Guides
Pandas
Before starting the guide, finish the First Steps to Use AG guide.
AG provides a comprehensive guide to present all the main functionalities of op_pandas. The guide is divided in four parts, which you can access using the links below. To have a better experience, follow the proposed order:
4. Functions, Joins and Statistical Methods
In addition to the comprehensive guide, you can also check reference pages for the API:
Pandas is a widespread open-source data manipulation and analysis library for Python. It provides data structures and functions to efficiently handle and manipulate structured data, such as tables or time series. Pandas offers powerful data cleaning, transformation, filtering, merging, and aggregation tools. It is widely used in data science, machine learning, and other domains for data preprocessing and analysis tasks, making it a valuable tool for working with structured data in Python.
AG provides a differentially private version of the Pandas Library (op_pandas), which lets users handle private data frames and series and perform various statistical analyses with differential privacy guarantees. Users familiar with Pandas will find minimal difficulty adjusting to the API methods.
To use AG’s op_pandas, you can import the library as presented in the following code block:
%%ag
from op_pandas import PrivateDataFrame, PrivateSeries
Private datasets can be loaded as PrivateDataFrames and PrivateSeries.
Resources
The following are additional resources that will be helpful when using the Pandas library:
- Official Pandas Documentation
- API Reference: Read more about the library's internal API.