The Data Analyst Toolkit

Functions:

• Function: A block of organized, reusable code that performs a specific task.
  • def greet(name): return "Hello, " + name
• Function Call: Executing a function to perform its defined task
  • message = greet("Alice")
• Arguments: Values passed to a function when it’s called.
  • def add(x, y): return x + y result = add(5, 3) # Result is 8
• Parameters: Variables in a function definition that receive arguments.
  • def multiply(a, b): return a * b
• Return Statement: Defines what a function should return.
  • def square(x): return x * x
• Default Arguments: Arguments with predefined values.
  • def power(base, exponent=2): return base ** exponent
• Variable Scope: The part of the code where a variable can be accessed.
  • def my_func(): x = 10 # Local variable
• Global Variable: A variable defined outside any function and accessible everywhere.
  • global_var = 100 def access_global(): print(global_var)
• Lambda Function: An anonymous function defined using the lambda keyword.
  • double = lambda x: x * 2

Data Types:

• Integer: Whole numbers without decimal points.
  • age = 25
• Float: Numbers with decimal points.
  • pi = 3.14
• String: A sequence of characters.
  • message = "Hello, Python"
• Boolean: Represents truth values True or False.
  • is_valid = True
• List: An ordered collection of elements.
  • numbers = [1, 2, 3, 4, 5]
• Tuple: Similar to a list but immutable.
  • coordinates = (3, 5)
• Dictionary: A collection of key-value pairs.
  • student = {"name": "Alice", "age": 20, "grade": "A"}
• Set: An unordered collection of unique elements.
  • unique_numbers = {1, 2, 3, 4, 5}
• DataFrame: A 2-dimensional labeled data structure in pandas.
  • import pandas as pd data = {'Name': ['Alice', 'Bob'], 'Age': [25, 30]} df = pd.DataFrame(data)
    
• Indexing: Accessing elements in a data structure using indices.
  • first_name = student["name"]
• Slicing: Extracting a subset of elements from a sequence.
  • sublist = numbers[1:4]
• Appending: Adding an element to the end of a list.
  • numbers.append(6)
• Updating: Modifying elements in a list.
  • numbers[0] = 0
• Keys: The unique identifiers in a dictionary.
  • keys = student.keys()
• Values: The data associated with the keys in a dictionary.
  • values = student.values()
• DataFrame Columns: Named series within a DataFrame.
  • ages = df['Age']
• DataFrame Rows: Individual records in a DataFrame.
  • row = df.iloc[0] # First row
• Concatenation: Joining two or more sequences together.
  • combined_list = numbers + unique_numbers
• Sorting: Arranging elements in a specific order.
  • sorted_list = sorted(numbers)
• List Comprehension: Creating a new list using concise syntax.
  • squares = [x ** 2 for x in numbers]
• Tuple Packing and Unpacking: Assigning multiple values in a single operation.
  • x, y = coordinates
• Key-Value Pair Manipulation: Adding, updating, or deleting entries in a dictionary.
  • student["grade"] = "A+"
• Set Operations: Union, intersection, and difference between sets.
  • common_numbers = numbers & unique_numbers
• DataFrame Operations: Filtering, grouping, and aggregating data in a DataFrame.
  • filtered_df = df[df['Age'] > 25]
• Data Type Conversion: Changing one data type to another.
  • num_str = str(42) # Convert to string
• Membership Testing: Checking if an element is present in a sequence.
  • is_present = 3 in numbers
• Dictionary Iteration: Looping through keys, values, or items in a dictionary.
  • for key, value in student.items(): print(key, value)
• List Length: Counting the number of elements in a list.
  • num_elements = len(numbers)
• Tuple Immutability: Preventing modification of tuple elements.
  • coordinates[0] = 4 # Error
• Dictionary Keys Immutability: Using immutable objects as dictionary keys.
  • immutable_dict = {(1, 2): 'value'}
• Index Out of Range Handling: Avoiding accessing elements beyond the list’s length.
  • if index < len(numbers): element = numbers[index]
• List Appending and Extending: Adding elements from another sequence to a list.
  • new_numbers = [6, 7, 8] numbers.extend(new_numbers)
• List Deletion: Removing elements by index or value.
  • del numbers[0] # Delete by index numbers.remove(5) # Delete by value
• DataFrame Filtering: Selecting rows based on certain conditions.
  • adults_df = df[df['Age'] >= 18]
• DataFrame Grouping: Grouping data by a certain column.
  • grouped = df.groupby('Age') ``