Martha Morrissey

When you plot time series data using the matplotlib package in Python, you often want to customize the date format that is presented on the plot. Learn how to customize the date format on time series plots created using matplotlib.

Sometimes you need to take time series data collected at a higher resolution (for instance many times a day) and summarize it to a daily, weekly or even monthly value. This process is called resampling in Python and can be done using pandas dataframes. Learn how to resample time series data in Python with Pandas.

Sometimes you have data over a longer time span than you need for your analysis or plot. Learn how to subset your data using a begin and end date in Python.

Python provides a datetime object for storing and working with dates. Learn how you can convert columns in a pandas dataframe containing dates and times as strings into datetime objects for more efficient analysis and plotting.

Python provides a datetime object for storing and working with dates. Learn how you can convert columns in a pandas dataframe containing dates and times as strings into datetime objects for more efficient analysis and plotting.

When you plot time series data in matplotlib, you often want to customize the date format that is presented on the plot. Learn how to customize the date format in a Python matplotlib plot.

Sometimes vector data are missing attribute data, and it can be helpful to clean up your data. Learn how to handle missing attribute data in Python using GeoPandas.

Sometimes you may want to spatially clip a vector data layer to a specified boundary for easier plotting and analysis of smaller spatial areas. Learn how to clip a vector data layer in Python using GeoPandas and Shapely.

Often when spatial data do not line up properly on a plot, it is because they are in different coordinate reference systems (CRS). Learn how to reproject a vector dataset to a different CRS in Python using the to_crs() function from GeoPandas.

Often when spatial data do not line up properly on a plot, it is because they are in different coordinate reference systems (CRS). Learn how to reproject a vector dataset to a different CRS in Python using the to_crs() function from GeoPandas.

GitHub.com can be used to store and access files in the cloud to share with others or simply as a backup of your local files. Learn how to create a copy of files on GitHub (fork) and to download files from GitHub to your computer (clone).

A version control system allows you to track and manage changes to your files. Learn benefits of version control for scientific workflows and how git and GitHub.com support version control.

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

This lesson introduces what vector data are and how to open vector data stored in shapefile format in Python.

Sometimes you need to manipulate multiple rasters to create a new raster output data set in Python. Learn how to create a CHM by subtracting an elevation raster dataset from a surface model dataset in Python.

This lesson introduces the raster geotiff file format - which is often used to store lidar raster data. You will learn the 3 key spatial attributes of a raster dataset including Coordinate reference system, spatial extent and resolution.

There are a suite of powerful open source python libraries that can be used to work with spatial data. Learn how to use geopandas, rasterio and matplotlib to plot and manipulate spatial data in Python.

Text editors can be used to edit code and for commit messages in git. Learn about features to look for in a text editor and how to change your default text editor at the command line.

Conda environments allow you to easily manage the Python package installations on your computer. Learn how to install a conda environment using a yml file.

This tutorial walks you through how access the shell through terminal, use basic commands in the terminal for file organization, and set up a working directory for the course.

Learn how to install Git, GitBash (a version of command line Bash) and the Miniconda Python distribution on your computer.

There are several core tools that are required to work with data. These include Shell/Bash, Git/Github and Python. Learn how to set all of these tools up on your computer so you can work with different types of data using open science workflows.

Folium is a Python package that can be used to create interactive maps in Jupyter Notebook. Learn how to create interactive maps with raster overlays in Python using Folium.

One way to analyze Twitter data is to analyze attitudes (or sentiment) in the tweet text. Learn how to analyze sentiments in Twitter data using open source 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.

You can use the Twitter RESTful API to access tweet data from Twitter. Learn how to use tweepy to download and work with twitter social media data in Python.

You can access twitter social media data using the twitter API automatically in Python. Learn about the basics of downloading twitter data using open source 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.

JSON is a powerful text based data format that contains hierarchical data. JSON and GeoJSON are common data formats that are returned when accessing automatically data using an API. Learn more about JSON and GeoJSON data.

API's allow you to automate access and downloading data in your code to support open reproducible science. Learn how how to use API's to download data from the internet using open source python.

Sometimes you will work with multiple rasters that are not in the same projections, and thus, need to reproject the rasters, so they are in the same coordinate reference system. Learn how to reproject raster data in Python using Rasterio.

Reclassifying raster data allows you to use a set of defined values to organize pixel values into new bins or categories. Learn how to classify a raster dataset and export it as a new raster in Python.

Often you need to process two raster datasets together to create a new raster output and then save that output as a new file. Learn how to subtract rasters and create a new GeoTIFF file using open source Python.

You can perform the same raster processing steps in Python that you would in a tool like ArcGIS. Learn how to process spatial raster data using Open Source Python.

Raster data are gridded data composed of pixels that store values, such as an image or elevation data file. Learn how to open, plot, and explore raster files in Python using Rasterio.

Metadata describe the key characteristics of a dataset such as a raster. For spatial data, these characteristics including the coordinate reference system (CRS), resolution and spatial extent. Learn about the use of TIF tags or metadata embedded within a GeoTIFF file to explore the metadata programatically.

Raster metadata includes the coordinate reference system (CRS), resolution, and spatial extent. Learn about these metadata and how to access them in Python

Histograms of raster data provide the distribution of pixel values in the dataset. Learn how to explore and plot the distribution of values within a raster using histograms.

Rasters are gridded data composed of pixels that store values. Learn more about the structure of raster data and how to use them to store data, such as imagery or elevation values.

Practice interpreting data on plots that show rainfall (precipitation) and stream flow (discharge) as it changes over time.

Connecting data to analysis and outputs is an important part of open reproducible science. In this lesson you will explore that value of a well documented workflow.

Learn how to use the time series feature in Google Earth to view before and after images of a location.