Jenny Palomino

Jenny 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

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:

Data Wrangling With Numpy Arrays

This lesson teaches you how to wrangle data (e.g. run multi-task functions, combine) with numpy arrays.

Data Wrangling With Pandas

This lesson teaches you how to wrangle data (e.g. subselect, update, and combine) with pandas dataframes.

Apply Functions to Numpy Arrays

This lesson teaches you how to apply functions to numpy arrays in Python.

Write Custom Functions

This lesson teaches you how to write custom functions in Python.

Intro to Functions

This lesson describes how functions are used in Python to write DRY and modular code.

Control Flow Using Conditional Statements

This lesson teaches you how to control the flow of your code using conditional statements.

Intro to Conditional Statements

This lesson describes the structure of conditional statements in Python and demonstrates how they are used for writing DRY code.

Automate Tasks With Loops

This lesson describes how to automate tasks with loops in Python.

Intro to Loops

This lesson describes the structure of loops in Python and how they are used to iteratively execute code.

Intro to DRY code

This lesson describes the DRY (i.e. Do Not Repeat Yourself) principle and lists key strategies for writing DRY code in Python.

Selections From Pandas Dataframes

This lesson walks you through using indexing to select data from pandas dataframes.

Manipulate and Plot Pandas Dataframes

This lesson walks you through describing, manipulating, and plotting pandas dataframes.

Import CSV Files Into Pandas Dataframes

This lesson walks you through importing tabular data from .csv files to pandas dataframes.

Intro to Pandas Dataframes

This lesson describes key characteristics of pandas dataframes, a data structure commonly used for scientific data.

Activity on Dry Code

This activity provides an opportunity to practice writing DRY code using loops, conditional statements, and functions.

Guided Activity on Git/Github.com For Collaboration

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.

Guided Activity on Undo Changes in Git

This lesson teaches you how to undo changes in Git after they have been added or committed.

Activity Data Structures

This activity provides an opportunity to practice working with commonly used Python data structures for scientific data: lists, numpy arrays, and pandas dataframes.

Manipulate, Summarize and Plot Numpy Arrays

This lesson walks you through manipulating, summarizing and plotting numpy arrays.

Import Text Data Into Numpy Arrays

This lesson walks you through importing text data from .txt and .csv files into numpy arrays.

Intro to Numpy Arrays

This lesson describes the key characteristics of a commonly used data structure in Python for scientific data: numpy arrays.

Guided Activity to Submit Pull Requests

This lesson teaches you how to submit pull requests on Github.com to suggest changes to another repository.

Guided Activity on Version Control with Git/GitHub

This lesson teaches you how to implement version control using Git and GitHub.

Markdown in Jupyter Notebook

This lesson teaches you how to add Markdown to Jupyter Notebook files.

Manage Jupyter Notebook Files

This lesson teaches you how to manage your Jupyter Notebook files and directories.

What Is Version Control

This lesson reviews the process and benefits of version control and how Git and GitHub support version control.

Plot Data in Python with Matplotlib

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.

Import Python Packages

Python packages are organized directories of code that provide functionality such as plotting data. Learn how to write Python Code to import packages.

Python Lists

This lesson walks you through creating and editing Python lists.

Variables in Python

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.

Crop a Spatial Raster Dataset Using a Shapefile in Python

This lesson covers how to crop a raster dataset and export it as a new raster in Python

How to Dissolve Polygons Using Geopandas: GIS 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.

How to Reproject Vector Data in Python Using Geopandas - GIS in Python

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.

Set Up Your Anaconda Python Environment

This tutorial walks you through installing a conda environment designed for this class.

The Jupyter Notebook Interface

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.

Get Files From GitHub

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.

Intro to Shell

This lesson walks you through using Bash/Shell to navigate and manage files and directories on your computer.

What Is Open Reproducible Science

This lesson reviews the importance and benefits of open reproducible science.

GEOG 4463 & 5463 - Earth Analytics Bootcamp: Final Project

GEOG 4463 & 5463 - Earth Analytics Bootcamp: Homework 4

GEOG 4463 & 5463 - Earth Analytics Bootcamp: Homework 3

GEOG 4463 & 5463 - Earth Analytics Bootcamp: Reference on PEP 8 Style Guide

GEOG 4463 & 5463 - Earth Analytics Bootcamp: Homework 2

GEOG 4463 & 5463 - Earth Analytics Bootcamp: Homework 1

GEOG 4463 & 5463 - Earth Analytics Bootcamp: Pre-Course Checklist

GEOG 4463 & 5463 - Earth Analytics Bootcamp: Pre-Course Learning Resources

GEOG 4463 & 5463 - Earth Analytics Bootcamp: August 2018 Syllabus

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.

Work With Datetime Format in Python - Time Series Data

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.

Interactive Maps in Python

This lesson covers creating interactive maps with Python in Jupyter Notebook.

Data tutorials

Nothing to list here yet!