## Customize Your Plots Using Matplotlib

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: 05 Oct 2019*

In Earth data science, often the greatest amount of time is spent figuring out how to open, clean up and explore your data. Once the data are cleaned up, you can then begin to visualize and analyze them. In the lessons below, learn the basic skills needed to open, clean up, plot and analyze scientific data.

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: 05 Oct 2019*

Matplotlib is the most commonly used plotting library in Python. Learn how to create plots using the matplotlib object oriented approach.

*last updated: 05 Oct 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: 03 Sep 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*

Learn how to perform a basic sentiment analysis using the tidytext package in R.

*last updated: 03 Sep 2019*

This lesson provides an example of modularizing code in R.

*last updated: 03 Sep 2019*

This lesson provides an example of modularizing code in R.

*last updated: 03 Sep 2019*

Text mining is used to extract useful information from text - such as Tweets. Learn how to use the Tidytext package in R to analyze twitter data.

*last updated: 03 Sep 2019*

You can use the Twitter RESTful API to access data about Twitter users and tweets. Learn how to use rtweet to download and analyze twitter social media data in R.

*last updated: 03 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: 03 Sep 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*