Jupyter Notebook

Comprehensive Jupyter Notebook shortcuts and workflows for data science and interactive computing.

Basic Navigation

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

Cell Operations

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

Running Code

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

File Operations

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

View and 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

Magic Commands

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

Data Loading and Exploration

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

Data Visualization

# 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)

Machine Learning Pipeline

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 Formatting

Headers

# Header 1
## Header 2
### Header 3
#### Header 4
##### Header 5
###### Header 6

Text Formatting

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

Lists

- Unordered list item 1
- Unordered list item 2

1. Ordered list item 1
2. Ordered list item 2

Links and Images

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

Tables

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

Math (LaTeX)

Inline math: $E = mc^2$

Block math:
$$\int_{-\infty}^{\infty} e^{-x^2} dx = \sqrt{\pi}$$

Best Practices

Code Organization

• Use meaningful variable names
• Add comments and docstrings
• Break complex operations into multiple cells
• Use functions for repeated operations
• Import libraries at the top

Data Analysis Workflow

1. Data Loading: Import and initial exploration
2. Data Cleaning: Handle missing values, outliers
3. Exploratory Data Analysis: Visualizations and statistics
4. Feature Engineering: Create new features
5. Modeling: Train and evaluate models
6. Results: Interpret and visualize results

Performance Tips

• Use vectorized operations with NumPy/Pandas
• Avoid loops when possible
• Use appropriate data types
• Clear output of large cells
• Restart kernel periodically

Documentation

• Use Markdown cells for explanations
• Document assumptions and decisions
• Include data source information
• Add conclusions and next steps
• Use clear section headers