- Home
- Computers
- Programming
- Numeric Python (Python Data Analysis with NumPy, Pandas, and Matplotlib)
Numeric Python (Python Data Analysis with NumPy, Pandas, and Matplotlib)
| Expected release date is May 15th 2026 |
- Availability: Confirm prior to ordering
- Branding: minimum 50 pieces (add’l costs below)
- Check Freight Rates (branded products only)
Branding Options (v), Availability & Lead Times
- 1-Color Imprint: $2.00 ea.
- Promo-Page Insert: $2.50 ea. (full-color printed, single-sided page)
- Belly-Band Wrap: $2.50 ea. (full-color printed)
- Set-Up Charge: $45 per decoration
- Availability: Product availability changes daily, so please confirm your quantity is available prior to placing an order.
- Branded Products: allow 10 business days from proof approval for production. Branding options may be limited or unavailable based on product design or cover artwork.
- Unbranded Products: allow 3-5 business days for shipping. All Unbranded items receive FREE ground shipping in the US. Inquire for international shipping.
- RETURNS/CANCELLATIONS: All orders, branded or unbranded, are NON-CANCELLABLE and NON-RETURNABLE once a purchase order has been received.
Product Details
Overview
- Numerical computing with NumPy arrays, dtypes, vectorized operations
- Data analysis using Pandas DataFrames, grouping, pivoting, and time series
- Scientific visualization with Matplotlib plots, layouts, and contour graphics
- Real-world data work: files, missing data, binning, and indexing
- Applied Python: image processing, probability, and practical projects
This book teaches the Python fundamentals required to solve numerical problems in data science and machine learning.
The first part focuses on NumPy as the foundation of numerical programming, covering arrays as the core data type, numerical operations, broadcasting, and universal functions, as well as statistics, probability, Boolean masking, and file handling.
The second part is devoted to data visualization with Matplotlib, ranging from core concepts to line, bar, histogram, and contour plots. The third part introduces Pandas, including Series and DataFrames, importing and exporting Excel, CSV, and JSON files, handling missing data, and visualization directly within Pandas.
The fourth part presents practical applications, including a household budget project, an incomeexpenditure analysis, and an introduction to image processing.
The book concludes with a fifth part containing solutions to the numerous exercises that accompany almost every one of the 33 chapters.
