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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

[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