Address pre-commit issues (#79)

.github/workflows/2023-PyConIT-workflow.yaml

@@ -35,7 +35,7 @@ jobs:
           - "2023_PyConIT/**/*.py"
           - "2023_PyConIT/**/*.ipynb"
           - "2023_PyConIT/requirements.txt"
-  
+
   lint:
     name: 🚨 Lint Python code
     if: needs.changes.outputs.changes == 'true'

.github/workflows/2023-PyData_Berlin-python-pdf-workflow.yaml

@@ -35,7 +35,7 @@ jobs:
           - "2023_PyData_Berlin/**/*.py"
           - "2023_PyData_Berlin/**/*.ipynb"
           - "2023_PyData_berlin/requirements.txt"
-          
+
   lint:
     name: 🚨 Lint Python code
     if: needs.changes.outputs.changes == 'true'

.pre-commit-config.yaml

@@ -6,3 +6,5 @@ repos:
     - id: check-yaml
       args: ['--unsafe']
     - id: check-toml
+    - id: trailing-whitespace
+    - id: end-of-file-fixer

2023_EuroSciPy/notes.md

@@ -63,7 +63,7 @@ packaging_tutorial/
 │       └── example.py
 └── tests/
     └── test_example.py
-``` 
+```
 
 And there are a lot to talk about here.
 First is a `pyproject.toml`. TOML is a configuration file format that has gained popularity lately.
@@ -586,4 +586,4 @@ that run your code however it is possible to self-host your own Runners I've nev
 wanted to point it out as an option if you don't want GitHub to handle the compute which would be running on Azure Cloud
 I mean who really knows what Microsoft is doing with all that code they're running in any case though GitHub actions is
 an awesome platform and I would highly recommend using these free robots to get stuff done thanks for watching and I
-will see you in the next one
+will see you in the next one

2023_EuroSciPy/pyproject.toml

@@ -13,4 +13,4 @@ dependencies = [
 dev = [
     "pytest==8.1.1",
     "hypothesis==6.100.1",
-]
+]

2023_PyConIT/README.md

@@ -362,5 +362,3 @@ collection, data processing, and availability of datasets, I know that everythin
 read, I quickly go and see what's wrong.
 And we looked at the example of continuous delivery pipeline, where we could use GitHub actions to verify the data
 visualisation code.
-
-

2023_PyData_Berlin/README.md

@@ -361,5 +361,3 @@ collection, data processing, and availability of datasets, I know that everythin
 read, I quickly go and see what's wrong.
 And we looked at the example of continuous delivery pipeline, where we could use GitHub actions to verify the data
 visualisation code.
-
-

2023_PyData_Berlin/data/global_temperature.txt

@@ -146,4 +146,4 @@ Year No_Smoothing  Lowess(5)
 2019      0.98      0.92
 2020      1.02      0.92
 2021      0.85      0.91
-2022      0.89      0.91
+2022      0.89      0.91

2023_PyData_Berlin/data/test_data.csv

@@ -7,4 +7,4 @@ x,y
 6,6
 7,7
 8,8
-9,9
+9,9

2023_PyData_Berlin/requirements.txt

@@ -3,4 +3,4 @@ pandas==2.2.2
 matplotlib==3.8.4
 pytest==8.1.1
 flake8==7.0.0
-black==24.4.0
+black==24.4.0