Meet Theodosia, the data assistant.
A few weeks ago a former co-worker (and overall awesome guy) posted this experiment/idea. Today, I finally gave it a try. And it delivered as promised. (note: I’m a cheapskate, so, putting down the CC to pay for OpenAI API was a mental hurdle I had to get over. And I’m glad I did, as you’ll see below).
Upon trying the example suggested in Kyle’s post, and seeing the following answer below — which is absolutely impressive in its own right — , I knew there were a lot more capabilities I could get out of it:
The dataset contains information about buildings in the city of Berkeley, California. Each row represents a building and each column represents a variable.
Some of the variables in the dataset are:
building_address: the address of the building
property_building_name: the name of the building
_2019_source_eui_kbtu_ft2_: the building’s Energy Use Intensity (EUI) within the 2019 calendar year, measured in thousand British Thermal Units per square foot per year (kBtu/ft²/year). This is a measure of the building’s energy efficiency.
_2021_energy_star_score: the building’s Energy Star score, which is a measure of the building’s energy efficiency on a scale of 1–100
_2021_site_eui_kbtu_ft2: the building’s Site Energy Use Intensity (EUI) within the 2021 calendar year, measured in thousand British Thermal Units per square foot per year (kBtu/ft²/year). This is a measure of the building’s total energy usage.
The dataset includes data from multiple years, including 2018, 2019, 2020, and 2021. The dataset also includes information about the status of each building’s energy assessments.
So, as with anything with GPT/ChatGPT the prompt is everything. When crafting a prompt for a prompt it is always helpful to think of a persona and role. You will tell GPT API to assume this role and persona. So for this example I chose Theodosia (name came to my head at the moment), and then their role (Data Assistant) and I wrote it down
You’re a data assistant Theodosia.
Then you must describe a high level task, with inputs and outputs.
I will provide a dataset and you will inspect it and analyze it.
Add further description of the task
After your analysis is complete you should be able to answer queries about its contents.
The following took some trial and error as GPT’s default output bias is to give ancillary information for some reason.
Just answer what you’re asked.
Sounds rude, but you have to tell the model what you want — sorry GPT. Then you have to tell it, what the dataset is called
The dataset is `df`.
And after this you provide the dataset by just running a cell containing df or whatever your dataset is named.
All put together, the prompt cell should look something like this
'''
You're data assistant Theodosia.
I will provide a dataset and you will inspect it and analyze it.
After your analysis is complete you should be able to answer queries about its contents.
Just answer what you're asked.
The dataset is `df`.
'''
dfPlease note that we have not told GPT anything about the dataset contents or whether it is a pandas dataframe or not. This is all part of the magic of GPT. Also big caveat that given GPT’s output is statistical, if you were to do everything identically as I have, answers will likely differ.
Alright, with that set up, let’s ask it some questions.
Let’s first ask it something simple.
Theodosia give me a summary of the data in the dataset
In order to summarize the data in the provided dataset (df), we can use the .describe() method to get descriptive statistics for each numerical column in the DataFrame. However, it's worth noting that some numerical columns may represent categorical variables, in which case the summary statistics may not be as meaningful. Here is the summary statistics for numerical columns in the dataset:
count mean std min 25% 50% 75% max
_2018_energy_star_score 308 65.970779 27.313019 13 52.00 67 84.25 100
_2019_energy_star_score 308 68.081169 26.263321 1 54.00 71 90.00 100
_2020_energy_star_score 308 65.725649 27.270817 10 50.00 66 84.00 100
_2021_energy_star_score 308 63.727273 28.178067 1 51.75 63 84.00 100
_2019_source_eui_kbtu_ft2_ 291 104.630821 205.815609 0 66.10 91 116.05 4550
_2019_site_eui_kbtu_ft2_ 291 44.738409 72.980597 0 20.35 31.6 49.15 797
_2020_source_eui_kbtu_ft2_ 309 102.894143 213.166032 0 63.30 84.8 108.45 5179
_floor_area 309 7848.302917 26885.515144 84 2285.00 4915.0 9255.00 435000
_2018_site_eui_kbtu_ft2_ 308 43.355806 59.524173 0 20.75 29.9 44.90 1077
_beso_id 309 3006.338917 12006.338242 1915 2040.00 2505.0 3220.00 147095
_2021_site_eui_kbtu_ft2_ 308 43.374221 57.444354 13 21.80 31.0 45.40 584The summary statistics show that the dataset contains a mix of categorical and numerical data. For example, the _beso_property_type column is a categorical variable that does not appear in this summary statistics. Additionally, some columns have a significantly higher maximum value than the 75th percentile, suggesting that there may be outliers in the data.
Good. That’s really cool. Let’s try to push it a little bit to test its powers of inference.
Theodosia what is the mean energy start score for 2018?
Based on the provided df, the mean energy star score for the year 2018 can be calculated using the following command:
mean_score_2018 = df['_2018_energy_star_score'].mean()Running this command on the df you provided, we get that the mean energy star score for 2018 is approximately 62.8.
Alright, this truly impressive. It inferred the column name, and the calculation. Let’s keep pushing.
Theodosia which property had the highest energy start score for 2018 (ignore beso_id Total)?
To find which property had the highest Energy Start score for 2018, you can use the following code snippet. This will locate the maximum score in the _2018_energy_star_score column and retrieve the property_building_name value for that row.
max_score_2018 = df[df['beso_id']!='Total']['_2018_energy_star_score'].idxmax()
property_name = df.loc[max_score_2018]['property_building_name']
print(property_name)Alternatively, you could also use the nlargest() function as follows:
property_name = df.loc[df[df['beso_id']!='Total']['_2018_energy_star_score'].nlargest(1).index[0], 'property_building_name']
print(property_name)This will return the name of the property which got the highest energy star score for 2018 excluding those with a ‘beso_id’ equal to ‘Total’.
I mean, that’s a fairly complex ask, and it answered it well. I tried the code and worked as expected:
For this final one I’m going to try to get it to do some inference and comparisons.
Theodosia did the average star energy scores gone up from 2020 to 2021 (ignore beso_id Total!)?
Yes, the average Energy Star score has gone up from 2020 to 2021 (excluding Total).
Goosebumps, but let’s check — let’s say Theodosia can be random at times ;)
In closing, I’m beyond astounded. We are truly living in the future. How do we even quantify the ‘value prop’ of this tech (other than saying it’s the highest value prop of any technology human beings have ever built)?
P.S.: I’m interested in hearing takes where LLMs/ChatGPT don’t absolutely bulldoze through the labor and tech market in 3–5 years.
