Pandas and R: Side by side

Welcome welcome: Comparing the code queries used in two popular data manipulation libraries: Pandas in Python and dplyr in R.

Written on July 4, 2023

Load library

Loading pandas

import pandas as pd

Loading dplyr

library(dplyr)

Create data frame

data_sales = {
    'group': ['A', 'B', 'C', 'A'],
    'sales': [2000, 1500, 3000, 2500],
    'gender': ['male', 'female', 
              'male', 'male'],
    'age': [25, 30, 35, 32]
}

# Create the DataFrame
df = pd.DataFrame(data_sales)

df <- tibble(
  group = c("A", "B", "C", "A"),
  sales = c(2000, 1500, 3000, 2500),
  gender = c("male", "female", 
             "male", "male"),
  age = c(25, 30, 35, 32)
)

Select column(s)

df[["group", "gender"]]

df |> 
  select(group, gender)

Filter

df.query('sales >= 2500')

df |> 
  filter(sales >= 2500)

Rename column

df.rename(
  columns={'group': 'working_class',
           'sales': 'product_sales'})

df |> 
  rename(working_class = group,
         product_sales = sales)

Create a variable

df['sales_times_2'] = df['sales'] * 2

df |> 
  mutate(sales_times_2 = sales * 2)

Modify a variable

df['sales'] = df['sales'] * 2

df |> 
  mutate(sales = sales * 2)

Summmarization

(
  df.groupby(['group'])
  .agg({'sales': 'sum'})
  .reset_index()
  )

df |> 
  group_by(group) |> 
  summarise(sales = sum(sales))

Sort

df.sort_values('sales')

df |> 
  arrange(sales)

Sort (Descending order)

df.sort_values('sales', 
                ascending=False)

df |> 
  arrange(desc(sales))