fix

Author: tobiasraabe

docs/source/how_to_guides/writing_custom_nodes.md

@@ -11,7 +11,7 @@ your own to improve your workflows.
 
 ## Use-case
 
-A typical task operation is to load data like a [pandas.DataFrame][]
+A typical task operation is to load data like a [`pandas.DataFrame`][]
 from a pickle file, transform it, and store it on disk. The usual way would be to use
 paths to point to inputs and outputs and call [`pandas.read_pickle`](https://pandas.pydata.org/docs/reference/api/pandas.read_pickle.html) and
 [`pandas.DataFrame.to_pickle`](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.to_pickle.html).
@@ -115,7 +115,7 @@ Here are some explanations.
 ## Improvements
 
 Usually, you would like your custom node to work with [`pathlib.Path`][] objects and
-[upath.UPath][] objects allowing to work with remote
+[`upath.UPath`][] objects allowing to work with remote
 filesystems. To simplify getting the state of the node, you can use the
 [`pytask.get_state_of_path`](../reference_guides/api/utilities_and_typing.md#pytask.get_state_of_path)
 function.

docs/source/tutorials/using_a_data_catalog.md

@@ -88,7 +88,7 @@ The following tabs show you how to use the data catalog given the interface you
 
     Use `data_catalog["data"]` as an default argument to access the
     [`pytask.PickleNode`](../reference_guides/api/nodes_and_tasks.md#pytask.PickleNode) within the task. When
-    you are done transforming your [pandas.DataFrame][], save it with
+    you are done transforming your [`pandas.DataFrame`][], save it with
     [`pytask.PNode.save`](../reference_guides/api/nodes_and_tasks.md#pytask.PNode.save).
 
     ```py hl_lines="11 22" title="task_data_preparation.py"
@@ -99,7 +99,7 @@ The following tabs show you how to use the data catalog given the interface you
 
     Use `data_catalog["data"]` as an default argument to access the
     [`pytask.PickleNode`](../reference_guides/api/nodes_and_tasks.md#pytask.PickleNode) within the task. When
-    you are done transforming your [pandas.DataFrame][], save it with
+    you are done transforming your [`pandas.DataFrame`][], save it with
     [`pytask.PNode.save`](../reference_guides/api/nodes_and_tasks.md#pytask.PNode.save).
 
     ```py hl_lines="7 17" title="task_data_preparation.py"
@@ -110,7 +110,7 @@ The following tabs show you how to use the data catalog given the interface you
 
     An elegant way to use the data catalog is via return type annotations. Add
     `data_catalog["data"]` to the annotated return and simply return the
-    [pandas.DataFrame][]
+    [`pandas.DataFrame`][]
     to store it.
 
     You can read more about return type annotations in
@@ -124,7 +124,7 @@ The following tabs show you how to use the data catalog given the interface you
 
 Next, we will define the second task that consumes the data set from the previous task.
 Following one of the interfaces gives you immediate access to the
-[pandas.DataFrame][] in the task without any additional line to load
+[`pandas.DataFrame`][] in the task without any additional line to load
 it.
 
 ```py hl_lines="13" title="task_plot_data.py"
@@ -223,6 +223,6 @@ WindowsPath('C:\Users\pytask-dev\git\my_project\file.csv')
 
 `data_catalog["data"]` was stored with a
 [`pytask.PickleNode`](../reference_guides/api/nodes_and_tasks.md#pytask.PickleNode) and returns the
-[pandas.DataFrame][] whereas `data_catalog["csv"]` becomes a
+[`pandas.DataFrame`][] whereas `data_catalog["csv"]` becomes a
 [`pytask.PathNode`](../reference_guides/api/nodes_and_tasks.md#pytask.PathNode) and
 [`pytask.PNode.load`](../reference_guides/api/nodes_and_tasks.md#pytask.PNode.load) returns the path.