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astanin logopython-tabulate

Pretty-print tabular data in Python, a library and a command-line utility. Repository migrated from bitbucket.org/astanin/python-tabulate.

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Quick Overview

Python-tabulate is a library for creating ASCII tables in Python. It provides a simple and flexible way to format tabular data into various table styles, making it easy to generate readable text-based tables for console output or plain text documents.

Pros

  • Easy to use with a simple API
  • Supports multiple table formats and styles
  • Handles various data types and structures
  • Customizable column alignment and formatting

Cons

  • Limited support for complex table structures
  • May not be suitable for very large datasets
  • Lacks advanced features like cell merging or nested tables
  • Output is text-based, not suitable for graphical interfaces

Code Examples

  1. Basic table creation:
from tabulate import tabulate

data = [["Name", "Age", "City"],
        ["Alice", 25, "New York"],
        ["Bob", 30, "San Francisco"],
        ["Charlie", 35, "London"]]

print(tabulate(data, headers="firstrow"))
  1. Using different table styles:
from tabulate import tabulate

data = [["Item", "Price"], ["Apple", 1.50], ["Banana", 0.75], ["Orange", 1.25]]

print(tabulate(data, headers="firstrow", tablefmt="grid"))
print("\n")
print(tabulate(data, headers="firstrow", tablefmt="fancy_grid"))
  1. Aligning columns and formatting numbers:
from tabulate import tabulate

data = [["Product", "Quantity", "Price"],
        ["Widget A", 100, 9.99],
        ["Gadget B", 50, 24.50],
        ["Gizmo C", 75, 14.95]]

print(tabulate(data, headers="firstrow", numalign="right", floatfmt=".2f"))

Getting Started

To use python-tabulate, first install it using pip:

pip install tabulate

Then, in your Python script:

from tabulate import tabulate

# Prepare your data as a list of lists or a dictionary
data = [["Name", "Age"], ["Alice", 25], ["Bob", 30]]

# Generate and print the table
print(tabulate(data, headers="firstrow"))

This will create a simple ASCII table with your data. Explore different table formats and options by referring to the library's documentation for more advanced usage.

Competitor Comparisons

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  • Specialized for analyzing PE (Portable Executable) files, providing in-depth Windows executable analysis
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Cons of pefile

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pefile:

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pe = pefile.PE('executable.exe')
print(pe.DOS_HEADER.e_magic)
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print(pe.FILE_HEADER.Machine)

python-tabulate:

from tabulate import tabulate
data = [["Name", "Age"], ["Alice", 24], ["Bob", 19]]
print(tabulate(data, headers="firstrow"))

Summary

pefile is a specialized library for analyzing Windows PE files, making it ideal for malware analysis and reverse engineering. python-tabulate, on the other hand, is a general-purpose library for creating formatted tables from various data sources. While pefile offers deep insights into executable files, python-tabulate excels at presenting tabular data in a readable format for a wide range of applications.

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Cons of rich

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Code comparison

rich:

from rich.console import Console
from rich.table import Table

table = Table(title="Sample Table")
table.add_column("Name", style="cyan")
table.add_column("Age", style="magenta")
table.add_row("Alice", "30")
table.add_row("Bob", "25")

console = Console()
console.print(table)

python-tabulate:

from tabulate import tabulate

data = [["Alice", 30], ["Bob", 25]]
headers = ["Name", "Age"]
print(tabulate(data, headers=headers, tablefmt="grid"))

Summary

rich is a more feature-rich library with extensive styling options and output types, while python-tabulate focuses primarily on table formatting. rich is better suited for projects requiring advanced console output, while python-tabulate is simpler and more lightweight for basic table needs.

Display tabular data in a visually appealing ASCII table format

Pros of PrettyTable

  • More feature-rich, including support for HTML output and custom styles
  • Actively maintained with regular updates and improvements
  • Better documentation and examples available

Cons of PrettyTable

  • Slightly more complex API, which may be overkill for simple use cases
  • Larger package size due to additional features

Code Comparison

PrettyTable:

from prettytable import PrettyTable
table = PrettyTable()
table.field_names = ["Name", "Age"]
table.add_row(["Alice", 24])
print(table)

python-tabulate:

from tabulate import tabulate
data = [["Name", "Age"], ["Alice", 24]]
print(tabulate(data, headers="firstrow"))

Both libraries provide similar functionality for creating simple tables, but PrettyTable offers more customization options and output formats. python-tabulate has a simpler API and is more lightweight, making it suitable for quick and straightforward table generation.

PrettyTable is better suited for complex tables with advanced formatting requirements, while python-tabulate excels in simplicity and ease of use for basic tabular data presentation.

A Python module for creating Excel XLSX files.

Pros of XlsxWriter

  • Specialized for creating Excel XLSX files with advanced features
  • Supports charts, images, and complex formatting options
  • Faster performance for large datasets

Cons of XlsxWriter

  • Limited to Excel output format
  • Steeper learning curve for basic use cases
  • Requires more code for simple tabular data

Code Comparison

XlsxWriter:

import xlsxwriter

workbook = xlsxwriter.Workbook('output.xlsx')
worksheet = workbook.add_worksheet()
worksheet.write('A1', 'Hello')
workbook.close()

python-tabulate:

from tabulate import tabulate

data = [["Name", "Age"], ["Alice", 25], ["Bob", 30]]
print(tabulate(data, headers="firstrow"))

Summary

XlsxWriter is ideal for creating complex Excel files with advanced features, while python-tabulate excels at quickly generating simple, formatted tables in various output formats. XlsxWriter offers more power and flexibility for Excel-specific tasks but requires more setup and code. python-tabulate is easier to use for basic tabular data presentation but lacks Excel-specific features.

43,532

Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more

Pros of pandas

  • Comprehensive data manipulation and analysis library
  • Powerful data structures like DataFrame and Series
  • Extensive functionality for handling various data formats and operations

Cons of pandas

  • Steeper learning curve due to its extensive features
  • Higher memory usage, especially for large datasets
  • More complex setup and dependencies

Code comparison

pandas:

import pandas as pd

data = {'Name': ['John', 'Alice', 'Bob'],
        'Age': [25, 30, 35]}
df = pd.DataFrame(data)
print(df)

python-tabulate:

from tabulate import tabulate

data = [['John', 25],
        ['Alice', 30],
        ['Bob', 35]]
headers = ['Name', 'Age']
print(tabulate(data, headers=headers))

Key differences

  • pandas is a full-featured data analysis library, while python-tabulate focuses on formatting tabular data
  • python-tabulate is lightweight and easy to use for simple table formatting tasks
  • pandas offers more advanced data manipulation and analysis capabilities
  • python-tabulate has a smaller footprint and fewer dependencies
  • pandas is better suited for complex data processing workflows, while python-tabulate excels at quick and simple table formatting
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xlwings is a Python library that makes it easy to call Python from Excel and vice versa. It works with Excel on Windows and macOS as well as with Google Sheets and Excel on the web.

Pros of xlwings

  • Provides direct integration with Excel, allowing for more complex spreadsheet operations
  • Supports both reading and writing Excel files, with the ability to manipulate Excel objects
  • Offers a COM API for Windows users, enabling automation of Excel tasks

Cons of xlwings

  • Requires Excel to be installed on the system, limiting portability
  • Has a steeper learning curve due to its more comprehensive feature set
  • May be overkill for simple tabular data formatting tasks

Code Comparison

xlwings:

import xlwings as xw

wb = xw.Book()
sheet = wb.sheets[0]
sheet.range('A1').value = [['Name', 'Age'], ['Alice', 30], ['Bob', 25]]
wb.save('output.xlsx')

python-tabulate:

from tabulate import tabulate

data = [['Alice', 30], ['Bob', 25]]
headers = ['Name', 'Age']
print(tabulate(data, headers=headers, tablefmt='grid'))

Summary

xlwings is a powerful library for Excel integration, offering extensive functionality for working with spreadsheets. It's ideal for complex Excel operations but requires Excel installation. python-tabulate, on the other hand, is a lightweight library focused on formatting tabular data for console output or simple text-based tables. It's more portable and easier to use for basic tabular data presentation tasks.

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README

python-tabulate

Pretty-print tabular data in Python, a library and a command-line utility.

The main use cases of the library are:

  • printing small tables without hassle: just one function call, formatting is guided by the data itself
  • authoring tabular data for lightweight plain-text markup: multiple output formats suitable for further editing or transformation
  • readable presentation of mixed textual and numeric data: smart column alignment, configurable number formatting, alignment by a decimal point

Installation

To install the Python library and the command line utility, run:

pip install tabulate

The command line utility will be installed as tabulate to bin on Linux (e.g. /usr/bin); or as tabulate.exe to Scripts in your Python installation on Windows (e.g. C:\Python39\Scripts\tabulate.exe).

You may consider installing the library only for the current user:

pip install tabulate --user

In this case the command line utility will be installed to ~/.local/bin/tabulate on Linux and to %APPDATA%\Python\Scripts\tabulate.exe on Windows.

To install just the library on Unix-like operating systems:

TABULATE_INSTALL=lib-only pip install tabulate

On Windows:

set TABULATE_INSTALL=lib-only
pip install tabulate

Build status

Build status Build status

Library usage

The module provides just one function, tabulate, which takes a list of lists or another tabular data type as the first argument, and outputs a nicely formatted plain-text table:

>>> from tabulate import tabulate

>>> table = [["Sun",696000,1989100000],["Earth",6371,5973.6],
...          ["Moon",1737,73.5],["Mars",3390,641.85]]
>>> print(tabulate(table))
-----  ------  -------------
Sun    696000     1.9891e+09
Earth    6371  5973.6
Moon     1737    73.5
Mars     3390   641.85
-----  ------  -------------

The following tabular data types are supported:

  • list of lists or another iterable of iterables
  • list or another iterable of dicts (keys as columns)
  • dict of iterables (keys as columns)
  • list of dataclasses (Python 3.7+ only, field names as columns)
  • two-dimensional NumPy array
  • NumPy record arrays (names as columns)
  • pandas.DataFrame

Tabulate is a Python3 library.

Headers

The second optional argument named headers defines a list of column headers to be used:

>>> print(tabulate(table, headers=["Planet","R (km)", "mass (x 10^29 kg)"]))
Planet      R (km)    mass (x 10^29 kg)
--------  --------  -------------------
Sun         696000           1.9891e+09
Earth         6371        5973.6
Moon          1737          73.5
Mars          3390         641.85

If headers="firstrow", then the first row of data is used:

>>> print(tabulate([["Name","Age"],["Alice",24],["Bob",19]],
...                headers="firstrow"))
Name      Age
------  -----
Alice      24
Bob        19

If headers="keys", then the keys of a dictionary/dataframe, or column indices are used. It also works for NumPy record arrays and lists of dictionaries or named tuples:

>>> print(tabulate({"Name": ["Alice", "Bob"],
...                 "Age": [24, 19]}, headers="keys"))
  Age  Name
-----  ------
   24  Alice
   19  Bob

Row Indices

By default, only pandas.DataFrame tables have an additional column called row index. To add a similar column to any other type of table, pass showindex="always" or showindex=True argument to tabulate(). To suppress row indices for all types of data, pass showindex="never" or showindex=False. To add a custom row index column, pass showindex=rowIDs, where rowIDs is some iterable:

>>> print(tabulate([["F",24],["M",19]], showindex="always"))
-  -  --
0  F  24
1  M  19
-  -  --

Table format

There is more than one way to format a table in plain text. The third optional argument named tablefmt defines how the table is formatted.

Supported table formats are:

  • "plain"
  • "simple"
  • "github"
  • "grid"
  • "simple_grid"
  • "rounded_grid"
  • "heavy_grid"
  • "mixed_grid"
  • "double_grid"
  • "fancy_grid"
  • "outline"
  • "simple_outline"
  • "rounded_outline"
  • "heavy_outline"
  • "mixed_outline"
  • "double_outline"
  • "fancy_outline"
  • "pipe"
  • "orgtbl"
  • "asciidoc"
  • "jira"
  • "presto"
  • "pretty"
  • "psql"
  • "rst"
  • "mediawiki"
  • "moinmoin"
  • "youtrack"
  • "html"
  • "unsafehtml"
  • "latex"
  • "latex_raw"
  • "latex_booktabs"
  • "latex_longtable"
  • "textile"
  • "tsv"

plain tables do not use any pseudo-graphics to draw lines:

>>> table = [["spam",42],["eggs",451],["bacon",0]]
>>> headers = ["item", "qty"]
>>> print(tabulate(table, headers, tablefmt="plain"))
item      qty
spam       42
eggs      451
bacon       0

simple is the default format (the default may change in future versions). It corresponds to simple_tables in Pandoc Markdown extensions:

>>> print(tabulate(table, headers, tablefmt="simple"))
item      qty
------  -----
spam       42
eggs      451
bacon       0

github follows the conventions of GitHub flavored Markdown. It corresponds to the pipe format without alignment colons:

>>> print(tabulate(table, headers, tablefmt="github"))
| item   | qty   |
|--------|-------|
| spam   | 42    |
| eggs   | 451   |
| bacon  | 0     |

grid is like tables formatted by Emacs' table.el package. It corresponds to grid_tables in Pandoc Markdown extensions:

>>> print(tabulate(table, headers, tablefmt="grid"))
+--------+-------+
| item   |   qty |
+========+=======+
| spam   |    42 |
+--------+-------+
| eggs   |   451 |
+--------+-------+
| bacon  |     0 |
+--------+-------+

simple_grid draws a grid using single-line box-drawing characters:

>>> print(tabulate(table, headers, tablefmt="simple_grid"))
┌────────┬───────┐
│ item   │   qty │
├────────┼───────┤
│ spam   │    42 │
├────────┼───────┤
│ eggs   │   451 │
├────────┼───────┤
│ bacon  │     0 │
└────────┴───────┘

rounded_grid draws a grid using single-line box-drawing characters with rounded corners:

>>> print(tabulate(table, headers, tablefmt="rounded_grid"))
╭────────┬───────╮
│ item   │   qty │
├────────┼───────┤
│ spam   │    42 │
├────────┼───────┤
│ eggs   │   451 │
├────────┼───────┤
│ bacon  │     0 │
╰────────┴───────╯

heavy_grid draws a grid using bold (thick) single-line box-drawing characters:

>>> print(tabulate(table, headers, tablefmt="heavy_grid"))
┏━━━━━━━━┳━━━━━━━┓
┃ item   ┃   qty ┃
┣━━━━━━━━╋━━━━━━━┫
┃ spam   ┃    42 ┃
┣━━━━━━━━╋━━━━━━━┫
┃ eggs   ┃   451 ┃
┣━━━━━━━━╋━━━━━━━┫
┃ bacon  ┃     0 ┃
┗━━━━━━━━┻━━━━━━━┛

mixed_grid draws a grid using a mix of light (thin) and heavy (thick) lines box-drawing characters:

>>> print(tabulate(table, headers, tablefmt="mixed_grid"))
┍━━━━━━━━┯━━━━━━━┑
│ item   │   qty │
┝━━━━━━━━┿━━━━━━━┥
│ spam   │    42 │
├────────┼───────┤
│ eggs   │   451 │
├────────┼───────┤
│ bacon  │     0 │
┕━━━━━━━━┷━━━━━━━┙

double_grid draws a grid using double-line box-drawing characters:

>>> print(tabulate(table, headers, tablefmt="double_grid"))
╔════════╦═══════╗
║ item   ║   qty ║
╠════════╬═══════╣
║ spam   ║    42 ║
╠════════╬═══════╣
║ eggs   ║   451 ║
╠════════╬═══════╣
║ bacon  ║     0 ║
╚════════╩═══════╝

fancy_grid draws a grid using a mix of single and double-line box-drawing characters:

>>> print(tabulate(table, headers, tablefmt="fancy_grid"))
╒════════╤═══════╕
│ item   │   qty │
╞════════╪═══════╡
│ spam   │    42 │
├────────┼───────┤
│ eggs   │   451 │
├────────┼───────┤
│ bacon  │     0 │
╘════════╧═══════╛

outline is the same as the grid format but doesn't draw lines between rows:

>>> print(tabulate(table, headers, tablefmt="outline"))
+--------+-------+
| item   |   qty |
+========+=======+
| spam   |    42 |
| eggs   |   451 |
| bacon  |     0 |
+--------+-------+

simple_outline is the same as the simple_grid format but doesn't draw lines between rows:

>>> print(tabulate(table, headers, tablefmt="simple_outline"))
┌────────┬───────┐
│ item   │   qty │
├────────┼───────┤
│ spam   │    42 │
│ eggs   │   451 │
│ bacon  │     0 │
└────────┴───────┘

rounded_outline is the same as the rounded_grid format but doesn't draw lines between rows:

>>> print(tabulate(table, headers, tablefmt="rounded_outline"))
╭────────┬───────╮
│ item   │   qty │
├────────┼───────┤
│ spam   │    42 │
│ eggs   │   451 │
│ bacon  │     0 │
╰────────┴───────╯

heavy_outline is the same as the heavy_grid format but doesn't draw lines between rows:

>>> print(tabulate(table, headers, tablefmt="heavy_outline"))
┏━━━━━━━━┳━━━━━━━┓
┃ item   ┃   qty ┃
┣━━━━━━━━╋━━━━━━━┫
┃ spam   ┃    42 ┃
┃ eggs   ┃   451 ┃
┃ bacon  ┃     0 ┃
┗━━━━━━━━┻━━━━━━━┛

mixed_outline is the same as the mixed_grid format but doesn't draw lines between rows:

>>> print(tabulate(table, headers, tablefmt="mixed_outline"))
┍━━━━━━━━┯━━━━━━━┑
│ item   │   qty │
┝━━━━━━━━┿━━━━━━━┥
│ spam   │    42 │
│ eggs   │   451 │
│ bacon  │     0 │
┕━━━━━━━━┷━━━━━━━┙

double_outline is the same as the double_grid format but doesn't draw lines between rows:

>>> print(tabulate(table, headers, tablefmt="double_outline"))
╔════════╦═══════╗
║ item   ║   qty ║
╠════════╬═══════╣
║ spam   ║    42 ║
║ eggs   ║   451 ║
║ bacon  ║     0 ║
╚════════╩═══════╝

fancy_outline is the same as the fancy_grid format but doesn't draw lines between rows:

>>> print(tabulate(table, headers, tablefmt="fancy_outline"))
╒════════╤═══════╕
│ item   │   qty │
╞════════╪═══════╡
│ spam   │    42 │
│ eggs   │   451 │
│ bacon  │     0 │
╘════════╧═══════╛

presto is like tables formatted by Presto cli:

>>> print(tabulate(table, headers, tablefmt="presto"))
 item   |   qty
--------+-------
 spam   |    42
 eggs   |   451
 bacon  |     0

pretty attempts to be close to the format emitted by the PrettyTables library:

>>> print(tabulate(table, headers, tablefmt="pretty"))
+-------+-----+
| item  | qty |
+-------+-----+
| spam  | 42  |
| eggs  | 451 |
| bacon |  0  |
+-------+-----+

psql is like tables formatted by Postgres' psql cli:

>>> print(tabulate(table, headers, tablefmt="psql"))
+--------+-------+
| item   |   qty |
|--------+-------|
| spam   |    42 |
| eggs   |   451 |
| bacon  |     0 |
+--------+-------+

pipe follows the conventions of PHP Markdown Extra extension. It corresponds to pipe_tables in Pandoc. This format uses colons to indicate column alignment:

>>> print(tabulate(table, headers, tablefmt="pipe"))
| item   |   qty |
|:-------|------:|
| spam   |    42 |
| eggs   |   451 |
| bacon  |     0 |

asciidoc formats data like a simple table of the AsciiDoctor format:

>>> print(tabulate(table, headers, tablefmt="asciidoc"))
[cols="8<,7>",options="header"]
|====
| item   |   qty
| spam   |    42
| eggs   |   451
| bacon  |     0
|====

orgtbl follows the conventions of Emacs org-mode, and is editable also in the minor orgtbl-mode. Hence its name:

>>> print(tabulate(table, headers, tablefmt="orgtbl"))
| item   |   qty |
|--------+-------|
| spam   |    42 |
| eggs   |   451 |
| bacon  |     0 |

jira follows the conventions of Atlassian Jira markup language:

>>> print(tabulate(table, headers, tablefmt="jira"))
|| item   ||   qty ||
| spam   |    42 |
| eggs   |   451 |
| bacon  |     0 |

rst formats data like a simple table of the reStructuredText format:

>>> print(tabulate(table, headers, tablefmt="rst"))
======  =====
item      qty
======  =====
spam       42
eggs      451
bacon       0
======  =====

mediawiki format produces a table markup used in Wikipedia and on other MediaWiki-based sites:

>>> print(tabulate(table, headers, tablefmt="mediawiki"))
{| class="wikitable" style="text-align: left;"
|+ <!-- caption -->
|-
! item   !! style="text-align: right;"|   qty
|-
| spam   || style="text-align: right;"|    42
|-
| eggs   || style="text-align: right;"|   451
|-
| bacon  || style="text-align: right;"|     0
|}

moinmoin format produces a table markup used in MoinMoin wikis:

>>> print(tabulate(table, headers, tablefmt="moinmoin"))
|| ''' item   ''' || ''' quantity   ''' ||
||  spam    ||  41.999      ||
||  eggs    ||  451         ||
||  bacon   ||              ||

youtrack format produces a table markup used in Youtrack tickets:

>>> print(tabulate(table, headers, tablefmt="youtrack"))
||  item    ||  quantity   ||
|   spam    |  41.999      |
|   eggs    |  451         |
|   bacon   |              |

textile format produces a table markup used in Textile format:

>>> print(tabulate(table, headers, tablefmt="textile"))
|_.  item   |_.   qty |
|<. spam    |>.    42 |
|<. eggs    |>.   451 |
|<. bacon   |>.     0 |

html produces standard HTML markup as an html.escape'd str with a .repr_html method so that Jupyter Lab and Notebook display the HTML and a .str property so that the raw HTML remains accessible. unsafehtml table format can be used if an unescaped HTML is required:

>>> print(tabulate(table, headers, tablefmt="html"))
<table>
<tbody>
<tr><th>item  </th><th style="text-align: right;">  qty</th></tr>
<tr><td>spam  </td><td style="text-align: right;">   42</td></tr>
<tr><td>eggs  </td><td style="text-align: right;">  451</td></tr>
<tr><td>bacon </td><td style="text-align: right;">    0</td></tr>
</tbody>
</table>

latex format creates a tabular environment for LaTeX markup, replacing special characters like _ or \ to their LaTeX correspondents:

>>> print(tabulate(table, headers, tablefmt="latex"))
\begin{tabular}{lr}
\hline
 item   &   qty \\
\hline
 spam   &    42 \\
 eggs   &   451 \\
 bacon  &     0 \\
\hline
\end{tabular}

latex_raw behaves like latex but does not escape LaTeX commands and special characters.

latex_booktabs creates a tabular environment for LaTeX markup using spacing and style from the booktabs package.

latex_longtable creates a table that can stretch along multiple pages, using the longtable package.

Column alignment

tabulate is smart about column alignment. It detects columns which contain only numbers, and aligns them by a decimal point (or flushes them to the right if they appear to be integers). Text columns are flushed to the left.

You can override the default alignment with numalign and stralign named arguments. Possible column alignments are: right, center, left, decimal (only for numbers), and None (to disable alignment).

Aligning by a decimal point works best when you need to compare numbers at a glance:

>>> print(tabulate([[1.2345],[123.45],[12.345],[12345],[1234.5]]))
----------
    1.2345
  123.45
   12.345
12345
 1234.5
----------

Compare this with a more common right alignment:

>>> print(tabulate([[1.2345],[123.45],[12.345],[12345],[1234.5]], numalign="right"))
------
1.2345
123.45
12.345
 12345
1234.5
------

For tabulate, anything which can be parsed as a number is a number. Even numbers represented as strings are aligned properly. This feature comes in handy when reading a mixed table of text and numbers from a file:

>>> import csv ; from StringIO import StringIO
>>> table = list(csv.reader(StringIO("spam, 42\neggs, 451\n")))
>>> table
[['spam', ' 42'], ['eggs', ' 451']]
>>> print(tabulate(table))
----  ----
spam    42
eggs   451
----  ----

To disable this feature use disable_numparse=True.

>>> print(tabulate.tabulate([["Ver1", "18.0"], ["Ver2","19.2"]], tablefmt="simple", disable_numparse=True))
----  ----
Ver1  18.0
Ver2  19.2
----  ----

Custom column alignment

tabulate allows a custom column alignment to override the smart alignment described above. Use colglobalalign to define a global setting. Possible alignments are: right, center, left, decimal (only for numbers). Furthermore, you can define colalign for column-specific alignment as a list or a tuple. Possible values are global (keeps global setting), right, center, left, decimal (only for numbers), None (to disable alignment). Missing alignments are treated as global.

>>> print(tabulate([[1,2,3,4],[111,222,333,444]], colglobalalign='center', colalign = ('global','left','right')))
---  ---  ---  ---
 1   2      3   4
111  222  333  444
---  ---  ---  ---

Custom header alignment

Headers' alignment can be defined separately from columns'. Like for columns, you can use:

  • headersglobalalign to define a header-specific global alignment setting. Possible values are right, center, left, None (to follow column alignment),
  • headersalign list or tuple to further specify header-wise alignment. Possible values are global (keeps global setting), same (follow column alignment), right, center, left, None (to disable alignment). Missing alignments are treated as global.
>>> print(tabulate([[1,2,3,4,5,6],[111,222,333,444,555,666]], colglobalalign = 'center', colalign = ('left',), headers = ['h','e','a','d','e','r'], headersglobalalign = 'right', headersalign = ('same','same','left','global','center')))

h     e   a      d   e     r
---  ---  ---  ---  ---  ---
1     2    3    4    5    6
111  222  333  444  555  666

Number formatting

tabulate allows to define custom number formatting applied to all columns of decimal numbers. Use floatfmt named argument:

>>> print(tabulate([["pi",3.141593],["e",2.718282]], floatfmt=".4f"))
--  ------
pi  3.1416
e   2.7183
--  ------

floatfmt argument can be a list or a tuple of format strings, one per column, in which case every column may have different number formatting:

>>> print(tabulate([[0.12345, 0.12345, 0.12345]], floatfmt=(".1f", ".3f")))
---  -----  -------
0.1  0.123  0.12345
---  -----  -------

intfmt works similarly for integers

>>> print(tabulate([["a",1000],["b",90000]], intfmt=","))
-  ------
a   1,000
b  90,000
-  ------

Text formatting

By default, tabulate removes leading and trailing whitespace from text columns. To disable whitespace removal, set the global module-level flag PRESERVE_WHITESPACE:

import tabulate
tabulate.PRESERVE_WHITESPACE = True

Wide (fullwidth CJK) symbols

To properly align tables which contain wide characters (typically fullwidth glyphs from Chinese, Japanese or Korean languages), the user should install wcwidth library. To install it together with tabulate:

pip install tabulate[widechars]

Wide character support is enabled automatically if wcwidth library is already installed. To disable wide characters support without uninstalling wcwidth, set the global module-level flag WIDE_CHARS_MODE:

import tabulate
tabulate.WIDE_CHARS_MODE = False

Multiline cells

Most table formats support multiline cell text (text containing newline characters). The newline characters are honored as line break characters.

Multiline cells are supported for data rows and for header rows.

Further automatic line breaks are not inserted. Of course, some output formats such as latex or html handle automatic formatting of the cell content on their own, but for those that don't, the newline characters in the input cell text are the only means to break a line in cell text.

Note that some output formats (e.g. simple, or plain) do not represent row delimiters, so that the representation of multiline cells in such formats may be ambiguous to the reader.

The following examples of formatted output use the following table with a multiline cell, and headers with a multiline cell:

>>> table = [["eggs",451],["more\nspam",42]]
>>> headers = ["item\nname", "qty"]

plain tables:

>>> print(tabulate(table, headers, tablefmt="plain"))
item      qty
name
eggs      451
more       42
spam

simple tables:

>>> print(tabulate(table, headers, tablefmt="simple"))
item      qty
name
------  -----
eggs      451
more       42
spam

grid tables:

>>> print(tabulate(table, headers, tablefmt="grid"))
+--------+-------+
| item   |   qty |
| name   |       |
+========+=======+
| eggs   |   451 |
+--------+-------+
| more   |    42 |
| spam   |       |
+--------+-------+

fancy_grid tables:

>>> print(tabulate(table, headers, tablefmt="fancy_grid"))
╒════════╤═══════╕
│ item   │   qty │
│ name   │       │
╞════════╪═══════╡
│ eggs   │   451 │
├────────┼───────┤
│ more   │    42 │
│ spam   │       │
╘════════╧═══════╛

pipe tables:

>>> print(tabulate(table, headers, tablefmt="pipe"))
| item   |   qty |
| name   |       |
|:-------|------:|
| eggs   |   451 |
| more   |    42 |
| spam   |       |

orgtbl tables:

>>> print(tabulate(table, headers, tablefmt="orgtbl"))
| item   |   qty |
| name   |       |
|--------+-------|
| eggs   |   451 |
| more   |    42 |
| spam   |       |

jira tables:

>>> print(tabulate(table, headers, tablefmt="jira"))
| item   |   qty |
| name   |       |
|:-------|------:|
| eggs   |   451 |
| more   |    42 |
| spam   |       |

presto tables:

>>> print(tabulate(table, headers, tablefmt="presto"))
 item   |   qty
 name   |
--------+-------
 eggs   |   451
 more   |    42
 spam   |

pretty tables:

>>> print(tabulate(table, headers, tablefmt="pretty"))
+------+-----+
| item | qty |
| name |     |
+------+-----+
| eggs | 451 |
| more | 42  |
| spam |     |
+------+-----+

psql tables:

>>> print(tabulate(table, headers, tablefmt="psql"))
+--------+-------+
| item   |   qty |
| name   |       |
|--------+-------|
| eggs   |   451 |
| more   |    42 |
| spam   |       |
+--------+-------+

rst tables:

>>> print(tabulate(table, headers, tablefmt="rst"))
======  =====
item      qty
name
======  =====
eggs      451
more       42
spam
======  =====

Multiline cells are not well-supported for the other table formats.

Automating Multilines

While tabulate supports data passed in with multilines entries explicitly provided, it also provides some support to help manage this work internally.

The maxcolwidths argument is a list where each entry specifies the max width for it's respective column. Any cell that will exceed this will automatically wrap the content. To assign the same max width for all columns, a singular int scaler can be used.

Use None for any columns where an explicit maximum does not need to be provided, and thus no automate multiline wrapping will take place.

The wrapping uses the python standard textwrap.wrap function with default parameters - aside from width.

This example demonstrates usage of automatic multiline wrapping, though typically the lines being wrapped would probably be significantly longer than this.

>>> print(tabulate([["John Smith", "Middle Manager"]], headers=["Name", "Title"], tablefmt="grid", maxcolwidths=[None, 8]))
+------------+---------+
| Name       | Title   |
+============+=========+
| John Smith | Middle  |
|            | Manager |
+------------+---------+

Adding Separating lines

One might want to add one or more separating lines to highlight different sections in a table.

The separating lines will be of the same type as the one defined by the specified formatter as either the linebetweenrows, linebelowheader, linebelow, lineabove or just a simple empty line when none is defined for the formatter

>>> from tabulate import tabulate, SEPARATING_LINE

table = [["Earth",6371],
         ["Mars",3390],
         SEPARATING_LINE,
         ["Moon",1737]]
print(tabulate(table, tablefmt="simple"))
-----  ----
Earth  6371
Mars   3390
-----  ----
Moon   1737
-----  ----

ANSI support

ANSI escape codes are non-printable byte sequences usually used for terminal operations like setting color output or modifying cursor positions. Because multi-byte ANSI sequences are inherently non-printable, they can still introduce unwanted extra length to strings. For example:

>>> len('\033[31mthis text is red\033[0m')  # printable length is 16
25

To deal with this, string lengths are calculated after first removing all ANSI escape sequences. This ensures that the actual printable length is used for column widths, rather than the byte length. In the final, printable table, however, ANSI escape sequences are not removed so the original styling is preserved.

Some terminals support a special grouping of ANSI escape sequences that are intended to display hyperlinks much in the same way they are shown in browsers. These are handled just as mentioned before: non-printable ANSI escape sequences are removed prior to string length calculation. The only diifference with escaped hyperlinks is that column width will be based on the length of the URL text rather than the URL itself (terminals would show this text). For example:

>>> len('\x1b]8;;https://example.com\x1b\\example\x1b]8;;\x1b\\')  # display length is 7, showing 'example'
45

Usage of the command line utility

Usage: tabulate [options] [FILE ...]

FILE                      a filename of the file with tabular data;
                          if "-" or missing, read data from stdin.

Options:

-h, --help                show this message
-1, --header              use the first row of data as a table header
-o FILE, --output FILE    print table to FILE (default: stdout)
-s REGEXP, --sep REGEXP   use a custom column separator (default: whitespace)
-F FPFMT, --float FPFMT   floating point number format (default: g)
-I INTFMT, --int INTFMT   integer point number format (default: "")
-f FMT, --format FMT      set output table format; supported formats:
                          plain, simple, github, grid, fancy_grid, pipe,
                          orgtbl, rst, mediawiki, html, latex, latex_raw,
                          latex_booktabs, latex_longtable, tsv
                          (default: simple)

Performance considerations

Such features as decimal point alignment and trying to parse everything as a number imply that tabulate:

  • has to "guess" how to print a particular tabular data type
  • needs to keep the entire table in-memory
  • has to "transpose" the table twice
  • does much more work than it may appear

It may not be suitable for serializing really big tables (but who's going to do that, anyway?) or printing tables in performance sensitive applications. tabulate is about two orders of magnitude slower than simply joining lists of values with a tab, comma, or other separator.

At the same time, tabulate is comparable to other table pretty-printers. Given a 10x10 table (a list of lists) of mixed text and numeric data, tabulate appears to be slower than asciitable, and faster than PrettyTable and texttable The following mini-benchmark was run in Python 3.9.13 on Windows 10:

=================================  ==========  ===========
Table formatter                      time, μs    rel. time
=================================  ==========  ===========
csv to StringIO                          12.5          1.0
join with tabs and newlines              14.6          1.2
asciitable (0.8.0)                      192.0         15.4
tabulate (0.9.0)                        483.5         38.7
tabulate (0.9.0, WIDE_CHARS_MODE)       637.6         51.1
PrettyTable (3.4.1)                    1080.6         86.6
texttable (1.6.4)                      1390.3        111.4
=================================  ==========  ===========

Version history

The full version history can be found at the changelog.

How to contribute

Contributions should include tests and an explanation for the changes they propose. Documentation (examples, docstrings, README.md) should be updated accordingly.

This project uses pytest testing framework and tox to automate testing in different environments. Add tests to one of the files in the test/ folder.

To run tests on all supported Python versions, make sure all Python interpreters, pytest and tox are installed, then run tox in the root of the project source tree.

On Linux tox expects to find executables like python3.7, python3.8 etc. On Windows it looks for C:\Python37\python.exe, C:\Python38\python.exe etc. respectively.

One way to install all the required versions of the Python interpreter is to use pyenv. All versions can then be easily installed with something like:

 pyenv install 3.7.12
 pyenv install 3.8.12
 ...

Don't forget to change your PATH so that tox knows how to find all the installed versions. Something like

 export PATH="${PATH}:${HOME}/.pyenv/shims"

To test only some Python environments, use -e option. For example, to test only against Python 3.7 and Python 3.10, run:

tox -e py37,py310

in the root of the project source tree.

To enable NumPy and Pandas tests, run:

tox -e py37-extra,py310-extra

(this may take a long time the first time, because NumPy and Pandas will have to be installed in the new virtual environments)

To fix code formatting:

tox -e lint

See tox.ini file to learn how to use to test individual Python versions.

Contributors

Sergey Astanin, Pau Tallada Crespí, Erwin Marsi, Mik Kocikowski, Bill Ryder, Zach Dwiel, Frederik Rietdijk, Philipp Bogensberger, Greg (anonymous), Stefan Tatschner, Emiel van Miltenburg, Brandon Bennett, Amjith Ramanujam, Jan Schulz, Simon Percivall, Javier Santacruz López-Cepero, Sam Denton, Alexey Ziyangirov, acaird, Cesar Sanchez, naught101, John Vandenberg, Zack Dever, Christian Clauss, Benjamin Maier, Andy MacKinlay, Thomas Roten, Jue Wang, Joe King, Samuel Phan, Nick Satterly, Daniel Robbins, Dmitry B, Lars Butler, Andreas Maier, Dick Marinus, Sébastien Celles, Yago González, Andrew Gaul, Wim Glenn, Jean Michel Rouly, Tim Gates, John Vandenberg, Sorin Sbarnea, Wes Turner, Andrew Tija, Marco Gorelli, Sean McGinnis, danja100, endolith, Dominic Davis-Foster, pavlocat, Daniel Aslau, paulc, Felix Yan, Shane Loretz, Frank Busse, Harsh Singh, Derek Weitzel, Vladimir Vrzić, 서승우 (chrd5273), Georgy Frolov, Christian Cwienk, Bart Broere, Vilhelm Prytz, Alexander Gažo, Hugo van Kemenade, jamescooke, Matt Warner, Jérôme Provensal, Kevin Deldycke, Kian-Meng Ang, Kevin Patterson, Shodhan Save, cleoold, KOLANICH, Vijaya Krishna Kasula, Furcy Pin, Christian Fibich, Shaun Duncan, Dimitri Papadopoulos, Élie Goudout.