Python Syntax Quick Reference

Python Syntax Quick Reference#

This page provides a quick reference for Python and pandas syntax commonly used in this course.

Bracket Types: When to Use What#

Python uses three types of brackets, each with a specific purpose:

Bracket Type

Symbol

Primary Uses

Examples

Square brackets

[ ]

Lists, indexing, selecting columns

my_list = [1, 2, 3]
df['column_name']
df.loc[0:5]

Parentheses

( )

Function calls, tuples, grouping

print('Hello')
my_tuple = (1, 2, 3)
(x + y) * z

Curly braces

{ }

Dictionaries, sets

my_dict = {'key': 'value'}
my_set = {1, 2, 3}

Common Mistakes#

# ❌ WRONG: Using parentheses for column selection
df('column_name')  # This won't work!

# ✅ CORRECT: Use square brackets
df['column_name']

# ❌ WRONG: Using square brackets for function calls
print['Hello']  # This won't work!

# ✅ CORRECT: Use parentheses
print('Hello')

DataFrame Column Selection#

# Select a single column (returns a Series)
ages = data['age']

# Select multiple columns (returns a DataFrame)
subset = data[['age', 'sex', 'education']]
# Note the double square brackets: outer for indexing, inner for the list

# Select columns by condition
numeric_cols = data[data.columns[data.dtypes == 'float64']]

DataFrame Indexing: .loc[] vs .iloc[]#

Method

What it uses

Example

.loc[]

Labels/names

df.loc[0:5, 'column_name']
df.loc[df['age'] > 30]

.iloc[]

Integer positions

df.iloc[0:5, 0:3]
df.iloc[0, 1]

Key Difference#

# .loc[] uses LABELS (inclusive on both ends)
df.loc[0:5]  # Includes rows 0, 1, 2, 3, 4, AND 5

# .iloc[] uses INTEGER POSITIONS (exclusive on the end)
df.iloc[0:5]  # Includes rows 0, 1, 2, 3, 4 (NOT 5)

Spacing and Formatting#

Spaces DON’T Matter (Mostly)#

# These are all equivalent:
data['age']
data[ 'age' ]
data  [  'age'  ]

# Function arguments with spaces
print('Hello', 'World')
print( 'Hello' , 'World' )

Commas DO Matter#

# ❌ WRONG: Missing comma
df[['age' 'sex']]  # This will cause an error!

# ✅ CORRECT: Comma separates items in a list
df[['age', 'sex']]

# ❌ WRONG: Extra comma at the end (in some contexts)
my_list = [1, 2, 3,]  # Works but not recommended

# ✅ CORRECT: No trailing comma
my_list = [1, 2, 3]

Quotes: Single vs Double#

# Both work the same for strings
name1 = 'Alice'
name2 = "Alice"

# Use the opposite quote type for nested quotes
message = "She said 'Hello'"
message = 'She said "Hello"'

# Or use escape characters
message = 'She said \'Hello\''

Common String Operations#

# Column names are strings
column_name = 'age'
data[column_name]  # Same as data['age']

# String values need quotes
data[data['sex'] == 'Male']  # ✅ CORRECT

data[data['sex'] == Male]  # ❌ WRONG: Male without quotes is a variable

Method Chaining#

# You can chain multiple operations
result = (data
          .query('age > 30')
          .groupby('ART')
          ['visuospatial_domain_z']
          .mean())

# Same as:
temp1 = data.query('age > 30')
temp2 = temp1.groupby('ART')
temp3 = temp2['visuospatial_domain_z']
result = temp3.mean()

Quick Troubleshooting Checklist#

When you encounter an error, check:

  1. ✅ Are you using the right bracket type?

  2. ✅ Do you have matching opening and closing brackets?

  3. ✅ Are all your commas in the right places?

  4. ✅ Are strings surrounded by quotes?

  5. ✅ Are you using .loc[] or .iloc[] correctly?

  6. ✅ Do column names match exactly (including capitalization)?

Resources#

Contents