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nicolaskruchtennicolaskruchten
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switch from graph_objects to px
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-135
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2 files changed

+43
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‎Examples.ipynb

+41-134
Original file line numberDiff line numberDiff line change
@@ -18,6 +18,7 @@
1818
"\n",
1919
"from plotly import figure_factory\n",
2020
"from plotly import graph_objects\n",
21+
"import plotly.express as px\n",
2122
"import plotly.io as pio\n",
2223
"from IPython.core.magic import Magics, magics_class, cell_magic\n",
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"\n",
@@ -49,6 +50,11 @@
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"templated_fig = pio.to_templated(fig)\n",
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"pio.templates['my_template'] = templated_fig.layout.template\n",
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"pio.templates.default = 'my_template'\n",
53+
"px.defaults.width = 100\n",
54+
"px.defaults.height = 100\n",
55+
"pio.renderers.default = \"png\"\n",
56+
"pio.renderers[\"png\"].width = 900\n",
57+
"pio.renderers[\"png\"].height = 900\n",
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"\n",
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"alt.renderers.enable('png', webdriver='firefox')"
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]
@@ -286,17 +292,8 @@
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},
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"outputs": [],
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"source": [
289-
"mpgGrouped = mpg.groupby('manufacturer').size()\n",
290-
"fig = graph_objects.Figure(layout={'title' : 'Number of Cars by Make'})\n",
291-
"bar = graph_objects.Bar({\n",
292-
" 'type' : 'bar',\n",
293-
" 'x' : mpgGrouped.values.tolist(),\n",
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" 'y' : mpgGrouped.index.tolist(),\n",
295-
" 'orientation' : 'h'\n",
296-
" \n",
297-
" })\n",
298-
"fig.add_trace(bar)\n",
299-
"Image(fig.to_image(format=\"png\", width=900, height=900))"
295+
"px.histogram(mpg, y=\"manufacturer\", \n",
296+
" title='Number of Cars by Make')"
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]
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},
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{
@@ -410,13 +407,7 @@
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},
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"outputs": [],
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"source": [
413-
"fig = graph_objects.Figure()\n",
414-
"hist = graph_objects.Histogram({\n",
415-
" 'type' : 'histogram',\n",
416-
" 'x' : mpg['cty'],\n",
417-
"})\n",
418-
"fig.add_trace(hist)\n",
419-
"Image(fig.to_image(format=\"png\", width=900, height=900))"
410+
"px.histogram(mpg, x=\"cty\")"
420411
]
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},
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{
@@ -547,23 +538,12 @@
547538
},
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"outputs": [],
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"source": [
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"fig = graph_objects.Figure(layout={\n",
551-
" 'title' : 'Engine Displacement in Liters vs Highway MPG',\n",
552-
" 'xaxis' : {\n",
553-
" 'title' : 'Engine Displacement in Liters'\n",
554-
" },\n",
555-
" 'yaxis' : {\n",
556-
" 'title' : 'Highway MPG'\n",
557-
" }\n",
558-
"})\n",
559-
"scatter = graph_objects.Scatter({\n",
560-
" 'type' : 'scatter',\n",
561-
" 'mode' : 'markers',\n",
562-
" 'x' : mpg.displ,\n",
563-
" 'y' : mpg.hwy \n",
564-
"})\n",
565-
"fig.add_trace(scatter)\n",
566-
"Image(fig.to_image(format=\"png\", width=900, height=900))"
541+
"px.scatter(mpg, x=\"displ\", y=\"hwy\", \n",
542+
" title='Engine Displacement in Liters vs Highway MPG',\n",
543+
" labels=dict(\n",
544+
" displ='Engine Displacement in Liters', \n",
545+
" hwy='Highway MPG')\n",
546+
" )"
567547
]
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},
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{
@@ -841,27 +821,12 @@
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},
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"outputs": [],
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"source": [
844-
"traces = []\n",
845-
"for cls in mpg[\"class\"].unique():\n",
846-
" traces.append(\n",
847-
" graph_objects.Scatter(\n",
848-
" {\n",
849-
" \"mode\": \"markers\",\n",
850-
" \"x\": mpg.displ[mpg[\"class\"] == cls],\n",
851-
" \"y\": mpg.hwy[mpg[\"class\"] == cls],\n",
852-
" \"name\": cls,\n",
853-
" }\n",
854-
" )\n",
855-
" )\n",
856-
"fig = graph_objects.Figure(\n",
857-
" layout={\n",
858-
" \"title\": \"Engine Displacement in Liters vs Highway MPG\",\n",
859-
" \"xaxis\": {\"title\": \"Engine Displacement in Liters\",},\n",
860-
" \"yaxis\": {\"title\": \"Highway MPG\"},\n",
861-
" },\n",
862-
" data=traces,\n",
863-
")\n",
864-
"Image(fig.to_image(format=\"png\", width=900, height=900))"
824+
"px.scatter(mpg, x=\"displ\", y=\"hwy\", color=\"class\", \n",
825+
" title='Engine Displacement in Liters vs Highway MPG',\n",
826+
" labels=dict(\n",
827+
" displ='Engine Displacement in Liters', \n",
828+
" hwy='Highway MPG')\n",
829+
" )"
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]
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},
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{
@@ -976,29 +941,10 @@
976941
},
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"outputs": [],
978943
"source": [
979-
"traces = [\n",
980-
" graph_objects.Scatter(\n",
981-
" {\n",
982-
" \"mode\": \"markers\",\n",
983-
" \"x\": mpg.cty,\n",
984-
" \"y\": mpg.hwy,\n",
985-
" \"marker\": {\"size\": mpg.cyl, \"color\": \"rgba(54,54,54,0.5)\"},\n",
986-
" \"name\": cls,\n",
987-
" }\n",
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" )\n",
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"]\n",
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"\n",
991-
"fig = graph_objects.Figure(\n",
992-
" **{\n",
993-
" \"data\": traces,\n",
994-
" \"layout\": {\n",
995-
" \"title\": \"Engine Displacement in Liters vs Highway MPG\",\n",
996-
" \"xaxis\": {\"title\": \"Engine Displacement in Liters\",},\n",
997-
" \"yaxis\": {\"title\": \"Highway MPG\"},\n",
998-
" },\n",
999-
" }\n",
1000-
")\n",
1001-
"Image(fig.to_image(format=\"png\", width=900, height=900))"
944+
"px.scatter(mpg, x=\"cty\", y=\"hwy\", size=\"cyl\", size_max=10,\n",
945+
" title='City MPG vs Highway MPG',\n",
946+
" labels=dict(cty='City MPG', hwy='Highway MPG')\n",
947+
" )"
1002948
]
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},
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{
@@ -1099,8 +1045,7 @@
10991045
},
11001046
"outputs": [],
11011047
"source": [
1102-
"fig = figure_factory.create_facet_grid(df=mpg, x=\"displ\", y=\"cty\", facet_col=\"class\")\n",
1103-
"Image(fig.to_image(format=\"png\", width=900, height=900))"
1048+
"px.scatter(mpg, x=\"displ\", y=\"hwy\", facet_col=\"class\", facet_col_wrap=4)"
11041049
]
11051050
},
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{
@@ -1205,14 +1150,9 @@
12051150
},
12061151
"outputs": [],
12071152
"source": [
1208-
"fig = figure_factory.create_facet_grid(\n",
1209-
" df=mpg, \n",
1210-
" x=\"displ\", \n",
1211-
" y=\"cty\", \n",
1212-
" facet_col=\"cyl\", \n",
1213-
" facet_row=\"drv\"\n",
1214-
")\n",
1215-
"Image(fig.to_image(format=\"png\", width=900, height=900))"
1153+
"px.scatter(mpg, x=\"displ\", y=\"hwy\", \n",
1154+
" facet_col=\"cyl\", facet_row=\"drv\",\n",
1155+
" category_orders=dict(cyl=[4,5,6,8]))"
12161156
]
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},
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{
@@ -1450,24 +1390,11 @@
14501390
},
14511391
"outputs": [],
14521392
"source": [
1453-
"traces = []\n",
1454-
"newDiamond = diamonds.groupby(['cut','clarity']).size().unstack()\n",
1455-
"for c in newDiamond.columns:\n",
1456-
" traces.append(graph_objects.Bar({\n",
1457-
" 'x' : newDiamond.index,\n",
1458-
" 'y' : newDiamond[c],\n",
1459-
" 'name' : c\n",
1460-
" }))\n",
1461-
"fig = graph_objects.Figure(**{\n",
1462-
" 'data' : traces,\n",
1463-
" 'layout' : {\n",
1464-
" 'barmode' : 'stack',\n",
1465-
" 'xaxis' : {\n",
1466-
" 'title' : 'cut'\n",
1467-
" }, \n",
1468-
" }\n",
1469-
"})\n",
1470-
"Image(fig.to_image(format=\"png\", width=900, height=900))"
1393+
"px.histogram(diamonds, x=\"cut\", color=\"clarity\",\n",
1394+
" category_orders=dict(cut=[\n",
1395+
" \"Fair\", \"Good\", \"Very Good\", \n",
1396+
" \"Premium\", \"Ideal\"])\n",
1397+
" )"
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]
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},
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{
@@ -1558,24 +1485,11 @@
15581485
},
15591486
"outputs": [],
15601487
"source": [
1561-
"traces = []\n",
1562-
"newDiamond = diamonds.groupby(['cut','clarity']).size().unstack()\n",
1563-
"for c in newDiamond.columns:\n",
1564-
" traces.append(graph_objects.Bar({\n",
1565-
" 'x' : newDiamond.index,\n",
1566-
" 'y' : newDiamond[c],\n",
1567-
" 'name' : c\n",
1568-
" }))\n",
1569-
"fig = graph_objects.Figure(**{\n",
1570-
" 'data' : traces,\n",
1571-
" 'layout' : {\n",
1572-
" 'barmode' : 'group',\n",
1573-
" 'xaxis' : {\n",
1574-
" 'title' : 'cut'\n",
1575-
" }, \n",
1576-
" }\n",
1577-
"})\n",
1578-
"Image(fig.to_image(format=\"png\", width=900, height=900))"
1488+
"px.histogram(diamonds, x=\"cut\", color=\"clarity\", barmode=\"group\",\n",
1489+
" category_orders=dict(cut=[\n",
1490+
" \"Fair\", \"Good\", \"Very Good\", \n",
1491+
" \"Premium\", \"Ideal\"])\n",
1492+
" )"
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]
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},
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{
@@ -1773,14 +1687,7 @@
17731687
},
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"outputs": [],
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"source": [
1776-
"fig = graph_objects.Figure(layout={'xaxis' : { 'title' : 'date'}})\n",
1777-
"scatter = graph_objects.Scatter({\n",
1778-
" 'mode' :'lines',\n",
1779-
" 'x' : ts.date,\n",
1780-
" 'y' : ts.value\n",
1781-
"})\n",
1782-
"fig.add_trace(scatter)\n",
1783-
"Image(fig.to_image(format=\"png\", width=900, height=900))"
1690+
"px.line(ts, x=\"date\", y=\"value\")"
17841691
]
17851692
},
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{
@@ -1818,7 +1725,7 @@
18181725
"name": "python",
18191726
"nbconvert_exporter": "python",
18201727
"pygments_lexer": "ipython3",
1821-
"version": "3.7.0"
1728+
"version": "3.7.7"
18221729
}
18231730
},
18241731
"nbformat": 4,

‎requirements.txt

+2-1
Original file line numberDiff line numberDiff line change
@@ -15,7 +15,8 @@ chart-studio
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statsmodels==0.11.1
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rpy2
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psutil
18-
plotly
18+
plotly==4.10.0
19+
kaleido
1920
tzlocal
2021
simplegeneric
2122
altair

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