Reference

Last updated on 2026-08-24 | Edit this page

Python Quick Reference

Common Data Types

PYTHON 

# Integers
x = 42
type(x)  # <class 'int'>

# Floats
pi = 3.14159
type(pi)  # <class 'float'>

# Strings
name = "Alice"
type(name)  # <class 'str'>

# Lists (mutable, ordered)
numbers = [1, 2, 3, 4, 5]
numbers[0]  # 1
numbers.append(6)

# Tuples (immutable, ordered)
coords = (10, 20)
coords[0]  # 10

# Dictionaries (key-value pairs)
person = {'name': 'Bob', 'age': 30}
person['name']  # 'Bob'

NumPy Quick Reference

Creating Arrays

PYTHON 

import numpy as np

# From Python lists
arr = np.array([1, 2, 3, 4, 5])

# Built-in functions
zeros = np.zeros(5)      # [0. 0. 0. 0. 0.]
ones = np.ones(3)        # [1. 1. 1.]
range_arr = np.arange(0, 10, 2)  # [0 2 4 6 8]

# Random
random_arr = np.random.random(5)  # 5 random floats [0, 1)

Array Operations

PYTHON 

# Element-wise operations
arr1 = np.array([1, 2, 3])
arr2 = np.array([4, 5, 6])

arr1 + arr2      # [5 7 9]
arr1 * arr2      # [4 10 18]
arr1 ** 2        # [1 4 9]

# Aggregation functions
arr.sum()        # Sum of all elements
arr.mean()       # Average
arr.std()        # Standard deviation
arr.min()        # Minimum value
arr.max()        # Maximum value

Array Indexing and Slicing

PYTHON 

arr = np.array([10, 20, 30, 40, 50])

arr[0]           # 10 (first element)
arr[-1]          # 50 (last element)
arr[1:4]         # [20 30 40] (slice)
arr[::2]         # [10 30 50] (every other element)

# 2D arrays
arr_2d = np.array([[1, 2, 3], [4, 5, 6]])
arr_2d[0, 1]     # 2 (row 0, column 1)
arr_2d[1, :]     # [4 5 6] (entire second row)

Pandas Quick Reference

Creating DataFrames

PYTHON 

import pandas as pd

# From dictionary
data = {'name': ['Alice', 'Bob', 'Charlie'],
        'age': [25, 30, 35]}
df = pd.DataFrame(data)

# From CSV file
df = pd.read_csv('gapminder_gdp_africa.csv')

# Display
df.head()        # First 5 rows
df.tail()        # Last 5 rows
df.info()        # Column info
df.describe()    # Statistical summary

Selecting Data

PYTHON 

# Column selection
df['name']                    # Single column as Series
df[['name', 'age']]          # Multiple columns as DataFrame

# Row selection
df.loc[0]                    # By label
df.iloc[0]                   # By position
df.loc[df['age'] > 30]       # Boolean indexing

# Cell selection
df.loc[0, 'name']            # Specific cell

Data Manipulation

PYTHON 

# Adding columns
df['age_squared'] = df['age'] ** 2

# Filtering
young = df[df['age'] < 30]

# Sorting
df.sort_values('age')           # Sort ascending
df.sort_values('age', ascending=False)  # Sort descending

# Grouping and aggregation
df.groupby('category')['age'].mean()

# Missing data
df.isnull()      # Check for NaN values
df.fillna(0)     # Replace NaN with 0
df.dropna()      # Remove rows with NaN

Matplotlib Quick Reference

Basic Plotting

PYTHON 

import matplotlib.pyplot as plt

# Line plot
plt.plot([1, 2, 3, 4], [1, 4, 2, 3])
plt.xlabel('X axis')
plt.ylabel('Y axis')
plt.title('Simple Line Plot')
plt.show()

# Scatter plot
plt.scatter([1, 2, 3, 4], [1, 4, 2, 3])
plt.show()

# Bar plot
plt.bar(['A', 'B', 'C'], [10, 24, 36])
plt.show()

Figure and Subplot Management

PYTHON 

# Create figure with specific size
fig, ax = plt.subplots(figsize=(10, 6))

# Create multiple subplots
fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 5))
ax1.plot([1, 2, 3])
ax2.scatter([1, 2, 3], [1, 4, 9])

plt.tight_layout()  # Prevent overlap
plt.show()

Customization

PYTHON 

plt.plot([1, 2, 3], label='Line 1')
plt.plot([3, 2, 1], label='Line 2')
plt.legend()           # Show legend

plt.xlim(0, 4)         # Set x-axis limits
plt.ylim(0, 5)         # Set y-axis limits

plt.grid(True)         # Show grid

# Color and style
plt.plot([1, 2, 3], 'r--')     # Red dashed line
plt.scatter([1, 2, 3], s=50, alpha=0.5)  # Size and transparency

# Save figure
plt.savefig('my_plot.png', dpi=300)

Common Plot Types

PYTHON 

# Histogram
plt.hist(data, bins=20)

# Box plot
plt.boxplot([data1, data2])

# Heatmap (requires matplotlib.image or seaborn)
import seaborn as sns
sns.heatmap(df)

# 3D plot
from mpl_toolkits.mplot3d import Axes3D
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')

Jupyter Notebook Keyboard Shortcuts

Shortcut Action
Enter Edit mode
Esc Command mode
Up/Down Previous/next cell
Shift+Enter Run cell and move to next
Ctrl+Enter Run cell (stay in place)
Alt+Enter Run cell and insert new below

Cell Operations (in Command Mode)

Shortcut Action
A Insert cell above
B Insert cell below
D, D Delete cell
M Change to Markdown
Y Change to Code
Z Undo
Ctrl+Shift+Z Redo

Useful Tricks

Action Command
Get help on function help(function_name) or function_name?
Show docstring function_name?
Show source code function_name??
Time execution %timeit statement
Run shell command !command
Magic commands %magic

Useful Resources