fix: Minor fixes for visualization tutorial, summary & heatmap

Runtime/safe-ds/safeds/data/tabular/_table.py

@@ -835,14 +835,14 @@ def summary(self) -> Table:
 
             for function in statistics.values():
                 try:
-                    values.append(function())
+                    values.append(str(function()))
                 except NonNumericColumnError:
                     values.append("-")
 
             result = pd.concat([result, pd.DataFrame(values)], axis=1)
 
         result = pd.concat([pd.DataFrame(list(statistics.keys())), result], axis=1)
-        result.columns = [""] + self.get_column_names()
+        result.columns = ["metrics"] + self.get_column_names()
 
         return Table(result)
 

Runtime/safe-ds/safeds/plotting/_correlation_heatmap.py

@@ -29,6 +29,7 @@ def correlation_heatmap(table: Table) -> None:
         vmax=1,
         xticklabels=table.get_column_names(),
         yticklabels=table.get_column_names(),
+        cmap="vlag",
     )
     plt.tight_layout()
     plt.show()

Runtime/safe-ds/tests/data/tabular/_table/test_summary.py

@@ -22,19 +22,31 @@ def test_summary() -> None:
                     "row count",
                 ],
                 "col1": [
-                    2.0,
-                    1.0,
-                    4.0 / 3,
-                    1.0,
-                    1.0,
-                    4.0,
-                    1.0 / 3,
-                    table._data[0].std(),
-                    2.0 / 3,
-                    2.0 / 3,
-                    3.0,
+                    "2",
+                    "1",
+                    str(4.0 / 3),
+                    "1",
+                    "1.0",
+                    "4",
+                    str(1.0 / 3),
+                    str(table._data[0].std()),
+                    str(2.0 / 3),
+                    str(2.0 / 3),
+                    "3",
+                ],
+                "col2": [
+                    "-",
+                    "-",
+                    "-",
+                    "a",
+                    "-",
+                    "-",
+                    "-",
+                    "-",
+                    "1.0",
+                    str(1.0 / 3),
+                    "3",
                 ],
-                "col2": ["-", "-", "-", "a", "-", "-", "-", "-", 1.0, 1.0 / 3, 3],
             }
         )
     )

docs/Stdlib/python/Tutorials/visualization.md

@@ -2,7 +2,8 @@
 
 The following code will use a Jupyter Notebook environment.
 
-First we need some data to visualize. For this we use the common example of the titanic disaster.
+## Table & Statistics
+First, we need some data to visualize. For this, we use the common example of the titanic disaster.
 
 !!! note
     You can download that dataset on [kaggle](https://www.kaggle.com/c/titanic).
@@ -12,33 +13,33 @@ from safeds.data.tabular import Table
 data = Table.from_csv("path/to/your/data.csv")
 ```
 
-Now we want to have look at what our dataset looks like. For this we use Jupyter Notebooks native display function.
+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)
 
 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)
 
-As you can see here, the **idness** of the column _PassangerId_ is 1. This means, that every row has a unique value for
+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.
 
 ```python
-data_cleaned = data.drop_columns(["PassangerId"])
+data_cleaned = data.drop_columns(["PassengerId"])
 ```
-
+## Heatmap
 Now we have a rough idea of what we are looking at. But we still don't really know a lot about our dataset.
-So next we can start to plot a our columns against each other in a so called Heatmap, to understand which values relate to each other.
+So next we can start to plot our columns against each other in a so called Heatmap, to understand which values relate to each other.
 
-But since this type of diagramm only works for numerical values, we are going to use only those.
+But since this type of diagram only works for numerical values, we are going to use only those.
 
 ```python
 from safeds.plotting import correlation_heatmap
@@ -49,8 +50,9 @@ correlation_heatmap(data_only_numerics)
 
 ![Heatmap](./Resources/Heatmap.png)
 
-As you can see, the columns _Fare_ and _Pclass_ (Passanger Class) seem to heavily correlate. Let's have another look at that.
-We'll use a linechart to better understand their relationship.
+As you can see, the columns _Fare_ and _Pclass_ (Passenger Class) seem to heavily correlate. Let's have another look at that.
+## Lineplot
+We'll use a lineplot to better understand their relationship.
 
 ```python
 from safeds.plotting import lineplot
@@ -61,19 +63,31 @@ lineplot(data_cleaned, "Pclass", "Fare")
 
 The line itself represents the central tendency and the hued area around it a confidence interval for that estimate.
 
-We can conclude that tickets for first class rooms are much more expensive compared to second and third class.
-Also the difference between second and third is less pronounced.
+We can conclude that tickets for first classrooms are much more expensive compared to second and third class.
+Also, the difference between second and third is less pronounced.
 
-Some other plots that might be useful are boxplots, histogams and scatterplots.
+## Other plots
+Some other plots that might be useful are boxplots, histograms and scatterplots.
 
 ```python
-from safeds.plotting import boxplot, histogram, scatterplot
+from safeds.plotting import boxplot
 
 boxplot(data_cleaned.get_column("Age"))
-histogram(data_cleaned.get_column("Fare"))
-scatterplot(data_cleaned, "Age", "Fare")
 ```
 
 ![Boxplot](./Resources/Boxplot.png)
+
+```python
+from safeds.plotting import histogram
+
+histogram(data_cleaned.get_column("Fare"))
+```
 ![Histogram](./Resources/Histogram.png)
+
+```python
+from safeds.plotting import scatterplot
+
+scatterplot(data_cleaned, "Age", "Fare")
+```
+
 ![Scatterplot](./Resources/Scatterplot.png)