Welcome to Time Series Visualization with Python: A Comprehensive Course by Tamas Vincent

In this course, we will explore the world of time series visualization using Python programming language. As students, we expect you to be comfortable with the basics of Python as covered in an intro to Python and intermediate Python for data science courses on Data Camp. The course is designed to provide practical knowledge on how to diagnose and visualize time series data using Python.

Time Series Analysis: A Fundamental Concept

Time series analysis is a crucial concept in the field of data science, particularly when dealing with financial and weather data. These types of data are best handled as time series, as they exhibit patterns over time that can be analyzed and predicted. The current explosion of Internet of Things data collected by sensors and other sources has also led to an increase in time series analysis. Therefore, it is not uncommon to encounter time series data in the field of data science.

Personal Experience with Time Series Data

As someone who has had the opportunity to work with time series data frequently, I hope that through this course, I will be able to share my knowledge and show you the power of time series visualization. I am excited to help you learn how to manipulate and clean time series data, produce time series graphs, extract and display summarized views of time series data, and analyze time series using sophisticated methods.

Course Outline

The course is divided into four chapters. In the first chapter, we will delve into manipulating and cleaning time series data and producing time series graphs with personalized aesthetics and information displayed. The second chapter will take things further by describing how to extract and display summarized views of time series data. The third chapter will introduce sophisticated methods for analyzing time series. The fourth chapter will focus on handling datasets containing multiple time series. Finally, the course will end with a case study that will review the content of the first four chapters.

Recap: Introduction to Pandas Library

Before we begin, let's take a quick recap of the pandas library. It is common practice to load the pandas library using the `pd` alias. We can then leverage the dot (`.`) read CSV function to import contents of a CSV file into a DataFrame. Once our file has been loaded into the DataFrame named `DF`, we can leverage additional pandas methods to display information about the DataFrame.

Displaying Information About the DataFrame

The dot head method allows us to display the first and last rows of our data frame. Similarly, the dot tail method returns the last and rows of our data frame when analyzing data. It is also recommended to check the type of each column in your data frame to understand the type of data you are working with. For that, we can use the `dtypes` method to print out the data type of each column. This will inform us whether the columns contain integers, floats, strings, etc.

Working with Time Series Data and Pandas

When working with time series data and pandas, it is recommended that dates are formatted as a datetime64 type. Fortunately, even if your data comes in the form of a string, we can use the dot (`.`) `datetime` function to convert those to the appropriate datetime64 type by default. However, if the dot (`.`) `datetime` function cannot pass the day like object, then it will raise an error. We can override this behavior by adding the argument `errors='coerce'`, which will return NaN (Not a Number) timestamp when the object cannot be passed.

Let's move on to the next chapter, where we will dive into manipulating and cleaning time series data and producing time series graphs with personalized aesthetics and information displayed.

"WEBVTTKind: captionsLanguage: enwelcome 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 turnwelcome 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\n"