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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

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

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

  1. *Datenbelastung: Import und erste Exploration
  2. ** Datenreinigung**: Zeigen Sie fehlende Werte, Ausreißer
  3. Datenanalyse : Visualisierungen und Statistiken
  4. Feature Engineering: Neue Features erstellen
  5. Modeling: Modelle trainieren und bewerten
  6. *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