| Name | tidy-viewer-py JSON |
| Version |
0.3.0
JSON |
| download |
| home_page | None |
| Summary | 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. |
| upload_time | 2025-08-20 23:39:52 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.8 |
| license | MIT OR Apache-2.0 |
| keywords |
table
formatting
terminal
pretty-print
csv
parquet
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
# Tidy Viewer Py
## Installation
```bash
pip install tidy-viewer-py
```
## Quick Start
### CSV File Pretty Printing
```python
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
```python
import pandas as pd
import tidy_viewer_py as tv
df = pd.read_csv(filename)
tv.print_dataframe(df)
```
### Polars DataFrames Pretty Printing
```python
import polars as pl
df_pl = pl.read_csv(filename)
tv.print_polars_dataframe(df_pl)
```
### Method Chaining API
```python
import tidy_viewer_py as tv
tv.tv().color_theme("gruvbox").max_rows(10).print_dataframe(df)
```
## Configuration Options
```python
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
```python
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
```python
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:
```python
# 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
```python
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
```bash
git clone https://github.com/yourusername/tidy-viewer-py
cd tidy-viewer-py
uv pip install .
```
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"description": "# Tidy Viewer Py\n\n## Installation\n\n```bash\npip install tidy-viewer-py\n```\n\n## Quick Start\n\n### CSV File Pretty Printing\n\n```python\nimport tidy_viewer_py as tv\nimport pandas as pd\nurl = \"https://raw.githubusercontent.com/mwaskom/seaborn-data/master/iris.csv\"\npd.read_csv(url).to_csv(\"iris.csv\", index=False) # Save to csv for demo\nfilename = \"iris.csv\"\ntv.print_csv(filename)\n```\n\n### Pandas DataFrames Pretty Printing\n\n```python\nimport pandas as pd\nimport tidy_viewer_py as tv\ndf = pd.read_csv(filename)\ntv.print_dataframe(df)\n```\n\n### Polars DataFrames Pretty Printing\n\n```python\nimport polars as pl\n\ndf_pl = pl.read_csv(filename)\ntv.print_polars_dataframe(df_pl)\n```\n\n### Method Chaining API\n\n```python\nimport tidy_viewer_py as tv\n\ntv.tv().color_theme(\"gruvbox\").max_rows(10).print_dataframe(df)\n```\n\n## Configuration Options\n\n```python\noptions = tv.FormatOptions(\n # Display options\n max_rows=25, # Maximum rows to display (None for all)\n max_col_width=20, # Maximum column width\n min_col_width=2, # Minimum column width\n \n # Styling\n use_color=True, # Enable/disable colored output\n color_theme=\"nord\", # Color theme\n \n # Data formatting\n delimiter=\",\", # CSV delimiter\n significant_figures=3, # Number of significant figures\n preserve_scientific=False,# Preserve scientific notation\n max_decimal_width=13, # Max width before scientific notation\n \n # Table elements\n no_dimensions=False, # Hide table dimensions\n no_row_numbering=False, # Hide row numbers\n title=\"My Table\", # Table title\n footer=\"End of data\", # Table footer\n)\n```\n\n## Data Type Display\n\nTidy 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.\n\n### Automatic Data Type Detection\n\n```python\nimport pandas as pd\nimport tidy_viewer_py as tv\n\n# Pandas DataFrame with automatic data type display\ndf = pd.DataFrame({\n 'name': ['Alice', 'Bob', 'Charlie'],\n 'age': [25, 30, 35],\n 'salary': [50000.0, 60000.0, 70000.0],\n 'active': [True, False, True]\n})\n\n# Data types are automatically detected and displayed\ntv.print_dataframe(df)\n```\n\n### Manual Data Type Specification\n\n```python\nimport tidy_viewer_py as tv\n\ndata = [['Alice', '25', 'Engineer'], ['Bob', '30', 'Designer']]\nheaders = ['Name', 'Age', 'Role']\ndata_types = ['<str>', '<i64>', '<str>']\n\n# Specify data types manually\ntv.print_table(data, headers, data_types)\n```\n\n### Data Type Mapping\n\nThe library automatically maps data types from different dataframe libraries to abbreviated format:\n\n#### Pandas Data Types\n| Pandas Type | Abbreviated |\n|-------------|-------------|\n| `object` | `<str>` |\n| `int64` | `<i64>` |\n| `float64` | `<f64>` |\n| `bool` | `<bool>` |\n| `datetime64[ns]` | `<dt>` |\n| `category` | `<cat>` |\n| `complex128` | `<cplx>` |\n\n#### Polars Data Types\n| Polars Type | Abbreviated |\n|-------------|-------------|\n| `String` | `<str>` |\n| `Int64` | `<i64>` |\n| `Float64` | `<f64>` |\n| `Boolean` | `<bool>` |\n| `Datetime` | `<dt>` |\n| `Categorical` | `<cat>` |\n| `List<Int64>` | `<list<i64>>` |\n\n#### Arrow Data Types\n| Arrow Type | Abbreviated |\n|------------|-------------|\n| `Utf8` | `<str>` |\n| `Int64` | `<i64>` |\n| `Float64` | `<f64>` |\n| `Boolean` | `<bool>` |\n| `Timestamp` | `<dt>` |\n| `List` | `<list>` |\n| `Struct` | `<struct>` |\n\n### Complex Type Handling\n\nComplex data types are automatically simplified:\n\n```python\n# These complex types are simplified:\n# List<Int64> \u2192 <list<i64>>\n# Struct<field1: String, field2: Int64> \u2192 <struct>\n# Map<String, Int64> \u2192 <map>\n# Union<Int64, String> \u2192 <union>\n# Int64? \u2192 <i64> (nullable types)\n```\n\n### Data Type Utilities\n\n```python\nfrom tidy_viewer_py import map_dtype, map_dtypes, auto_map_dtypes\n\n# Map individual data types\nmap_dtype('int64', 'pandas') # Returns '<i64>'\nmap_dtype('String', 'polars') # Returns '<str>'\n\n# Map lists of data types\ndtypes = ['object', 'int64', 'float64']\nmapped = map_dtypes(dtypes, 'pandas') # Returns ['<str>', '<i64>', '<f64>']\n\n# Auto-detect library and map\nauto_mapped = auto_map_dtypes(dtypes) # Automatically detects pandas\n```\n\n## Color Themes\n\nAvailable themes:\n- `nord` (default) - Arctic, north-bluish color palette\n- `gruvbox` - Retro groove color scheme\n- `dracula` - Dark theme with vibrant colors\n- `one_dark` - Atom One Dark inspired\n- `solarized_light` - Precision colors for readability\n\n\n### Building from Source\n\nRequirements:\n- Python 3.8+\n- Rust 1.70+\n- uv (recommended) or pip\n\n```bash\ngit clone https://github.com/yourusername/tidy-viewer-py\ncd tidy-viewer-py\nuv pip install .\n```\n\n",
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