polars-info 0.1.0

A tiny helper to print clean, readable info summaries for Polars DataFrames.

polars-info

Pandas-like df.info() for Polars.

Polars has no built-in .info(). This library brings the familiar summary view to polars.DataFrame — column dtypes, null counts, memory usage — just like pandas.DataFrame.info(), plus extras like head/tail column truncation.

With print_df_info(), you can print a clean, friendly summary of:

• shape (rows and columns)
• estimated size (bytes)
• per-column dtypes
• null / non-null stats (optional)
• sample rows from the head (optional)

Even when your table has tons of columns, head + tail display keeps it readable.

✏️ Pandas df.info() vs polars_info

Pandas df.info()	polars-info print_df_info(df)
<class 'pandas.core.frame.DataFrame'> RangeIndex: 3 entries, 0 to 2 Data columns (total 3 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 id 3 non-null int64 1 name 2 non-null object 2 score 2 non-null float64 dtypes: float64(1), int64(1), object(1) memory usage: 200.0+ bytes	<class 'polars.dataframe.frame.DataFrame'> Shape: (3, 3) Estimated size: 51 B Columns: # Column Dtype Non-Null Null Null% 0 id Int64 3 0 0.00% 1 name String 2 1 33.33% 2 score Float64 2 1 33.33%

Key differences from Pandas:

• Null% column — instantly spot columns with high missing rates
• Head/tail truncation — DataFrames with 100+ columns stay readable
• Returns a summary object — DFInfoSummary for programmatic use

📦 Install

pip install polars-info

🚀 Quick Start

import polars as pl
from polars_info import print_df_info

df = pl.DataFrame(
    {
        "id": [1, 2, 3],
        "name": ["A", "B", None],
        "score": [10.2, None, 8.7],
    }
)

summary = print_df_info(df)

<class 'polars.dataframe.frame.DataFrame'>
Shape: (3, 3)
Estimated size: 51 B
Columns:
  #  Column  Dtype    Non-Null    Null   Null%
  0  id      Int64           3       0   0.00%
  1  name    String          2       1  33.33%
  2  score   Float64         2       1  33.33%

Use name and show_sample to add a label and preview rows.

summary = print_df_info(df, name="demo_df", show_sample=2)

<class 'polars.dataframe.frame.DataFrame'>
Name: demo_df
Shape: (3, 3)
Estimated size: 51 B
Columns:
  #  Column  Dtype    Non-Null    Null   Null%
  0  id      Int64           3       0   0.00%
  1  name    String          2       1  33.33%
  2  score   Float64         2       1  33.33%
Sample (head 2):
shape: (2, 3)
┌─────┬──────┬───────┐
│ id  ┆ name ┆ score │
│ --- ┆ ---  ┆ ---   │
│ i64 ┆ str  ┆ f64   │
╞═════╪══════╪═══════╡
│ 1   ┆ A    ┆ 10.2  │
│ 2   ┆ B    ┆ null  │
└─────┴──────┴───────┘

The returned DFInfoSummary gives you programmatic access to the metadata.

print(summary.rows, summary.cols)  # 3 3
print(summary.dtypes)              # {'id': Int64, 'name': String, 'score': Float64}

🌟 Great For

• fixing "too many columns, can't read anything" in notebooks
• quickly checking dtype / null health before preprocessing
• running lightweight, repeatable data sanity checks

⚙️ Main Options

print_df_info(
    df,
    name="train_df",
    display="auto",      # "auto" | "head_tail" | "full"
    head=5,              # head columns shown in head_tail mode
    tail=5,              # tail columns shown in head_tail mode
    max_cols=60,         # full-display limit in auto/full mode
    show_null_stats=True,
    show_sample=3,       # print first 3 rows
)

📄 License

MIT