36 Module 2 Cheat Sheet

Key concepts, definitions, and code from Chapters 4–6

A quick-reference summary of the essential ideas from Module 2. Click any section heading to jump to the full coverage in the book.

Cell Types

A Jupyter notebook is a document that blends executable code, formatted text, and output (tables, plots) in a single shareable file.

Cell type	Purpose	Run with
Code	Write and execute Python	`Shift + Enter`
Markdown	Add headings, explanations, equations	`Shift + Enter`

Markdown quick reference:

# Heading 1     ## Heading 2      ### Heading 3
**bold**         *italic*          `inline code`
- bullet         1. numbered list
$a^2 + b^2 = c^2$   (inline math equation)

Kernel & Execution State

The kernel is the running Python process behind your notebook. All variables defined in executed cells persist in memory until the kernel is restarted.

Cells can be run out of order. Always Restart & Run All before sharing or submitting — this is the only way to confirm the notebook runs top-to-bottom without hidden state.

Action	When to use
Restart kernel	Clear all variables and start fresh
Restart & Run All	Verify notebook works top-to-bottom
Clear outputs	Before committing or sharing

Notebooks vs .py Scripts

Use notebooks for…	Use `.py` scripts for…
Exploration & EDA	Reusable functions and modules
Teaching & presentations	Production pipelines
One-off analysis	Code that needs unit tests or version control

Running a .py script from inside a notebook:

%run my_script.py      # execute script; its variables enter notebook scope
%load my_script.py     # paste script contents into a new cell to edit
import my_module       # import functions from a .py file like a package

Lists

A list is an ordered, mutable collection. Use it when items need to stay in sequence and may change over time.

scores = [85, 90, 88, 92]

scores[0]           # 85  — first element (zero-based indexing)
scores[-1]          # 92  — last element
scores.append(95)   # add to the end
scores[1] = 91      # update in place
len(scores)         # number of elements → 5
scores.sort()       # sort in place (ascending)
'apple' in scores   # membership check → False
scores.remove(85)   # remove first occurrence of 85
scores.pop()        # remove and return the last item

Python uses zero-based indexing: scores[0] is the first element, scores[1] is the second. Accessing scores[5] on a 5-item list raises an IndexError.

Tuples

A tuple is an ordered, immutable collection. Use it for fixed groupings that should never change (coordinates, dates, function return values).

coordinates = (39.76, -84.19)
birthday = (7, 14, 1998)

coordinates[0]           # 39.76 — same indexing as lists
# coordinates[0] = 41.0  # ← TypeError: tuples are immutable

# Tuple unpacking — assign each element to its own variable
lat, lon = coordinates
month, day, year = birthday

Dictionaries

A dictionary is a mutable collection of key-value pairs. Use it when data has meaningful labels (like a row of named fields).

student = {'name': 'Jordan', 'score': 95, 'major': 'Data Science'}

student['score']              # 95 — access by key
student['score'] = 98         # update existing value
student['grad_year'] = 2025   # add new key-value pair
del student['major']          # remove a key

student.keys()                # all keys
student.values()              # all values
student.items()               # all (key, value) pairs as tuples
'name' in student             # True — membership check on keys
student.get('gpa', 0)         # safe access — returns 0 if key missing

Data Structure Comparison

Structure	Syntax	Ordered	Mutable	Best for
`list`	`[1, 2, 3]`	✓	✓	Sequences that change
`tuple`	`(1, 2, 3)`	✓	✗	Fixed groupings, function returns
`dict`	`{'k': v}`	✓*	✓	Labeled data, fast lookups

*Insertion order preserved in Python 3.7+

Standard Library

The standard library ships with every Python installation — no pip needed.

import math
math.sqrt(144)        # → 12.0
math.factorial(6)     # → 720
math.ceil(9.2)        # → 10

import os
os.getcwd()           # current working directory
os.listdir()          # list files in current directory

import datetime
today = datetime.date.today()
birthday = datetime.date(1998, 7, 14)
(today - birthday).days   # days between two dates

import random
random.randint(1, 6)  # roll a 6-sided die

Standard vs Third-Party Libraries

	Standard library	Third-party library
Available	Automatically with Python	Install via `pip`
Examples	`math`, `os`, `datetime`, `random`	`pandas`, `numpy`, `matplotlib`, `seaborn`
Internet needed	No	Yes (to install)

Install a third-party package:

# In terminal
pip install pandas

# Inside a Jupyter notebook cell
!pip install pandas

Importing Libraries

Import once at the top of your notebook. Use the conventional aliases — they are universal in data science code.

import numpy as np              # numerical arrays and vectorized math
import pandas as pd             # DataFrames and data manipulation
import matplotlib.pyplot as plt # static plotting
import seaborn as sns           # statistical visualization
import math                     # standard library — no alias needed

Dot notation accesses functions inside a library:

math.sqrt(144)          # → 12.0
np.mean([1, 2, 3])      # → 2.0
pd.read_csv('data.csv') # load a CSV into a DataFrame

Key Data Science Libraries

Library	Primary use	Signature example
NumPy	Fast arrays, vectorized math	`arr ** 2` squares every element at once
Pandas	DataFrames, tabular data	`df.groupby('label').sum()`
Matplotlib	Static plots	`plt.plot(x, y)`
Seaborn	Statistical visualizations	`sns.histplot(df, x='col')`
SciPy	Scientific computing (stats, optimization)	`scipy.stats.ttest_ind(a, b)`
scikit-learn	Machine learning	`model.fit(X, y)`

NumPy vectorization — operate on every element without a loop:

import numpy as np

x = np.arange(1, 10)   # array([1, 2, 3, 4, 5, 6, 7, 8, 9])
x ** 2                  # array([ 1,  4,  9, 16, 25, 36, 49, 64, 81])
np.mean(x)              # → 5.0
x.reshape((3, 3))       # reshape into a 3×3 matrix

Common Pitfalls

Mistake	Why it happens	Fix
`scores[5]` on a 5-item list raises `IndexError`	Indexing starts at 0; valid indices are 0–4	Use `scores[-1]` for the last item
Cells run out of order break later cells	Kernel state depends on execution order, not cell position	Restart & Run All before submitting
`coordinates[0] = 5.0` raises `TypeError`	Tuples are immutable	Create a new tuple or convert to a list first
`student['gpa']` raises `KeyError`	Key doesn’t exist in the dictionary	Use `student.get('gpa', default)`
`import pandas` then `pd.read_csv(...)` raises `NameError`	Alias `pd` not defined when importing without it	Use `import pandas as pd`
`x_list ** 2` raises `TypeError` on a plain Python list	Lists don’t support vectorized operations	Convert to NumPy: `x = np.array(x_list)`