|
18 | 18 | "\n", |
19 | 19 | "from plotly import figure_factory\n", |
20 | 20 | "from plotly import graph_objects\n", |
21 | | - "import plotly.io as pio\n", |
| 21 | + "import plotly.express as px\n", |
22 | 22 | "from IPython.core.magic import Magics, magics_class, cell_magic\n", |
23 | 23 | "\n", |
24 | 24 | "from IPython.display import Image\n", |
|
44 | 44 | ") # for plotnine\n", |
45 | 45 | "\n", |
46 | 46 | "\n", |
47 | | - "fig = graph_objects.Figure(layout = dict(width=100, height=100))\n", |
48 | | - "\n", |
49 | | - "templated_fig = pio.to_templated(fig)\n", |
50 | | - "pio.templates['my_template'] = templated_fig.layout.template\n", |
51 | | - "pio.templates.default = 'my_template'\n", |
| 47 | + "import plotly.io as pio\n", |
| 48 | + "pio.renderers.default = \"png\"\n", |
| 49 | + "pio.renderers[\"png\"].width = 750\n", |
| 50 | + "pio.renderers[\"png\"].height = 750\n", |
52 | 51 | "\n", |
53 | 52 | "alt.renderers.enable('png', webdriver='firefox')" |
54 | 53 | ] |
|
286 | 285 | }, |
287 | 286 | "outputs": [], |
288 | 287 | "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))" |
| 288 | + "px.histogram(\n", |
| 289 | + " mpg, y=\"manufacturer\", \n", |
| 290 | + " title='Number of Cars by Make'\n", |
| 291 | + ")" |
300 | 292 | ] |
301 | 293 | }, |
302 | 294 | { |
|
410 | 402 | }, |
411 | 403 | "outputs": [], |
412 | 404 | "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))" |
| 405 | + "px.histogram(\n", |
| 406 | + " mpg, x=\"cty\"\n", |
| 407 | + ")" |
420 | 408 | ] |
421 | 409 | }, |
422 | 410 | { |
|
547 | 535 | }, |
548 | 536 | "outputs": [], |
549 | 537 | "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))" |
| 538 | + "px.scatter(\n", |
| 539 | + " mpg, x=\"displ\", y=\"hwy\", \n", |
| 540 | + " title='Engine Displacement in Liters vs Highway MPG',\n", |
| 541 | + " labels=dict(\n", |
| 542 | + " displ='Engine Displacement in Liters', \n", |
| 543 | + " hwy='Highway MPG')\n", |
| 544 | + ")" |
567 | 545 | ] |
568 | 546 | }, |
569 | 547 | { |
|
702 | 680 | "fig.add_trace(p2)\n", |
703 | 681 | "fig.add_trace(p3)\n", |
704 | 682 | "fig.add_trace(p4)\n", |
705 | | - "Image(fig.to_image(format=\"png\", width=900, height=900))" |
| 683 | + "Image(fig.to_image(format=\"png\", width=750, height=750))" |
706 | 684 | ] |
707 | 685 | }, |
708 | 686 | { |
|
841 | 819 | }, |
842 | 820 | "outputs": [], |
843 | 821 | "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))" |
| 822 | + "px.scatter(\n", |
| 823 | + " mpg, x=\"displ\", y=\"hwy\", color=\"class\", \n", |
| 824 | + " title='Engine Displacement in Liters vs Highway MPG',\n", |
| 825 | + " labels=dict(\n", |
| 826 | + " displ='Engine Displacement in Liters', \n", |
| 827 | + " hwy='Highway MPG')\n", |
| 828 | + ")" |
865 | 829 | ] |
866 | 830 | }, |
867 | 831 | { |
|
904 | 868 | "(\n", |
905 | 869 | " alt.Chart(\n", |
906 | 870 | " mpg,\n", |
907 | | - " title=\"Engine Displacement in Liters vs Highway MPG\",\n", |
| 871 | + " title=\"City MPG vs Highway MPG\",\n", |
908 | 872 | " )\n", |
909 | 873 | " .mark_circle(opacity=0.3)\n", |
910 | 874 | " .encode(\n", |
|
942 | 906 | " y='hwy', \n", |
943 | 907 | " s=10*mpg['cyl'],\n", |
944 | 908 | " alpha=.5))\n", |
945 | | - "ax.set_title('Engine Displacement in Liters vs Highway MPG')\n", |
946 | | - "ax.set_xlabel('Engine Displacement in Liters')\n", |
| 909 | + "ax.set_title('City MPG vs Highway MPG')\n", |
| 910 | + "ax.set_xlabel('City MPG')\n", |
947 | 911 | "ax.set_ylabel('Highway MPG');" |
948 | 912 | ] |
949 | 913 | }, |
|
976 | 940 | }, |
977 | 941 | "outputs": [], |
978 | 942 | "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))" |
| 943 | + "px.scatter(\n", |
| 944 | + " mpg, x=\"cty\", y=\"hwy\", \n", |
| 945 | + " size=\"cyl\", size_max=10,\n", |
| 946 | + " title='City MPG vs Highway MPG',\n", |
| 947 | + " labels=dict(cty='City MPG', hwy='Highway MPG')\n", |
| 948 | + ")" |
1002 | 949 | ] |
1003 | 950 | }, |
1004 | 951 | { |
|
1099 | 1046 | }, |
1100 | 1047 | "outputs": [], |
1101 | 1048 | "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))" |
| 1049 | + "px.scatter(\n", |
| 1050 | + " mpg, x=\"displ\", y=\"hwy\", \n", |
| 1051 | + " facet_col=\"class\", facet_col_wrap=4\n", |
| 1052 | + ")" |
1104 | 1053 | ] |
1105 | 1054 | }, |
1106 | 1055 | { |
|
1205 | 1154 | }, |
1206 | 1155 | "outputs": [], |
1207 | 1156 | "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))" |
| 1157 | + "px.scatter(\n", |
| 1158 | + " mpg, x=\"displ\", y=\"hwy\", \n", |
| 1159 | + " facet_col=\"cyl\", facet_row=\"drv\",\n", |
| 1160 | + " category_orders=dict(cyl=[4,5,6,8])\n", |
| 1161 | + ")" |
1216 | 1162 | ] |
1217 | 1163 | }, |
1218 | 1164 | { |
|
1362 | 1308 | " }\n", |
1363 | 1309 | " }\n", |
1364 | 1310 | "})\n", |
1365 | | - "Image(fig.to_image(format=\"png\", width=900, height=900))" |
| 1311 | + "Image(fig.to_image(format=\"png\", width=750, height=750))" |
1366 | 1312 | ] |
1367 | 1313 | }, |
1368 | 1314 | { |
|
1450 | 1396 | }, |
1451 | 1397 | "outputs": [], |
1452 | 1398 | "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))" |
| 1399 | + "px.histogram(\n", |
| 1400 | + " diamonds, x=\"cut\", color=\"clarity\",\n", |
| 1401 | + " category_orders=dict(cut=[\n", |
| 1402 | + " \"Fair\", \"Good\", \"Very Good\", \n", |
| 1403 | + " \"Premium\", \"Ideal\"])\n", |
| 1404 | + ")" |
1471 | 1405 | ] |
1472 | 1406 | }, |
1473 | 1407 | { |
|
1558 | 1492 | }, |
1559 | 1493 | "outputs": [], |
1560 | 1494 | "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))" |
| 1495 | + "px.histogram(\n", |
| 1496 | + " diamonds, x=\"cut\", color=\"clarity\", barmode=\"group\",\n", |
| 1497 | + " category_orders=dict(cut=[\n", |
| 1498 | + " \"Fair\", \"Good\", \"Very Good\", \n", |
| 1499 | + " \"Premium\", \"Ideal\"])\n", |
| 1500 | + ")" |
1579 | 1501 | ] |
1580 | 1502 | }, |
1581 | 1503 | { |
|
1710 | 1632 | ")\n", |
1711 | 1633 | "for d in fig[\"data\"]:\n", |
1712 | 1634 | " d.update({\"fill\": \"tozeroy\"})\n", |
1713 | | - "Image(fig.to_image(format=\"png\", width=900, height=900))" |
| 1635 | + "Image(fig.to_image(format=\"png\", width=750, height=750))" |
1714 | 1636 | ] |
1715 | 1637 | }, |
1716 | 1638 | { |
|
1773 | 1695 | }, |
1774 | 1696 | "outputs": [], |
1775 | 1697 | "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))" |
| 1698 | + "px.line(\n", |
| 1699 | + " ts, x=\"date\", y=\"value\"\n", |
| 1700 | + ")" |
1784 | 1701 | ] |
1785 | 1702 | }, |
1786 | 1703 | { |
|
1818 | 1735 | "name": "python", |
1819 | 1736 | "nbconvert_exporter": "python", |
1820 | 1737 | "pygments_lexer": "ipython3", |
1821 | | - "version": "3.7.0" |
| 1738 | + "version": "3.7.7" |
1822 | 1739 | } |
1823 | 1740 | }, |
1824 | 1741 | "nbformat": 4, |
|
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