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I developed a Python-based dataset exploration script to efficiently load, inspect, and summarize structured data (e.g., Iris dataset in CSV format). The script provides quick insights into dataset structure, column metadata, row counts, and statistical distributions—helping data analysts, students, and professionals understand their data before applying deeper analytics or machine learning techniques. This script serves as a foundation for data analysis pipelines, ensuring data is validated, clean, and well-understood before advanced modeling.
- Column Names → Displays all feature/column names in the dataset.
- Number of Rows → Provides dataset size and total entries for initial checks.
- Summary Statistics → Generates descriptive statistics (mean, median, standard deviation, min, max, quartiles) for numerical columns.
- Data Types & Structure → Identifies numeric vs categorical features and highlights potential preprocessing needs.
Prints number of rows, columns, and non-null counts.
Outputs list of feature names to quickly verify dataset schema.
Provides measures of central tendency (mean, median), spread (std, min, max), and distribution (quartiles).
Displays the first 5 rows of the dataset for rapid inspection.
The dashboard was built using the following tools and technologies:
- Python (Jupyter Notebook / Script) → Core language for data loading and processing
- Pandas → DataFrame creation, summary statistics, and structural overview
- 📊 Enabled quick and reliable dataset understanding before running advanced analysis
- 💡 Helped identify data types and missing values early, reducing errors in modeling
- 📈 Streamlined the data exploration phase for data science workflows
- 🌍 Built a reusable Python script template for loading and analyzing any CSV dataset
💼 Portfolio: https://linktr.ee/AbdullahUmar.DataAnalyst
📧 Email: [email protected]
Show what the Code and Output looks like.
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