Regenerate visualization landscape notebook

Author: jorisvandenbossche

notebooks/visualization_03_landscape.ipynb

@@ -4,7 +4,7 @@
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-    "<p><font size=\"6\"><b>Visualzation - Python's Visualization Landscape</b></font></p>\n",
+    "<p><font size=\"6\"><b>Visualization - Python's Visualization Landscape</b></font></p>\n",
     "\n",
     "> *DS Data manipulation, analysis and visualization in Python*  \n",
     "> *May/June, 2021*\n",
@@ -31,13 +31,14 @@
     "\n",
     "To have support of plotly inside the Jupyter Lab environment\n",
     "```\n",
+    "conda install -c conda-forge nodejs\n",
     "jupyter labextension install [email protected]\n",
     "```\n",
     "\n",
     "To run the large data set section, additional package installations are required:\n",
     "\n",
     "```\n",
-    "conda install -c bokeh datashader holoviews\n",
+    "conda install -c conda-forge datashader holoviews\n",
     "```\n",
     "\n",
     "To run the 'bokeh-pandas' backend:\n",
@@ -113,7 +114,9 @@
   {
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    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
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    "source": [
     "with plt.style.context('seaborn-whitegrid'):  # context manager for styling the figure\n",
@@ -182,40 +185,40 @@
    "source": [
     "* When your data consists of only **1 categorical variable**, such as\n",
     "\n",
-    "| ID | variable 1 | variable 2 | variabel ... | \n",
-    "|------------|-------------| ---- | ----- |\n",
-    "| 1 | 0.2 | 0.8 | ... |\n",
-    "| 2 | 0.3 | 0.1 | ... |\n",
-    "| 3 | 0.9 | 0.6 | ... |\n",
-    "| 4 | 0.1 | 0.7 | ... |\n",
-    "| ...  | ... | ... | ...|\n",
+    "  | ID | variable 1 | variable 2 | variabel ... | \n",
+    "  |------------|-------------| ---- | ----- |\n",
+    "  | 1 | 0.2 | 0.8 | ... |\n",
+    "  | 2 | 0.3 | 0.1 | ... |\n",
+    "  | 3 | 0.9 | 0.6 | ... |\n",
+    "  | 4 | 0.1 | 0.7 | ... |\n",
+    "  | ...  | ... | ... | ...|\n",
     "\n",
-    "the added value of using Seaborn approach is LOW. Pandas `.plot()` will probably suffice.\n",
+    "  the added value of using Seaborn approach is LOW. Pandas `.plot()` will probably suffice.\n",
     "\n",
     "* When working with **timeseries data** from sensors or continuous logging, such as\n",
     "\n",
-    "| datetime | station 1 | station 2 | station ... | \n",
-    "|------------|-------------| ---- | ----- |\n",
-    "| 2017-12-20T17:50:46Z | 0.2 | 0.8 | ... |\n",
-    "| 2017-12-20T17:50:52Z | 0.3 | 0.1 | ... |\n",
-    "| 2017-12-20T17:51:03Z | 0.9 | 0.6 | ... |\n",
-    "| 2017-12-20T17:51:40Z | 0.1 | 0.7 | ... |\n",
-    "| ...  | ... | ... | ...|\n",
+    "  | datetime | station 1 | station 2 | station ... | \n",
+    "  |------------|-------------| ---- | ----- |\n",
+    "  | 2017-12-20T17:50:46Z | 0.2 | 0.8 | ... |\n",
+    "  | 2017-12-20T17:50:52Z | 0.3 | 0.1 | ... |\n",
+    "  | 2017-12-20T17:51:03Z | 0.9 | 0.6 | ... |\n",
+    "  | 2017-12-20T17:51:40Z | 0.1 | 0.7 | ... |\n",
+    "  | ...  | ... | ... | ...|\n",
     "\n",
-    "the added value of using a grammar of graphics approach is LOW. Pandas `.plot()` will probably suffice.\n",
+    "  the added value of using a grammar of graphics approach is LOW. Pandas `.plot()` will probably suffice.\n",
     "\n",
     "* When working with different experiments, different conditions, (factorial) **experimental designs**, such as\n",
     "\n",
-    "| ID | origin | addition (ml)  | measured_value | \n",
-    "|----|-----------| ----- | ------ |\n",
-    "| 1  | Eindhoven | 0.3 | 7.2 |\n",
-    "| 2  | Eindhoven | 0.6 | 6.7 |\n",
-    "| 3  | Eindhoven | 0.9 | 5.2 |\n",
-    "| 4  | Destelbergen | 0.3 | 7.2 |\n",
-    "| 5  | Destelbergen | 0.6 | 6.8 |\n",
-    "| ...  | ... | ... | ...|\n",
+    "  | ID | origin | addition (ml)  | measured_value | \n",
+    "  |----|-----------| ----- | ------ |\n",
+    "  | 1  | Eindhoven | 0.3 | 7.2 |\n",
+    "  | 2  | Eindhoven | 0.6 | 6.7 |\n",
+    "  | 3  | Eindhoven | 0.9 | 5.2 |\n",
+    "  | 4  | Destelbergen | 0.3 | 7.2 |\n",
+    "  | 5  | Destelbergen | 0.6 | 6.8 |\n",
+    "  | ...  | ... | ... | ...|\n",
     "\n",
-    "the added value of using Seaborn approach is HIGH. Represent your data [`tidy`](http://www.jeannicholashould.com/tidy-data-in-python.html) to achieve maximal benefit!\n",
+    "  the added value of using Seaborn approach is HIGH. Represent your data [`tidy`](http://www.jeannicholashould.com/tidy-data-in-python.html) to achieve maximal benefit!\n",
     "\n",
     "* When you want to visualize __distributions__ of data or __regressions__ between variables, the added value of using Seaborn approach is HIGH."
    ]
@@ -1191,6 +1194,13 @@
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