ChatGPT, Data Scientist

ChatGPT (with a plus account) have launched an application in BETA to try - Code Interpreter that can be used to produce the start of of an analysis of data. Producing Python code for the analysis  - Pandas based.

Prompt 1

So load in a file of COVID data from 2021 and type in the prompt "Acts as a data scientist and analze the datashet amd make charts and graphs to show major trends" Even with the spelling mistakes it did the following: 

 

Acts as a data scientist and analze the datashet amd make charts and graphs to show major trends

covid_1.csv

ChatGPT

Great, let's start by loading the dataset and examining its structure.

Finished working

Show work

It seems like the dataset does not have any column names. The dataset consists of three columns. Could you please provide some context about what these columns represent? This will help me to analyze the data more accurately.

Prompt 2

Follow-up prompt "first column is days, the second is group under 35 years old and third colum is a group over 35 years old"

first column is days, the second is group under 35 years old and third colum is a group over 35 years old

ChatGPT

Thank you for the clarification. Let's assign appropriate column names and take a look at some basic statistics of the data.

Finished working

RESULT

              Day  Group_Under_35  Group_Over_35

count  158.000000      158.000000     158.000000

mean    80.500000        0.009439       0.012779

std     45.754781        0.005514       0.007603

min      2.000000        0.000696       0.000414

25%     41.250000        0.005688       0.007028

50%     80.500000        0.008524       0.012666

75%    119.750000        0.013676       0.018399

max    159.000000        0.019054       0.026756

The dataset contains 158 records, spanning from day 2 to day 159.

Here are some basic statistics for each column:

• Group_Under_35: The average is around 0.0094, with a minimum value of 0.0007 and a maximum value of 0.0190. The data varies around this average by about 0.0055 (standard deviation).
• Group_Over_35: The average is around 0.0128, with a minimum value of 0.0004 and a maximum value of 0.0268. The data varies around this average by about 0.0076 (standard deviation).

Now, let's create some plots to visualize these trends. We'll make line plots for each group over the given days. This can give us a sense of how the values change over time for each age group.

Finished working

The line graph provides a visual representation of the trend in the dataset for both groups. From the graph, we can observe that:

• For both groups, the trend is generally increasing over time, suggesting that the value for both groups under 35 and over 35 grows as days pass.
• The 'Group Over 35' consistently has higher values than the 'Group Under 35'. This could suggest that the factor being measured impacts the 'Group Over 35' more significantly, or it could mean that there are more people in the 'Group Over 35'.

Remember, the precise interpretation would depend on what these values represent. For a more in-depth analysis, one could also look into statistical tests, correlation analysis, and more.

Do you have any specific questions or areas you'd like to explore in this data?

 

Thoughts

I haven't included the code here but it produced the code you would expect using Pandas in Python to perform basic analysis - all with two prompts. Not bad, need to explore it further. A more detailed introduction can be found at “ChatGPT’s Code Interpreter Was Just Released. Here’s How It Will Change Data Science Forever.” by The PyCoach

https://link.medium.com/LSxLIoY8pBb

All opinions in this blog are the Author's and should not in any way be seen as reflecting the views of any organisation the Author has any association with. Twitter @scottturneruon