This course covers the use of pandas arrays for data analysis and manipulation, including handling time data, categorical data, and sparse data. Hands-on exercises provide practical experience using pandas arrays to solve real-world problems.
Pandas is one of the most powerful libraries for data science and it provides powerful and easy to use data structures for handling different kinds of data. In this course, Pandas Arrays and Data Structures, you’ll learn a range of topics, including handling time delta and time interval data, working with categorical and sparse data, and using pandas arrays to manipulate and analyze string data. First, you'll explore through hands-on exercises and case studies that are practical experiences using pandas arrays to solve real-world problems in data analysis and manipulation. Next, you'll discover pandas Aries, handling date and time operations with timestamp time delta and in turbo handling messy data using concepts like sparse and categorical data. Finally, you'll learn and become familiar with pandas function to analyze large datasets efficiently. By the end of this course, you'll have a strong understanding of the pandas library, including its data structures and tools for data manipulation and will be able to apply this knowledge to real world datasets to gain insights and make data driving decisions.
Pandas is one of the most powerful libraries for data science and it provides powerful and easy to use data structures for handling different kinds of data. In this course, Pandas Arrays and Data Structures, you’ll learn a range of topics, including handling time delta and time interval data, working with categorical and sparse data, and using pandas arrays to manipulate and analyze string data. First, you'll explore through hands-on exercises and case studies that are practical experiences using pandas arrays to solve real-world problems in data analysis and manipulation. Next, you'll discover pandas Aries, handling date and time operations with timestamp time delta and in turbo handling messy data using concepts like sparse and categorical data. Finally, you'll learn and become familiar with pandas function to analyze large datasets efficiently. By the end of this course, you'll have a strong understanding of the pandas library, including its data structures and tools for data manipulation and will be able to apply this knowledge to real world datasets to gain insights and make data driving decisions.
