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Create a CSV Dataset Explorer

Level: Beginner · Time: 25–35 min · Category: Data app

Tags: Python · Streamlit · Prompting

Use AI coding assistance to build a small Streamlit app for previewing, filtering, and summarizing CSV files.

This is a good first AI-assisted project because every change is visible in the browser and the app can stay local.

Prerequisites​

  • LLM API Guide — set up API access so an assistant can help explain and extend the app.
  • Use Python with the LLM gateway — review basic Python environment and SDK setup.
  • Python — install Python if your workstation does not already have it.
  • Sample CSV — download a small lab-results CSV for testing the tutorial app.

Confirm Python 3 and pip are available before you start:

python3 --version      # expect 3.9 or newer
python3 -m pip --version

If Python is not found, install it from python.org. On the supercomputer, load a Python module instead (for example module load python), then run the checks again.

What you will do​

  • Install Streamlit and pandas.
  • Prompt for a first local data explorer.
  • Ask for careful improvements like filters and downloads.
  • Troubleshoot common CSV and dependency problems.

Build it step by step​

1. Install dependencies​

Create a small Python environment for the app. This keeps Streamlit dependencies separate from other projects.

mkdir dataset-explorer
cd dataset-explorer
python3 -m venv .venv
source .venv/bin/activate
pip install streamlit pandas
Keep this out of Git

If you track this project with Git, do not commit your virtual environment or any secrets. Create a .gitignore file in the project folder with at least these lines:

.gitignore
.venv/
.env
__pycache__/

.venv/ is large and specific to your machine, and .env holds your API key — neither belongs in a shared repository. New to Git? See Track a New Project with Git.

2. Ask for the first app​

Give the assistant the app goal, the dependencies it may use, and the safety constraints around uploaded files.

You are helping me build a beginner Streamlit app.

Project goal:
- Let a user upload a CSV.
- Show the first rows.
- Show row count, column count, and missing value count.
- Let the user choose one column and show a simple summary.

Constraints:
- Use Streamlit and pandas.
- Keep the first version in app.py.
- Add friendly errors for malformed CSV files.
- Do not add a database or login.
- I will test with a sample file named lab-results-example.csv.

Please:
1. Create app.py.
2. Explain how to run it locally.
3. Suggest two small next improvements.

3. Run and test with the sample CSV​

Start Streamlit and test with the sample lab-results file before trying a large research dataset.

curl -L -o lab-results-example.csv https://docs.rc.asu.edu/tutorials/lab-results-example.csv
streamlit run app.py

Or create the sample CSV manually:

cat > lab-results-example.csv <<'CSV'
sample_id,condition,replicate,day,value,unit,passed_qc,notes
S001,control,1,0,12.4,ng/uL,true,Baseline sample
S002,control,2,0,11.8,ng/uL,true,Baseline sample
S003,control,3,0,13.1,ng/uL,true,Baseline sample
S004,treatment-a,1,1,18.6,ng/uL,true,Clear response
S005,treatment-a,2,1,17.9,ng/uL,true,Clear response
S006,treatment-a,3,1,,ng/uL,false,Instrument timeout
S007,treatment-b,1,1,15.2,ng/uL,true,Moderate response
S008,treatment-b,2,1,14.7,ng/uL,true,Moderate response
S009,treatment-b,3,1,15.9,ng/uL,true,Moderate response
S010,control,1,7,12.9,ng/uL,true,Follow-up
S011,treatment-a,1,7,21.4,ng/uL,true,Follow-up
S012,treatment-b,1,7,16.8,ng/uL,true,Follow-up
CSV
streamlit run app.py

4. Ask for one practical improvement​

The best follow-up prompts name one feature and ask how to test it with a small file.

Please improve the dataset explorer with one focused feature:

Feature:
- Add a download button for the filtered dataframe.

Constraints:
- Keep the app beginner-readable.
- Do not rewrite the whole file if a smaller change works.
- Explain how to test the new button with lab-results-example.csv.

More prompts to try​

  • Add a date range filter, but only show it when a date-like column exists.
  • Review this app for friendly errors when the uploaded CSV is malformed.
  • Suggest three improvements for lab-results-example.csv that would help a collaborator who does not write Python, then wait for me to choose one.

Troubleshooting​

  • If streamlit is not found, activate the virtual environment again and run pip install streamlit pandas.
  • If curl cannot download the sample file, use the manual CSV block above instead.
  • If pandas cannot read the file, ask the assistant to add try/catch handling around pd.read_csv.
  • If the app is slow with a large CSV, ask for row limits, cached loading, or column selection before display.
  • If the assistant adds too many charts at once, ask it to revert and add one chart tied to one selected column.

Next steps​