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Pandas pennsauken menu3/9/2024 Hierarchical axes to add additional depth to your data.Powerful time series functionality, such as frequency conversion, moving windows, and lagging.Simple and easy to understand merging and joining of datasets.Simple plotting interface for quick data visualization.Versatile reshaping of datasets, such as moving from wide to long or long to wide.Familiar ways of aggregating data using Pandas pivot_table and grouping data using the group_by method.Simple ways of working with missing data.Manipulating DataFrames to add, delete, and insert data.Reading, accessing, and viewing data in familiar tabular formats.Let’s take a look at some of the things the library does very well: It’s flexible, easy to understand, and incredibly powerful. Pandas is the quintessential data analysis library in Python (and arguable, in other languages, too). We’ll dive more into these in a second, but let’s take a little moment to dive into some of the many benefits the pandas library provides. Pandas provides two main data structures to work with: a one-dimension pandas Series and a two-dimensional pandas DataFrame. Other structured datasets, such as those coming from web data, like JSON files.Time series data, either at fixed-frequency or not.Tabular data with columns of different data types, such as that from Excel spreadsheets, CSV files from the internet, and SQL database tables.The pandas library allows you to work with the following types of data: It’s definitely well on its way to achieving this! The library itself has the goal of becoming the most powerful and flexible open-source data analysis tool in any language. pandas is intended to work with any industry, including with finance, statistics, social sciences, and engineering. The library provides a high-level syntax that allows you to work with familiar functions and methods. ![]() ![]() Pandas is a Python library that allows you to work with fast and flexible data structures: the pandas Series and the pandas DataFrame.
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