Python Tabulate: A Full Guide

Advanced Features of Python Tabulate

The tabulate library offers advanced features that allow you to customize the appearance of complex tables. Let us discuss some of these features.

Customizing table appearance

The tabulate library offers different ways to customize table appearance, such as adjusting alignment, setting column widths, and using different table formats. The following are some of the key formatting options.

• numalign: Controls the alignment of numeric columns. Acceptable values are "left", "center", and "right".

• stralign: Controls the alignment of string columns. Acceptable values are "left", "center", and "right".

• colalign: Allows for specific alignment of individual columns by providing a list of alignment options.

The example below shows the application of these formatting options.

from tabulate import tabulate

# Sample data: list of lists
data = [
    ["Alice", 24, 1200.50],
    ["Bob", 30, 3500.75],
    ["Charlie", 28, 2500.00]
]

# Customizing table appearance
table = tabulate(
    data,
    headers=["Name", "Age", "Salary"],
    tablefmt="fancy_grid",
    numalign="right",
    stralign="center",
    colalign=("center", "center", "right")
)

print(table)

Customized table output using Python tabulate. Image by Author.

Handling multiline cells

The tabulate library also lets you handle text data exceeding the typical column width. The maxcolwidths argument specifies the maximum width of each cell in the output table to manage text wrapping within cells. This functionality allows you to display lengthy information in a more readable way.

In the example below, the maxcolwidths is set to 30 characters to wrap the text data into multiple lines if it exceeds the specified number of characters.

# Import tabulate library
from tabulate import tabulate

# Sample text data
data = [
    ["Alice", "Lorem ipsum dolor sit amet, consectetur adipiscing elit."],
    ["Bob", "Sed do eiusmod tempor incididunt ut labore et dolore magna aliqua."],
    ["Charlie", "Ut enim ad minim veniam, quis nostrud exercitation ullamco."]
]

# Creating a table with multiline cells
table = tabulate(
    data,
    headers=["Name", "Description"],
    tablefmt="grid",
    maxcolwidths=[None, 30]  # Set maximum width for the Description column
)

print(table)

Table output handling multiple lines using Python tabulate. Image by Author.

Using the command line utility

The tabulate library provides a command-line utility to help you display tables directly from the command line or terminal. This command-line utility allows you to generate the tables without writing additional Python code.

Assume you have a CSV file named data.csv. You will use the following command prompt to generate a table using tabulate.

cat data.csv | tabulate -d , -f grid

Where:

• cat data.csv reads the content of the CSV file.

• -d , specifies the delimiter (comma for CSV).

• -f grid sets the output format to grid style.

Practical Examples and Use Cases

In this section, we will explore the different use cases of the tabulate library to display tables in Python.

Displaying sales data

Assume we have the following sales data.

# Sample sales data
sales_data = [
    ["Q1", 15000, 12000, 13000],
    ["Q2", 17000, 16000, 14500],
    ["Q3", 18000, 15000, 16000],
    ["Q4", 20000, 21000, 19000]
]

headers = ["Quarter", "Product A", "Product B", "Product C"]

You can display the data in a table using the fancy_grid format.

# Import tabulate library
from tabulate import tabulate

# Displaying sales data using the 'fancy_grid' format
table_fancy = tabulate(sales_data, headers=headers, tablefmt="fancy_grid")

print(table_fancy)

Table output using fancy_grid formatting in Python tabulate. Image by Author.

You can also display the data in HTML format by setting tablefmt to html.

# Displaying sales data using the 'html' format
table_html = tabulate(sales_data, headers=headers, tablefmt="HTML")

print(table_html)

Output:

<table>
<thead>
<tr><th>Quarter  </th><th style="text-align: right;">  Product A</th><th style="text-align: right;">  Product B</th><th style="text-align: right;">  Product C</th></tr>
</thead>
<tbody>
<tr><td>Q1       </td><td style="text-align: right;">      15000</td><td style="text-align: right;">      12000</td><td style="text-align: right;">      13000</td></tr>
<tr><td>Q2       </td><td style="text-align: right;">      17000</td><td style="text-align: right;">      16000</td><td style="text-align: right;">      14500</td></tr>
<tr><td>Q3       </td><td style="text-align: right;">      18000</td><td style="text-align: right;">      15000</td><td style="text-align: right;">      16000</td></tr>
<tr><td>Q4       </td><td style="text-align: right;">      20000</td><td style="text-align: right;">      21000</td><td style="text-align: right;">      19000</td></tr>
</tbody>
</table>

The HTML table output is useful in web integrations if you want to embed the table output into websites. You can also further customize the HTML table output using CSS to have a more appealing look to the user.

Integration with Jupyter Notebooks

The tabulate library can easily be integrated with Jupyter Notebooks for easier data presentation. The example below shows how to create a sample DataFrame in a Jupyter Notebook and display it in a grid-formatted table.

# Import required library
import pandas as pd
from tabulate import tabulate

# Sample student exam scores DataFrame
exam_scores_df = pd.DataFrame({
    "Student": ["Alice", "Bob", "Charlie", "David"],
    "Math": [85, 90, 78, 92],
    "Science": [88, 76, 95, 85],
    "English": [90, 88, 84, 91]
})

# Displaying the DataFrame as a formatted table using 'grid' format
table_jupyter = tabulate(exam_scores_df, headers='keys', tablefmt='grid')

print(table_jupyter)

Table output using Python tabulate integrated with a Jupyter Notebook. Image by Author.

The advantage of using the tabulate library in a Jupyter environment is that it enhances readability and data comparison, especially for data analysis tasks.

Conclusion

The tabulate library is important for creating nicely formatted tables in Python. As you have seen, it also offers flexibility for use in different environments, such as Jupyter Notebooks, command lines, and web applications, including options for the output, such as HTML. 

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