Python vs R for Data Analysts: A Practical Comparison

import pandas as pd

raw = pd.DataFrame({
    "city": [" delhi", "Delhi ", "MUMBAI", None],
    "order_date": ["2026-10-01", "2026-10-01", "2026-10-03", "2026-10-04"],
    "amount": [500, 500, 1200, 300],
})
clean = (raw.assign(city=raw["city"].str.strip().str.title().fillna("Unknown"),
                    order_date=pd.to_datetime(raw["order_date"]))
            .drop_duplicates())
print(clean)

# install.packages(c("dplyr", "stringr", "lubridate", "tidyr"))
library(dplyr); library(stringr); library(lubridate); library(tidyr)

raw <- data.frame(
  city = c(" delhi", "Delhi ", "MUMBAI", NA),
  order_date = c("2026-10-01", "2026-10-01", "2026-10-03", "2026-10-04"),
  amount = c(500, 500, 1200, 300)
)
clean <- raw %>%
  mutate(city = str_to_title(str_trim(city)),
         order_date = ymd(order_date)) %>%
  replace_na(list(city = "Unknown")) %>%
  distinct()
print(clean)

import sqlite3
import pandas as pd

con = sqlite3.connect(":memory:")
pd.DataFrame({"region": ["North", "South", "North", "East"],
              "amount": [500, 700, 300, 400]}).to_sql("orders", con, index=False)

query = """
SELECT region, COUNT(*) AS orders, SUM(amount) AS revenue
FROM orders
GROUP BY region
ORDER BY revenue DESC
"""
print(pd.read_sql(query, con))

# install.packages(c("DBI", "RSQLite"))
library(DBI)

con <- dbConnect(RSQLite::SQLite(), ":memory:")
dbWriteTable(con, "orders", data.frame(
  region = c("North", "South", "North", "East"),
  amount = c(500, 700, 300, 400)))

res <- dbGetQuery(con, "
  SELECT region, COUNT(*) AS orders, SUM(amount) AS revenue
  FROM orders
  GROUP BY region
  ORDER BY revenue DESC")
print(res)
dbDisconnect(con)

library(ggplot2)

sales <- data.frame(
  region = c("North", "South", "East", "West"),
  revenue = c(800, 700, 400, 650)
)
p <- ggplot(sales, aes(x = reorder(region, -revenue), y = revenue)) +
  geom_col(fill = "steelblue") +
  labs(title = "Revenue by region", x = NULL, y = "Revenue") +
  theme_minimal()
ggsave("revenue_by_region.png", p, width = 6, height = 4)

# pip install pandas openpyxl
import pandas as pd
from datetime import date

orders = pd.DataFrame({
    "region": ["North", "South", "North", "East"],
    "amount": [500, 700, 300, 400],
})
summary = orders.groupby("region", as_index=False)["amount"].sum()
file = f"weekly_sales_{date.today():%Y_%m_%d}.xlsx"
with pd.ExcelWriter(file) as xl:
    summary.to_excel(xl, sheet_name="Summary", index=False)
    orders.to_excel(xl, sheet_name="Detail", index=False)
print("saved", file)
# Schedule with cron or Task Scheduler to run every Monday.

If your target team...                       Choose
Uses SQL + Excel + BI, automates reports      Python
Works in research, surveys, statistics        R
Already uses one language                     That one
You are not sure                              Python (plus SQL)

Either way: SQL and Excel first, then the language.

Weeks 1-2  Language basics and data frames
Weeks 3-4  Cleaning: text, dates, missing values, duplicates
Week 5     Running SQL from code
Week 6     Charts (Seaborn or ggplot2)
Week 7     One automated weekly report
Week 8     Portfolio project with a short written report

Project: Automated weekly sales report

1. SQL query pulls last week's orders
2. Code cleans and summarises by region and category
3. Charts saved as images
4. Excel or HTML report generated automatically
5. README explains how to schedule it

Works in either language; shows real analyst value.