Python Tutorial: Plot your first time series

Want to learn more? Take the full course at https://learn.datacamp.com/courses/visualizing-time-series-data-in-python at your own pace. More than a video, you'll learn hands-on coding & quickly apply skills to your daily work. --- We covered how to leverage pandas to read and process time series data, but there is so much more you can do! In this section of the course, you will get your first taste of time series visualization in Python. Let's get started! In Python, matplotlib is an extensive package used to plot data. The library is built in a hierarchy, and most functions that can be used to add elements to your plots can be accessed via the matplotlib dot pyplot module. As a result, it is common to see Python practitioners import matplotlib dot pyplot using the alias plt. matplotlib is the most widely used plotting library in Python and fortunately for us, the authors of the pandas library have implemented a dot plot() method on both Series and DataFrames objects that work as a simple wrapper around the plt dot plot() function in matplotlib, therefore allowing for fast and simple plotting. In case of time series data, if the index consists of dates, pandas will automatically call a separate function to format the x-axis nicely as shown in the figure here. Therefore, it is always recommended to set the dates of your time series as the index of your DataFrame using the dot set_index() method. Once you have finished defining the parameters of your figure, call plt dot show() to display the current figure that you are working on. The default style for matplotlib plot may not necessarily be your preferred style, but it is possible to change that. Because it would be time-consuming to customize each plot or to create your own template, several matplotlib style templates have been made available to use. These can be invoked by using the plt dot style dot use command, and will automatically add pre-specified defaults for fonts, lines and points, background colors