Appendix C — From pandas to dplyr

D From pandas to dplyr

D.1 Introduction

Switching from Python? The ideas transfer one-to-one; only the verbs change. pandas snippets below are static reference (never executed by this book); the dplyr side runs on the ecommerce data from @dplyr-basics.

library(dplyr)
library(readr)

ecom <-
  read_csv('https://raw.githubusercontent.com/rsquaredacademy/datasets/master/web.csv',
    col_types = cols_only(device = col_factor(levels = c("laptop", "tablet", "mobile")),
      referrer = col_factor(levels = c("bing", "direct", "social", "yahoo", "google")),
      purchase = col_logical(), n_pages = col_double(), n_visit = col_double(),
      duration = col_double(), order_value = col_double(), order_items = col_double()
    )
  )

D.2 Verb map

Task	pandas	dplyr (this book)
Read CSV	pd.read_csv("web.csv")	read_csv("web.csv")
First rows	df.head(10)	head(ecom, 10) / slice_head(ecom, n = 10)
Filter rows	df[df.purchase] or df.query("purchase")	filter(ecom, purchase)
Pick columns	df[["device", "order_value"]]	select(ecom, device, order_value)
Sort	df.sort_values("n_pages")	arrange(ecom, n_pages)
New column	df.assign(aov=df.revenue/df.orders)	mutate(ecom4, aov = revenue / orders)
Group + aggregate	df.groupby("device").agg(revenue=("order_value","sum"))	summarise(ecom, revenue = sum(order_value), .by = device)
Join	pd.merge(customer, order, on="id", how="left")	left_join(customer, order, by = join_by(id))
Drop missing	df.dropna()	drop_na(df)
Reshape long/wide	df.melt(...) / df.pivot(...)	pivot_longer() / pivot_wider()
Strings	s.str.contains(pat), s.str.extract(pat)	str_detect(), str_extract()
Datetimes	pd.to_datetime(s), s.dt.year	ymd(), year(), month(), day()
Categories	astype("category"), cat.reorder_categories	factor(), fct_relevel(), fct_lump_*()

D.3 Worked pair: grouped means

pandas:

ecom.groupby("purchase")["n_pages"].mean()

dplyr (runs here):

ecom |>
  summarise(mean_pages = mean(n_pages), .by = purchase)

# A tibble: 2 × 2
  purchase mean_pages
  <lgl>         <dbl>
1 FALSE          4.79
2 TRUE          15.8 

Same split-apply-combine, same answer. The rest of the grammar maps the same way: chain with |> where pandas chains with ., and reach for Chapters @dplyr-basics–@categorical-data-in-r for the full drill.

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