rustytables 0.1.1

A Rust library for pretty tables in Python

RustyTable

RustyTable is a high-performance, feature-rich table formatting library for Python, built using Rust for maximum efficiency. It offers a variety of functionalities to create, format, and manipulate tables for different output formats, including plain text, CSV, HTML, and Markdown.

Features

• Add and remove rows and columns
• Sort and filter data
• Apply themes for better readability
• Export tables to CSV, HTML, and Markdown formats
• Compute summary statistics (sum, average, min, max)
• Conditional formatting
• Paginate large tables

Installation

You can install RustyTable from PyPI using pip:

pip install rustytable

Usage

Here are detailed usage examples for all the features provided by RustyTable.

1. Creating a Table

from rustytable import Table, Cell, Theme

headers = ["Name", "Age", "City"]
table = Table(headers, None, True)

# Add some rows
table.add_row([
    Cell("Alice", None, False, None, None),
    Cell("30", None, False, None, None),
    Cell("New York", None, False, None, None)
])
table.add_row([
    Cell("Bob", None, False, None, None),
    Cell("25", None, False, None, None),
    Cell("San Francisco", None, False, None, None)
])

# Set title and subtitle
table.set_title("User Information")
table.set_subtitle("A table displaying user data")

Output:

User Information
A table displaying user data
+---------------+---------------+---------------+
| Name          | Age           | City          |
+---------------+---------------+---------------+
| Alice         | 30            | New York      |
| Bob           | 25            | San Francisco |
+---------------+---------------+---------------+

2. Adding and Removing Columns

# Add a column
table.add_column("Country", "USA")

# Add a row with the new column
table.add_row([
    Cell("Charlie", None, False, None, None),
    Cell("35", None, False, None, None),
    Cell("Los Angeles", None, False, None, None),
    Cell("USA", None, False, None, None)
])

# Remove a column
table.remove_column(3)  # Remove the "Country" column

Output:

+---------------+---------------+---------------+
| Name          | Age           | City          |
+---------------+---------------+---------------+
| Alice         | 30            | New York      |
| Bob           | 25            | San Francisco |
| Charlie       | 35            | Los Angeles   |
+---------------+---------------+---------------+

3. Exporting Tables

# Export to CSV
csv_data = table.to_csv()

# Export to HTML
html_data = table.to_html()

# Export to Markdown
markdown_data = table.to_markdown()

# Print table as a string
print(table.to_string())

Output: CSV:

Name,Age,City
Alice,30,New York
Bob,25,San Francisco
Charlie,35,Los Angeles

HTML:

<table>
<caption>User Information</caption>
<thead>
<tr><th>Name</th><th>Age</th><th>City</th></tr>
</thead>
<tbody>
<tr><td>Alice</td><td>30</td><td>New York</td></tr>
<tr><td>Bob</td><td>25</td><td>San Francisco</td></tr>
<tr><td>Charlie</td><td>35</td><td>Los Angeles</td></tr>
</tbody>
</table>

Markdown:

| Name | Age | City |
| --- | --- | --- |
| Alice | 30 | New York |
| Bob | 25 | San Francisco |
| Charlie | 35 | Los Angeles |

4. Setting Title and Subtitle

table.set_title("Employee Details")
table.set_subtitle("Detailed Information")

Output:

Employee Details
Detailed Information
+---------------+---------------+---------------+
| Name          | Age           | City          |
+---------------+---------------+---------------+
| Alice         | 30            | New York      |
| Bob           | 25            | San Francisco |
| Charlie       | 35            | Los Angeles   |
+---------------+---------------+---------------+

5. Statistical Functions

sum_age = table.sum("Age")
avg_age = table.average("Age")
min_age = table.min("Age")
max_age = table.max("Age")

Output:

Sum of ages: 90
Average age: 30.0
Minimum age: 25
Maximum age: 35

6. Conditional Formatting

table.conditional_format("City", "San Francisco", "SF")

Output:

Employee Details
Detailed Information
+---------------+---------------+---------------+
| Name          | Age           | City          |
+---------------+---------------+---------------+
| Alice         | 30            | New York      |
| Bob           | 25            | SF            |
| Charlie       | 35            | Los Angeles   |
+---------------+---------------+---------------+

7. Sorting and Filtering

# Sorting
table.sort_by("Age", ascending=True)

# Filtering
filtered_table = table.filter("City", "SF")

Output (Sorting):

Employee Details
Detailed Information
+---------------+---------------+---------------+
| Name          | Age           | City          |
+---------------+---------------+---------------+
| Bob           | 25            | SF            |
| Alice         | 30            | New York      |
| Charlie       | 35            | Los Angeles   |
+---------------+---------------+---------------+

Output (Filtering):

Employee Details
Detailed Information
+---------------+---------------+---------------+
| Name          | Age           | City          |
+---------------+---------------+---------------+
| Bob           | 25            | SF            |
+---------------+---------------+---------------+

8. Pagination

paginated_tables = table.paginate(2)

Output:

Page 1:
Employee Details
Detailed Information
+---------------+---------------+---------------+
| Name          | Age           | City          |
+---------------+---------------+---------------+
| Bob           | 25            | SF            |
| Alice         | 30            | New York      |
+---------------+---------------+---------------+

Page 2:
Employee Details
Detailed Information
+---------------+---------------+---------------+
| Name          | Age           | City          |
+---------------+---------------+---------------+
| Charlie       | 35            | Los Angeles   |
+---------------+---------------+---------------+

9. Theming

theme = Theme(header_color="32", row_colors=["31", "34"])
table.set_theme(theme)
print("
Table with theme applied:")
print(table.to_string())

Output:

Table with theme applied:
Employee Details
Detailed Information
+---------------+---------------+---------------+
| Name          | Age           | City          |
+---------------+---------------+---------------+
| Bob           | 25            | SF            |
| Alice         | 30            | New York      |
| Charlie       | 35            | Los Angeles   |
+---------------+---------------+---------------+

10. Dynamic Data Loading

data = [
    {"Name": "Alice", "Age": "30", "City": "New York"},
    {"Name": "Bob", "Age": "25", "City": "San Francisco"},
    {"Name": "Charlie", "Age": "40", "City": "Los Angeles"}
]
table.load_data(data)
print("
Table after loading data dynamically:")
print(table.to_string())

Output:

Table after loading data dynamically:
Employee Details
Detailed Information
+---------------+---------------+---------------+
| Name          | Age           | City          |
+---------------+---------------+---------------+
| Alice         | 30            | New York      |
| Bob           | 25            | San Francisco |
| Charlie       | 40            | Los Angeles   |
+---------------+---------------+---------------+

Contributing

We welcome contributions to RustyTable! If you'd like to contribute, please fork the repository and submit a pull request.

License

RustyTable is licensed under the MIT License. See the LICENSE file for more information.

Contact

For any questions or feedback, feel free to reach out to the maintainer at [e-mail].

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Enjoy using RustyTable for all your table formatting needs!