pandas.io.formats.style.Styler.format

Styler.format(formatter=None, subset=None, na_rep=None, precision=None, decimal='.', thousands=None, escape=None)[source]

Format the text display value of cells.

Parameters
formatterstr, callable, dict or None

Object to define how values are displayed. See notes.

subsetlabel, array-like, IndexSlice, optional

A valid 2d input to DataFrame.loc[<subset>], or, in the case of a 1d input or single key, to DataFrame.loc[:, <subset>] where the columns are prioritised, to limit data to before applying the function.

na_repstr, optional

Representation for missing values. If na_rep is None, no special formatting is applied.

New in version 1.0.0.

precisionint, optional

Floating point precision to use for display purposes, if not determined by the specified formatter.

New in version 1.3.0.

decimalstr, default “.”

Character used as decimal separator for floats, complex and integers

New in version 1.3.0.

thousandsstr, optional, default None

Character used as thousands separator for floats, complex and integers

New in version 1.3.0.

escapestr, optional

Use ‘html’ to replace the characters &, <, >, ', and " in cell display string with HTML-safe sequences. Use ‘latex’ to replace the characters &, %, $, #, _, {, }, ~, ^, and \ in the cell display string with LaTeX-safe sequences. Escaping is done before formatter.

New in version 1.3.0.

Returns
selfStyler

Notes

This method assigns a formatting function, formatter, to each cell in the DataFrame. If formatter is None, then the default formatter is used. If a callable then that function should take a data value as input and return a displayable representation, such as a string. If formatter is given as a string this is assumed to be a valid Python format specification and is wrapped to a callable as string.format(x). If a dict is given, keys should correspond to column names, and values should be string or callable, as above.

The default formatter currently expresses floats and complex numbers with the pandas display precision unless using the precision argument here. The default formatter does not adjust the representation of missing values unless the na_rep argument is used.

The subset argument defines which region to apply the formatting function to. If the formatter argument is given in dict form but does not include all columns within the subset then these columns will have the default formatter applied. Any columns in the formatter dict excluded from the subset will be ignored.

When using a formatter string the dtypes must be compatible, otherwise a ValueError will be raised.

When instantiating a Styler, default formatting can be applied be setting the pandas.options:

  • styler.format.formatter: default None.

  • styler.format.na_rep: default None.

  • styler.format.precision: default 6.

  • styler.format.decimal: default “.”.

  • styler.format.thousands: default None.

  • styler.format.escape: default None.

Examples

Using na_rep and precision with the default formatter

>>> df = pd.DataFrame([[np.nan, 1.0, 'A'], [2.0, np.nan, 3.0]])
>>> df.style.format(na_rep='MISS', precision=3)  
        0       1       2
0    MISS   1.000       A
1   2.000    MISS   3.000

Using a formatter specification on consistent column dtypes

>>> df.style.format('{:.2f}', na_rep='MISS', subset=[0,1])  
        0      1          2
0    MISS   1.00          A
1    2.00   MISS   3.000000

Using the default formatter for unspecified columns

>>> df.style.format({0: '{:.2f}', 1: {:.1f}'}, na_rep='MISS', precision=1)
...  
         0      1     2
0    MISS   £ 1.0     A
1    2.00    MISS   3.0

Multiple na_rep or precision specifications under the default formatter.

>>> df.style.format(na_rep='MISS', precision=1, subset=[0])
...     .format(na_rep='PASS', precision=2, subset=[1, 2])  
        0      1      2
0    MISS   1.00      A
1     2.0   PASS   3.00

Using a callable formatter function.

>>> func = lambda s: 'STRING' if isinstance(s, str) else 'FLOAT'
>>> df.style.format({0: '{:.1f}', 2: func}, precision=4, na_rep='MISS')
...  
        0        1        2
0    MISS   1.0000   STRING
1     2.0     MISS    FLOAT

Using a formatter with HTML escape and na_rep.

>>> df = pd.DataFrame([['<div></div>', '"A&B"', None]])
>>> s = df.style.format(
...     '<a href="a.com/{0}">{0}</a>', escape="html", na_rep="NA"
...     )
>>> s.to_html()  
...
<td .. ><a href="a.com/&lt;div&gt;&lt;/div&gt;">&lt;div&gt;&lt;/div&gt;</a></td>
<td .. ><a href="a.com/&#34;A&amp;B&#34;">&#34;A&amp;B&#34;</a></td>
<td .. >NA</td>
...

Using a formatter with LaTeX escape.

>>> df = pd.DataFrame([["123"], ["~ ^"], ["$%#"]])
>>> df.style.format("\\textbf{{{}}}", escape="latex").to_latex()
...  
\begin{tabular}{ll}
{} & {0} \\
0 & \textbf{123} \\
1 & \textbf{\textasciitilde \space \textasciicircum } \\
2 & \textbf{\$\%\#} \\
\end{tabular}