Python Tutorial: Visualizing Time Series Data in Python | Intro

Key Takeaways

welcome to the course my name is tamas Vincent and I am currently the head of data science and Getty Images in this course you will learn how to become an advanced user of time-series visualization in the Python programming language we expect you are comfortable with the basics of Python as covered an intro to Python an intermediate python for data science courses on data camp several data sets can be analyzed using the concept of time series analysis financial and weather data are best handled as time series and the current explosion of Internet of Things data collected by sensors and other sources can also be analyzed as time series therefore it is frequent to counter time series in the field of data science I personally have had the opportunity to work with time series data very often and I hope that through this course I will be able to show you the power of time series visualization this course will provide practical knowledge on how to diagnose and visualize time series data using Python in the first chapter we will show how to manipulate and clean time series data and produce time series graph in which personalized aesthetics and information is displayed in the second chapter we will take things further by describing how to extract and display summarized views of time series data while the third chapter will introduce sophisticated methods to analyze time series the fourth chapter will take a different turn and describe in detail how to handle data sets containing multiple time series finally the course will end with a case study that will review the content of the first four chapters this course will heavily leverage the pandas library to process and clean time series data so before we kick things off let's do a quick recap of the pandas library as shown in line one it is common practice to load the pandas library using the PD areas we can then leverage the dot read CSV function to import contents of the CSV file into a data frame now that your file has been loaded into the data frame named DF you can leverage additional pandas methods to display information about TF the dot head method allows to display the first and Rose of your data frame similarly the dot tail method returns the last and rows of your data frame when analyzing data it is also recommended to check the type of each column in your data frame which will help you understand the type of data you are working with for that you can use a dot d theis method to print out the data type of each column this will inform you whether the columns contain integers floats strings etc in this case you can see that a DF data frame contains a date stand column of the object type and a co2 column of the flow type when working with time sweetie data and pandas it is recommended that dates are formatted as a date time 64 type fortunately even if your data comes in the form of a string you can use the dot - date/time function to convert those to the appropriate date time 64 type by default if the dot - day time function cannot pass the day like object then it will raise an error however you can override this behavior by adding the argument errors equals coerce which will return an n/a T timestamp when the object cannot be passed now it's your turn