¿Cómo analizar datos con ChatGPT?

How does ChatGPT use its linguistic capabilities to perform data analysis?

As many users already know, ChatGPT is not designed to do mathematical calculations. Instead, it focuses on language manipulation. This is because it is a linguistic model, also known as a Large Language Model (LLM), trained primarily on texts. Although it has been exposed to mathematical texts, its specialty is not calculating, but predicting and generating the next word with the highest probability.

OpenAI, the company behind ChatGPT, has addressed this limitation by using its ability to generate text and trained it programmatically. This allows ChatGPT to run calculations using an ephemeral programming environment, similar to a calculator. Thus, you can perform data analysis by creating mini-programs that generate results based on the input provided.

How to submit data to ChatGPT?

In order for ChatGPT to analyze data, you must first provide it with the information. This process is straightforward:

1. Attach file: Use the option to attach the file from the computer.
2. Select and upload: Make sure the file is in .xlsx format, compatible with ChatGPT and uploaded correctly.
3. Send a prompt: Once ChatGPT recognizes the file, you can prompt it for specific descriptions or analysis about the data.

For example, when loading a payment file, ChatGPT can identify the number of records and columns, and provide detailed descriptions.

How can ChatGPT analyze and describe data?

1. Preliminary Analysis: ChatGPT can scan the contents of the files and provide a synopsis of the data. In doing so, it delivers a preview of the file and even highlights important data before processing everything.

2. Use of Python: Although users do not need to know Python, it is useful to understand that ChatGPT generates Python code to parse data. This code allows opening and reading the first few rows of the file, accurately describing its structure.

# Example of Python code generated by ChatGPT to inspect dataimport pandas as pd
 # Load Excel filefile = pd.ExcelFile('file_path.xlsx')
 # Read the first rows and display themdata = file.parse(sheet_name=file.sheet_names[0])print(data.head())

3. Verification and Validation: It is the user's responsibility to confirm the accuracy of the data parsed by ChatGPT. Although the model is sophisticated, it can make errors due to its linguistic nature.

How does ChatGPT help to detect anomalies in the data?

When ChatGPT is asked to analyze a specific column, such as the amount of payments, it can calculate relevant statistics:

• Averages
• Minimums and maximums
• Standard deviation

These indicators provide insight into possible anomalies within the data set. For example, a high average combined with anomalies in small amounts points to inconsistencies that may require attention.

Thus, ChatGPT not only identifies, but also indicates how these irregularities could be corrected. This makes ChatGPT a powerful tool not only for data analysis, but also for cleaning and improving data sets in practical applications.

With this knowledge, users have the power to use ChatGPT to interpret and deduce valuable information from their data. Keep exploring and maximizing the potential of this tool as you continue your learning!