Python data science handbook: essential tools for working with data

IPython: beyond normal Python

Introduction to NumPy

Data manipulation with Pandas

Visualization with Matplatlib

Machine learning.

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.