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leo 2.0,Installation and Setup

google 28 1 月, 2025

Are you ready to dive into the world of Leo 2.0? This revolutionary library is designed to simplify your data processing tasks, making your code more readable and efficient. Let’s explore the features and capabilities of Leo 2.0, and see how it can enhance your data handling experience.

Installation and Setup

To get started with Leo 2.0, you’ll need to install luarocks, a package manager for Lua. Once you have luarocks installed, you can proceed to install Leo 2.0 by running the following command in your terminal:

luarocks install leo

After the installation is complete, you’ll be ready to use Leo 2.0 in your Lua projects.

Functionality Overview

Leo 2.0 offers a wide range of functions to help you manage and process data efficiently. Here’s a brief overview of some of the key features:

Function Name	Functionality	Remark
Factor()	Create factor objects	Encode categorical data into integer values, generating factor objects that retain original category information and provide a numerical representation for statistical analysis and data processing.
Array()	Create multidimensional arrays	Supports 2D or 3D arrays, allowing for both cyclic and linear filling.
Matrix()	Create 2D matrices	Specifically designed for creating 2D matrices and initializing each element’s value, suitable for mathematical calculations and image processing scenarios.
DataFrame()	Create data frame objects	Store structured data and provide convenient data manipulation interfaces. Supports column-wise storage, allowing for independent operations on each column, such as filtering, sorting, and aggregation.
List()	Create list objects	Supports extracting elements from variable-length arguments or a single table. List objects support dynamic addition and deletion of elements, suitable for scenarios where data structures are frequently modified.
Pipe()	Create pipe objects	Allow for a series of operations on data through chained calls. Simplify complex transformation processes and reduce code complexity caused by nested calls, achieving efficient pipeline-style data processing.
Summary()	Calculate statistical summaries	Calculate the minimum, first quartile, median, mean, third quartile, and maximum of a numerical array. Provides functionality similar to the R language’s summary() function, allowing you to quickly understand the basic statistical information of data.
Which()	Find the index of elements that meet the condition	Find the index of elements that meet the given condition. The condition can be a function or a logical array, and it returns a list of indices that meet the condition, commonly used for data filtering and anomaly detection.
Isna()	Check nil elements	Check whether each element in a numerical array is nil, and return a logical array indicating the element’s status.
Naomit()	Remove nil elements	Remove all nil elements from a numerical array and return a new array.
Narm	Ignore nil when calculating the mean	Calculate the mean of a numerical array, ignoring nil elements. Effectively handle missing data and ensure the accuracy of subsequent analysis.
Format Output	Output information directly to the console	The Show() function is used to output information directly to the console. It can handle any type of value and format it appropriately based on the value’s type. For tables, it recursively prints the table’s content.