Analyse de données CSV avec LangChain et GPT4

Analyse de données CSV avec LangChain et GPT4

Table of Contents

1. Introduction to Agent Concept in Lang Chain
2. Understanding the Documentation for Agents
3. Tools and Large Language Model (LLM)
4. Creating a CSV Agent
5. Loading Agents and Dependencies
6. Analyzing a CSV File with Lang Chain Agent
7. Basic Data Analysis with Lang Chain Agent
8. Exploring the Data Frame Shape and Columns
9. Finding Correlation Metrics in the Data Frame
10. Handling Missing Values in the Data Frame
11. Finding Unique Regions in the Data Frame
12. Plotting the Top 10 Regions by Happiness Score
13. Horizontal Bar Plot of Factors Affecting Happiness Score

Introduction to Agent Concept in Lang Chain

The agent concept in Lang Chain is a powerful tool that allows applications to Create a chain of calls to large language models and other tools Based on user input. This allows for a dynamic and flexible approach to data analysis and problem-solving. In this article, we will explore the concept of agents in Lang Chain and how they can be used to perform basic data analysis on a standard CSV file.

Understanding the Documentation for Agents

Before diving into the details of using agents in Lang Chain, it is important to understand the official documentation. The documentation explains that agents use a large language model (LLM) to determine which actions to take and in what order. An action can involve using a specific tool or returning information to the user. When used correctly, agents can be extremely powerful and enable creative and innovative work.

Tools and Large Language Model (LLM)

In Lang Chain, agents have access to a suite of tools depending on the user input. These tools can include Google search, database Lookup, Python or EPL scripts, and more. The interface for tools is a function that takes a STRING as input and returns a string as output. The large language model powers the agent and allows it to perform complex tasks and access values and functions in the data frame.

Creating a CSV Agent

To perform data analysis on a CSV file, we will use the CSV agent provided by Lang Chain. The CSV agent is one of many available agents, such as JSON agents, OpenAI agents, natural language API agents, pandas data frame agents, Python agents, SQL database agents, and vector store agents. By using the create CSV agent method, we can configure the agent to Read a CSV file and perform various operations on the data.

Loading Agents and Dependencies

Before using a Lang Chain agent, we need to install Lang Chain and OpenAI. This can be done by running a simple command. Additionally, we need to set our OpenAI API token using OS.environment. Once the dependencies are set up, we can proceed with loading the Lang Chain agent and the necessary modules for data analysis.

Analyzing a CSV File with Lang Chain Agent

To perform data analysis on a CSV file, we first need to load the data into a data frame using a library like pandas. We can then explore the basic information about the data set, such as its shape and column names. This manual analysis gives us a general understanding of the data before using the Lang Chain agent for more complex tasks.

Basic Data Analysis with Lang Chain Agent

To leverage the power of the Lang Chain agent, we activate the agent and define its configuration. We set parameters such as temperature, which controls the creativity of the agent's answers. We also pass the input CSV file and set the verbose parameter to true, which allows us to see the agent's thinking process and the steps it takes to arrive at its final answer.

Exploring the Data Frame Shape and Columns

Using the Lang Chain agent, we can ask specific questions about the data frame. For example, we can Inquire about the number of rows and columns in the data frame. The agent's thoughts are displayed, showing its decision-making process. The agent uses the Python AST or APL tool to execute actions and provide the final answer.

Finding Correlation Metrics in the Data Frame

Another question we can ask the agent is to provide correlation metrics among specific columns in the data frame. The agent calculates the correlations and presents them in a tabular format. This information allows us to analyze the relationships between different variables in the data.

Handling Missing Values in the Data Frame

The Lang Chain agent is also capable of checking for missing values in the data frame. By executing the Relevant action, we can ensure that our data set is complete and does not contain any missing values. The agent's final answer confirms whether there are any missing values in the data set.

Finding Unique Regions in the Data Frame

If our data frame includes a column containing regions, we can use the Lang Chain agent to find the unique values in that column. This action helps us understand the diversity of regions represented in our data set. The agent provides the unique regions as its final answer.

Plotting the Top 10 Regions by Happiness Score

Data visualization is an essential part of data analysis. With Lang Chain, we can instruct the agent to plot a bar Chart showing the top 10 regions ranked by their happiness scores. By specifying the x-axis and y-axis values, we can generate an informative and visually appealing plot.

Horizontal Bar Plot of Factors Affecting Happiness Score

To gain further insights into the factors affecting the happiness score, we can ask the agent to create a horizontal bar plot. By instructing the agent to consider only the top 10 regions with the highest happiness scores, we can Visualize the ratios of different factors affecting happiness. This helps us understand the contribution of each factor to the overall happiness score.

Conclusion

The agent concept in Lang Chain is a powerful tool for performing data analysis and problem-solving. By leveraging the capabilities of large language models and a suite of tools, we can gain valuable insights and make informed decisions. The Lang Chain agent simplifies the process of analyzing a CSV file and provides Meaningful results. Experimenting with various questions and creative statements allows us to explore the full potential of Lang Chain agents and uncover Hidden Patterns in our data.