File Upload & Profiling Pipeline

Generated from prompt:

Create a professional, technical presentation visualizing the process described in the provided input. Title: 'STEP 1 – File Upload & Profiling'. Slide 1 should visually depict the following: 1️⃣ STEP 1.1 – File Upload & Reading - Show user uploading files via Streamlit UI. - Function: read_any_from_upload() reads each file. - Supported formats: CSV (pd.read_csv, latin-1 fallback), XLSX (pd.read_excel), Parquet (pd.read_parquet). - Output: df_by_name = {“filename.csv”: DataFrame, ...} 2️⃣ STEP 1.2 – File Profiling (node_profile_files) - Functions: build_file_metadata() + _column_stats() - Output: metadata_by_file = { filename.csv: { file_name, n_rows, n_cols, columns, column_stats, sample_data, extension } } Include a sub-box showing: ⚙️ Numerical Column Identification - Method: dtype check - Rule: dtype contains 'int' or 'float' → Numerical - LLM: Not involved Use a clear data pipeline flow diagram (arrows connecting steps), professional typography, icons for data files, functions, and outputs. Color scheme: shades of blue and gray with data-tech aesthetic.

This technical presentation outlines Step 1 of a data processing workflow, visualizing user file uploads via Streamlit, multi-format reading (CSV, XLSX, Parquet) into DataFrames, and profiling with me

December 2, 20253 slides
Slide 1 of 3

Slide 1 - STEP 1 – File Upload & Profiling

This title slide marks Step 1 of a process, focusing on File Upload & Profiling. It introduces the file upload and profiling mechanisms within a data pipeline.

STEP 1 – File Upload & Profiling

Introducing the file upload and profiling process in a data pipeline.

Speaker Notes
Welcome slide introducing the file upload and profiling process in a data pipeline. Visualize with flow diagram: user upload via Streamlit → read_any_from_upload() supports CSV/XLSX/Parquet → df_by_name output. Then node_profile_files: build_file_metadata + _column_stats → metadata_by_file. Sub-box: Numerical cols via dtype check (int/float). No LLM. Use blue/gray scheme, icons, arrows.
Slide 1 - STEP 1 – File Upload & Profiling
Slide 2 of 3

Slide 2 - PHASE 1 — FILE INGESTION & PROFILING 1.1 File Upload & Reading

  • Users upload multiple files (CSV, XLSX, Parquet) through a Streamlit UI.
  • Each file is read into a dataframe with format-specific loaders.
  • Output: A dictionary of dataframes keyed by file name.
Slide 2 - PHASE 1 — FILE INGESTION & PROFILING 1.1 File Upload & Reading
Slide 3 of 3

Slide 3 - Key Takeaways

The slide highlights efficient file handling for CSV, XLSX, and Parquet formats, along with the generation of comprehensive metadata including stats and samples, plus accurate numerical detection through dtype checks. It positions the pipeline as ready for subsequent steps, streamlining data ingestion to enable robust analysis.

Key Takeaways

Efficient file handling supports CSV, XLSX, and Parquet formats. Generates comprehensive metadata with stats and samples. Accurate numerical detection via dtype checks. Pipeline ready for subsequent steps.

Streamlining data ingestion for robust analysis.

Slide 3 - Key Takeaways

Discover More Presentations

Explore thousands of AI-generated presentations for inspiration

Browse Presentations
Powered by AI

Create Your Own Presentation

Generate professional presentations in seconds with Karaf's AI. Customize this presentation or start from scratch.

Create New Presentation

Powered by Karaf.ai — AI-Powered Presentation Generator