Jupyter Notebook¶

generieren

Umfassende Jupyter Notebook verkürzt und Workflows für Datenwissenschaft und interaktives Computing.

Hauptnavigation¶

Shortcut	Mode	Description
`Enter`	Command	Enter Edit Mode
`Esc`	Edit	Enter Command Mode
`Shift+Enter`	Both	Run Cell and Select Below
`Ctrl+Enter`	Both	Run Cell
`Alt+Enter`	Both	Run Cell and Insert Below
`↑/↓`	Command	Select Cell Above/Below
`A`	Command	Insert Cell Above
`B`	Command	Insert Cell Below
`X`	Command	Cut Cell
`C`	Command	Copy Cell
`V`	Command	Paste Cell Below
`Shift+V`	Command	Paste Cell Above
`DD`	Command	Delete Cell
`Z`	Command	Undo Cell Deletion

Stammzellen¶

Shortcut	Mode	Description
`M`	Command	Change to Markdown Cell
`Y`	Command	Change to Code Cell
`R`	Command	Change to Raw Cell
`1-6`	Command	Change to Heading 1-6
`Shift+M`	Command	Merge Selected Cells
`Ctrl+Shift+-`	Edit	Split Cell at Cursor
`Shift+J/K`	Command	Extend Selection Below/Above
`Shift+↑/↓`	Command	Extend Selection

Code Editing¶

Shortcut	Mode	Description
`Tab`	Edit	Code Completion or Indent
`Shift+Tab`	Edit	Tooltip
`Ctrl+]`	Edit	Indent
`Ctrl+[`	Edit	Dedent
`Ctrl+A`	Edit	Select All
`Ctrl+Z`	Edit	Undo
`Ctrl+Shift+Z`	Edit	Redo
`Ctrl+Y`	Edit	Redo
`Ctrl+Home`	Edit	Go to Cell Start
`Ctrl+End`	Edit	Go to Cell End
`Ctrl+Left/Right`	Edit	Go Left/Right One Word
`Ctrl+Backspace`	Edit	Delete Word Before
`Ctrl+Delete`	Edit	Delete Word After

Laufkodex¶

Shortcut	Mode	Description
`Shift+Enter`	Both	Run Cell, Select Below
`Ctrl+Enter`	Both	Run Cell
`Alt+Enter`	Both	Run Cell, Insert Below
`Ctrl+K`	Command	Interrupt Kernel
`0,0`	Command	Restart Kernel
`Shift+L`	Command	Toggle Line Numbers
`Shift+O`	Command	Toggle Output

Dateioperationen¶

Shortcut	Mode	Description
`Ctrl+S`	Both	Save and Checkpoint
`Ctrl+Shift+S`	Command	Save As
`Ctrl+O`	Command	Open
`Ctrl+N`	Command	New Notebook
`Ctrl+Shift+P`	Command	Command Palette

Ansicht und Layout¶

Shortcut	Mode	Description
`Shift+Space`	Command	Scroll Up
`Space`	Command	Scroll Down
`Ctrl+Shift+L`	Command	Toggle All Line Numbers
`F`	Command	Find and Replace
`O`	Command	Toggle Output
`Shift+O`	Command	Toggle Output Scrolling

Magische Befehle¶

Command	Description
`%run script.py`	Run Python script
`%load script.py`	Load script into cell
`%who`	List variables
`%whos`	List variables with details
`%time statement`	Time execution of statement
`%timeit statement`	Time execution multiple times
`%matplotlib inline`	Enable inline plots
`%pwd`	Print working directory
`%cd directory`	Change directory
`%ls`	List directory contents
`%history`	Show command history
`%reset`	Reset namespace
`%debug`	Enter debugger
`%pdb on/off`	Toggle automatic debugger

Cell Magic Commands¶

Command	Description
`%%time`	Time execution of entire cell
`%%timeit`	Time execution of cell multiple times
`%%bash`	Run cell as bash script
`%%html`	Render cell as HTML
`%%javascript`	Run cell as JavaScript
`%%latex`	Render cell as LaTeX
`%%markdown`	Render cell as Markdown
`%%python2`	Run cell with Python 2
`%%python3`	Run cell with Python 3
`%%writefile filename`	Write cell contents to file

Data Science Workflows¶

Daten laden und Exploration¶

```python import pandas as pd import numpy as np import matplotlib.pyplot as plt import seaborn as sns

Load data¶

df = pd.read_csv('data.csv')

Quick exploration¶

df.head() df.info() df.describe() df.shape ```_

Datenvisualisierung¶

```python

Matplotlib inline¶

%matplotlib inline

Basic plots¶

plt.figure(figsize=(10, 6)) plt.plot(x, y) plt.title('Title') plt.xlabel('X Label') plt.ylabel('Y Label') plt.show()

Seaborn plots¶

sns.scatterplot(data=df, x='col1', y='col2') sns.heatmap(df.corr(), annot=True) ```_

Maschine lernen Pipeline¶

```python from sklearn.model_selection import train_test_split from sklearn.linear_model import LinearRegression from sklearn.metrics import mean_squared_error

Prepare data¶

X = df[['feature1', 'feature2']] y = df['target']

Split data¶

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)

Train model¶

model = LinearRegression() model.fit(X_train, y_train)

Evaluate¶

predictions = model.predict(X_test) mse = mean_squared_error(y_test, predictions) ```_

Markdown Formatierung¶

Kopf¶

```markdown

Header 1¶

Header 2¶

Header 3¶

Header 4¶

Header 5¶

Header 6¶

```_

Textformatierung¶

markdown **Bold text** *Italic text* `Code text` ~~Strikethrough~~_

Listen¶

```markdown - Unordered list item 1 - Unordered list item 2

Ordered list item 1
Ordered list item 2 ```_

Links und Bilder¶

markdown [Link text](URL) ![Alt text](image_url)_

Tabellen¶

markdown |Column 1|Column 2|Column 3| |----------|----------|----------| |Row 1|Data|Data| |Row 2|Data|Data|_

Math (LaTeX)¶

```markdown Inline math: \(E = mc^2\)

Block math: \(\int_\\\\{-\infty\\\\}^\\\\{\infty\\\\} e^\\\\{-x^2\\\\} dx = \sqrt\\\\{\pi\\\\}\) ```_

Best Practices¶

Code Organisation¶

sinnvolle Variablennamen verwenden
Kommentare und Docstrings hinzufügen
Komplexe Operationen in mehreren Zellen brechen
Funktionen für wiederholte Operationen verwenden
Bibliotheken nach oben importieren

Datenanalyse Workflow¶

**Datenbelastung*: Import und erste Exploration
** Datenreinigung**: Zeigen Sie fehlende Werte, Ausreißer
**Datenanalyse **: Visualisierungen und Statistiken
Feature Engineering: Neue Features erstellen
Modeling: Modelle trainieren und bewerten
**Ergebnisse*: Ergebnisse interpretieren und visualisieren

Leistungsspitzen¶

Verwenden Sie vektorisierte Operationen mit NumPy/Pandas
Vermeiden Sie Schlaufen, wenn möglich
geeignete Datentypen verwenden
Klare Ausgabe großer Zellen
Kernel periodisch wiederfinden

Dokumentation¶

Markdown-Zellen für Erläuterungen verwenden
Annahmen und Entscheidungen von Dokumenten
Datenquelleninformationen einschließen
Schlussfolgerungen und nächste Schritte hinzufügen
Verwenden Sie klare Schnittkopf