diff --git a/doc/source/_static/style-excel.png b/doc/source/_static/style-excel.png
new file mode 100644
index 0000000000000..ee8a14a410d48
Binary files /dev/null and b/doc/source/_static/style-excel.png differ
diff --git a/doc/source/html-styling.ipynb b/doc/source/html-styling.ipynb
index 1a97378fd30b1..331bee4c7a209 100644
--- a/doc/source/html-styling.ipynb
+++ b/doc/source/html-styling.ipynb
@@ -26,6 +26,8 @@
"- [Builtin Styles](#Builtin-Styles)\n",
"- [Other options](#Other-options)\n",
"- [Sharing Styles](#Sharing-Styles)\n",
+ "- [CSS Classes](#CSS-Classes)\n",
+ "- [Export to Excel](#Export-to-Excel)\n",
"- [Limitations](#Limitations)\n",
"- [Terms](#Terms)\n",
"- [Extensibility](#Extensibility)"
@@ -57,9 +59,7 @@
{
"cell_type": "code",
"execution_count": null,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
@@ -82,9 +82,7 @@
{
"cell_type": "code",
"execution_count": null,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [],
"source": [
"df.style"
@@ -102,9 +100,7 @@
{
"cell_type": "code",
"execution_count": null,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [],
"source": [
"df.style.highlight_null().render().split('\\n')[:10]"
@@ -129,9 +125,7 @@
{
"cell_type": "code",
"execution_count": null,
- "metadata": {
- "collapsed": true
- },
+ "metadata": {},
"outputs": [],
"source": [
"def color_negative_red(val):\n",
@@ -155,9 +149,7 @@
{
"cell_type": "code",
"execution_count": null,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [],
"source": [
"s = df.style.applymap(color_negative_red)\n",
@@ -187,9 +179,7 @@
{
"cell_type": "code",
"execution_count": null,
- "metadata": {
- "collapsed": true
- },
+ "metadata": {},
"outputs": [],
"source": [
"def highlight_max(s):\n",
@@ -203,9 +193,7 @@
{
"cell_type": "code",
"execution_count": null,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [],
"source": [
"df.style.apply(highlight_max)"
@@ -229,9 +217,7 @@
{
"cell_type": "code",
"execution_count": null,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [],
"source": [
"df.style.\\\n",
@@ -256,9 +242,7 @@
{
"cell_type": "code",
"execution_count": null,
- "metadata": {
- "collapsed": true
- },
+ "metadata": {},
"outputs": [],
"source": [
"def highlight_max(data, color='yellow'):\n",
@@ -285,9 +269,7 @@
{
"cell_type": "code",
"execution_count": null,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [],
"source": [
"df.style.apply(highlight_max, color='darkorange', axis=None)"
@@ -335,9 +317,7 @@
{
"cell_type": "code",
"execution_count": null,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [],
"source": [
"df.style.apply(highlight_max, subset=['B', 'C', 'D'])"
@@ -353,9 +333,7 @@
{
"cell_type": "code",
"execution_count": null,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [],
"source": [
"df.style.applymap(color_negative_red,\n",
@@ -388,9 +366,7 @@
{
"cell_type": "code",
"execution_count": null,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [],
"source": [
"df.style.format(\"{:.2%}\")"
@@ -406,9 +382,7 @@
{
"cell_type": "code",
"execution_count": null,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [],
"source": [
"df.style.format({'B': \"{:0<4.0f}\", 'D': '{:+.2f}'})"
@@ -424,9 +398,7 @@
{
"cell_type": "code",
"execution_count": null,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [],
"source": [
"df.style.format({\"B\": lambda x: \"±{:.2f}\".format(abs(x))})"
@@ -449,9 +421,7 @@
{
"cell_type": "code",
"execution_count": null,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [],
"source": [
"df.style.highlight_null(null_color='red')"
@@ -467,9 +437,7 @@
{
"cell_type": "code",
"execution_count": null,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [],
"source": [
"import seaborn as sns\n",
@@ -490,9 +458,7 @@
{
"cell_type": "code",
"execution_count": null,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [],
"source": [
"# Uses the full color range\n",
@@ -502,9 +468,7 @@
{
"cell_type": "code",
"execution_count": null,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [],
"source": [
"# Compreess the color range\n",
@@ -524,9 +488,7 @@
{
"cell_type": "code",
"execution_count": null,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [],
"source": [
"df.style.bar(subset=['A', 'B'], color='#d65f5f')"
@@ -542,9 +504,7 @@
{
"cell_type": "code",
"execution_count": null,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [],
"source": [
"df.style.highlight_max(axis=0)"
@@ -553,9 +513,7 @@
{
"cell_type": "code",
"execution_count": null,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [],
"source": [
"df.style.highlight_min(axis=0)"
@@ -571,9 +529,7 @@
{
"cell_type": "code",
"execution_count": null,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [],
"source": [
"df.style.set_properties(**{'background-color': 'black',\n",
@@ -598,9 +554,7 @@
{
"cell_type": "code",
"execution_count": null,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [],
"source": [
"df2 = -df\n",
@@ -611,9 +565,7 @@
{
"cell_type": "code",
"execution_count": null,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [],
"source": [
"style2 = df2.style\n",
@@ -666,9 +618,7 @@
{
"cell_type": "code",
"execution_count": null,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [],
"source": [
"with pd.option_context('display.precision', 2):\n",
@@ -688,9 +638,7 @@
{
"cell_type": "code",
"execution_count": null,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [],
"source": [
"df.style\\\n",
@@ -723,9 +671,7 @@
{
"cell_type": "code",
"execution_count": null,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [],
"source": [
"df.style.set_caption('Colormaps, with a caption.')\\\n",
@@ -751,9 +697,7 @@
{
"cell_type": "code",
"execution_count": null,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [],
"source": [
"from IPython.display import HTML\n",
@@ -809,6 +753,44 @@
"- Data cells include `data`"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Export to Excel\n",
+ "\n",
+ "*New in version 0.19.0*\n",
+ "\n",
+ "*Experimental: This is a new feature and still under development. We'll be adding features and possibly making breaking changes in future releases. We'd love to hear your [feedback](https://github.com/pandas-dev/pandas/issues).*
\n",
+ "\n",
+ "Some support is available for exporting styled `DataFrames` to Excel worksheets using the `OpenPyXL` engine. CSS2.2 properties handled include:\n",
+ "\n",
+ "- `background-color`\n",
+ "- `border-style`, `border-width`, `border-color` and their {`top`, `right`, `bottom`, `left` variants}\n",
+ "- `color`\n",
+ "- `font-family`\n",
+ "- `font-style`\n",
+ "- `font-weight`\n",
+ "- `text-align`\n",
+ "- `text-decoration`\n",
+ "- `vertical-align`\n",
+ "- `white-space: nowrap`\n",
+ "\n",
+ "Only CSS2 named colors and hex colors of the form `#rgb` or `#rrggbb` are currently supported."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df.style.\\\n",
+ " applymap(color_negative_red).\\\n",
+ " apply(highlight_max).\\\n",
+ " to_excel('styled.xlsx', engine='openpyxl')"
+ ]
+ },
{
"cell_type": "markdown",
"metadata": {},
@@ -849,9 +831,7 @@
{
"cell_type": "code",
"execution_count": null,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [],
"source": [
"from IPython.html import widgets\n",
@@ -866,9 +846,7 @@
{
"cell_type": "code",
"execution_count": null,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [],
"source": [
"def magnify():\n",
@@ -887,9 +865,7 @@
{
"cell_type": "code",
"execution_count": null,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [],
"source": [
"np.random.seed(25)\n",
@@ -960,8 +936,7 @@
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.5.1"
+ "pygments_lexer": "ipython3"
}
},
"nbformat": 4,
diff --git a/doc/source/whatsnew/v0.20.0.txt b/doc/source/whatsnew/v0.20.0.txt
index d571c0f2d9620..b8845ddd2fd64 100644
--- a/doc/source/whatsnew/v0.20.0.txt
+++ b/doc/source/whatsnew/v0.20.0.txt
@@ -316,6 +316,32 @@ To convert a ``SparseDataFrame`` back to sparse SciPy matrix in COO format, you
.. _whatsnew_0200.enhancements.other:
+Stylers now support Excel output
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+Experimental support has been added to export ``DataFrame.style`` to Excel using the ``openpyxl`` engine. (:issue:`15530`)
+
+For example, after running the following, ``styled.xlsx`` renders as below:
+
+.. ipython:: python
+
+ import pandas as pd
+ import numpy as np
+
+ np.random.seed(24)
+ df = pd.DataFrame({'A': np.linspace(1, 10, 10)})
+ df = pd.concat([df, pd.DataFrame(np.random.randn(10, 4), columns=list('BCDE'))],
+ axis=1)
+ df.iloc[0, 2] = np.nan
+ df.style.\
+ applymap(color_negative_red).\
+ apply(highlight_max).\
+ to_excel('styled.xlsx', engine='openpyxl')
+
+.. image:: _static/style-excel.png
+
+See the :ref:`Style documentation