Pandas and Plots for Data Analysis
summary
Concrete workflow: live notebooks on a Google Colab live notebook (Binder link fails), using the Anaconda Python distribution, JupyterLab, numpy then pandas and seaborn to import CSV/Excel/SQL, handle NA values, create dummy variables, run group‑by split‑apply‑combine, estimate means/SDs and basic regressions (linear and logistic), and produce publication‑quality plots. Examples include generating random draws from normal/binomial/Poisson and showing which commands actually transform data and which do not.
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chapters (14)
- 0:00Introduction
- 1:14Audience expectations
- 3:01What is pandas
- 5:51Anaconda for data science
- 6:35Core Python packages
- 8:00Pandas objectives
- 9:52Seaborn for visualization
- 10:37Python data structures
- 19:10Importing Python packages
- 21:16Numpy arrays challenges
- 23:28Pandas Series and DataFrame
- 28:47Jupyter notebooks
- 31:43DataFrames from dictionaries
- 33:50Data ingestion
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