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.
last updated: 10 Jan 2020
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.
last updated: 10 Jan 2020
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.
last updated: 10 Jan 2020
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.
last updated: 10 Jan 2020
Matplotlib is the most commonly used plotting library in Python. Learn how to customize the colors, symbols, and labels on your plots using matplotlib.
last updated: 13 Jan 2020
Matplotlib is the most commonly used plotting library in Python. Learn how to create plots using the matplotlib object oriented approach.
last updated: 21 Oct 2019
Sometimes you want to customize the colorbar and range of values plotted in a raster map. Learn how to create breaks to plot rasters in Python.
last updated: 21 Jan 2020
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.
last updated: 21 Jan 2020
A hillshade is a representation of the earth's surface as it would look with shade and shadows from the sun. Learn how to overlay raster data on top of a hillshade in Python.
last updated: 21 Jan 2020
Learn how to create a cumulative sum plot in Pandas to better understand stream discharge in a watershed.
last updated: 13 Jan 2020
Learn how to calculate exceedance probability and return periods associated with a flood in Python.
last updated: 13 Jan 2020
This lesson will discuss some of the challenges associated with working with social media data in science. These challenges include working with non standard text, large volumes of data, API limitations, and geolocation issues.
last updated: 26 Nov 2019
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.
last updated: 04 Sep 2019
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.
last updated: 04 Sep 2019
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.
last updated: 04 Sep 2019
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.
last updated: 04 Sep 2019
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.
last updated: 04 Sep 2019
Sometimes you want to customize the colorbar and range of values plotted in a raster map. Learn how to create breaks to plot rasters in Python.
last updated: 03 Sep 2019
This lesson covers creating interactive maps with Python in Jupyter Notebook.
last updated: 04 Sep 2019
This lesson covers how to overlay raster data on top of a hillshade in Python and layer opacity arguments.
last updated: 04 Sep 2019
This lesson will discuss some of the challenges associated with working with social media data in science. These challenges include working with non standard text, large volumes of data, API limitations, and geolocation issues.
last updated: 15 Nov 2019
In this lesson you will review how to adjust the extent of a spatial plot in R using the ext() or extent argument and the extent of another layer.
last updated: 03 Sep 2019
In this lesson you learn to use the par() or parameter settings in R to plot several raster RGB plots in R in a grid.
last updated: 03 Sep 2019
In this lesson you review how to remove those pesky borders from a raster plot using base plot in R. We also cover adding legends to your plot outside of the plot extent.
last updated: 03 Sep 2019
This lesson covers how to overlay raster data on a hillshade in R using baseplot and layer opacity arguments.
last updated: 03 Sep 2019
This lesson covers creating a basemap with the ggmap package in R. Given some ongoing bugs with ggmap it also covers the map package as a backup!
last updated: 03 Sep 2019
This lesson introduces the mutate() and group_by() dplyr functions - which allow you to aggregate or summarize time series data by a particular field - in this case you will aggregate data by day to get daily precipitation totals for Boulder during the 2013 floods.
last updated: 03 Sep 2019
This lesson illustrated what your final stream discharge homework plots should look like for the week. Use all of the skills that you've learned in the previous lessons to complete it.
last updated: 03 Sep 2019
Learn how to summarize time series data by day, month or year with Tidyverse pipes in R.
last updated: 03 Sep 2019
Learn how to extract and plot data by a range of dates using pipes in R.
last updated: 03 Sep 2019
Times series data can be manipulated efficiently in R. Learn how to work with, plot and subset data with dates in R.
last updated: 03 Sep 2019
Learn how to plot data and customize your plots using ggplot in R.
last updated: 03 Sep 2019