Chris Holdgraf

Chris Holdgraf has contributed to the materials listed below. Chris is a core member of the Jupyter team at University of California

Course Lessons

Course lessons are developed as a part of a course curriculum. They teach specific learning objectives associated with data and scientific programming. Chris Holdgraf has contributed to the following lessons:

Export Numpy Arrays to Geotiff Format Using Rasterio and Python

You often create outputs in Python that you want to use in another tool like QGIS or ArcGIS. Learn how to export a numpy array created through a rasterio workflow in Python to spatial geotiff.

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.

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 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.

GIS in Python: Introduction to Vector Format Spatial Data - Points, Lines and Polygons

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

Subtract Raster Data in Python Using Numpy and Rasterio

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.

Open, Plot and Explore Lidar Data in Raster Format with 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.

Calculate NDVI Using NAIP Remote Sensing Data in the Python Programming Language

A vegetation index is a single value that quantifies vegetation health or structure. Learn how to calculate the NDVI vegetation index using NAIP data in Python.

Handle missing spatial attribute data Python: GIS in Python

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

GIS in Python: Reproject Vector Data.

In this lesson we cover how to reproject a vector dataset in `Python` using the `to_crs()` `Geopandas` function.

Geographic vs projected coordinate reference systems - GIS in Python

GIS in Python: Intro to Coordinate Reference Systems in Python

This lesson introduces what a coordinate reference system is. You will use the `Python` programming language to explore and reproject data into geographic and projected CRSs.

GIS in Python: Introduction to Vector Format Spatial Data - Points, Lines and Polygons

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

Customize your Maps in Python: GIS in Python

In this lesson you will learn how to adjust the x and y limits of your matplotlib and geopandas map to change the spatial extent..

Customize your Maps in Python using Matplotlib: GIS in Python

In this lesson you will review how to customize matplotlib maps created using vector data in Python. You will review how to add legends, titles and how to customize map colors.

Get Help with Python

This tutorial covers ways to get help when you are stuck in Python.

Customize matplotlib plots in Python - earth analytics - data science for scientists

Matplotlib is one of the most commonly used plotting library in Python. This lesson covers how to create a plot using matplotlib and how to customize matplotlib plot colors and label axes in Python.

About data types in Python - Data Science for scientists 101

This tutorial introduces numpy arrays in Python. It also introduces the differences between strings, numbers and boolean values (True / False) in Python.

Objects and variables in Python

This tutorial introduces the Python programming language. It is designed for someone who has not used Python before. You will work with precipitation and stream discharge data for Boulder County.

Get to Know Python & Jupyter Notebooks

This tutorial introduces the Python scientific programming language. It is designed for someone who has not used Python before. You will work with precipitation and stream discharge data for Boulder County in Python but also learn the basics of working with python.

Customize Matplotlibe Dates Ticks on the x-axis in Python

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.

Resample or Summarize Time Series Data in Python With Pandas - Hourly to Daily Summary

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.

Subset Time Series By Dates Python Using Pandas

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

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.

Classify and Plot Raster Data in Python

This lesson presents how to classify a raster dataset and export it as a new raster in Python.

Subtract One Raster from Another and Export a New Geotiff in Python

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

About the Geotiff (.tif) Raster File Format: Raster Data in Python

This lesson introduces the geotiff file format. Further it introduces the concept of metadata - or data about the data. Metadata describe key characteristics of a data set. For spatial data these characteristics including CRS, resolution and spatial extent. Here you learn about the use of tif tags or metadata embedded within a geotiff file as they can be used to explore data programatically.

Plot Histograms of Raster Values in Python

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

Spatial Raster Metadata: CRS, Resolution, and Extent in Python

This lesson introduces the raster meta data. You will learn about CRS, resolution, and spatial extent.

Open, Plot and Explore Lidar Data in Raster Format with 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.

Extract raster values using vector boundaries in Python

This lesson reviews how to extract data from a raster dataset using a vector dataset.

Extract Raster Values At Point Locations in Python

This lesson reviews how to extract data from a raster dataset using a vector dataset.

Compare Lidar With Human Measured Tree Heights - Remote Sensing Uncertainty

In this lesson, we cover the topic of uncertainty. We focus on the types of uncertainty that you can expect when working with tree height data both derived from lidar remote sensing and human measurements. Further we cover sources of error including systematic vs. random error.

Data tutorials

Nothing to list here yet!