Handling Null Values — Pandas vs Polars

Imagine you’re working with a dataset that has missing values for some columns.
How do you clean or fill them? Pandas and Polars provide simple tools to handle nulls.

Example data

import pandas as pd
import polars as pl
import numpy as np

data = {"name": ["Alice", "Bob", None], "score": [10, None, 30]}
df_pd = pd.DataFrame(data)
df_pl = pl.DataFrame(data)

Dropping nulls

# Pandas
print(df_pd.dropna())

#    name  score
# 0  Alice   10.0
# 2   None   30.0

# Polars
print(df_pl.drop_nulls())

# shape: (1, 2)
# ┌───────┬───────┐
# │ name  ┆ score │
# │ ---   ┆ ---   │
# │ str   ┆ i64   │
# ╞═══════╪═══════╡
# │ Alice ┆ 10    │
# └───────┴───────┘

Filling nulls

# Pandas
print(df_pd.fillna({"name": "Unknown", "score": 0}))

#     name  score
# 0  Alice   10.0
# 1    Bob    0.0
# 2 Unknown   30.0

# Polars
print(df_pl.fill_null(strategy="forward"))

# shape: (3, 2)
# ┌───────┬───────┐
# │ name  ┆ score │
# │ ---   ┆ ---   │
# │ str   ┆ i64   │
# ╞═══════╪═══════╡
# │ Alice ┆ 10    │
# │ Bob   ┆ 10    │
# │ Bob   ┆ 30    │
# └───────┴───────┘

Conclusion

• Pandas: dropna(), fillna() are the main methods.
• Polars: drop_nulls(), fill_null() provide similar functionality.
• Both give flexible options to handle missing values efficiently.