What is the primary function of the Pandas library?

The Pandas library is primarily designed to perform data cleaning and analysis using dataframes, which are two-dimensional labeled data structures similar to tables in a database or spreadsheets. This library provides a plethora of functions that facilitate the manipulation of numerical tables and time series data, making it an essential tool for data scientists and analysts.

With Pandas, users can easily perform operations such as filtering, grouping, and aggregating data, as well as handling missing values. The dataframe structure allows for intuitive data manipulation and analysis, enabling users to effectively handle large datasets in a more manageable and readable format. This makes it particularly powerful for exploratory data analysis, ensuring data is in the right shape and format for further processing or visualization.

In contrast, creating complex algorithms typically involves libraries like TensorFlow or scikit-learn, which focus on machine learning and predictive modeling. Visualizing data in 3D is generally addressed through libraries such as Matplotlib or Plotly, which specialize in graphical representation of data. Developing mobile applications is outside the scope of Pandas and usually relates to frameworks specific to app development. Thus, the primary function of the Pandas library distinctly stands out as focused on data cleaning and analysis.