Styler Object and HTML¶

Styling should be performed after the data in a DataFrame has been processed. The Styler creates an HTML <table> and leverages CSS styling language to manipulate many parameters including colors, fonts, borders, background, etc. See here for more information on styling HTML tables. This allows a lot of flexibility out of the box, and even enables web developers to integrate DataFrames into their exiting user interface designs.

The DataFrame.style attribute is a property that returns a Styler object. It has a _repr_html_ method defined on it so they are rendered automatically in Jupyter Notebook.

[2]:

import pandas as pd
import numpy as np

df = pd.DataFrame([[38.0, 2.0, 18.0, 22.0, 21, np.nan],[19, 439, 6, 452, 226,232]],
                  index=pd.Index(['Tumour (Positive)', 'Non-Tumour (Negative)'], name='Actual Label:'),
                  columns=pd.MultiIndex.from_product([['Decision Tree', 'Regression', 'Random'],['Tumour', 'Non-Tumour']], names=['Model:', 'Predicted:']))
df.style

[2]:

Model:	Decision Tree		Regression		Random
Predicted:	Tumour	Non-Tumour	Tumour	Non-Tumour	Tumour	Non-Tumour
Actual Label:
Tumour (Positive)	38.000000	2.000000	18.000000	22.000000	21	nan
Non-Tumour (Negative)	19.000000	439.000000	6.000000	452.000000	226	232.000000

The above output looks very similar to the standard DataFrame HTML representation. But the HTML here has already attached some CSS classes to each cell, even if we haven’t yet created any styles. We can view these by calling the .render() method, which returns the raw HTML as string, which is useful for further processing or adding to a file - read on in More about CSS and HTML. Below we will show how we can use these to format the DataFrame to be more communicative. For example how we can build s:

[4]:

[4]:

Confusion matrix for multiple cancer prediction models.
Model:	Decision Tree		Regression
Predicted:	Tumour	Non-Tumour	Tumour	Non-Tumour
Actual Label:
Tumour (Positive)	38	2	18	22
Non-Tumour (Negative)	19	439	6	452

Formatting the Display¶

Formatting Values¶

Before adding styles it is useful to show that the Styler can distinguish the display value from the actual value. To control the display value, the text is printed in each cell, and we can use the .format() method to manipulate this according to a format spec string or a callable that takes a single value and returns a string. It is possible to define this for the whole table or for individual columns.

Additionally, the format function has a precision argument to specifically help formatting floats, an na_rep argument to display missing data, and an escape argument to help displaying safe-HTML. The default formatter is configured to adopt pandas’ regular display.precision option, controllable using with pd.option_context('display.precision', 2):

Here is an example of using the multiple options to control the formatting generally and with specific column formatters.

[5]:

df.style.format(precision=0, na_rep='MISSING',
                formatter={('Decision Tree', 'Tumour'): "{:.2f}",
                           ('Regression', 'Non-Tumour'): lambda x: "$ {:,.1f}".format(x*-1e3)
                          })

[5]:

Model:	Decision Tree		Regression		Random
Predicted:	Tumour	Non-Tumour	Tumour	Non-Tumour	Tumour	Non-Tumour
Actual Label:
Tumour (Positive)	38.00	2	18	$ -22,000.0	21	MISSING
Non-Tumour (Negative)	19.00	439	6	$ -452,000.0	226	232

Hiding Data¶

The index can be hidden from rendering by calling .hide_index(), which might be useful if your index is integer based.

Columns can be hidden from rendering by calling .hide_columns() and passing in the name of a column, or a slice of columns.

Hiding does not change the integer arrangement of CSS classes, e.g. hiding the first two columns of a DataFrame means the column class indexing will start at col2, since col0 and col1 are simply ignored.

We can update our Styler object to hide some data and format the values.

[6]:

s = df.style.format('{:.0f}').hide_columns([('Random', 'Tumour'), ('Random', 'Non-Tumour')])
s

[6]:

Model:	Decision Tree		Regression
Predicted:	Tumour	Non-Tumour	Tumour	Non-Tumour
Actual Label:
Tumour (Positive)	38	2	18	22
Non-Tumour (Negative)	19	439	6	452

Methods to Add Styles¶

There are 3 primary methods of adding custom CSS styles to Styler:

Using .set_table_styles() to control broader areas of the table with specified internal CSS. Although table styles allow the flexibility to add CSS selectors and properties controlling all individual parts of the table, they are unwieldy for individual cell specifications. Also, note that table styles cannot be exported to Excel.
Using .set_td_classes() to directly link either external CSS classes to your data cells or link the internal CSS classes created by .set_table_styles(). See here. These cannot be used on column header rows or indexes, and also won’t export to Excel.
Using the .apply() and .applymap() functions to add direct internal CSS to specific data cells. See here. These cannot be used on column header rows or indexes, but only these methods add styles that will export to Excel. These methods work in a similar way to DataFrame.apply() and DataFrame.applymap().

Table Styles¶

Table styles are flexible enough to control all individual parts of the table, including column headers and indexes. However, they can be unwieldy to type for individual data cells or for any kind of conditional formatting, so we recommend that table styles are used for broad styling, such as entire rows or columns at a time.

Table styles are also used to control features which can apply to the whole table at once such as greating a generic hover functionality. The :hover pseudo-selector, as well as other pseudo-selectors, can only be used this way.

To replicate the normal format of CSS selectors and properties (attribute value pairs), e.g.

tr:hover {
  background-color: #ffff99;
}

the necessary format to pass styles to .set_table_styles() is as a list of dicts, each with a CSS-selector tag and CSS-properties. Properties can either be a list of 2-tuples, or a regular CSS-string, for example:

[8]:

cell_hover = {  # for row hover use <tr> instead of <td>
    'selector': 'td:hover',
    'props': [('background-color', '#ffffb3')]
}
index_names = {
    'selector': '.index_name',
    'props': 'font-style: italic; color: darkgrey; font-weight:normal;'
}
headers = {
    'selector': 'th:not(.index_name)',
    'props': 'background-color: #000066; color: white;'
}
s.set_table_styles([cell_hover, index_names, headers])

[8]:

Model:	Decision Tree		Regression
Predicted:	Tumour	Non-Tumour	Tumour	Non-Tumour
Actual Label:
Tumour (Positive)	38	2	18	22
Non-Tumour (Negative)	19	439	6	452

Next we just add a couple more styling artifacts targeting specific parts of the table, and we add some internally defined CSS classes that we need for the next section. Be careful here, since we are chaining methods we need to explicitly instruct the method not to overwrite the existing styles.

[10]:

s.set_table_styles([
    {'selector': 'th.col_heading', 'props': 'text-align: center;'},
    {'selector': 'th.col_heading.level0', 'props': 'font-size: 1.5em;'},
    {'selector': 'td', 'props': 'text-align: center; font-weight: bold;'},
    # internal CSS classes
    {'selector': '.true', 'props': 'background-color: #e6ffe6;'},
    {'selector': '.false', 'props': 'background-color: #ffe6e6;'},
    {'selector': '.border-red', 'props': 'border: 2px dashed red;'},
    {'selector': '.border-green', 'props': 'border: 2px dashed green;'},
], overwrite=False)

[10]:

Model:	Decision Tree		Regression
Predicted:	Tumour	Non-Tumour	Tumour	Non-Tumour
Actual Label:
Tumour (Positive)	38	2	18	22
Non-Tumour (Negative)	19	439	6	452

As a convenience method (since version 1.2.0) we can also pass a dict to .set_table_styles() which contains row or column keys. Behind the scenes Styler just indexes the keys and adds relevant .col<m> or .row<n> classes as necessary to the given CSS selectors.

[12]:

s.set_table_styles({
    ('Regression', 'Tumour'): [{'selector': 'th', 'props': 'border-left: 1px solid white'},
                               {'selector': 'td', 'props': 'border-left: 1px solid #000066'}]
}, overwrite=False, axis=0)

[12]:

Model:	Decision Tree		Regression
Predicted:	Tumour	Non-Tumour	Tumour	Non-Tumour
Actual Label:
Tumour (Positive)	38	2	18	22
Non-Tumour (Negative)	19	439	6	452

Setting Classes and Linking to External CSS¶

If you have designed a website then it is likely you will already have an external CSS file that controls the styling of table and cell objects within it. You may want to use these native files rather than duplicate all the CSS in python (and duplicate any maintenance work).

Table Attributes¶

It is very easy to add a class to the main <table> using .set_table_attributes(). This method can also attach inline styles - read more in CSS Hierarchies.

[14]:

out = s.set_table_attributes('class="my-table-cls"').render()
print(out[out.find('<table'):][:109])

<table id="T_xyz01" class="my-table-cls">
  <thead>
    <tr>
      <th class="index_name level0" >Model:</th>

Data Cell CSS Classes¶

New in version 1.2.0

The .set_td_classes() method accepts a DataFrame with matching indices and columns to the underlying Styler’s DataFrame. That DataFrame will contain strings as css-classes to add to individual data cells: the <td> elements of the <table>. Here we add our .true and .false classes that we created previously. We will save adding the borders until the section on tooltips.

[15]:

cell_color = pd.DataFrame([['true ', 'false ', 'true ', 'false '],
                           ['false ', 'true ', 'false ', 'true ']],
                          index=df.index,
                          columns=df.columns[:4])
s.set_td_classes(cell_color)

[15]:

Model:	Decision Tree		Regression
Predicted:	Tumour	Non-Tumour	Tumour	Non-Tumour
Actual Label:
Tumour (Positive)	38	2	18	22
Non-Tumour (Negative)	19	439	6	452

Styler Functions¶

We use the following methods to pass your style functions. Both of those methods take a function (and some other keyword arguments) and apply it to the DataFrame in a certain way, rendering CSS styles.

.applymap() (elementwise): accepts a function that takes a single value and returns a string with the CSS attribute-value pair.
.apply() (column-/row-/table-wise): accepts a function that takes a Series or DataFrame and returns a Series, DataFrame, or numpy array with an identical shape where each element is a string with a CSS attribute-value pair. This method passes each column or row of your DataFrame one-at-a-time or the entire table at once, depending on the axis keyword argument. For columnwise use axis=0, rowwise use axis=1, and for the entire table at once use axis=None.

This method is powerful for applying multiple, complex logic to data cells. We create a new DataFrame to demonstrate this.

[17]:

np.random.seed(0)
df2 = pd.DataFrame(np.random.randn(10,4), columns=['A','B','C','D'])
df2.style

[17]:

	A	B	C	D
0	1.764052	0.400157	0.978738	2.240893
1	1.867558	-0.977278	0.950088	-0.151357
2	-0.103219	0.410599	0.144044	1.454274
3	0.761038	0.121675	0.443863	0.333674
4	1.494079	-0.205158	0.313068	-0.854096
5	-2.552990	0.653619	0.864436	-0.742165
6	2.269755	-1.454366	0.045759	-0.187184
7	1.532779	1.469359	0.154947	0.378163
8	-0.887786	-1.980796	-0.347912	0.156349
9	1.230291	1.202380	-0.387327	-0.302303

For example we can build a function that colors text if it is negative, and chain this with a function that partially fades cells of negligible value. Since this looks at each element in turn we use applymap.

[18]:

def style_negative(v, props=''):
    return props if v < 0 else None
s2 = df2.style.applymap(style_negative, props='color:red;')\
              .applymap(lambda v: 'opacity: 20%;' if (v < 0.3) and (v > -0.3) else None)
s2

[18]:

	A	B	C	D
0	1.764052	0.400157	0.978738	2.240893
1	1.867558	-0.977278	0.950088	-0.151357
2	-0.103219	0.410599	0.144044	1.454274
3	0.761038	0.121675	0.443863	0.333674
4	1.494079	-0.205158	0.313068	-0.854096
5	-2.552990	0.653619	0.864436	-0.742165
6	2.269755	-1.454366	0.045759	-0.187184
7	1.532779	1.469359	0.154947	0.378163
8	-0.887786	-1.980796	-0.347912	0.156349
9	1.230291	1.202380	-0.387327	-0.302303

We can also build a function that highlights the maximum value across rows, cols, and the DataFrame all at once. In this case we use apply. Below we highlight the maximum in a column.

[20]:

def highlight_max(s, props=''):
    return np.where(s == np.nanmax(s.values), props, '')
s2.apply(highlight_max, props='color:white;background-color:darkblue', axis=0)

[20]:

	A	B	C	D
0	1.764052	0.400157	0.978738	2.240893
1	1.867558	-0.977278	0.950088	-0.151357
2	-0.103219	0.410599	0.144044	1.454274
3	0.761038	0.121675	0.443863	0.333674
4	1.494079	-0.205158	0.313068	-0.854096
5	-2.552990	0.653619	0.864436	-0.742165
6	2.269755	-1.454366	0.045759	-0.187184
7	1.532779	1.469359	0.154947	0.378163
8	-0.887786	-1.980796	-0.347912	0.156349
9	1.230291	1.202380	-0.387327	-0.302303

We can use the same function across the different axes, highlighting here the DataFrame maximum in purple, and row maximums in pink.

[22]:

s2.apply(highlight_max, props='color:white;background-color:pink;', axis=1)\
  .apply(highlight_max, props='color:white;background-color:purple', axis=None)

[22]:

	A	B	C	D
0	1.764052	0.400157	0.978738	2.240893
1	1.867558	-0.977278	0.950088	-0.151357
2	-0.103219	0.410599	0.144044	1.454274
3	0.761038	0.121675	0.443863	0.333674
4	1.494079	-0.205158	0.313068	-0.854096
5	-2.552990	0.653619	0.864436	-0.742165
6	2.269755	-1.454366	0.045759	-0.187184
7	1.532779	1.469359	0.154947	0.378163
8	-0.887786	-1.980796	-0.347912	0.156349
9	1.230291	1.202380	-0.387327	-0.302303

This last example shows how some styles have been overwritten by others. In general the most recent style applied is active but you can read more in the section on CSS hierarchies. You can also apply these styles to more granular parts of the DataFrame - read more in section on subset slicing.

It is possible to replicate some of this functionality using just classes but it can be more cumbersome. See item 3) of Optimization

Debugging Tip: If you’re having trouble writing your style function, try just passing it into DataFrame.apply. Internally, Styler.apply uses DataFrame.apply so the result should be the same.

Tooltips and Captions¶

Table captions can be added with the .set_caption() method. You can use table styles to control the CSS relevant to the caption.

[23]:

s.set_caption("Confusion matrix for multiple cancer prediction models.")\
 .set_table_styles([{
     'selector': 'caption',
     'props': 'caption-side: bottom; font-size:1.25em;'
 }], overwrite=False)

[23]:

Confusion matrix for multiple cancer prediction models.
Model:	Decision Tree		Regression
Predicted:	Tumour	Non-Tumour	Tumour	Non-Tumour
Actual Label:
Tumour (Positive)	38	2	18	22
Non-Tumour (Negative)	19	439	6	452

Adding tooltips (since version 1.3.0) can be done using the .set_tooltips() method in the same way you can add CSS classes to data cells by providing a string based DataFrame with intersecting indices and columns. You don’t have to specify a css_class name or any css props for the tooltips, since there are standard defaults, but the option is there if you want more visual control.

[25]:

tt = pd.DataFrame([['This model has a very strong true positive rate',
                    "This model's total number of false negatives is too high"]],
                  index=['Tumour (Positive)'], columns=df.columns[[0,3]])
s.set_tooltips(tt, props='visibility: hidden; position: absolute; z-index: 1; border: 1px solid #000066;'
                         'background-color: white; color: #000066; font-size: 0.8em;'
                         'transform: translate(0px, -24px); padding: 0.6em; border-radius: 0.5em;')

[25]:

Confusion matrix for multiple cancer prediction models.
Model:	Decision Tree		Regression
Predicted:	Tumour	Non-Tumour	Tumour	Non-Tumour
Actual Label:
Tumour (Positive)	38	2	18	22
Non-Tumour (Negative)	19	439	6	452

The only thing left to do for our table is to add the highlighting borders to draw the audience attention to the tooltips. Setting classes always overwrites so we need to make sure we add the previous classes.

[27]:

cell_border = pd.DataFrame([['border-green ', ' ', ' ', 'border-red '],
                           [' ', ' ', ' ', ' ']],
                          index=df.index,
                          columns=df.columns[:4])
s.set_td_classes(cell_color + cell_border)

[27]:

Confusion matrix for multiple cancer prediction models.
Model:	Decision Tree		Regression
Predicted:	Tumour	Non-Tumour	Tumour	Non-Tumour
Actual Label:
Tumour (Positive)	38	2	18	22
Non-Tumour (Negative)	19	439	6	452

Finer Control with Slicing¶

The examples we have shown so far for the Styler.apply and Styler.applymap functions have not demonstrated the use of the subset argument. This is a useful argument which permits a lot of flexibility: it allows you to apply styles to specific rows or columns, without having to code that logic into your style function.

The value passed to subset behaves similar to slicing a DataFrame;

A scalar is treated as a column label
A list (or Series or NumPy array) is treated as multiple column labels
A tuple is treated as (row_indexer, column_indexer)

Consider using pd.IndexSlice to construct the tuple for the last one. We will create a MultiIndexed DataFrame to demonstrate the functionality.

[29]:

df3 = pd.DataFrame(np.random.randn(4,4),
                   pd.MultiIndex.from_product([['A', 'B'], ['r1', 'r2']]),
                   columns=['c1','c2','c3','c4'])
df3

[29]:

		c1	c2	c3	c4
A	r1	-1.048553	-1.420018	-1.706270	1.950775
A	r2	-0.509652	-0.438074	-1.252795	0.777490
B	r1	-1.613898	-0.212740	-0.895467	0.386902
B	r2	-0.510805	-1.180632	-0.028182	0.428332

We will use subset to highlight the maximum in the third and fourth columns with red text. We will highlight the subset sliced region in yellow.

[30]:

slice_ = ['c3', 'c4']
df3.style.apply(highlight_max, props='color:red;', axis=0, subset=slice_)\
         .set_properties(**{'background-color': '#ffffb3'}, subset=slice_)

[30]:

		c1	c2	c3	c4
A	r1	-1.048553	-1.420018	-1.706270	1.950775
A	r2	-0.509652	-0.438074	-1.252795	0.777490
B	r1	-1.613898	-0.212740	-0.895467	0.386902
B	r2	-0.510805	-1.180632	-0.028182	0.428332

If combined with the IndexSlice as suggested then it can index across both dimensions with greater flexibility.

[31]:

idx = pd.IndexSlice
slice_ = idx[idx[:,'r1'], idx['c2':'c4']]
df3.style.apply(highlight_max, props='color:red;', axis=0, subset=slice_)\
         .set_properties(**{'background-color': '#ffffb3'}, subset=slice_)

[31]:

		c1	c2	c3	c4
A	r1	-1.048553	-1.420018	-1.706270	1.950775
A	r2	-0.509652	-0.438074	-1.252795	0.777490
B	r1	-1.613898	-0.212740	-0.895467	0.386902
B	r2	-0.510805	-1.180632	-0.028182	0.428332

This also provides the flexibility to sub select rows when used with the axis=1.

[32]:

slice_ = idx[idx[:,'r2'], :]
df3.style.apply(highlight_max, props='color:red;', axis=1, subset=slice_)\
         .set_properties(**{'background-color': '#ffffb3'}, subset=slice_)

[32]:

		c1	c2	c3	c4
A	r1	-1.048553	-1.420018	-1.706270	1.950775
A	r2	-0.509652	-0.438074	-1.252795	0.777490
B	r1	-1.613898	-0.212740	-0.895467	0.386902
B	r2	-0.510805	-1.180632	-0.028182	0.428332

There is also scope to provide conditional filtering.

Suppose we want to highlight the maximum across columns 2 and 4 only in the case that the sum of columns 1 and 3 is less than -2.0 (essentially excluding rows (:,'r2')).

[33]:

slice_ = idx[idx[(df3['c1'] + df3['c3']) < -2.0], ['c2', 'c4']]
df3.style.apply(highlight_max, props='color:red;', axis=1, subset=slice_)\
         .set_properties(**{'background-color': '#ffffb3'}, subset=slice_)

[33]:

		c1	c2	c3	c4
A	r1	-1.048553	-1.420018	-1.706270	1.950775
A	r2	-0.509652	-0.438074	-1.252795	0.777490
B	r1	-1.613898	-0.212740	-0.895467	0.386902
B	r2	-0.510805	-1.180632	-0.028182	0.428332

Only label-based slicing is supported right now, not positional, and not callables.

If your style function uses a subset or axis keyword argument, consider wrapping your function in a functools.partial, partialing out that keyword.

my_func2 = functools.partial(my_func, subset=42)

Optimization¶

Generally, for smaller tables and most cases, the rendered HTML does not need to be optimized, and we don’t really recommend it. There are two cases where it is worth considering:

If you are rendering and styling a very large HTML table, certain browsers have performance issues.
If you are using Styler to dynamically create part of online user interfaces and want to improve network performance.

Here we recommend the following steps to implement:

Ignore the uuid and set cell_ids to False. This will prevent unnecessary HTML.

This is sub-optimal:

[34]:

df4 = pd.DataFrame([[1,2],[3,4]])
s4 = df4.style

This is better:

[35]:

from pandas.io.formats.style import Styler
s4 = Styler(df4, uuid_len=0, cell_ids=False)

Use table styles where possible (e.g. for all cells or rows or columns at a time) since the CSS is nearly always more efficient than other formats.

This is sub-optimal:

[36]:

props = 'font-family: "Times New Roman", Times, serif; color: #e83e8c; font-size:1.3em;'
df4.style.applymap(lambda x: props, subset=[1])

[36]:

	0	1
0	1	2
1	3	4

This is better:

[37]:

df4.style.set_table_styles([{'selector': 'td.col1', 'props': props}])

[37]:

	0	1
0	1	2
1	3	4

For large DataFrames where the same style is applied to many cells it can be more efficient to declare the styles as classes and then apply those classes to data cells, rather than directly applying styles to cells. It is, however, probably still easier to use the Styler function api when you are not concerned about optimization.

This is sub-optimal:

[38]:

df2.style.apply(highlight_max, props='color:white;background-color:darkblue;', axis=0)\
         .apply(highlight_max, props='color:white;background-color:pink;', axis=1)\
         .apply(highlight_max, props='color:white;background-color:purple', axis=None)

[38]:

	A	B	C	D
0	1.764052	0.400157	0.978738	2.240893
1	1.867558	-0.977278	0.950088	-0.151357
2	-0.103219	0.410599	0.144044	1.454274
3	0.761038	0.121675	0.443863	0.333674
4	1.494079	-0.205158	0.313068	-0.854096
5	-2.552990	0.653619	0.864436	-0.742165
6	2.269755	-1.454366	0.045759	-0.187184
7	1.532779	1.469359	0.154947	0.378163
8	-0.887786	-1.980796	-0.347912	0.156349
9	1.230291	1.202380	-0.387327	-0.302303

This is better:

[39]:

build = lambda x: pd.DataFrame(x, index=df2.index, columns=df2.columns)
cls1 = build(df2.apply(highlight_max, props='cls-1 ', axis=0))
cls2 = build(df2.apply(highlight_max, props='cls-2 ', axis=1, result_type='expand').values)
cls3 = build(highlight_max(df2, props='cls-3 '))
df2.style.set_table_styles([
    {'selector': '.cls-1', 'props': 'color:white;background-color:darkblue;'},
    {'selector': '.cls-2', 'props': 'color:white;background-color:pink;'},
    {'selector': '.cls-3', 'props': 'color:white;background-color:purple;'}
]).set_td_classes(cls1 + cls2 + cls3)

[39]:

	A	B	C	D
0	1.764052	0.400157	0.978738	2.240893
1	1.867558	-0.977278	0.950088	-0.151357
2	-0.103219	0.410599	0.144044	1.454274
3	0.761038	0.121675	0.443863	0.333674
4	1.494079	-0.205158	0.313068	-0.854096
5	-2.552990	0.653619	0.864436	-0.742165
6	2.269755	-1.454366	0.045759	-0.187184
7	1.532779	1.469359	0.154947	0.378163
8	-0.887786	-1.980796	-0.347912	0.156349
9	1.230291	1.202380	-0.387327	-0.302303

Tooltips require cell_ids to work and they generate extra HTML elements for every data cell.

You can remove unnecessary HTML, or shorten the default class names with string replace functions.

[40]:

html = Styler(df4, uuid_len=0, cell_ids=False)\
      .set_table_styles([{'selector': 'td', 'props': props},
                         {'selector': '.col1', 'props': 'color:green;'},
                         {'selector': '.level0', 'props': 'color:blue;'}])\
      .render()\
      .replace('blank', '')\
      .replace('data', '')\
      .replace('level0', 'l0')\
      .replace('col_heading', '')\
      .replace('row_heading', '')

import re
html = re.sub(r'col[0-9]+', lambda x: x.group().replace('col', 'c'), html)
html = re.sub(r'row[0-9]+', lambda x: x.group().replace('row', 'r'), html)
print(html)

<style type="text/css">
#T__ td {
  font-family: "Times New Roman", Times, serif;
  color: #e83e8c;
  font-size: 1.3em;
}
#T__ .c1 {
  color: green;
}
#T__ .l0 {
  color: blue;
}
</style>
<table id="T__">
  <thead>
    <tr>
      <th class=" l0" >&nbsp;</th>
      <th class=" l0 c0" >0</th>
      <th class=" l0 c1" >1</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th id="T__l0_r0" class=" l0 r0" >0</th>
      <td  class=" r0 c0" >1</td>
      <td  class=" r0 c1" >2</td>
    </tr>
    <tr>
      <th id="T__l0_r1" class=" l0 r1" >1</th>
      <td  class=" r1 c0" >3</td>
      <td  class=" r1 c1" >4</td>
    </tr>
  </tbody>
</table>

[41]:

from IPython.display import HTML
HTML(html)

[41]:

	0	1
0	1	2
1	3	4

Builtin Styles¶

We expect certain styling functions to be common enough that we’ve included a few “built-in” to the Styler, so you don’t have to write them yourself.

[42]:

df2.iloc[0,2] = np.nan
df2.iloc[4,3] = np.nan
df2.loc[:4].style.highlight_null(null_color='red')

[42]:

	A	B	C	D
0	1.764052	0.400157	nan	2.240893
1	1.867558	-0.977278	0.950088	-0.151357
2	-0.103219	0.410599	0.144044	1.454274
3	0.761038	0.121675	0.443863	0.333674
4	1.494079	-0.205158	0.313068	nan

You can create “heatmaps” with the background_gradient method. These require matplotlib, and we’ll use Seaborn to get a nice colormap.

[43]:

import seaborn as sns
cm = sns.light_palette("green", as_cmap=True)

df2.style.background_gradient(cmap=cm)

[43]:

	A	B	C	D
0	1.764052	0.400157	nan	2.240893
1	1.867558	-0.977278	0.950088	-0.151357
2	-0.103219	0.410599	0.144044	1.454274
3	0.761038	0.121675	0.443863	0.333674
4	1.494079	-0.205158	0.313068	nan
5	-2.552990	0.653619	0.864436	-0.742165
6	2.269755	-1.454366	0.045759	-0.187184
7	1.532779	1.469359	0.154947	0.378163
8	-0.887786	-1.980796	-0.347912	0.156349
9	1.230291	1.202380	-0.387327	-0.302303

Styler.background_gradient takes the keyword arguments low and high. Roughly speaking these extend the range of your data by low and high percent so that when we convert the colors, the colormap’s entire range isn’t used. This is useful so that you can actually read the text still.

[44]:

# Uses the full color range
df2.loc[:4].style.background_gradient(cmap='viridis')

[44]:

	A	B	C	D
0	1.764052	0.400157	nan	2.240893
1	1.867558	-0.977278	0.950088	-0.151357
2	-0.103219	0.410599	0.144044	1.454274
3	0.761038	0.121675	0.443863	0.333674
4	1.494079	-0.205158	0.313068	nan

[45]:

# Compress the color range
df2.loc[:4].style\
   .background_gradient(cmap='viridis', low=.5, high=0)\
   .highlight_null('red')

[45]:

	A	B	C	D
0	1.764052	0.400157	nan	2.240893
1	1.867558	-0.977278	0.950088	-0.151357
2	-0.103219	0.410599	0.144044	1.454274
3	0.761038	0.121675	0.443863	0.333674
4	1.494079	-0.205158	0.313068	nan

There’s also .highlight_min and .highlight_max, which is almost identical to the user defined version we created above, and also a .highlight_null method.

[46]:

df2.loc[:4].style.highlight_max(axis=0)

[46]:

	A	B	C	D
0	1.764052	0.400157	nan	2.240893
1	1.867558	-0.977278	0.950088	-0.151357
2	-0.103219	0.410599	0.144044	1.454274
3	0.761038	0.121675	0.443863	0.333674
4	1.494079	-0.205158	0.313068	nan

Use Styler.set_properties when the style doesn’t actually depend on the values. This is just a simple wrapper for .applymap where the function returns the same properties for all cells.

[47]:

df2.loc[:4].style.set_properties(**{'background-color': 'black',
                           'color': 'lawngreen',
                           'border-color': 'white'})

[47]:

	A	B	C	D
0	1.764052	0.400157	nan	2.240893
1	1.867558	-0.977278	0.950088	-0.151357
2	-0.103219	0.410599	0.144044	1.454274
3	0.761038	0.121675	0.443863	0.333674
4	1.494079	-0.205158	0.313068	nan

Bar charts¶

You can include “bar charts” in your DataFrame.

[48]:

df2.style.bar(subset=['A', 'B'], color='#d65f5f')

[48]:

	A	B	C	D
0	1.764052	0.400157	nan	2.240893
1	1.867558	-0.977278	0.950088	-0.151357
2	-0.103219	0.410599	0.144044	1.454274
3	0.761038	0.121675	0.443863	0.333674
4	1.494079	-0.205158	0.313068	nan
5	-2.552990	0.653619	0.864436	-0.742165
6	2.269755	-1.454366	0.045759	-0.187184
7	1.532779	1.469359	0.154947	0.378163
8	-0.887786	-1.980796	-0.347912	0.156349
9	1.230291	1.202380	-0.387327	-0.302303

In version 0.20.0 the ability to customize the bar chart further was given. You can now have the df.style.bar be centered on zero or midpoint value (in addition to the already existing way of having the min value at the left side of the cell), and you can pass a list of [color_negative, color_positive].

Here’s how you can change the above with the new align='mid' option:

[49]:

df2.style.bar(subset=['A', 'B'], align='mid', color=['#d65f5f', '#5fba7d'])

[49]:

	A	B	C	D
0	1.764052	0.400157	nan	2.240893
1	1.867558	-0.977278	0.950088	-0.151357
2	-0.103219	0.410599	0.144044	1.454274
3	0.761038	0.121675	0.443863	0.333674
4	1.494079	-0.205158	0.313068	nan
5	-2.552990	0.653619	0.864436	-0.742165
6	2.269755	-1.454366	0.045759	-0.187184
7	1.532779	1.469359	0.154947	0.378163
8	-0.887786	-1.980796	-0.347912	0.156349
9	1.230291	1.202380	-0.387327	-0.302303

The following example aims to give a highlight of the behavior of the new align options:

[51]:

HTML(head)

[51]:

Align

All Negative

All Positive

Both Neg and Pos

left


0	-100
1	-60
2	-30
3	-20


0	10
1	20
2	50
3	100


0	-10
1	-5
2	0
3	90

zero


0	-100
1	-60
2	-30
3	-20


0	10
1	20
2	50
3	100


0	-10
1	-5
2	0
3	90

mid


0	-100
1	-60
2	-30
3	-20


0	10
1	20
2	50
3	100


0	-10
1	-5
2	0
3	90

Sharing styles¶

Say you have a lovely style built up for a DataFrame, and now you want to apply the same style to a second DataFrame. Export the style with df1.style.export, and import it on the second DataFrame with df1.style.set

[52]:

style1 = df2.style.applymap(style_negative, props='color:red;')\
         .applymap(lambda v: 'opacity: 20%;' if (v < 0.3) and (v > -0.3) else None)

[53]:

style2 = df3.style
style2.use(style1.export())
style2

[53]:

		c1	c2	c3	c4
A	r1	-1.048553	-1.420018	-1.706270	1.950775
A	r2	-0.509652	-0.438074	-1.252795	0.777490
B	r1	-1.613898	-0.212740	-0.895467	0.386902
B	r2	-0.510805	-1.180632	-0.028182	0.428332

Notice that you’re able to share the styles even though they’re data aware. The styles are re-evaluated on the new DataFrame they’ve been used upon.

Limitations¶

DataFrame only (use Series.to_frame().style)
The index and columns must be unique
No large repr, and construction performance isn’t great; although we have some HTML optimizations
You can only style the values, not the index or columns (except with table_styles above)
You can only apply styles, you can’t insert new HTML entities

Some of these might be addressed in the future.

Other Fun and Useful Stuff¶

Here are a few interesting examples.

Widgets¶

Styler interacts pretty well with widgets. If you’re viewing this online instead of running the notebook yourself, you’re missing out on interactively adjusting the color palette.

[54]:

from ipywidgets import widgets
@widgets.interact
def f(h_neg=(0, 359, 1), h_pos=(0, 359), s=(0., 99.9), l=(0., 99.9)):
    return df2.style.background_gradient(
        cmap=sns.palettes.diverging_palette(h_neg=h_neg, h_pos=h_pos, s=s, l=l,
                                            as_cmap=True)
    )

Magnify¶

[55]:

def magnify():
    return [dict(selector="th",
                 props=[("font-size", "4pt")]),
            dict(selector="td",
                 props=[('padding', "0em 0em")]),
            dict(selector="th:hover",
                 props=[("font-size", "12pt")]),
            dict(selector="tr:hover td:hover",
                 props=[('max-width', '200px'),
                        ('font-size', '12pt')])
]

[56]:

np.random.seed(25)
cmap = cmap=sns.diverging_palette(5, 250, as_cmap=True)
bigdf = pd.DataFrame(np.random.randn(20, 25)).cumsum()

bigdf.style.background_gradient(cmap, axis=1)\
    .set_properties(**{'max-width': '80px', 'font-size': '1pt'})\
    .set_caption("Hover to magnify")\
    .format(precision=2)\
    .set_table_styles(magnify())

[56]:

Hover to magnify
	0	1	2	3	4	5	6	7	8	9	10	11	12	13	14	15	16	17	18	19	20	21	22	23	24
0	0.23	1.03	-0.84	-0.59	-0.96	-0.22	-0.62	1.84	-2.05	0.87	-0.92	-0.23	2.15	-1.33	0.08	-1.25	1.20	-1.05	1.06	-0.42	2.29	-2.59	2.82	0.68	-1.58
1	-1.75	1.56	-1.13	-1.10	1.03	0.00	-2.46	3.45	-1.66	1.27	-0.52	-0.02	1.52	-1.09	-1.86	-1.13	-0.68	-0.81	0.35	-0.06	1.79	-2.82	2.26	0.78	0.44
2	-0.65	3.22	-1.76	0.52	2.20	-0.37	-3.00	3.73	-1.87	2.46	0.21	-0.24	-0.10	-0.78	-3.02	-0.82	-0.21	-0.23	0.86	-0.68	1.45	-4.89	3.03	1.91	0.61
3	-1.62	3.71	-2.31	0.43	4.17	-0.43	-3.86	4.16	-2.15	1.08	0.12	0.60	-0.89	0.27	-3.67	-2.71	-0.31	-1.59	1.35	-1.83	0.91	-5.80	2.81	2.11	0.28
4	-3.35	4.48	-1.86	-1.70	5.19	-1.02	-3.81	4.72	-0.72	1.08	-0.18	0.83	-0.22	-1.08	-4.27	-2.88	-0.97	-1.78	1.53	-1.80	2.21	-6.34	3.34	2.49	2.09
5	-0.84	4.23	-1.65	-2.00	5.34	-0.99	-4.13	3.94	-1.06	-0.94	1.24	0.09	-1.78	-0.11	-4.45	-0.85	-2.06	-1.35	0.80	-1.63	1.54	-6.51	2.80	2.14	3.77
6	-0.74	5.35	-2.11	-1.13	4.20	-1.85	-3.20	3.76	-3.22	-1.23	0.34	0.57	-1.82	0.54	-4.43	-1.83	-4.03	-2.62	-0.20	-4.68	1.93	-8.46	3.34	2.52	5.81
7	-0.44	4.69	-2.30	-0.21	5.93	-2.63	-1.83	5.46	-4.50	-3.16	-1.73	0.18	0.11	0.04	-5.99	-0.45	-6.20	-3.89	0.71	-3.95	0.67	-7.26	2.97	3.39	6.66
8	0.92	5.80	-3.33	-0.65	5.99	-3.19	-1.83	5.63	-3.53	-1.30	-1.61	0.82	-2.45	-0.40	-6.06	-0.52	-6.60	-3.48	-0.04	-4.60	0.51	-5.85	3.23	2.40	5.08
9	0.38	5.54	-4.49	-0.80	7.05	-2.64	-0.44	5.35	-1.96	-0.33	-0.80	0.26	-3.37	-0.82	-6.05	-2.61	-8.45	-4.45	0.41	-4.71	1.89	-6.93	2.14	3.00	5.16
10	2.06	5.84	-3.90	-0.98	7.78	-2.49	-0.59	5.59	-2.22	-0.71	-0.46	1.80	-2.79	0.48	-5.97	-3.44	-7.77	-5.49	-0.70	-4.61	-0.52	-7.72	1.54	5.02	5.81
11	1.86	4.47	-2.17	-1.38	5.90	-0.49	0.02	5.78	-1.04	-0.60	0.49	1.96	-1.47	1.88	-5.92	-4.55	-8.15	-3.42	-2.24	-4.33	-1.17	-7.90	1.36	5.31	5.83
12	3.19	4.22	-3.06	-2.27	5.93	-2.64	0.33	6.72	-2.84	-0.20	1.89	2.63	-1.53	0.75	-5.27	-4.53	-7.57	-2.85	-2.17	-4.78	-1.13	-8.99	2.11	6.42	5.60
13	2.31	4.45	-3.87	-2.05	6.76	-3.25	-2.17	7.99	-2.56	-0.80	0.71	2.33	-0.16	-0.46	-5.10	-3.79	-7.58	-4.00	0.33	-3.67	-1.05	-8.71	2.47	5.87	6.71
14	3.78	4.33	-3.88	-1.58	6.22	-3.23	-1.46	5.57	-2.93	-0.33	-0.97	1.72	3.61	0.29	-4.21	-4.10	-6.68	-4.50	-2.19	-2.43	-1.64	-9.36	3.36	6.11	7.53
15	5.64	5.31	-3.98	-2.26	5.91	-3.30	-1.03	5.68	-3.06	-0.33	-1.16	2.19	4.20	1.01	-3.22	-4.31	-5.74	-4.44	-2.30	-1.36	-1.20	-11.27	2.59	6.69	5.91
16	4.08	4.34	-2.44	-3.30	6.04	-2.52	-0.47	5.28	-4.84	1.58	0.23	0.10	5.79	1.80	-3.13	-3.85	-5.53	-2.97	-2.13	-1.15	-0.56	-13.13	2.07	6.16	4.94
17	5.64	4.57	-3.53	-3.76	6.58	-2.58	-0.75	6.58	-4.78	3.63	-0.29	0.56	5.76	2.05	-2.27	-2.31	-4.95	-3.16	-3.06	-2.43	0.84	-12.57	3.56	7.36	4.70
18	5.99	5.82	-2.85	-4.15	7.12	-3.32	-1.21	7.93	-4.85	1.44	-0.63	0.35	7.47	0.87	-1.52	-2.09	-4.23	-2.55	-2.46	-2.89	1.90	-9.74	3.43	7.07	4.39
19	4.03	6.23	-4.10	-4.11	7.19	-4.10	-1.52	6.53	-5.21	-0.24	0.01	1.16	6.43	-1.97	-2.64	-1.66	-5.20	-3.25	-2.87	-1.65	1.64	-10.66	2.83	7.48	3.94

Sticky Headers¶

If you display a large matrix or DataFrame in a notebook, but you want to always see the column and row headers you can use the following CSS to make them stick. We might make this into an API function later.

[57]:

bigdf = pd.DataFrame(np.random.randn(15, 100))
bigdf.style.set_table_styles([
    {'selector': 'thead th', 'props': 'position: sticky; top:0; background-color:salmon;'},
    {'selector': 'tbody th', 'props': 'position: sticky; left:0; background-color:lightgreen;'}
])

[57]:

	0	1	2	3	4	5	6	7	8	9	10	11	12	13	14	15	16	17	18	19	20	21	22	23	24	25	26	27	28	29	30	31	32	33	34	35	36	37	38	39	40	41	42	43	44	45	46	47	48	49	50	51	52	53	54	55	56	57	58	59	60	61	62	63	64	65	66	67	68	69	70	71	72	73	74	75	76	77	78	79	80	81	82	83	84	85	86	87	88	89	90	91	92	93	94	95	96	97	98	99
0	-0.773866	-0.240521	-0.217165	1.173609	0.686390	0.008358	0.696232	0.173166	0.620498	0.504067	0.428066	-0.051824	0.719915	0.057165	0.562808	-0.369536	0.483399	0.620765	-0.354342	-1.469471	-1.937266	0.038031	-1.518162	-0.417599	0.386717	0.716193	0.489961	0.733957	0.914415	0.679894	0.255448	-0.508338	0.332030	-0.111107	-0.251983	-1.456620	0.409630	1.062320	-0.577115	0.718796	-0.399260	-1.311389	0.649122	0.091566	0.628872	0.297894	-0.142290	-0.542291	-0.914290	1.144514	0.313584	1.182635	1.214235	-0.416446	-1.653940	-2.550787	0.442473	0.052127	-0.464469	-0.523852	0.989726	-1.325539	-0.199687	-1.226727	0.290018	1.164574	0.817841	-0.309509	0.496599	0.943536	-0.091850	-2.802658	2.126219	-0.521161	0.288098	-0.454663	-1.676143	-0.357661	-0.788960	0.185911	-0.017106	2.454020	1.832706	-0.911743	-0.655873	-0.000514	-2.226997	0.677285	-0.140249	-0.408407	-0.838665	0.482228	1.243458	-0.477394	-0.220343	-2.463966	0.237325	-0.307380	1.172478	0.819492
1	0.405906	-0.978919	1.267526	0.145250	-1.066786	-2.114192	-1.128346	-1.082523	0.372216	0.004127	-0.211984	0.937326	-0.935890	-1.704118	0.611789	-1.030015	0.636123	-1.506193	1.736609	1.392958	1.009424	0.353266	0.697339	-0.297424	0.428702	-0.145346	-0.333553	-0.974699	0.665314	0.971944	0.121950	-1.439668	1.018808	1.442399	-0.199585	-1.165916	0.645656	1.436466	-0.921215	1.293906	-2.706443	1.460928	-0.823197	0.292952	-1.448992	0.026692	-0.975883	0.392823	0.442166	0.745741	1.187982	-0.218570	0.305288	0.054932	-1.476953	-0.114434	0.014103	0.825394	-0.060654	-0.413688	0.974836	1.339210	1.034838	0.040775	0.705001	0.017796	1.867681	-0.390173	2.285277	2.311464	-0.085070	-0.648115	0.576300	-0.790087	-1.183798	-1.334558	-0.454118	0.319302	1.706488	0.830429	0.502476	-0.079631	0.414635	0.332511	0.042935	-0.160910	0.918553	-0.292697	-1.303834	-0.199604	0.871023	-1.370681	-0.205701	-0.492973	1.123083	-0.081842	-0.118527	0.245838	-0.315742	-0.511806
2	0.011470	-0.036104	1.399603	-0.418176	-0.412229	-1.234783	-1.121500	1.196478	-0.569522	0.422022	-0.220484	0.804338	2.892667	-0.511055	-0.168722	-1.477996	-1.969917	0.471354	1.698548	0.137105	-0.762052	0.199379	-0.964346	-0.256692	1.265275	0.848762	-0.784161	1.863776	-0.355569	0.854552	0.768061	-2.075718	-2.501069	1.109868	0.957545	-0.683276	0.307764	0.733073	1.706250	-1.118091	0.374961	-1.414503	-0.524183	-1.662696	0.687921	0.521732	1.451396	-0.833491	-0.362796	-1.174444	-0.813893	-0.893220	0.770743	1.156647	-0.647444	0.125929	0.513600	-0.537874	1.992052	-1.946584	-0.104759	0.484779	-0.290936	-0.441075	0.542993	-1.050038	1.630482	0.239771	-1.177310	0.464804	-0.966995	0.646086	0.486899	1.022196	-2.267827	-1.229616	1.313805	1.073292	2.324940	-0.542720	-1.504292	0.777643	-0.618553	0.011342	1.385062	1.363552	-0.549834	0.688896	1.361288	-0.381137	0.797812	-1.128198	0.369208	0.540132	0.413853	-0.200308	-0.969126	0.981293	-0.009783	-0.320020
3	-0.574816	1.419977	0.434813	-1.101217	-1.586275	1.979573	0.378298	0.782326	2.178987	0.657564	0.683774	-0.091000	-0.059552	-0.738908	-0.907653	-0.701936	0.580039	-0.618757	0.453684	1.665382	-0.152321	0.880077	0.571073	-0.604736	0.532359	0.515031	-0.959844	-0.887184	0.435781	0.862093	-0.956321	-0.625909	0.194472	0.442490	0.526503	-0.215274	0.090711	0.932592	0.811999	-2.497026	0.631545	0.321418	-0.425549	-1.078832	0.753444	0.199790	-0.360526	-0.013448	-0.819476	0.814869	0.442118	-0.972048	-0.060603	-2.349825	1.265445	-0.573257	0.429124	1.049783	1.954773	0.071883	-0.094209	0.265616	0.948318	0.331645	1.343401	-0.167934	-1.105252	-0.167077	-0.096576	-0.838161	-0.208564	0.394534	0.762533	1.235357	-0.207282	-0.202946	-0.468025	0.256944	2.587584	1.186697	-1.031903	1.428316	0.658899	-0.046582	-0.075422	1.329359	-0.684267	-1.524182	2.014061	3.770933	0.647353	-1.021377	-0.345493	0.582811	0.797812	1.326020	1.422857	-3.077007	0.184083	1.478935
4	-0.600142	1.929561	-2.346771	-0.669700	-1.165258	0.814788	0.444449	-0.576758	0.353091	0.408893	0.091391	-2.294389	0.485506	-0.081304	-0.716272	-1.648010	1.005361	-1.489603	0.363098	0.758602	-1.373847	-0.972057	1.988537	0.319829	1.169060	0.146585	1.030388	1.165984	1.369563	0.730984	-1.383696	-0.515189	-0.808927	-1.174651	-1.631502	-1.123414	-0.478155	-1.583067	1.419074	1.668777	1.567517	0.222103	-0.336040	-1.352064	0.251032	-0.401695	0.268413	-0.012299	-0.918953	2.921208	-0.581588	0.672848	1.251136	1.382263	1.429897	1.290990	-1.272673	-0.308611	-0.422988	-0.675642	0.874441	1.305736	-0.262585	-1.099395	-0.667101	-0.646737	-0.556338	-0.196591	0.119306	-0.266455	-0.524267	2.650951	0.097318	-0.974697	0.189964	1.141155	-0.064434	1.104971	-1.508908	-0.031833	0.803919	-0.659221	0.939145	0.214041	-0.531805	0.956060	0.249328	0.637903	-0.510158	1.850287	-0.348407	2.001376	-0.389643	-0.024786	-0.470973	0.869339	0.170667	0.598062	1.217262	1.274013
5	-0.389981	-0.752441	-0.734871	3.517318	-1.173559	-0.004956	0.145419	2.151368	-3.086037	-1.569139	1.449784	-0.868951	-1.687716	-0.994401	1.153266	1.803045	-0.819059	0.847970	0.227102	-0.500762	0.868210	1.823540	1.161007	-0.307606	-0.713416	0.363560	-0.822162	2.427681	-0.129537	-0.078716	1.345644	-1.286094	0.237242	-0.136056	0.596664	-1.412381	1.206341	0.299860	0.705238	0.142412	-1.059382	0.833468	1.060015	-0.527045	-1.135732	-1.140983	-0.779540	-0.640875	-1.217196	-1.675663	0.241263	-0.273322	-1.697936	-0.594943	0.101154	1.391735	-0.426953	1.008344	-0.818577	1.924570	-0.578900	-0.457395	-1.096705	0.418522	-0.155623	0.169706	-2.533706	0.018904	1.434160	0.744095	0.647626	-0.770309	2.329141	-0.141547	-1.761594	0.702091	-1.531450	-0.788427	-0.184622	-1.942321	1.530113	0.503406	1.105845	-0.935120	-1.115483	-2.249762	1.307135	0.788412	-0.441091	0.073561	0.812101	-0.916146	1.573714	-0.309508	0.499987	0.187594	0.558913	0.903246	0.317901	-0.809797
6	1.128248	1.516826	-0.186735	-0.668157	1.132259	-0.246648	-0.855167	0.732283	0.931802	1.318684	-1.198418	-1.149318	0.586321	-1.171937	-0.607731	2.753747	1.479287	-1.136365	-0.020485	0.320444	-1.955755	0.660402	-1.545371	0.200519	-0.017263	1.634686	0.599246	0.462989	0.023721	0.225546	0.170972	-0.027496	-0.061233	-0.566411	-0.669567	0.601618	0.503656	-0.678253	-2.907108	-1.717123	0.397631	1.300108	0.215821	-0.593075	-0.225944	-0.946057	1.000308	0.393160	1.342074	-0.370687	-0.166413	-0.419814	-0.255931	1.789478	0.282378	0.742260	-0.050498	1.415309	0.838166	-1.400292	-0.937976	-1.499148	0.801859	0.224824	0.283572	0.643703	-1.198465	0.527206	0.215202	0.437048	1.312868	0.741243	0.077988	0.006123	0.190370	0.018007	-1.026036	-2.378430	-1.069949	0.843822	1.289216	-1.423369	-0.462887	0.197330	-0.935076	0.441271	0.414643	-0.377887	-0.530515	0.621592	1.009572	0.569718	0.175291	-0.656279	-0.112273	-0.392137	-1.043558	-0.467318	-0.384329	-2.009207
7	0.658598	0.101830	-0.682781	0.229349	-0.305657	0.404877	0.252244	-0.837784	-0.039624	0.329457	0.751694	1.469070	-0.157199	1.032628	-0.584639	-0.925544	0.342474	-0.969363	0.133480	-0.385974	-0.600278	0.281939	0.868579	1.129803	-0.041898	0.961193	0.131521	-0.792889	-1.285737	0.073934	-1.333315	-1.044125	1.277338	1.492257	0.411379	1.771805	-1.111128	1.123233	-1.019449	1.738357	-0.690764	-0.120710	-0.421359	-0.727294	-0.857759	-0.069436	-0.328334	-0.558180	1.063474	-0.519133	-0.496902	1.089589	-1.615801	0.080174	-0.229938	-0.498420	-0.624615	0.059481	-0.093158	-1.784549	-0.503789	-0.140528	0.002653	-0.484930	0.055914	-0.680948	-0.994271	1.277052	0.037651	2.155421	-0.437589	0.696404	0.417752	-0.544785	1.190690	0.978262	0.752102	0.504472	0.139853	-0.505089	-0.264975	-1.603194	0.731847	0.010903	-1.165346	-0.125195	-1.032685	-0.465520	1.514808	0.304762	0.793414	0.314635	-1.638279	0.111737	-0.777037	0.251783	1.126303	-0.808798	0.422064	-0.349264
8	-0.356362	-0.089227	0.609373	0.542382	-0.768681	-0.048074	2.015458	-1.552351	0.251552	1.459635	0.949707	0.339465	-0.001372	1.798589	1.559163	0.231783	0.423141	-0.310530	0.353795	2.173336	-0.196247	-0.375636	-0.858221	0.258410	0.656430	0.960819	1.137893	1.553405	0.038981	-0.632038	-0.132009	-1.834997	-0.242576	-0.297879	-0.441559	-0.769691	0.224077	-0.153009	0.519526	-0.680188	0.535851	0.671496	-0.183064	0.301234	1.288256	-2.478240	-0.360403	0.424067	-0.834659	-0.128464	-0.489013	-0.014888	-1.461230	-1.435223	-1.319802	1.083675	0.979140	-0.375291	1.110189	-1.011351	0.587886	-0.822775	-1.183865	1.455173	1.134328	0.239403	-0.837991	-1.130932	0.783168	1.845520	1.437072	-1.198443	1.379098	2.129113	0.260096	-0.011975	0.043302	0.722941	1.028152	-0.235806	1.145245	-1.359598	0.232189	0.503712	-0.614264	-0.530606	-2.435803	-0.255238	-0.064423	0.784643	0.256346	0.128023	1.414103	-1.118659	0.877353	0.500561	0.463651	-2.034512	-0.981683	-0.691944
9	-1.113376	-1.169402	0.680539	-1.534212	1.653817	-1.295181	-0.566826	0.477014	1.413371	0.517105	1.401153	-0.872685	0.830957	0.181507	-0.145616	0.694592	-0.751208	0.324444	0.681973	-0.054972	0.917776	-1.024810	-0.206446	-0.600113	0.852805	1.455109	-0.079769	0.076076	0.207699	-1.850458	-0.124124	-0.610871	-0.883362	0.219049	-0.685094	-0.645330	-0.242805	-0.775602	0.233070	2.422642	-1.423040	-0.582421	0.968304	-0.701025	-0.167850	0.277264	1.301231	0.301205	-3.081249	-0.562868	0.192944	-0.664592	0.565686	0.190913	-0.841858	-1.856545	-1.022777	1.295968	0.451921	0.659955	0.065818	-0.319586	0.253495	-1.144646	-0.483404	0.555902	0.807069	0.714196	0.661196	0.053667	0.346833	-1.288977	-0.386734	-1.262127	0.477495	-0.494034	-0.911414	1.152963	-0.342365	-0.160187	0.470054	-0.853063	-1.387949	-0.257257	-1.030690	-0.110210	0.328911	-0.555923	0.987713	-0.501957	2.069887	-0.067503	0.316029	-1.506232	2.201621	0.492097	-0.085193	-0.977822	1.039147	-0.653932
10	-0.405638	-1.402027	-1.166242	1.306184	0.856283	-1.236170	-0.646721	-1.474064	0.082960	0.090310	-0.169977	0.406345	0.915427	-0.974503	0.271637	1.539184	-0.098866	-0.525149	1.063933	0.085827	-0.129622	0.947959	-0.072496	-0.237592	0.012549	1.065761	0.996596	-0.172481	2.583139	-0.028578	-0.254856	1.328794	-1.592951	2.434350	-0.341500	-0.307719	-1.333273	-1.100845	0.209097	1.734777	0.639632	0.424779	-0.129327	0.905029	-0.482909	1.731628	-2.783425	-0.333677	-0.110895	1.212636	-0.208412	0.427117	1.348563	0.043859	1.772519	-1.416106	0.401155	0.807157	0.303427	-1.246288	0.178774	-0.066126	-1.862288	1.241295	0.377021	-0.822320	-0.749014	1.463652	1.602268	-1.043877	1.185290	-0.565783	-1.076879	1.360241	-0.121991	0.991043	1.007952	0.450185	-0.744376	1.388876	-0.316847	-0.841655	-1.056842	-0.500226	0.096959	1.176896	-2.939652	1.792213	0.316340	0.303218	1.024967	-0.590871	-0.453326	-0.795981	-0.393301	-0.374372	-1.270199	1.618372	1.197727	-0.914863
11	-0.625210	0.288911	0.288374	-1.372667	-0.591395	-0.478942	1.335664	-0.459855	-1.615975	-1.189676	0.374767	-2.488733	0.586656	-1.422008	0.496030	1.911128	-0.560660	-0.499614	-0.372171	-1.833069	0.237124	-0.944446	0.912140	0.359790	-1.359235	0.166966	-0.047107	-0.279789	-0.594454	-0.739013	-1.527645	0.401668	1.791252	-2.774848	0.523873	2.207585	0.488999	-0.339283	0.131711	0.018409	1.186551	-0.424318	1.554994	-0.205917	-0.934975	0.654102	-1.227761	-0.461025	-0.421201	-0.058615	-0.584563	0.336913	-0.477102	-1.381463	0.757745	-0.268968	0.034870	1.231686	0.236600	1.234720	-0.040247	0.029582	1.034905	0.380204	-0.012108	-0.859511	-0.990340	-1.205172	-1.030178	0.426676	0.497796	-0.876808	0.957963	0.173016	0.131612	-1.003556	-1.069908	-1.799207	1.429598	-0.116015	-1.454980	0.261917	0.444412	0.273290	0.844115	0.218745	-1.033350	-1.188295	0.058373	0.800523	-1.627068	0.861651	0.871018	-0.003733	-0.243354	0.947296	0.509406	0.044546	0.266896	1.337165
12	0.699142	-1.928033	0.105363	1.042322	0.715206	-0.763783	0.098798	-1.157898	0.134105	0.042041	0.674826	0.165649	-1.622970	-3.131274	0.597649	-1.880331	0.663980	-0.256033	-1.524058	0.492799	0.221163	0.429622	-0.659584	1.264506	-0.032131	-2.114907	-0.264043	0.457835	-0.676837	-0.629003	0.489145	-0.551686	0.942622	-0.512043	-0.455893	0.021244	-0.178035	-2.498073	-0.171292	0.323510	-0.545163	-0.668909	-0.150031	0.521620	-0.428980	0.676463	0.369081	-0.724832	0.793542	1.237422	0.401275	2.141523	0.249012	0.486755	-0.163274	0.592222	-0.292600	-0.547168	0.619104	-0.013605	0.776734	0.131424	1.189480	-0.666317	-0.939036	1.105515	0.621452	1.586605	-0.760970	1.649646	0.283199	1.275812	-0.452012	0.301361	-0.976951	-0.268106	-0.079255	-1.258332	2.216658	-1.175988	-0.863497	-1.653022	-0.561514	0.450753	0.417200	0.094676	-2.231054	1.316862	-0.477441	0.646654	-0.200252	1.074354	-0.058176	0.120990	0.222522	-0.179507	0.421655	-0.914341	-0.234178	0.741524
13	0.932714	1.423761	-1.280835	0.347882	-0.863171	-0.852580	1.044933	2.094536	0.806206	0.416201	-1.109503	0.145302	-0.996871	0.325456	-0.605081	1.175326	1.645054	0.293432	-2.766822	1.032849	0.079115	-1.414132	1.463376	2.335486	0.411951	-0.048543	0.159284	-0.651554	-1.093128	1.568390	-0.077807	-2.390779	-0.842346	-0.229675	-0.999072	-1.367219	-0.792042	-1.878575	1.451452	1.266250	-0.734315	0.266152	0.735523	-0.430860	0.229864	0.850083	-2.241241	1.063850	0.289409	-0.354360	0.113063	-0.173006	1.386998	1.886236	0.587119	-0.961133	0.399295	1.461560	0.310823	0.280220	-0.879103	-1.326348	0.003337	-1.085908	-0.436723	2.111926	0.106068	0.615597	2.152996	-0.196155	0.025747	-0.039061	0.656823	-0.347105	2.513979	1.758070	1.288473	-0.739185	-0.691592	-0.098728	-0.276386	0.489981	0.516278	-0.838258	0.596673	-0.331053	0.521174	-0.145023	0.836693	-1.092166	0.361733	-1.169981	0.046731	0.655377	-0.756852	1.285805	-0.095019	0.360253	1.370621	0.083010
14	0.888893	2.288725	-1.032332	0.212273	-1.091826	1.692498	1.025367	0.550854	0.679430	-1.335712	-0.798341	2.265351	-1.006938	2.059761	0.420266	-1.189657	0.506674	0.260847	-0.533145	0.727267	1.412276	1.482106	-0.996258	0.588641	-0.412642	-0.920733	-0.874691	0.839002	0.501668	-0.342493	-0.533806	-2.146352	-0.597339	0.115726	0.850683	-0.752239	0.377263	-0.561982	0.262783	-0.356676	-0.367462	0.753611	-1.267414	-1.330698	-0.536453	0.840938	-0.763108	-0.268100	-0.677424	1.606831	0.151732	-2.085701	1.219296	0.400863	0.591165	-1.485213	1.501979	1.196569	-0.214154	0.339554	-0.034446	1.176452	0.546340	-1.255630	-1.309210	-0.445437	0.189437	-0.737463	0.843767	-0.605632	-0.060777	0.409310	1.285569	-0.622638	1.018193	0.880680	0.046805	-1.818058	-0.809829	0.875224	0.409569	-0.116621	-1.238919	3.305724	-0.024121	-1.756500	1.328958	0.507593	-0.866554	-2.240848	-0.661376	-0.671824	0.215720	-0.296326	0.481402	0.829645	-0.721025	1.263914	0.549047	-1.234945

Hiding Headers¶

We don’t yet have any API to hide headers so a quick fix is:

[58]:

df3.style.set_table_styles([{'selector': 'thead tr', 'props': 'display: none;'}])  # or 'thead th'

[58]:

		c1	c2	c3	c4
A	r1	-1.048553	-1.420018	-1.706270	1.950775
A	r2	-0.509652	-0.438074	-1.252795	0.777490
B	r1	-1.613898	-0.212740	-0.895467	0.386902
B	r2	-0.510805	-1.180632	-0.028182	0.428332

HTML Escaping¶

Suppose you have to display HTML within HTML, that can be a bit of pain when the renderer can’t distinguish. You can use the escape formatting option to handle this. Even use it within a formatter that contains HTML itself.

[59]:

df4 = pd.DataFrame([['<div></div>', '"&other"', '<span></span>']])
df4.style

[59]:

	0	1	2
0		"&other"

[60]:

# df4.style.format(escape=True)

Export to Excel¶

Some support (since version 0.20.0) is available for exporting styled DataFrames to Excel worksheets using the OpenPyXL or XlsxWriter engines. CSS2.2 properties handled include:

background-color
color
font-family
font-style
font-weight
text-align
text-decoration
vertical-align
white-space: nowrap
Currently broken: border-style, border-width, border-color and their {top, right, bottom, left variants}
Only CSS2 named colors and hex colors of the form #rgb or #rrggbb are currently supported.
The following pseudo CSS properties are also available to set excel specific style properties:
- number-format

Table level styles, and data cell CSS-classes are not included in the export to Excel: individual cells must have their properties mapped by the Styler.apply and/or Styler.applymap methods.

[61]:

df2.style.\
    applymap(style_negative, props='color:red;').\
    highlight_max(axis=0).\
    to_excel('styled.xlsx', engine='openpyxl')

A screenshot of the output:

More About CSS and HTML¶

Cascading Style Sheet (CSS) language, which is designed to influence how a browser renders HTML elements, has its own peculiarities. It never reports errors: it just silently ignores them and doesn’t render your objects how you intend so can sometimes be frustrating. Here is a very brief primer on how Styler creates HTML and interacts with CSS, with advice on common pitfalls to avoid.

CSS Classes and Ids¶

The precise structure of the CSS class attached to each cell is as follows.

Cells with Index and Column names include index_name and level<k> where k is its level in a MultiIndex
Index label cells include
- row_heading
- level<k> where k is the level in a MultiIndex
- row<m> where m is the numeric position of the row
Column label cells include
- col_heading
- level<k> where k is the level in a MultiIndex
- col<n> where n is the numeric position of the column
Data cells include
- data
- row<m>, where m is the numeric position of the cell.
- col<n>, where n is the numeric position of the cell.
Blank cells include blank

The structure of the id is T_uuid_level<k>_row<m>_col<n> where level<k> is used only on headings, and headings will only have either row<m> or col<n> whichever is needed. By default we’ve also prepended each row/column identifier with a UUID unique to each DataFrame so that the style from one doesn’t collide with the styling from another within the same notebook or page. You can read more about the use of UUIDs in Optimization.

We can see example of the HTML by calling the .render() method.

[62]:

print(pd.DataFrame([[1,2],[3,4]], index=['i1', 'i2'], columns=['c1', 'c2']).style.render())

<style type="text/css">
</style>
<table id="T_1a528_">
  <thead>
    <tr>
      <th class="blank level0" >&nbsp;</th>
      <th class="col_heading level0 col0" >c1</th>
      <th class="col_heading level0 col1" >c2</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th id="T_1a528_level0_row0" class="row_heading level0 row0" >i1</th>
      <td id="T_1a528_row0_col0" class="data row0 col0" >1</td>
      <td id="T_1a528_row0_col1" class="data row0 col1" >2</td>
    </tr>
    <tr>
      <th id="T_1a528_level0_row1" class="row_heading level0 row1" >i2</th>
      <td id="T_1a528_row1_col0" class="data row1 col0" >3</td>
      <td id="T_1a528_row1_col1" class="data row1 col1" >4</td>
    </tr>
  </tbody>
</table>

CSS Hierarchies¶

The examples have shown that when CSS styles overlap, the one that comes last in the HTML render, takes precedence. So the following yield different results:

[63]:

df4 = pd.DataFrame([['text']])
df4.style.applymap(lambda x: 'color:green;')\
         .applymap(lambda x: 'color:red;')

[63]:

	0
0	text

[64]:

df4.style.applymap(lambda x: 'color:red;')\
         .applymap(lambda x: 'color:green;')

[64]:

	0
0	text

This is only true for CSS rules that are equivalent in hierarchy, or importance. You can read more about CSS specificity here but for our purposes it suffices to summarize the key points:

A CSS importance score for each HTML element is derived by starting at zero and adding:

1000 for an inline style attribute
100 for each ID
10 for each attribute, class or pseudo-class
1 for each element name or pseudo-element

Let’s use this to describe the action of the following configurations

[65]:

df4.style.set_uuid('a_')\
         .set_table_styles([{'selector': 'td', 'props': 'color:red;'}])\
         .applymap(lambda x: 'color:green;')

[65]:

	0
0	text

This text is red because the generated selector #T_a_ td is worth 101 (ID plus element), whereas #T_a_row0_col0 is only worth 100 (ID), so is considered inferior even though in the HTML it comes after the previous.

[66]:

df4.style.set_uuid('b_')\
         .set_table_styles([{'selector': 'td', 'props': 'color:red;'},
                            {'selector': '.cls-1', 'props': 'color:blue;'}])\
         .applymap(lambda x: 'color:green;')\
         .set_td_classes(pd.DataFrame([['cls-1']]))

[66]:

	0
0	text

In the above case the text is blue because the selector #T_b_ .cls-1 is worth 110 (ID plus class), which takes precendence.

[67]:

df4.style.set_uuid('c_')\
         .set_table_styles([{'selector': 'td', 'props': 'color:red;'},
                            {'selector': '.cls-1', 'props': 'color:blue;'},
                            {'selector': 'td.data', 'props': 'color:yellow;'}])\
         .applymap(lambda x: 'color:green;')\
         .set_td_classes(pd.DataFrame([['cls-1']]))

[67]:

	0
0	text

Now we have created another table style this time the selector T_c_ td.data (ID plus element plus class) gets bumped up to 111.

If your style fails to be applied, and its really frustrating, try the !important trump card.

[68]:

df4.style.set_uuid('d_')\
         .set_table_styles([{'selector': 'td', 'props': 'color:red;'},
                            {'selector': '.cls-1', 'props': 'color:blue;'},
                            {'selector': 'td.data', 'props': 'color:yellow;'}])\
         .applymap(lambda x: 'color:green !important;')\
         .set_td_classes(pd.DataFrame([['cls-1']]))

[68]:

	0
0	text

Finally got that green text after all!

Extensibility¶

The core of pandas is, and will remain, its “high-performance, easy-to-use data structures”. With that in mind, we hope that DataFrame.style accomplishes two goals

Provide an API that is pleasing to use interactively and is “good enough” for many tasks
Provide the foundations for dedicated libraries to build on

If you build a great library on top of this, let us know and we’ll link to it.

Subclassing¶

If the default template doesn’t quite suit your needs, you can subclass Styler and extend or override the template. We’ll show an example of extending the default template to insert a custom header before each table.

[69]:

from jinja2 import Environment, ChoiceLoader, FileSystemLoader
from IPython.display import HTML
from pandas.io.formats.style import Styler

We’ll use the following template:

[70]:

with open("templates/myhtml.tpl") as f:
    print(f.read())

{% extends "html.tpl" %}
{% block table %}
<h1>{{ table_title|default("My Table") }}</h1>
{{ super() }}
{% endblock table %}

Now that we’ve created a template, we need to set up a subclass of Styler that knows about it.

[71]:

class MyStyler(Styler):
    env = Environment(
        loader=ChoiceLoader([
            FileSystemLoader("templates"),  # contains ours
            Styler.loader,  # the default
        ])
    )
    template = env.get_template("myhtml.tpl")

Notice that we include the original loader in our environment’s loader. That’s because we extend the original template, so the Jinja environment needs to be able to find it.

Now we can use that custom styler. It’s __init__ takes a DataFrame.

[72]:

MyStyler(df3)

[72]:

My Table

		c1	c2	c3	c4
A	r1	-1.048553	-1.420018	-1.706270	1.950775
A	r2	-0.509652	-0.438074	-1.252795	0.777490
B	r1	-1.613898	-0.212740	-0.895467	0.386902
B	r2	-0.510805	-1.180632	-0.028182	0.428332

Our custom template accepts a table_title keyword. We can provide the value in the .render method.

[73]:

HTML(MyStyler(df3).render(table_title="Extending Example"))

[73]:

Extending Example

		c1	c2	c3	c4
A	r1	-1.048553	-1.420018	-1.706270	1.950775
A	r2	-0.509652	-0.438074	-1.252795	0.777490
B	r1	-1.613898	-0.212740	-0.895467	0.386902
B	r2	-0.510805	-1.180632	-0.028182	0.428332

For convenience, we provide the Styler.from_custom_template method that does the same as the custom subclass.

[74]:

EasyStyler = Styler.from_custom_template("templates", "myhtml.tpl")
EasyStyler(df3)

[74]:

My Table

		c1	c2	c3	c4
A	r1	-1.048553	-1.420018	-1.706270	1.950775
A	r2	-0.509652	-0.438074	-1.252795	0.777490
B	r1	-1.613898	-0.212740	-0.895467	0.386902
B	r2	-0.510805	-1.180632	-0.028182	0.428332

Here’s the template structure:

[75]:

with open("templates/template_structure.html") as f:
    structure = f.read()

HTML(structure)

[75]:

before_style

style

<style type="text/css">

table_styles

before_cellstyle

cellstyle

</style>

before_table

table

<table ...>

caption

thead

before_head_rows

head_tr (loop over headers)

after_head_rows

tbody

before_rows

tr (loop over data rows)

after_rows

</table>

after_table

See the template in the GitHub repo for more details.

Table Visualization¶

Styler Object and HTML¶

Formatting the Display¶

Formatting Values¶

Hiding Data¶

Methods to Add Styles¶

Table Styles¶

Setting Classes and Linking to External CSS¶

Table Attributes¶

Data Cell CSS Classes¶

Styler Functions¶

Tooltips and Captions¶

Finer Control with Slicing¶

Optimization¶

Builtin Styles¶

Bar charts¶

Sharing styles¶

Limitations¶

Other Fun and Useful Stuff¶

Widgets¶

Magnify¶

Sticky Headers¶

Hiding Headers¶

HTML Escaping¶

Export to Excel¶

More About CSS and HTML¶

CSS Classes and Ids¶

CSS Hierarchies¶

Extensibility¶

Subclassing¶

My Table

Extending Example

My Table