Python Fundamentals and String Methods for Data Preparation

Day 1: Python Basics for AI/ML Preparation

Output Statements

• print() – Displays output to the console.
• end=" " – Prevents a new line after the print statement.
• sep="," – Specifies the character used to separate multiple items in the output.

Input and Type Casting

• The input() function always returns a string data type.
• Adding strings results in *concatenation* (e.g., "5" + "10" = "510").
• Use int() or float() for numerical input conversion:

  Example: num = int(input("Enter a number: "))

Variables and Data Types

• Common types include int (integer), float (decimal), str (string), and bool (boolean).
• Use type(variable) to check the data type of any variable.

Formatted Strings (f-Strings)

• Used for easy string formatting and embedding variables:

  Example: print(f"Value: {num}")

• Limiting decimal places:

  Use {num:.2f} to display the number with 2 decimal places.

Naming Convention

• Use snake_case for variable and function names (all lowercase, words separated by underscores).

  Example: my_variable_name = 10

Day 2: Python Data Cleaning String Methods

Essential String Trimming and Replacement

These methods are used to remove unwanted characters or spaces from strings, crucial for data preparation.

• .lstrip(): Removes spaces or specified characters from the left side.

  Example 1: " Kamal" becomes "Kamal"

  Example 2: "__Kamal".lstrip("__") becomes "Kamal"

• .rstrip(): Removes spaces or specified characters from the right side.

  Example: "Kamal " becomes "Kamal"

• .strip(): Removes spaces or specified characters from both sides.

  Example 1: " Kamal " becomes "Kamal"

  Example 2: "1kamal3".strip("13") becomes "kamal"

• .replace(): Replaces all occurrences of a specified substring.

  Example 1: "Ram ** Shrestha".replace(" ** ", " ") becomes "Ram Shrestha"

  Note: You can also replace letters, like replacing "R" with "Hari".

Cleaning Messy Text Workflow

Combine methods to handle complex data issues:

• Use strip() to clean leading/trailing characters.
• Use replace() to clean internal parts of the string.
• *Complex Example:* To clean "--My ----name is kamal 123___":

  Apply strip(' -123_') followed by replace(' ----', ' ').

Day 3: Advanced String Methods for Data Normalization

1. .title() Method

   • Capitalizes the first letter of each word in a string.
   • Example: 'john doe'.title() results in 'John Doe'.
   • Often combined with strip() and replace() to clean text and properly capitalize names.

2. .split() Method

   • Splits a string into a list of substrings based on a delimiter (default is whitespace).
   • Example: 'John Doe'.split() results in ['John', 'Doe'].
   • Can unpack results directly into variables:

     first_name, last_name = name.split()

   • Works with custom delimiters:

     'Kamal,Raj,Paudel'.split(',') results in ['Kamal', 'Raj', 'Paudel']

3. .capitalize() Method

   • Capitalizes only the first letter of the entire string, converting all other letters to lowercase.
   • Example: 'kamal paudel'.capitalize() results in 'Kamal paudel'.

Practical Data Cleaning Tasks and Examples

• Name Cleaning: Use strip() + replace() + title() sequentially to handle messy characters and ensure proper capitalization of names.
• Extraction: Extract first and last names efficiently using split() after initial cleaning.
• Phone Number Normalization: Use replace() to remove unwanted characters or country codes (e.g., removing (+977)).
• Complex String Processing: Combine strip(), replace(), title(), and split() to clean and parse complex strings containing mixed data like names and phone numbers.