python-tabulate
Pretty-print tabular data in Python, a library and a command-line utility. Repository migrated from bitbucket.org/astanin/python-tabulate.
Top Related Projects
pefile is a Python module to read and work with PE (Portable Executable) files
Rich is a Python library for rich text and beautiful formatting in the terminal.
Display tabular data in a visually appealing ASCII table format
A Python module for creating Excel XLSX files.
Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more
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.
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
- 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"))
- 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"))
- 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
pefile is a Python module to read and work with PE (Portable Executable) files
Pros of pefile
- Specialized for analyzing PE (Portable Executable) files, providing in-depth Windows executable analysis
- Offers extensive features for parsing and manipulating PE file structures
- Widely used in malware analysis and reverse engineering
Cons of pefile
- Limited to PE file analysis, lacking general-purpose data presentation capabilities
- Steeper learning curve due to its specialized nature
- Less suitable for quick data visualization or reporting tasks
Code Comparison
pefile:
import pefile
pe = pefile.PE('executable.exe')
print(pe.DOS_HEADER.e_magic)
print(pe.NT_HEADERS.Signature)
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.
Rich is a Python library for rich text and beautiful formatting in the terminal.
Pros of rich
- More comprehensive feature set, including color, styling, and various output types
- Actively maintained with frequent updates and improvements
- Extensive documentation and examples
Cons of rich
- Larger library size and potentially more complex to use for simple tasks
- May be overkill for projects that only need basic table formatting
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.
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
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.
Convert designs to code with AI
Introducing Visual Copilot: A new AI model to turn Figma designs to high quality code using your components.
Try Visual CopilotREADME
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
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"))
ââââââââââ¬ââââââââ
â item â qty â
ââââââââââ¼ââââââââ¤
â spam â 42 â
ââââââââââ¼ââââââââ¤
â eggs â 451 â
ââââââââââ¼ââââââââ¤
â bacon â 0 â
ââââââââââ´ââââââââ
rounded_grid
draws a grid using single-line box-drawing characters with rounded corners:
>>> print(tabulate(table, headers, tablefmt="rounded_grid"))
ââââââââââ¬ââââââââ®
â item â qty â
ââââââââââ¼ââââââââ¤
â spam â 42 â
ââââââââââ¼ââââââââ¤
â eggs â 451 â
ââââââââââ¼ââââââââ¤
â bacon â 0 â
â°âââââââââ´ââââââââ¯
heavy_grid
draws a grid using bold (thick) single-line box-drawing characters:
>>> print(tabulate(table, headers, tablefmt="heavy_grid"))
ââââââââââ³ââââââââ
â item â qty â
â£âââââââââââââââââ«
â spam â 42 â
â£âââââââââââââââââ«
â eggs â 451 â
â£âââââââââââââââââ«
â bacon â 0 â
ââââââââââ»ââââââââ
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"))
ââââââââââ¯ââââââââ
â item â qty â
ââââââââââ¿ââââââââ¥
â spam â 42 â
ââââââââââ¼ââââââââ¤
â eggs â 451 â
ââââââââââ¼ââââââââ¤
â bacon â 0 â
ââââââââââ·ââââââââ
double_grid
draws a grid using double-line box-drawing characters:
>>> print(tabulate(table, headers, tablefmt="double_grid"))
ââââââââââ¦ââââââââ
â item â qty â
â âââââââââ¬ââââââââ£
â spam â 42 â
â âââââââââ¬ââââââââ£
â eggs â 451 â
â âââââââââ¬ââââââââ£
â bacon â 0 â
ââââââââââ©ââââââââ
fancy_grid
draws a grid using a mix of single and
double-line box-drawing characters:
>>> print(tabulate(table, headers, tablefmt="fancy_grid"))
ââââââââââ¤ââââââââ
â item â qty â
ââââââââââªââââââââ¡
â spam â 42 â
ââââââââââ¼ââââââââ¤
â eggs â 451 â
ââââââââââ¼ââââââââ¤
â bacon â 0 â
ââââââââââ§ââââââââ
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"))
ââââââââââ¬ââââââââ
â item â qty â
ââââââââââ¼ââââââââ¤
â spam â 42 â
â eggs â 451 â
â bacon â 0 â
ââââââââââ´ââââââââ
rounded_outline
is the same as the rounded_grid
format but doesn't draw lines between rows:
>>> print(tabulate(table, headers, tablefmt="rounded_outline"))
ââââââââââ¬ââââââââ®
â item â qty â
ââââââââââ¼ââââââââ¤
â spam â 42 â
â eggs â 451 â
â bacon â 0 â
â°âââââââââ´ââââââââ¯
heavy_outline
is the same as the heavy_grid
format but doesn't draw lines between rows:
>>> print(tabulate(table, headers, tablefmt="heavy_outline"))
ââââââââââ³ââââââââ
â item â qty â
â£âââââââââââââââââ«
â spam â 42 â
â eggs â 451 â
â bacon â 0 â
ââââââââââ»ââââââââ
mixed_outline
is the same as the mixed_grid
format but doesn't draw lines between rows:
>>> print(tabulate(table, headers, tablefmt="mixed_outline"))
ââââââââââ¯ââââââââ
â item â qty â
ââââââââââ¿ââââââââ¥
â spam â 42 â
â eggs â 451 â
â bacon â 0 â
ââââââââââ·ââââââââ
double_outline
is the same as the double_grid
format but doesn't draw lines between rows:
>>> print(tabulate(table, headers, tablefmt="double_outline"))
ââââââââââ¦ââââââââ
â item â qty â
â âââââââââ¬ââââââââ£
â spam â 42 â
â eggs â 451 â
â bacon â 0 â
ââââââââââ©ââââââââ
fancy_outline
is the same as the fancy_grid
format but doesn't draw lines between rows:
>>> print(tabulate(table, headers, tablefmt="fancy_outline"))
ââââââââââ¤ââââââââ
â item â qty â
ââââââââââªââââââââ¡
â spam â 42 â
â eggs â 451 â
â bacon â 0 â
ââââââââââ§ââââââââ
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 areright
,center
,left
,None
(to follow column alignment),headersalign
list or tuple to further specify header-wise alignment. Possible values areglobal
(keeps global setting),same
(follow column alignment),right
,center
,left
,None
(to disable alignment). Missing alignments are treated asglobal
.
>>> 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"))
ââââââââââ¤ââââââââ
â item â qty â
â name â â
ââââââââââªââââââââ¡
â eggs â 451 â
ââââââââââ¼ââââââââ¤
â more â 42 â
â spam â â
ââââââââââ§ââââââââ
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 CrespÃ, 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 VrziÄ, ìì¹ì° (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, Ãlie Goudout.
Top Related Projects
pefile is a Python module to read and work with PE (Portable Executable) files
Rich is a Python library for rich text and beautiful formatting in the terminal.
Display tabular data in a visually appealing ASCII table format
A Python module for creating Excel XLSX files.
Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more
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.
Convert designs to code with AI
Introducing Visual Copilot: A new AI model to turn Figma designs to high quality code using your components.
Try Visual Copilot