tidy-viewer-py 0.3.0

A cross-platform data pretty printer that uses column styling to maximize viewer enjoyment. Supports CSV, Parquet, Pandas, and Polars DataFrames with automatic data type detection and display.

Tidy Viewer Py

Installation

pip install tidy-viewer-py

Quick Start

CSV File Pretty Printing

import tidy_viewer_py as tv
import pandas as pd
url = "https://raw.githubusercontent.com/mwaskom/seaborn-data/master/iris.csv"
pd.read_csv(url).to_csv("iris.csv", index=False)  # Save to csv for demo
filename = "iris.csv"
tv.print_csv(filename)

Pandas DataFrames Pretty Printing

import pandas as pd
import tidy_viewer_py as tv
df = pd.read_csv(filename)
tv.print_dataframe(df)

Polars DataFrames Pretty Printing

import polars as pl

df_pl = pl.read_csv(filename)
tv.print_polars_dataframe(df_pl)

Method Chaining API

import tidy_viewer_py as tv

tv.tv().color_theme("gruvbox").max_rows(10).print_dataframe(df)

Configuration Options

options = tv.FormatOptions(
    # Display options
    max_rows=25,              # Maximum rows to display (None for all)
    max_col_width=20,         # Maximum column width
    min_col_width=2,          # Minimum column width
    
    # Styling
    use_color=True,           # Enable/disable colored output
    color_theme="nord",       # Color theme
    
    # Data formatting
    delimiter=",",            # CSV delimiter
    significant_figures=3,    # Number of significant figures
    preserve_scientific=False,# Preserve scientific notation
    max_decimal_width=13,     # Max width before scientific notation
    
    # Table elements
    no_dimensions=False,      # Hide table dimensions
    no_row_numbering=False,   # Hide row numbers
    title="My Table",         # Table title
    footer="End of data",     # Table footer
)

Data Type Display

Tidy Viewer Py can display data types from various dataframe libraries in an abbreviated format. Data types appear as a row below the headers with slightly dimmed styling.

Automatic Data Type Detection

import pandas as pd
import tidy_viewer_py as tv

# Pandas DataFrame with automatic data type display
df = pd.DataFrame({
    'name': ['Alice', 'Bob', 'Charlie'],
    'age': [25, 30, 35],
    'salary': [50000.0, 60000.0, 70000.0],
    'active': [True, False, True]
})

# Data types are automatically detected and displayed
tv.print_dataframe(df)

Manual Data Type Specification

import tidy_viewer_py as tv

data = [['Alice', '25', 'Engineer'], ['Bob', '30', 'Designer']]
headers = ['Name', 'Age', 'Role']
data_types = ['<str>', '<i64>', '<str>']

# Specify data types manually
tv.print_table(data, headers, data_types)

Data Type Mapping

The library automatically maps data types from different dataframe libraries to abbreviated format:

Pandas Data Types

Pandas Type	Abbreviated
object	<str>
int64	<i64>
float64	<f64>
bool	<bool>
datetime64[ns]	<dt>
category	<cat>
complex128	<cplx>

Polars Data Types

Polars Type	Abbreviated
String	<str>
Int64	<i64>
Float64	<f64>
Boolean	<bool>
Datetime	<dt>
Categorical	<cat>
List<Int64>	<list<i64>>

Arrow Data Types

Arrow Type	Abbreviated
Utf8	<str>
Int64	<i64>
Float64	<f64>
Boolean	<bool>
Timestamp	<dt>
List	<list>
Struct	<struct>

Complex Type Handling

Complex data types are automatically simplified:

# These complex types are simplified:
# List<Int64> → <list<i64>>
# Struct<field1: String, field2: Int64> → <struct>
# Map<String, Int64> → <map>
# Union<Int64, String> → <union>
# Int64? → <i64> (nullable types)

Data Type Utilities

from tidy_viewer_py import map_dtype, map_dtypes, auto_map_dtypes

# Map individual data types
map_dtype('int64', 'pandas')  # Returns '<i64>'
map_dtype('String', 'polars')  # Returns '<str>'

# Map lists of data types
dtypes = ['object', 'int64', 'float64']
mapped = map_dtypes(dtypes, 'pandas')  # Returns ['<str>', '<i64>', '<f64>']

# Auto-detect library and map
auto_mapped = auto_map_dtypes(dtypes)  # Automatically detects pandas

Color Themes

Available themes:

• nord (default) - Arctic, north-bluish color palette
• gruvbox - Retro groove color scheme
• dracula - Dark theme with vibrant colors
• one_dark - Atom One Dark inspired
• solarized_light - Precision colors for readability

Building from Source

Requirements:

• Python 3.8+
• Rust 1.70+
• uv (recommended) or pip

git clone https://github.com/yourusername/tidy-viewer-py
cd tidy-viewer-py
uv pip install .