Python Projects

Python Skills

• Used pandas for data wrangling: dropna(), groupby(), pivot_table(), merge()
• Performed EDA using describe(), value_counts(), and correlation analysis
• Cleaned and formatted dates, strings, and categorical variables with str.replace(), pd.to_datetime(), etc.
• Built visualisations with matplotlib and seaborn including boxplots, heatmaps, and time series plots
• Generated trend insights using resampling: resample('W'), rolling(), diff()
• Applied statistical measures: mean, median, standard deviation, skewness, and kurtosis
• Detected and handled outliers using IQR method and z-scores
• Used loc, iloc, and boolean indexing for advanced filtering and slicing
• Exported analysis-ready data to Excel, CSV, or SQL databases
• Documented workflows with Jupyter Notebook markdown and code comments

Sales Analysis – Python Project

This project explores apparel sales data in Australia during Q4 2020. Below is a step-by-step breakdown of the workflow including data cleaning, exploration, and insights using Python.

1. Load Dataset

2. Preview Data

3. DataFrame Info

4. Data Types

5. Nulls Check

6. Duplicates Check

7. Boxplot of Sales

8. Summary Statistics

9. Weekly Sales Trend

---