Creating DataFrames in Pandas vs Polars

Creating a DataFrame is often the very first step in any data analysis workflow.
While Pandas is the most widely used library, Polars is becoming popular for its speed and clean syntax.
Here’s how to create DataFrames in both libraries, side by side.

From a dict of lists

# Pandas
import pandas as pd

data = {"name": ["Alice", "Bob"], "age": [25, 30]}
df_pd = pd.DataFrame(data)
print(df_pd)

#    name  age
# 0  Alice   25
# 1    Bob   30

# Polars
import polars as pl

data = {"name": ["Alice", "Bob"], "age": [25, 30]}
df_pl = pl.DataFrame(data)
print(df_pl)

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

✔ Both accept a dictionary of lists, but Polars prints with a richer table view.

From a list of dicts (records)

# Pandas
records = [{"name": "Alice", "age": 25}, {"name": "Bob", "age": 30}]
df_pd = pd.DataFrame(records)
print(df_pd)

#    name  age
# 0  Alice   25
# 1    Bob   30

# Polars
records = [{"name": "Alice", "age": 25}, {"name": "Bob", "age": 30}]
df_pl = pl.DataFrame(records)
print(df_pl)

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

✔ Very similar behavior in both libraries.

From a list of lists with column names

# Pandas
df_pd = pd.DataFrame([[1, "A"], [2, "B"]], columns=["id", "label"])
print(df_pd)

#    id label
# 0   1     A
# 1   2     B

# Polars
df_pl = pl.DataFrame([[1, "A"], [2, "B"]], schema=["id", "label"])
print(df_pl)

# shape: (2, 2)
# ┌─────┬───────┐
# │ id  ┆ label │
# │ --- ┆ ---   │
# │ i64 ┆ str   │
# ╞═════╪═══════╡
# │ 1   ┆ A     │
# │ 2   ┆ B     │
# └─────┴───────┘

✔ Pandas uses columns=..., Polars uses schema=....

Conclusion

• Pandas and Polars share very similar syntax for creating DataFrames.
• Polars provides a richer printout and better performance on large data.
• If you already know Pandas, moving to Polars feels very natural.

👉 Next step: in the following post, we’ll see how to select rows and columns in both libraries.