Selecting Rows and Columns — Pandas vs Polars

Imagine you just loaded a dataset of customer information into a DataFrame.
The first thing you’ll probably want to do is explore: see only the name column, filter customers older than 25, or grab a specific row.
Both Pandas and Polars let you do this, but with slightly different syntax. Let’s compare.

Selecting a single column

# Pandas
import pandas as pd
df = pd.DataFrame({"name": ["Alice", "Bob"], "age": [25, 30]})
print(df["name"])

# 0    Alice
# 1      Bob
# Name: name, dtype: object

# Polars
import polars as pl
df = pl.DataFrame({"name": ["Alice", "Bob"], "age": [25, 30]})
print(df.select("name"))

# shape: (2, 1)
# ┌───────┐
# │ name  │
# │ ---   │
# │ str   │
# ╞═══════╡
# │ Alice │
# │ Bob   │
# └───────┘

Selecting multiple columns

# Pandas
print(df[["name", "age"]])

#     name  age
# 0  Alice   25
# 1    Bob   30

# Polars
print(df.select(["name", "age"]))

# shape: (2, 2)
# ┌───────┬─────┐
# │ name  ┆ age │
# │ ---   ┆ --- │
# │ str   ┆ i64 │
# ╞═══════╪═════╡
# │ Alice ┆ 25  │
# │ Bob   ┆ 30  │
# └───────┴─────┘

Filtering rows

# Pandas
print(df[df["age"] > 25])

#   name  age
# 1  Bob   30

# Polars
print(df.filter(pl.col("age") > 25))

# shape: (1, 2)
# ┌───────┬─────┐
# │ name  ┆ age │
# │ ---   ┆ --- │
# │ str   ┆ i64 │
# ╞═══════╪═════╡
# │ Bob   ┆ 30  │
# └───────┴─────┘

Selecting rows by index

# Pandas
print(df.iloc[0])

# name    Alice
# age        25
# Name: 0, dtype: object

⚠️ In Polars, rows are not accessed directly by index — you filter them by conditions instead.

Conclusion

• Pandas: use loc/iloc for rows, [] for columns.
• Polars: use .select() for columns and .filter() for rows.

👉 Next step: in the following post, we’ll compare CSV read performance between Pandas and Polars.