesson teaches you how to wrangle data (e.g. run multi-task functions, combine) with numpy arrays.
This lesson teaches you how to wrangle data (e.g. subselect, update, and combine) with pandas dataframes.
This lesson teaches you how to apply functions to numpy arrays in Python.
This lesson teaches you how to write custom functions in Python.
This lesson describes how functions are used in Python to write DRY and modular code.
This lesson teaches you how to control the flow of your code using conditional statements.
This lesson describes the structure of conditional statements in Python and demonstrates how they are used for writing DRY code.
This lesson describes how to automate tasks with loops in Python.
This lesson describes the structure of loops in Python and how they are used to iteratively execute code.
This lesson describes the DRY (i.e. Do Not Repeat Yourself) principle and lists key strategies for writing DRY code in Python.
This lesson walks you through using indexing to select data from pandas dataframes.
This lesson walks you through describing, manipulating, and plotting pandas dataframes.
This lesson walks you through importing tabular data from .csv files to pandas dataframes.
This lesson describes key characteristics of pandas dataframes, a data structure commonly used for scientific data.
When automating workflows, it is helpful to be able to programmatically check for and create directories and to parse directory and file names to extract information. Learn how to manipulate directories and strings using Python.
Identifying aspects of a workflow that can be modularized can help you design data workflows. Learn best practices for designing efficient data workflows.
Designing and developing data workflows can complete your work more efficiently by allowing you to repeat and automate data tasks. Learn how to design and develop efficient workflows to automate data analyses in Python.
Open source means that you can view and contribute to software code like packages you use in Python. Learn about the ways that you can contribute without being an expert progammer.
This activity provides an opportunity to practice writing DRY code using loops, conditional statements, and functions.
This lesson teaches you how to collaborate with others in a project, including tasks such as notifying others that an assigned task has been completed.
This lesson teaches you how to undo changes in Git after they have been added or committed.
This activity provides an opportunity to practice working with commonly used Python data structures for scientific data: lists, numpy arrays, and pandas dataframes.
This lesson walks you through manipulating, summarizing and plotting numpy arrays.
This lesson walks you through importing text data from .txt and .csv files into numpy arrays.
This lesson describes the key characteristics of a commonly used data structure in Python for scientific data: numpy arrays.
This lesson teaches you how to submit pull requests on Github.com to suggest changes to another repository.
This lesson teaches you how to implement version control using Git and GitHub.
This lesson teaches you how to add Markdown to Jupyter Notebook files.
This lesson teaches you how to manage your Jupyter Notebook files and directories.
This lesson reviews the process and benefits of version control and how Git and GitHub support version control.
Matplotlib is one of the most commonly used packages for plotting in Python. This lesson covers how to create a plot and customize plot colors and label axes using matplotlib.
Python packages are organized directories of code that provide functionality such as plotting data. Learn how to write Python Code to import packages.
This lesson walks you through creating and editing Python lists.
Variables store data (i.e. information) that you want to re-use in your code (e.g. a single value, list of values, path to a directory, filename). Learn how to write Python code to work with variables.
This lesson covers how to crop a raster dataset and export it as a new raster in Python
In this lesson you review how to dissolve polygons in python. A spatial join is when you assign attributes from one shapefile to another based upon its spatial location.
Sometimes two shapefiles do not line up properly even if they cover the same area because they are in different coordinate reference systems. Learn how to reproject vector data in Python using geopandas to ensure your data line up.
This tutorial walks you through installing a conda environment designed for this class.
Jupyter Notebooks is an interactive environment where you can write and run code and also add text that describes your workflow using Markdown. Learn how to use Jupyter Notebook to run Python Code and Markdown Text.
GitHub can be used to store and access files. Learn how to create a copy of files on Github (forking) and to use the Terminal to download the copy to your computer (cloning). You will also learn how to to update your forked repository with changes made in the original Github repository.
This lesson walks you through using Bash/Shell to navigate and manage files and directories on your computer.
This lesson reviews the importance and benefits of open reproducible science.
One common way to analyze Twitter data is to analyze attitudes (i.e. sentiment) in the tweet text. Learn how to analyze sentiments in Twitter data using Python.
One common way to analyze Twitter data is to identify the co-occurrence and networks of words in Tweets. Learn how to analyze word co-occurrence (i.e. bigrams) and networks of words using Python.
One common way to analyze Twitter data is to calculate word frequencies to understand how often words are used in tweets on a particular topic. To complete any analysis, you need to first prepare the data. Learn how to clean Twitter data and calculate word frequencies using Python.
This lesson walks through the process of retrieving and manipulating surface water data housed in the Colorado Information Warehouse. These data are stored in JSON format with spatial x, y information that support mapping.
This lesson introduces how to work with the JSON data structure using Python using the JSON and Pandas libraries to create and convert JSON objects.
How to Join Attributes From One Shapefile to Another in Open Source Python Using Geopandas: GIS in Python
In this lesson you review how to perform spatial joins in python. A spatial join is when you assign attributes from one shapefile to another based upon it's spatial location.
This lesson covers how to deal with dates in Python. It reviews how to convert a field containing dates as strings to a datetime object that Python can understand and plot efficiently. This tutorial also covers how to handle missing data values in Python.
This lesson covers creating interactive maps with Python in Jupyter Notebook.
MODIS is a satellite remote sensing instrument that collects data daily across the globe at 250-500 m resolution. Learn how to import, clean up and plot MODIS data in Python.