Python data science handbook essential tools for working with data
Python is a first-class tool for many researchers, primarily because of its libraries for storing, manipulating, and gaining insight from data. Several resources exist for individual pieces of this data science stack, but only with the new edition of Python Data Science Handbook do you get them all;...
| Otros Autores: | |
|---|---|
| Formato: | Libro electrónico |
| Idioma: | Inglés |
| Publicado: |
Sebastopol, CA :
O'Reilly Media, Incorporated
2023.
|
| Edición: | Second edition |
| Materias: | |
| Ver en Biblioteca Universitat Ramon Llull: | https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009707502706719 |
Tabla de Contenidos:
- Part I: Jupyter : beyond normal Pythong. Getting started in IPython and Jupyter ; Enhanced interactive features ; Debugging and profiling
- Part II: Introduction to NumPy. Understanding data types in Python ; The basics of NumPy arrays ; Computation on NumPy arrays : universal functions ; Aggregations : min, max, and everything in between ; Computation on arrays : broadcasting ; Comparisons, masks, and Boolean logic ; Fancy indexing ; Sorting arrays ; Structured data : NumPy's structured arrays
- Part III: Data manipulation with Pandas. Introducing Pandas objects ; Data indexing and selection ; Operating on data in Pandas ; Handling missing data ; Hierarchical indexing ; Combining datasets : concat and append ; Combining datasets : merge and join ; Aggregation and grouping ; Pivot tables ; Vectorized string operations ; Working with time series ; High-performance Pandas : eval and query
- Part IV: Visualization with Matplotlib. General Matplotlib tips ; Simple line plots ; Simple scatter plots ; Density and contour plots ; Customizing plot legends ; Customizing colorbars ; Multiple subplots ; Text and annotation ; Customizing ticks ; Customizing Matplotlib : configurations and stylesheets ; Three-dimensional plotting in Matplotlib ; Visualization with Seaborn
- Part V: Machine learning. What is machine learning? ; Introducing Scikit-Learn ; Hyperparameters and model validation ; Feature engineering ; In depth : Naive Bayes classification ; In depth : linear regression ; In depth : support vector machines ; In depth : decision trees and random forests ; In depth : principal component analysis ; In depth : manifold learning ; In depth : k-means clustering ; In depth : Gaussian mixture models ; In depth : kernel density estimation ; Application : a face detection pipeline.
