instead of \n",
- " 'selector': 'td:hover',\n",
- " 'props': [('background-color', '#ffffb3')]\n",
+ " \"selector\": \"td:hover\",\n",
+ " \"props\": [(\"background-color\", \"#ffffb3\")],\n",
"}\n",
"index_names = {\n",
- " 'selector': '.index_name',\n",
- " 'props': 'font-style: italic; color: darkgrey; font-weight:normal;'\n",
+ " \"selector\": \".index_name\",\n",
+ " \"props\": \"font-style: italic; color: darkgrey; font-weight:normal;\",\n",
"}\n",
"headers = {\n",
- " 'selector': 'th:not(.index_name)',\n",
- " 'props': 'background-color: #000066; color: white;'\n",
+ " \"selector\": \"th:not(.index_name)\",\n",
+ " \"props\": \"background-color: #000066; color: white;\",\n",
"}\n",
"s.set_table_styles([cell_hover, index_names, headers])"
]
@@ -417,8 +417,8 @@
},
"outputs": [],
"source": [
- "# Hidden cell to avoid CSS clashes and latter code upcoding previous formatting \n",
- "s.set_uuid('after_tab_styles1')"
+ "# Hidden cell to avoid CSS clashes and latter code upcoding previous formatting\n",
+ "s.set_uuid(\"after_tab_styles1\")"
]
},
{
@@ -434,11 +434,14 @@
"metadata": {},
"outputs": [],
"source": [
- "s.set_table_styles([\n",
- " {'selector': 'th.col_heading', 'props': 'text-align: center;'},\n",
- " {'selector': 'th.col_heading.level0', 'props': 'font-size: 1.5em;'},\n",
- " {'selector': 'td', 'props': 'text-align: center; font-weight: bold;'},\n",
- "], overwrite=False)"
+ "s.set_table_styles(\n",
+ " [\n",
+ " {\"selector\": \"th.col_heading\", \"props\": \"text-align: center;\"},\n",
+ " {\"selector\": \"th.col_heading.level0\", \"props\": \"font-size: 1.5em;\"},\n",
+ " {\"selector\": \"td\", \"props\": \"text-align: center; font-weight: bold;\"},\n",
+ " ],\n",
+ " overwrite=False,\n",
+ ")"
]
},
{
@@ -449,8 +452,8 @@
},
"outputs": [],
"source": [
- "# Hidden cell to avoid CSS clashes and latter code upcoding previous formatting \n",
- "s.set_uuid('after_tab_styles2')"
+ "# Hidden cell to avoid CSS clashes and latter code upcoding previous formatting\n",
+ "s.set_uuid(\"after_tab_styles2\")"
]
},
{
@@ -468,10 +471,16 @@
"metadata": {},
"outputs": [],
"source": [
- "s.set_table_styles({\n",
- " ('Regression', 'Tumour'): [{'selector': 'th', 'props': 'border-left: 1px solid white'},\n",
- " {'selector': 'td', 'props': 'border-left: 1px solid #000066'}]\n",
- "}, overwrite=False, axis=0)"
+ "s.set_table_styles(\n",
+ " {\n",
+ " (\"Regression\", \"Tumour\"): [\n",
+ " {\"selector\": \"th\", \"props\": \"border-left: 1px solid white\"},\n",
+ " {\"selector\": \"td\", \"props\": \"border-left: 1px solid #000066\"},\n",
+ " ]\n",
+ " },\n",
+ " overwrite=False,\n",
+ " axis=0,\n",
+ ")"
]
},
{
@@ -482,8 +491,8 @@
},
"outputs": [],
"source": [
- "# Hidden cell to avoid CSS clashes and latter code upcoding previous formatting \n",
- "s.set_uuid('xyz01')"
+ "# Hidden cell to avoid CSS clashes and latter code upcoding previous formatting\n",
+ "s.set_uuid(\"xyz01\")"
]
},
{
@@ -508,7 +517,7 @@
"outputs": [],
"source": [
"out = s.set_table_attributes('class=\"my-table-cls\"').to_html()\n",
- "print(out[out.find(' -0.3) else None)\n",
+ "\n",
+ "\n",
+ "s2 = df2.style.map(style_negative, props=\"color:red;\").map(\n",
+ " lambda v: \"opacity: 20%;\" if (v < 0.3) and (v > -0.3) else None\n",
+ ")\n",
"s2"
]
},
@@ -612,8 +629,8 @@
},
"outputs": [],
"source": [
- "# Hidden cell to avoid CSS clashes and latter code upcoding previous formatting \n",
- "s2.set_uuid('after_applymap')"
+ "# Hidden cell to avoid CSS clashes and latter code upcoding previous formatting\n",
+ "s2.set_uuid(\"after_applymap\")"
]
},
{
@@ -629,9 +646,11 @@
"metadata": {},
"outputs": [],
"source": [
- "def highlight_max(s, props=''):\n",
- " return np.where(s == np.nanmax(s.values), props, '')\n",
- "s2.apply(highlight_max, props='color:white;background-color:darkblue', axis=0)"
+ "def highlight_max(s, props=\"\"):\n",
+ " return np.where(s == np.nanmax(s.values), props, \"\")\n",
+ "\n",
+ "\n",
+ "s2.apply(highlight_max, props=\"color:white;background-color:darkblue\", axis=0)"
]
},
{
@@ -642,8 +661,8 @@
},
"outputs": [],
"source": [
- "# Hidden cell to avoid CSS clashes and latter code upcoding previous formatting \n",
- "s2.set_uuid('after_apply')"
+ "# Hidden cell to avoid CSS clashes and latter code upcoding previous formatting\n",
+ "s2.set_uuid(\"after_apply\")"
]
},
{
@@ -659,8 +678,9 @@
"metadata": {},
"outputs": [],
"source": [
- "s2.apply(highlight_max, props='color:white;background-color:pink;', axis=1)\\\n",
- " .apply(highlight_max, props='color:white;background-color:purple', axis=None)"
+ "s2.apply(highlight_max, props=\"color:white;background-color:pink;\", axis=1).apply(\n",
+ " highlight_max, props=\"color:white;background-color:purple\", axis=None\n",
+ ")"
]
},
{
@@ -671,8 +691,8 @@
},
"outputs": [],
"source": [
- "# Hidden cell to avoid CSS clashes and latter code upcoding previous formatting \n",
- "s2.set_uuid('after_apply_again')"
+ "# Hidden cell to avoid CSS clashes and latter code upcoding previous formatting\n",
+ "s2.set_uuid(\"after_apply_again\")"
]
},
{
@@ -713,8 +733,10 @@
"metadata": {},
"outputs": [],
"source": [
- "s2.map_index(lambda v: \"color:pink;\" if v>4 else \"color:darkblue;\", axis=0)\n",
- "s2.apply_index(lambda s: np.where(s.isin([\"A\", \"B\"]), \"color:pink;\", \"color:darkblue;\"), axis=1)"
+ "s2.map_index(lambda v: \"color:pink;\" if v > 4 else \"color:darkblue;\", axis=0)\n",
+ "s2.apply_index(\n",
+ " lambda s: np.where(s.isin([\"A\", \"B\"]), \"color:pink;\", \"color:darkblue;\"), axis=1\n",
+ ")"
]
},
{
@@ -734,11 +756,12 @@
"metadata": {},
"outputs": [],
"source": [
- "s.set_caption(\"Confusion matrix for multiple cancer prediction models.\")\\\n",
- " .set_table_styles([{\n",
- " 'selector': 'caption',\n",
- " 'props': 'caption-side: bottom; font-size:1.25em;'\n",
- " }], overwrite=False)"
+ "s.set_caption(\n",
+ " \"Confusion matrix for multiple cancer prediction models.\"\n",
+ ").set_table_styles(\n",
+ " [{\"selector\": \"caption\", \"props\": \"caption-side: bottom; font-size:1.25em;\"}],\n",
+ " overwrite=False,\n",
+ ")"
]
},
{
@@ -749,8 +772,8 @@
},
"outputs": [],
"source": [
- "# Hidden cell to avoid CSS clashes and latter code upcoding previous formatting \n",
- "s.set_uuid('after_caption')"
+ "# Hidden cell to avoid CSS clashes and latter code upcoding previous formatting\n",
+ "s.set_uuid(\"after_caption\")"
]
},
{
@@ -768,12 +791,24 @@
"metadata": {},
"outputs": [],
"source": [
- "tt = pd.DataFrame([['This model has a very strong true positive rate', \n",
- " \"This model's total number of false negatives is too high\"]], \n",
- " index=['Tumour (Positive)'], columns=df.columns[[0,3]])\n",
- "s.set_tooltips(tt, props='visibility: hidden; position: absolute; z-index: 1; border: 1px solid #000066;'\n",
- " 'background-color: white; color: #000066; font-size: 0.8em;' \n",
- " 'transform: translate(0px, -24px); padding: 0.6em; border-radius: 0.5em;')"
+ "tt = pd.DataFrame(\n",
+ " [\n",
+ " [\n",
+ " \"This model has a very strong true positive rate\",\n",
+ " \"This model's total number of false negatives is too high\",\n",
+ " ]\n",
+ " ],\n",
+ " index=[\"Tumour (Positive)\"],\n",
+ " columns=df.columns[[0, 3]],\n",
+ ")\n",
+ "s.set_tooltips(\n",
+ " tt,\n",
+ " props=\"visibility: hidden; position: absolute; z-index: 1; \"\n",
+ " \"border: 1px solid #000066;\"\n",
+ " \"background-color: white; color: #000066; font-size: 0.8em;\"\n",
+ " \"transform: translate(0px, -24px); padding: 0.6em; \"\n",
+ " \"border-radius: 0.5em;\",\n",
+ ")"
]
},
{
@@ -784,8 +819,8 @@
},
"outputs": [],
"source": [
- "# Hidden cell to avoid CSS clashes and latter code upcoding previous formatting \n",
- "s.set_uuid('after_tooltips')"
+ "# Hidden cell to avoid CSS clashes and latter code upcoding previous formatting\n",
+ "s.set_uuid(\"after_tooltips\")"
]
},
{
@@ -801,14 +836,18 @@
"metadata": {},
"outputs": [],
"source": [
- "s.set_table_styles([ # create internal CSS classes\n",
- " {'selector': '.border-red', 'props': 'border: 2px dashed red;'},\n",
- " {'selector': '.border-green', 'props': 'border: 2px dashed green;'},\n",
- "], overwrite=False)\n",
- "cell_border = pd.DataFrame([['border-green ', ' ', ' ', 'border-red '], \n",
- " [' ', ' ', ' ', ' ']], \n",
- " index=df.index, \n",
- " columns=df.columns[:4])\n",
+ "s.set_table_styles(\n",
+ " [ # create internal CSS classes\n",
+ " {\"selector\": \".border-red\", \"props\": \"border: 2px dashed red;\"},\n",
+ " {\"selector\": \".border-green\", \"props\": \"border: 2px dashed green;\"},\n",
+ " ],\n",
+ " overwrite=False,\n",
+ ")\n",
+ "cell_border = pd.DataFrame(\n",
+ " [[\"border-green \", \" \", \" \", \"border-red \"], [\" \", \" \", \" \", \" \"]],\n",
+ " index=df.index,\n",
+ " columns=df.columns[:4],\n",
+ ")\n",
"s.set_td_classes(cell_color + cell_border)"
]
},
@@ -820,8 +859,8 @@
},
"outputs": [],
"source": [
- "# Hidden cell to avoid CSS clashes and latter code upcoding previous formatting \n",
- "s.set_uuid('after_borders')"
+ "# Hidden cell to avoid CSS clashes and latter code upcoding previous formatting\n",
+ "s.set_uuid(\"after_borders\")"
]
},
{
@@ -847,9 +886,11 @@
"metadata": {},
"outputs": [],
"source": [
- "df3 = pd.DataFrame(np.random.randn(4,4), \n",
- " pd.MultiIndex.from_product([['A', 'B'], ['r1', 'r2']]),\n",
- " columns=['c1','c2','c3','c4'])\n",
+ "df3 = pd.DataFrame(\n",
+ " np.random.default_rng().standard_normal((4, 4)),\n",
+ " pd.MultiIndex.from_product([[\"A\", \"B\"], [\"r1\", \"r2\"]]),\n",
+ " columns=[\"c1\", \"c2\", \"c3\", \"c4\"],\n",
+ ")\n",
"df3"
]
},
@@ -866,9 +907,10 @@
"metadata": {},
"outputs": [],
"source": [
- "slice_ = ['c3', 'c4']\n",
- "df3.style.apply(highlight_max, props='color:red;', axis=0, subset=slice_)\\\n",
- " .set_properties(**{'background-color': '#ffffb3'}, subset=slice_)"
+ "slice_ = [\"c3\", \"c4\"]\n",
+ "df3.style.apply(\n",
+ " highlight_max, props=\"color:red;\", axis=0, subset=slice_\n",
+ ").set_properties(**{\"background-color\": \"#ffffb3\"}, subset=slice_)"
]
},
{
@@ -885,9 +927,10 @@
"outputs": [],
"source": [
"idx = pd.IndexSlice\n",
- "slice_ = idx[idx[:,'r1'], idx['c2':'c4']]\n",
- "df3.style.apply(highlight_max, props='color:red;', axis=0, subset=slice_)\\\n",
- " .set_properties(**{'background-color': '#ffffb3'}, subset=slice_)"
+ "slice_ = idx[idx[:, \"r1\"], idx[\"c2\":\"c4\"]]\n",
+ "df3.style.apply(\n",
+ " highlight_max, props=\"color:red;\", axis=0, subset=slice_\n",
+ ").set_properties(**{\"background-color\": \"#ffffb3\"}, subset=slice_)"
]
},
{
@@ -903,9 +946,10 @@
"metadata": {},
"outputs": [],
"source": [
- "slice_ = idx[idx[:,'r2'], :]\n",
- "df3.style.apply(highlight_max, props='color:red;', axis=1, subset=slice_)\\\n",
- " .set_properties(**{'background-color': '#ffffb3'}, subset=slice_)"
+ "slice_ = idx[idx[:, \"r2\"], :]\n",
+ "df3.style.apply(\n",
+ " highlight_max, props=\"color:red;\", axis=1, subset=slice_\n",
+ ").set_properties(**{\"background-color\": \"#ffffb3\"}, subset=slice_)"
]
},
{
@@ -923,9 +967,10 @@
"metadata": {},
"outputs": [],
"source": [
- "slice_ = idx[idx[(df3['c1'] + df3['c3']) < -2.0], ['c2', 'c4']]\n",
- "df3.style.apply(highlight_max, props='color:red;', axis=1, subset=slice_)\\\n",
- " .set_properties(**{'background-color': '#ffffb3'}, subset=slice_)"
+ "slice_ = idx[idx[(df3[\"c1\"] + df3[\"c3\"]) < -2.0], [\"c2\", \"c4\"]]\n",
+ "df3.style.apply(\n",
+ " highlight_max, props=\"color:red;\", axis=1, subset=slice_\n",
+ ").set_properties(**{\"background-color\": \"#ffffb3\"}, subset=slice_)"
]
},
{
@@ -981,7 +1026,7 @@
"metadata": {},
"outputs": [],
"source": [
- "df4 = pd.DataFrame([[1,2],[3,4]])\n",
+ "df4 = pd.DataFrame([[1, 2], [3, 4]])\n",
"s4 = df4.style"
]
},
@@ -1003,6 +1048,7 @@
"outputs": [],
"source": [
"from pandas.io.formats.style import Styler\n",
+ "\n",
"s4 = Styler(df4, uuid_len=0, cell_ids=False)"
]
},
@@ -1053,7 +1099,7 @@
"metadata": {},
"outputs": [],
"source": [
- "df4.style.set_table_styles([{'selector': 'td.col1', 'props': props}])"
+ "df4.style.set_table_styles([{\"selector\": \"td.col1\", \"props\": props}])"
]
},
{
@@ -1082,9 +1128,11 @@
"metadata": {},
"outputs": [],
"source": [
- "df2.style.apply(highlight_max, props='color:white;background-color:darkblue;', axis=0)\\\n",
- " .apply(highlight_max, props='color:white;background-color:pink;', axis=1)\\\n",
- " .apply(highlight_max, props='color:white;background-color:purple', axis=None)"
+ "df2.style.apply(\n",
+ " highlight_max, props=\"color:white;background-color:darkblue;\", axis=0\n",
+ ").apply(highlight_max, props=\"color:white;background-color:pink;\", axis=1).apply(\n",
+ " highlight_max, props=\"color:white;background-color:purple\", axis=None\n",
+ ")"
]
},
{
@@ -1105,14 +1153,18 @@
"outputs": [],
"source": [
"build = lambda x: pd.DataFrame(x, index=df2.index, columns=df2.columns)\n",
- "cls1 = build(df2.apply(highlight_max, props='cls-1 ', axis=0))\n",
- "cls2 = build(df2.apply(highlight_max, props='cls-2 ', axis=1, result_type='expand').values)\n",
- "cls3 = build(highlight_max(df2, props='cls-3 '))\n",
- "df2.style.set_table_styles([\n",
- " {'selector': '.cls-1', 'props': 'color:white;background-color:darkblue;'},\n",
- " {'selector': '.cls-2', 'props': 'color:white;background-color:pink;'},\n",
- " {'selector': '.cls-3', 'props': 'color:white;background-color:purple;'}\n",
- "]).set_td_classes(cls1 + cls2 + cls3)"
+ "cls1 = build(df2.apply(highlight_max, props=\"cls-1 \", axis=0))\n",
+ "cls2 = build(\n",
+ " df2.apply(highlight_max, props=\"cls-2 \", axis=1, result_type=\"expand\").values\n",
+ ")\n",
+ "cls3 = build(highlight_max(df2, props=\"cls-3 \"))\n",
+ "df2.style.set_table_styles(\n",
+ " [\n",
+ " {\"selector\": \".cls-1\", \"props\": \"color:white;background-color:darkblue;\"},\n",
+ " {\"selector\": \".cls-2\", \"props\": \"color:white;background-color:pink;\"},\n",
+ " {\"selector\": \".cls-3\", \"props\": \"color:white;background-color:purple;\"},\n",
+ " ]\n",
+ ").set_td_classes(cls1 + cls2 + cls3)"
]
},
{
@@ -1152,10 +1204,14 @@
" \"blank\": \"\",\n",
"}\n",
"html = Styler(df4, uuid_len=0, cell_ids=False)\n",
- "html.set_table_styles([{'selector': 'td', 'props': props},\n",
- " {'selector': '.c1', 'props': 'color:green;'},\n",
- " {'selector': '.l0', 'props': 'color:blue;'}],\n",
- " css_class_names=my_css)\n",
+ "html.set_table_styles(\n",
+ " [\n",
+ " {\"selector\": \"td\", \"props\": props},\n",
+ " {\"selector\": \".c1\", \"props\": \"color:green;\"},\n",
+ " {\"selector\": \".l0\", \"props\": \"color:blue;\"},\n",
+ " ],\n",
+ " css_class_names=my_css,\n",
+ ")\n",
"print(html.to_html())"
]
},
@@ -1213,9 +1269,9 @@
"metadata": {},
"outputs": [],
"source": [
- "df2.iloc[0,2] = np.nan\n",
- "df2.iloc[4,3] = np.nan\n",
- "df2.loc[:4].style.highlight_null(color='yellow')"
+ "df2.iloc[0, 2] = np.nan\n",
+ "df2.iloc[4, 3] = np.nan\n",
+ "df2.loc[:4].style.highlight_null(color=\"yellow\")"
]
},
{
@@ -1231,7 +1287,9 @@
"metadata": {},
"outputs": [],
"source": [
- "df2.loc[:4].style.highlight_max(axis=1, props='color:white; font-weight:bold; background-color:darkblue;')"
+ "df2.loc[:4].style.highlight_max(\n",
+ " axis=1, props=(\"color:white; \" \"font-weight:bold; \" \"background-color:darkblue;\")\n",
+ ")"
]
},
{
@@ -1249,7 +1307,9 @@
"outputs": [],
"source": [
"left = pd.Series([1.0, 0.0, 1.0], index=[\"A\", \"B\", \"D\"])\n",
- "df2.loc[:4].style.highlight_between(left=left, right=1.5, axis=1, props='color:white; background-color:purple;')"
+ "df2.loc[:4].style.highlight_between(\n",
+ " left=left, right=1.5, axis=1, props=\"color:white; background-color:purple;\"\n",
+ ")"
]
},
{
@@ -1266,7 +1326,7 @@
"metadata": {},
"outputs": [],
"source": [
- "df2.loc[:4].style.highlight_quantile(q_left=0.85, axis=None, color='yellow')"
+ "df2.loc[:4].style.highlight_quantile(q_left=0.85, axis=None, color=\"yellow\")"
]
},
{
@@ -1290,6 +1350,7 @@
"outputs": [],
"source": [
"import seaborn as sns\n",
+ "\n",
"cm = sns.light_palette(\"green\", as_cmap=True)\n",
"\n",
"df2.style.background_gradient(cmap=cm)"
@@ -1329,9 +1390,9 @@
"metadata": {},
"outputs": [],
"source": [
- "df2.loc[:4].style.set_properties(**{'background-color': 'black',\n",
- " 'color': 'lawngreen',\n",
- " 'border-color': 'white'})"
+ "df2.loc[:4].style.set_properties(\n",
+ " **{\"background-color\": \"black\", \"color\": \"lawngreen\", \"border-color\": \"white\"}\n",
+ ")"
]
},
{
@@ -1354,7 +1415,7 @@
"metadata": {},
"outputs": [],
"source": [
- "df2.style.bar(subset=['A', 'B'], color='#d65f5f')"
+ "df2.style.bar(subset=[\"A\", \"B\"], color=\"#d65f5f\")"
]
},
{
@@ -1372,10 +1433,15 @@
"metadata": {},
"outputs": [],
"source": [
- "df2.style.format('{:.3f}', na_rep=\"\")\\\n",
- " .bar(align=0, vmin=-2.5, vmax=2.5, cmap=\"bwr\", height=50,\n",
- " width=60, props=\"width: 120px; border-right: 1px solid black;\")\\\n",
- " .text_gradient(cmap=\"bwr\", vmin=-2.5, vmax=2.5)"
+ "df2.style.format(\"{:.3f}\", na_rep=\"\").bar(\n",
+ " align=0,\n",
+ " vmin=-2.5,\n",
+ " vmax=2.5,\n",
+ " cmap=\"bwr\",\n",
+ " height=50,\n",
+ " width=60,\n",
+ " props=\"width: 120px; border-right: 1px solid black;\",\n",
+ ").text_gradient(cmap=\"bwr\", vmin=-2.5, vmax=2.5)"
]
},
{
@@ -1398,10 +1464,10 @@
"from IPython.display import HTML\n",
"\n",
"# Test series\n",
- "test1 = pd.Series([-100,-60,-30,-20], name='All Negative')\n",
- "test2 = pd.Series([-10,-5,0,90], name='Both Pos and Neg')\n",
- "test3 = pd.Series([10,20,50,100], name='All Positive')\n",
- "test4 = pd.Series([100, 103, 101, 102], name='Large Positive')\n",
+ "test1 = pd.Series([-100, -60, -30, -20], name=\"All Negative\")\n",
+ "test2 = pd.Series([-10, -5, 0, 90], name=\"Both Pos and Neg\")\n",
+ "test3 = pd.Series([10, 20, 50, 100], name=\"All Positive\")\n",
+ "test4 = pd.Series([100, 103, 101, 102], name=\"Large Positive\")\n",
"\n",
"\n",
"head = \"\"\"\n",
@@ -1417,19 +1483,22 @@
"\n",
"\"\"\"\n",
"\n",
- "aligns = ['left', 'right', 'zero', 'mid', 'mean', 99]\n",
+ "aligns = [\"left\", \"right\", \"zero\", \"mid\", \"mean\", 99]\n",
"for align in aligns:\n",
" row = \"| {} | \".format(align)\n",
- " for series in [test1,test2,test3, test4]:\n",
+ " for series in [test1, test2, test3, test4]:\n",
" s = series.copy()\n",
- " s.name=''\n",
- " row += \"{} | \".format(s.to_frame().style.hide(axis='index').bar(align=align, \n",
- " color=['#d65f5f', '#5fba7d'], \n",
- " width=100).to_html()) #testn['width']\n",
- " row += '
|---|
'\n",
+ " s.name = \"\"\n",
+ " row += \"{} | \".format(\n",
+ " s.to_frame()\n",
+ " .style.hide(axis=\"index\")\n",
+ " .bar(align=align, color=[\"#d65f5f\", \"#5fba7d\"], width=100)\n",
+ " .to_html()\n",
+ " ) # testn['width']\n",
+ " row += \" |
\"\n",
" head += row\n",
- " \n",
- "head+= \"\"\"\n",
+ "\n",
+ "head += \"\"\"\n",
"\n",
"\"\"\""
]
@@ -1463,11 +1532,12 @@
"metadata": {},
"outputs": [],
"source": [
- "style1 = df2.style\\\n",
- " .map(style_negative, props='color:red;')\\\n",
- " .map(lambda v: 'opacity: 20%;' if (v < 0.3) and (v > -0.3) else None)\\\n",
- " .set_table_styles([{\"selector\": \"th\", \"props\": \"color: blue;\"}])\\\n",
- " .hide(axis=\"index\")\n",
+ "style1 = (\n",
+ " df2.style.map(style_negative, props=\"color:red;\")\n",
+ " .map(lambda v: \"opacity: 20%;\" if (v < 0.3) and (v > -0.3) else None)\n",
+ " .set_table_styles([{\"selector\": \"th\", \"props\": \"color: blue;\"}])\n",
+ " .hide(axis=\"index\")\n",
+ ")\n",
"style1"
]
},
@@ -1526,11 +1596,14 @@
"outputs": [],
"source": [
"from ipywidgets import widgets\n",
+ "\n",
+ "\n",
"@widgets.interact\n",
- "def f(h_neg=(0, 359, 1), h_pos=(0, 359), s=(0., 99.9), l=(0., 99.9)):\n",
+ "def f(h_neg=(0, 359, 1), h_pos=(0, 359), s=(0.0, 99.9), l_post=(0.0, 99.9)):\n",
" return df2.style.background_gradient(\n",
- " cmap=sns.palettes.diverging_palette(h_neg=h_neg, h_pos=h_pos, s=s, l=l,\n",
- " as_cmap=True)\n",
+ " cmap=sns.palettes.diverging_palette(\n",
+ " h_neg=h_neg, h_pos=h_pos, s=s, l=l_post, as_cmap=True\n",
+ " )\n",
" )"
]
},
@@ -1548,16 +1621,15 @@
"outputs": [],
"source": [
"def magnify():\n",
- " return [dict(selector=\"th\",\n",
- " props=[(\"font-size\", \"4pt\")]),\n",
- " dict(selector=\"td\",\n",
- " props=[('padding', \"0em 0em\")]),\n",
- " dict(selector=\"th:hover\",\n",
- " props=[(\"font-size\", \"12pt\")]),\n",
- " dict(selector=\"tr:hover td:hover\",\n",
- " props=[('max-width', '200px'),\n",
- " ('font-size', '12pt')])\n",
- "]"
+ " return [\n",
+ " {\"selector\": \"th\", \"props\": [(\"font-size\", \"4pt\")]},\n",
+ " {\"selector\": \"td\", \"props\": [(\"padding\", \"0em 0em\")]},\n",
+ " {\"selector\": \"th:hover\", \"props\": [(\"font-size\", \"12pt\")]},\n",
+ " {\n",
+ " \"selector\": \"tr:hover td:hover\",\n",
+ " \"props\": [(\"max-width\", \"200px\"), (\"font-size\", \"12pt\")],\n",
+ " },\n",
+ " ]"
]
},
{
@@ -1566,15 +1638,12 @@
"metadata": {},
"outputs": [],
"source": [
- "np.random.seed(25)\n",
- "cmap = cmap=sns.diverging_palette(5, 250, as_cmap=True)\n",
- "bigdf = pd.DataFrame(np.random.randn(20, 25)).cumsum()\n",
+ "cmap = sns.diverging_palette(5, 250, as_cmap=True)\n",
+ "bigdf = pd.DataFrame(np.random.default_rng(25).standard_normal((20, 25))).cumsum()\n",
"\n",
- "bigdf.style.background_gradient(cmap, axis=1)\\\n",
- " .set_properties(**{'max-width': '80px', 'font-size': '1pt'})\\\n",
- " .set_caption(\"Hover to magnify\")\\\n",
- " .format(precision=2)\\\n",
- " .set_table_styles(magnify())"
+ "bigdf.style.background_gradient(cmap, axis=1).set_properties(\n",
+ " **{\"max-width\": \"80px\", \"font-size\": \"1pt\"}\n",
+ ").set_caption(\"Hover to magnify\").format(precision=2).set_table_styles(magnify())"
]
},
{
@@ -1594,7 +1663,7 @@
"metadata": {},
"outputs": [],
"source": [
- "bigdf = pd.DataFrame(np.random.randn(16, 100))\n",
+ "bigdf = pd.DataFrame(np.random.default_rng().standard_normal((16, 100)))\n",
"bigdf.style.set_sticky(axis=\"index\")"
]
},
@@ -1611,8 +1680,8 @@
"metadata": {},
"outputs": [],
"source": [
- "bigdf.index = pd.MultiIndex.from_product([[\"A\",\"B\"],[0,1],[0,1,2,3]])\n",
- "bigdf.style.set_sticky(axis=\"index\", pixel_size=18, levels=[1,2])"
+ "bigdf.index = pd.MultiIndex.from_product([[\"A\", \"B\"], [0, 1], [0, 1, 2, 3]])\n",
+ "bigdf.style.set_sticky(axis=\"index\", pixel_size=18, levels=[1, 2])"
]
},
{
@@ -1632,7 +1701,7 @@
"metadata": {},
"outputs": [],
"source": [
- "df4 = pd.DataFrame([['