|
18 | 18 | "\n", |
19 | 19 | "from plotly import figure_factory\n", |
20 | 20 | "from plotly import graph_objects\n", |
| 21 | + "import plotly.express as px\n", |
21 | 22 | "import plotly.io as pio\n", |
22 | 23 | "from IPython.core.magic import Magics, magics_class, cell_magic\n", |
23 | 24 | "\n", |
|
49 | 50 | "templated_fig = pio.to_templated(fig)\n", |
50 | 51 | "pio.templates['my_template'] = templated_fig.layout.template\n", |
51 | 52 | "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", |
52 | 58 | "\n", |
53 | 59 | "alt.renderers.enable('png', webdriver='firefox')" |
54 | 60 | ] |
|
286 | 292 | }, |
287 | 293 | "outputs": [], |
288 | 294 | "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", |
294 | | - " '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')" |
300 | 297 | ] |
301 | 298 | }, |
302 | 299 | { |
|
410 | 407 | }, |
411 | 408 | "outputs": [], |
412 | 409 | "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\")" |
420 | 411 | ] |
421 | 412 | }, |
422 | 413 | { |
|
547 | 538 | }, |
548 | 539 | "outputs": [], |
549 | 540 | "source": [ |
550 | | - "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 | + " )" |
567 | 547 | ] |
568 | 548 | }, |
569 | 549 | { |
|
841 | 821 | }, |
842 | 822 | "outputs": [], |
843 | 823 | "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 | + " )" |
865 | 830 | ] |
866 | 831 | }, |
867 | 832 | { |
|
976 | 941 | }, |
977 | 942 | "outputs": [], |
978 | 943 | "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", |
988 | | - " )\n", |
989 | | - "]\n", |
990 | | - "\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 | + " )" |
1002 | 948 | ] |
1003 | 949 | }, |
1004 | 950 | { |
|
1099 | 1045 | }, |
1100 | 1046 | "outputs": [], |
1101 | 1047 | "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)" |
1104 | 1049 | ] |
1105 | 1050 | }, |
1106 | 1051 | { |
|
1205 | 1150 | }, |
1206 | 1151 | "outputs": [], |
1207 | 1152 | "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]))" |
1216 | 1156 | ] |
1217 | 1157 | }, |
1218 | 1158 | { |
|
1450 | 1390 | }, |
1451 | 1391 | "outputs": [], |
1452 | 1392 | "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 | + " )" |
1471 | 1398 | ] |
1472 | 1399 | }, |
1473 | 1400 | { |
|
1558 | 1485 | }, |
1559 | 1486 | "outputs": [], |
1560 | 1487 | "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 | + " )" |
1579 | 1493 | ] |
1580 | 1494 | }, |
1581 | 1495 | { |
|
1773 | 1687 | }, |
1774 | 1688 | "outputs": [], |
1775 | 1689 | "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\")" |
1784 | 1691 | ] |
1785 | 1692 | }, |
1786 | 1693 | { |
|
1818 | 1725 | "name": "python", |
1819 | 1726 | "nbconvert_exporter": "python", |
1820 | 1727 | "pygments_lexer": "ipython3", |
1821 | | - "version": "3.7.0" |
| 1728 | + "version": "3.7.7" |
1822 | 1729 | } |
1823 | 1730 | }, |
1824 | 1731 | "nbformat": 4, |
|
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