Python useRs
summary
Practical comparison of workflows and failures: example datasets are Ebola, a survey set, and Gapminder; Python demos use pandas, Jupyter/Anaconda, concat/merge/groupby/apply and scikit‑learn linear models; R demos use base read.csv, the tidyverse/dplyr verbs, formula LM and broom for tidy model output. Data interchange tries feather (fast but not production ready) and rpy2 (integration attempt fails), and performance notes point to data.table / dplyr for speed.
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chapters (31)
- 0:00Python and R for data analysis
- 1:51Python and R value
- 2:56Python for analytics
- 5:03Pandas dataframes
- 8:18Subsetting data in pandas
- 11:50Missing values and types in pandas
- 13:12New columns in pandas
- 15:17Combining datasets in pandas
- 18:32Merging datasets in pandas
- 19:58Vectorized operations in pandas
- 23:06Grouped operations in pandas
- 25:12Linear models in Python
- 26:17Jupyter slideshows
- 26:39R and RStudio
- 27:43Importing data in R
- 28:45Factors and strings in R
- 30:10Tidyverse approach
- 31:13Namespaces and functions in R
- 32:17Subsetting data in R
- 33:45Boolean subsetting in R
- 34:27Data types in R
- 35:31New columns in R with magrittr
- 36:56Data manipulation with dplyr
- 37:38Combining datasets in R
- 39:03Vectorized operations in R
- 40:08Grouped operations with dplyr
- 40:50Linear models in R
- 41:34Cleaning model outputs
- 41:55Slides with R Markdown
- 42:15Sharing data with feather
- 43:38Installing R packages
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