I will do python data analysis and data visualization using pandas numpy seaborn
Python Data Analyst specializing in Pandas and Seaborn
About this Gig
Do you have raw, messy data and need actionable insights to drive your business decisions? Look no further!
I am a professional Python Data Analyst specializing in data manipulation, statistical analysis, and interactive visualization. Using powerful libraries like NumPy, Pandas, Matplotlib, and Seaborn, I turn your unstructured data files into clean, professional, and visually stunning executive reports.
WHAT I OFFER:
Data Cleaning & Wrangling: Handling missing values, filtering outliers, and merging datasets using Pandas & NumPy.
Exploratory Data Analysis (EDA): Uncovering hidden trends, correlations, and key performance indicators (KPIs).
Custom Data Visualizations: Creating high-quality scatter plots, heatmaps, bar charts, and histograms ready for presentations.
Domain Expertise: Real-world experience analyzing E-commerce patterns, Real Estate valuations, and Healthcare metrics.
WHAT YOU WILL RECEIVE:
A fully documented, clean .ipynb (Jupyter Notebook) file or Python script.
High-resolution data charts ready for presentations or business reports.
Clear markdown comments explaining the core data insights step-by-step.
️ REQUISITES:
Please message me before placing an order
FAQ
What tools do you use for analysis and visualization?
I write my Python code using VS Code and export the workflows into fully compatible Jupyter Notebook (.ipynb) files. For core analysis, I rely strictly on Pandas and NumPy, and I use Seaborn and Matplotlib for data visualizations.
Can you handle large or messy datasets?
Yes! Pandas and NumPy are built specifically to process and clean large datasets efficiently. I can handle structural issues, duplicates, null values, and complex data formats seamlessly.
What file formats can you work with?
I can work with almost any data format, including CSV, Excel (.xlsx), JSON, SQL databases, and TXT files. I will deliver the cleaned data back to you in your preferred format.

