Pandas in Cheat sheet
Pandas is an open-source data manipulation and data analysis library in Python. It provides data structures for efficiently storing large datasets and tools for working with them. The two main classes in Pandas are Series and DataFrame.
A Series is a one-dimensional labeled array that can hold any data type. A DataFrame is a two-dimensional labeled table, where each column can have a different data type. With Pandas, you can perform operations such as filtering, aggregating, transforming, and visualizing data with ease. It also supports reading and writing data from various sources, such as CSV, Excel, SQL databases, and more.
Pandas is widely used in data science and is known for its ability to handle missing data, deal with time series data, and provide efficient and expressive data processing tools.
SQL and Pandas are two powerful tools for managing and analyzing data. SQL is the standard language for interacting with relational databases and is used for tasks such as retrieving, modifying, and deleting data. Pandas, on the other hand, is a library in Python that provides data structures and tools for working with large datasets. It is widely used in data science and provides features such as data cleaning, aggregation, transformation, and visualization. Both SQL and Pandas have their strengths and weaknesses and choosing the right tool for a specific task depends on the nature of the data and the requirements of the task at hand.