Python: pandas

pandas is a popular open-source library for data analytics in Python. It provides powerful tools for working with tabular data, such as data frames and series. With pandas, you can easily read, manipulate, and analyze data in a variety of formats, including CSV, Excel, SQL databases, and more.

One of the key features of pandas is its ability to handle missing data. pandas provides a number of methods for filling in missing data, interpolating values, and dropping missing data altogether. This is a critical feature for data analytics, as real-world data is often incomplete or inconsistent.

pandas performs complex data transformations and aggregations. With pandas, you can group data by one or more columns, apply functions to subsets of data, and pivot data to reshape it in different ways.

pandas provides tools for merging and joining data from multiple sources, making it easy to combine data from different sources into a single data set.

Being good with pandas is a valuable skill.

Faster Options

pandas can be a bit slow. Options include:

• Moving to the faster pandas 2.0
• Trying Polars

New! Read More about this important 2.0 update

• ⭐ Pandas 2.0 and its Ecosystem (Arrow, Polars, DuckDB) ⭐

pandas 2.0

pandas 2.0 is a significant update the to the beloved pandas.

Learn more at:

• pandas 2.0 (official)
• pandas 2.0 and its Ecosystem (Arrow, Polars, DuckDB)

Polars

Polars is a data manipulation library written with Rust that aims to provide a fast, memory-efficient alternative to pandas for large-scale data processing. It’s still a relatively new library, having been first released in 2019, a nd its user base and ecosystem are still growing.

Polars has a lot of potential as a fast and memory-efficient data manipulation library for large datasets, but it’s still a relatively new library and may not have the same level of maturity and ecosystem as pandas.