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Python for Data Analysis, 3rd Edition

by Wes McKinney · O'Reilly Media

Wes McKinney, who created pandas, walks through NumPy arrays, Series and DataFrames, data cleaning, joins, group-by aggregation, time series, and plotting. After it you can load messy real datasets and reshape them into analysis-ready tables.

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numpy-financial – Financial Functions for Python

Official documentation for numpy-financial, the NumPy project's package of elementary financial functions split out of NumPy itself. It covers net present value, internal rate of return, payments, interest rates and future value, so you can compute loan and investment cash-flow figures in Python.

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Scikit-learn Official Documentation

Official user guide, API reference and worked examples for scikit-learn, the standard Python machine learning library. Covers classification, regression, clustering, dimensionality reduction, pipelines, preprocessing and model selection, so readers can build, tune and evaluate models with cross-validation.

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Kaggle Learn

Free interactive micro-courses from Kaggle, each a few hours of short lessons with in-browser coding exercises. Topics include Python, pandas, data visualization, SQL, feature engineering and introductory machine learning, giving learners working code skills for basic data analysis and modelling.

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Harvard University's CS109 Data Science

Harvard's two-semester data science course with public lectures, labs and homework. CS109A covers data collection, cleaning, visualization and regression and classification models; CS109B adds deep learning and probabilistic methods. Students learn to run a full analysis in Python.

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Effective Pandas 2: Opinionated Patterns for Data Manipulation

Opinionated guide to idiomatic pandas 2: chaining operations instead of intermediate variables, choosing correct dtypes, avoiding SettingWithCopy problems, and writing pipelines that stay readable and testable. Aimed at people who already use pandas but produce tangled code.

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Pandas Tutorials (Corey Schafer)

Video series building from installation and loading CSV files through selecting rows and columns, filtering, updating data, sorting, grouping and aggregating, and handling missing values, each demonstrated live in a notebook. Suits people who prefer coding along to reading documentation.

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