# ChatGPT struggliing with this request

I asked GPT the following: I have the table below and i need to build a table with column the regions and the rows the sectors showing the % of the total NAV (sum of the NAV of the funds). can you please code this in Python.
i have 3 funds with the following data:
data = {
‘sector1’: [0.25, 0.5, 0.25],
‘sector2’: [0.25, 0, 0.25],
‘sector3’: [0.5, 0.5, 0.25],
‘sector4’: [0, 0, 0.25],
‘region1’: [0.5, 0.25, 1],
‘region2’: [0.5, 0.25, 0],
‘region3’: [0, 0.5, 0],
‘strategy1’: [0.5, 0.5, 1],
‘strategy2’: [0.25, 0, 0],
‘strategy3’: [0.25, 0.5, 0],
‘NAV’: [100, 150, 200]
}
df = pd.DataFrame(data, index=[‘fund1’, ‘fund2’, ‘fund3’])
the figures that are inside are the weight of inside Fund1 of sector1.
I am interested to have a table for the full portfolio ( with the 3 funds) having as rows the sectors and as columns the regions. inside you will have the weight expressed as Total NAV of the portfolio for each sector/region.

he doesn’t look to understand this request and he proposed several incorrect solutions.

Try using the word ‘matrix’ instead of ‘table’. You are expecting a matrix actually. That may be the key.

sure. let me try. thanks.

is this also an example of where “one-shot” learning might be helpful? From reading the prompt, I don’t understand what the requested table should look like, so it doesn’t surprise me that chatGPT struggles with the request. Or maybe write the pseudocode into the prompt…“the table values are determined by dividing the total NAV by the column NAV and multiplying by sector and region” or something like that. Seems like the closer the prompt is to a design document, the less room for improvisation on the part of the LLM. Thoughts?

ps: what happens to strategy? is it just there to confuse the model? doesn’t seem to have a role

I’ve moved this thread to the ChatGPT Prompt Engineering forum (instead of leaving it in “General Discussions”).

My understanding is that the expected result is a matrix where rows are sectors and columns are regions. The content of each cell may be and element-wise multiplication. As for the strategy, it doesn’t seem to play any function here, I think it just happens to be in the dataset.

Try a prompt like this:

lets say I have this dataset:

data = {
‘sector1’: [0.25, 0.5, 0.25],
‘sector2’: [0.25, 0, 0.25],
‘sector3’: [0.5, 0.5, 0.25],
‘sector4’: [0, 0, 0.25],
‘region1’: [0.5, 0.25, 1],
‘region2’: [0.5, 0.25, 0],
‘region3’: [0, 0.5, 0],
‘strategy1’: [0.5, 0.5, 1],
‘strategy2’: [0.25, 0, 0],
‘strategy3’: [0.25, 0.5, 0],
‘NAV’: [100, 150, 200]
}

Can you draw a matrix where sector’i’ is in the rows, region’i’ is in the columns, and the intersection of each is the element-wise multiplication of their respective values? as for ‘strategy’i’, just ignore it. Can you try to create the matrix where columns are region, sector1, sector2, sector3 and the table has 3 rows, region1, region2, region3, and in the intersections we put the element-wise multiplication? I am not expecting python code but an attempt to draw with characters this matrix.

It kind of creates the matrix:

It is not exactly the expected result but is somehow heading that way.

I had to code it this way :
import pandas as pd
import numpy as np

data = {
‘sector1’: [0.25, 0.5, 0.25],
‘sector2’: [0.25, 0, 0.25],
‘sector3’: [0.5, 0.5, 0.25],
‘sector4’: [0, 0, 0.25],
‘region1’: [0.5, 0.25, 1],
‘region2’: [0.5, 0.25, 0],
‘region3’: [0, 0.5, 0],
‘strategy1’: [0.5, 0.5, 1],
‘strategy2’: [0.25, 0, 0],
‘strategy3’: [0.25, 0.5, 0],
‘NAV’: [100, 150, 200]
}

df = pd.DataFrame(data, index=[‘fund1’, ‘fund2’, ‘fund3’])

# Initialize an empty dataframe to store the results

result = pd.DataFrame(index=[‘sector1’, ‘sector2’, ‘sector3’, ‘sector4’],
columns=[‘region1’, ‘region2’, ‘region3’])

# Calculate the total NAV for each sector and region pair

for sector in result.index:
for region in result.columns:
result.loc[sector, region] = np.sum(df[sector] * df[region] * df[‘NAV’])

# Convert the result to percentage of total NAV

result = result / df[‘NAV’].sum() * 100

# Add the row and column totals

result[‘Total’] = result.sum(axis=1)
result.loc[‘Total’] = result.sum(axis=0)

print(result)

It is working and gives a matrix as below

``````    region1 region2 region3   Total
``````

sector1 18.06% 6.94% 8.33% 33.33%
sector2 13.89% 2.78% 0.00% 16.67%
sector3 20.83% 9.72% 8.33% 38.89%
sector4 11.11% 0.00% 0.00% 11.11%
Total 63.89% 19.44% 16.67% 100.00%

Definitively with python this was an easy task. I thought you wanted ChatGPT to produce the actual matrix. Good job!