Python

• Solid understanding of Python fundamentals: variables, data types, lists, dictionaries, sets, and tuples

• Experienced in using control flow: if statements, for and while loops

• Familiar with error handling using try-except blocks

• Strong use of list comprehensions, string formatting, and Pythonic coding style

• Able to work with files using open(), read(), write(), and context managers (with)
  
  

 Pandas for Data Analysis

• Load and explore datasets using:

  • df.head(), df.tail(), df.sample(), df.info(), df.describe(), df.columns, df.shape, etc.

• Clean and preprocess data:

  • Handle missing values with isna(), dropna(), fillna()

  • Remove duplicates with drop_duplicates()

• Filter, sort, and slice data using Boolean indexing, loc[], and iloc[]

• Visualize data efficiently by integrating Pandas with Matplotlib and Seaborn for insights 

• Aggregate data using groupby()

• Merge and join datasets with merge(), concat()

• Export data with to_csv(), to_excel()