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lars-reimann merged 2 commits into from
Mar 4, 2023
Merged

docs: fix generation of reference pages #1

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8df0d48
docs: fix generation of reference pages
lars-reimann Mar 4, 2023
8592486
docs: fix dead links
lars-reimann Mar 4, 2023
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7 changes: 4 additions & 3 deletions docs/Reference/generate_reference_pages.py → docs/reference/generate_reference_pages.py
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Original file line number Diff line number Diff line change
Expand Up @@ -20,10 +20,11 @@ def list_class_and_function_names_in_module(module_name: str) -> list[str]:


nav = mkdocs_gen_files.Nav()
root = "src"

for path in sorted(Path("Runtime/safe-ds").rglob("__init__.py")):
module_path = path.relative_to("Runtime/safe-ds").with_suffix("")
doc_path = path.relative_to("Runtime/safe-ds").with_suffix(".md")
for path in sorted(Path(root).rglob("__init__.py")):
module_path = path.relative_to(root).with_suffix("")
doc_path = path.relative_to(root).with_suffix(".md")
full_doc_path = Path("reference", doc_path)

# Skip demos, tests, etc.
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0 docs/Tutorials/README.md → docs/tutorials/README.md
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0 docs/Tutorials/data_processing.md → docs/tutorials/data_processing.md
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0 docs/Tutorials/machine_learning.md → docs/tutorials/machine_learning.md
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0 docs/Tutorials/Resources/Boxplot.png → docs/tutorials/resources/boxplot.png
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0 docs/Tutorials/Resources/Heatmap.png → docs/tutorials/resources/heatmap.png
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0 docs/Tutorials/Resources/Histogram.png → docs/tutorials/resources/histogram.png
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0 docs/Tutorials/Resources/Lineplot.png → docs/tutorials/resources/lineplot.png
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0 docs/Tutorials/Resources/Scatterplot.png → docs/tutorials/resources/scatterplot.png
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0 docs/Tutorials/Resources/Summary.png → docs/tutorials/resources/summary.png
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0 docs/Tutorials/Resources/Table.png → docs/tutorials/resources/table.png
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18 changes: 9 additions & 9 deletions docs/Tutorials/visualization.md → docs/tutorials/visualization.md
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Expand Up @@ -16,18 +16,18 @@ data = Table.from_csv("path/to/your/data.csv")
Now we want to have a look at what our dataset looks like. For this, we use Jupyter Notebooks native display function.

```python
data    # calls display(data)
data # calls display(data)
```

![Table](./Resources/Table.png)
![Table](resources/table.png)

Next some statistics.

```python
data.summary()  # returns a table with various statistics for each column
data.summary() # returns a table with various statistics for each column
```

![Summary](./Resources/Summary.png)
![Summary](resources/summary.png)

As you can see here, the **idness** of the column _PassengerId_ is 1. This means, that every row has a unique value for
this column. Since this isn't helpful for our usecase we can drop it.
Expand All @@ -48,7 +48,7 @@ data_only_numerics = Table.from_columns(data_cleaned.list_columns_with_numerical
correlation_heatmap(data_only_numerics)
```

![Heatmap](./Resources/Heatmap.png)
![Heatmap](resources/heatmap.png)

As you can see, the columns _Fare_ and _Pclass_ (Passenger Class) seem to heavily correlate. Let's have another look at that.
## Lineplot
Expand All @@ -59,7 +59,7 @@ from safeds.plotting import lineplot
lineplot(data_cleaned, "Pclass", "Fare")
```

![Lineplot](./Resources/Lineplot.png)
![Lineplot](resources/lineplot.png)

The line itself represents the central tendency and the hued area around it a confidence interval for that estimate.

Expand All @@ -75,19 +75,19 @@ from safeds.plotting import boxplot
boxplot(data_cleaned.get_column("Age"))
```

![Boxplot](./Resources/Boxplot.png)
![Boxplot](resources/boxplot.png)

```python
from safeds.plotting import histogram

histogram(data_cleaned.get_column("Fare"))
```
![Histogram](./Resources/Histogram.png)
![Histogram](resources/histogram.png)

```python
from safeds.plotting import scatterplot

scatterplot(data_cleaned, "Age", "Fare")
```

![Scatterplot](./Resources/Scatterplot.png)
![Scatterplot](resources/scatterplot.png)