Python Data Types for Data Science

Introduction to Python Data Types

Python provides several built-in data types that are essential for data science work. Understanding these types is crucial for effective data manipulation and analysis.

Core Data Types in Python

Numeric Types for Data Science

# Integer operations
age = 25
year = 2024
count = 1000

# Float operations
price = 99.99
temperature = 36.6
pi = 3.141592653589793

# Type conversion for data science
int_to_float = float(42)          # 42.0
float_to_int = int(3.99)          # 3 (truncates)
str_to_int = int("100")           # 100
str_to_float = float("3.14")      # 3.14

# Precision handling (important for financial data)
from decimal import Decimal
price = Decimal('19.99')
total = price * 3  # Decimal('59.97')

String Operations for Data Science

# String creation and basic operations
text = "Data Science"
text_upper = text.upper()          # "DATA SCIENCE"
text_lower = text.lower()          # "data science"

# String slicing (essential for text processing)
sample = "Machine Learning"
print(sample[0:7])                 # "Machine"
print(sample[-8:])                 # "Learning"
print(sample[::2])                 # "Mchin ernn"

# String methods for data cleaning
dirty_text = "  Hello, World!  "
clean = dirty_text.strip()        # "Hello, World!"
lower = clean.lower()             # "hello, world!"
split = clean.split(',')          # ["Hello", " World!"]

# String formatting for data science
name = "Alice"
score = 95.5
f"Student: {name}, Score: {score:.1f}"  # "Student: Alice, Score: 95.5"

List Operations for Data Analysis

# Creating lists for data storage
data = [23, 45, 67, 89, 12, 34, 56, 78, 90, 11]

# List indexing and slicing
first_three = data[0:3]            # [23, 45, 67]
last_three = data[-3:]            # [78, 90, 11]
every_other = data[::2]           # [23, 67, 12, 56, 90]

# List methods for data manipulation
data.append(100)                  # Add element
data.extend([101, 102])           # Add multiple
data.insert(0, 0)                  # Insert at position
data.remove(23)                   # Remove by value
popped = data.pop()               # Remove and return last

# List comprehension for data transformation
squares = [x**2 for x in range(10)]  # [0, 1, 4, 9, 16, 25, 36, 49, 64, 81]
evens = [x for x in data if x % 2 == 0]  # Filter even numbers

Dictionary Operations for Feature Storage

# Dictionary for structured data storage
student = {
    'name': 'Alice',
    'age': 25,
    'gpa': 3.8,
    'courses': ['Math', 'Physics', 'CS'],
    'grades': {'Math': 'A', 'Physics': 'B', 'CS': 'A'}
}

# Accessing and modifying
print(student['name'])            # "Alice"
print(student.get('major', 'N/A')) # "N/A" (default)
student['age'] = 26               # Update value
student['major'] = 'Data Science' # Add new key

# Dictionary iteration
for key, value in student.items():
    print(f"{key}: {value}")

# Dictionary comprehension for feature engineering
features = ['age', 'salary', 'experience']
scaled = {f: 0 for f in features}  # {'age': 0, 'salary': 0, 'experience': 0}

Tuple for Fixed Data Structures

# Tuples for fixed data (coordinates, RGB values, etc.)
point = (10, 20)
rgb_color = (255, 128, 0)
date = (2024, 1, 15)

# Tuple unpacking
x, y = point
r, g, b = rgb_color
year, month, day = date

# Named tuples for clarity (useful for data science)
from collections import namedtuple
DataPoint = namedtuple('DataPoint', ['x', 'y', 'label'])
point1 = DataPoint(x=10, y=20, label='A')
print(point1.x, point1.label)  # 10, 'A'

# Multiple return values from functions
def calculate_stats(numbers):
    return min(numbers), max(numbers), sum(numbers)/len(numbers)

Set Operations for Unique Values

# Sets for unique value handling
data_with_duplicates = [1, 2, 2, 3, 3, 3, 4, 4, 5]
unique_values = set(data_with_duplicates)  # {1, 2, 3, 4, 5}

# Set operations
set1 = {1, 2, 3, 4}
set2 = {3, 4, 5, 6}
union = set1 | set2              # {1, 2, 3, 4, 5, 6}
intersection = set1 & set2      # {3, 4}
difference = set1 - set2         # {1, 2}
sym_diff = set1 ^ set2           # {1, 2, 5, 6}

# Set comprehension
squares_set = {x**2 for x in range(10)}  # {0, 1, 4, 9, 16, 25, 36, 49, 64, 81}

Type Checking and Conversion

# Type checking (important for data validation)
value = 42
print(type(value))              # <class 'int'>
print(isinstance(value, int))   # True
print(isinstance(value, (int, float)))  # True

# Check for numeric types
def is_numeric(x):
    return isinstance(x, (int, float))

# Type conversion in data pipelines
data = ['1', '2', '3', '4', '5']
int_data = [int(x) for x in data]  # [1, 2, 3, 4, 5]

# Handle mixed types in data
mixed = [1, 'two', 3.0, True]
clean = [x for x in mixed if isinstance(x, (int, float))]

Data Types in NumPy and Pandas

import numpy as np
import pandas as pd

# NumPy data types
arr_int = np.array([1, 2, 3], dtype=np.int32)
arr_float = np.array([1.0, 2.0, 3.0], dtype=np.float64)
arr_bool = np.array([True, False, True], dtype=np.bool_)

print(arr_int.dtype)   # int32
print(arr_float.dtype)  # float64

# Pandas data types
df = pd.DataFrame({
    'int_col': [1, 2, 3],
    'float_col': [1.1, 2.2, 3.3],
    'str_col': ['a', 'b', 'c'],
    'bool_col': [True, False, True]
})

print(df.dtypes)
# int_col       int64
# float_col   float64
# str_col     object
# bool_col      bool

Key Takeaways

1. Choose appropriate types - Use int/float for numeric data, str for text
2. Handle type conversion - Convert strings to numbers for calculations
3. Use collections wisely - Lists for ordered data, dicts for key-value pairs
4. Consider memory - Use appropriate dtypes in NumPy/Pandas for large datasets

Common Data Science Type Operations