Data Wrangling Using Python

Data wrangling is the process of cleaning and transforming raw data into a structured, usable format for analysis and modeling. This includes fixing missing values, converting data types, and standardizing inconsistent formats such as gender labels or date formats. A synthetic dataset of 1,000 records was generated with fields like Age, Gender, Income, Region, and Education. Data preprocessing steps included renaming columns (e.g., Income to Annual_Income), filtering rows based on conditions (e.g., high-income youth), and creating new features such as income tiers. Aggregation was demonstrated by grouping data to compute average income by education level. Missing values were handled using techniques like median imputation and row removal. Categorical values were mapped to numerical codes, and records were sorted by income for better inspection. Pivot tables summarized data across multiple dimensions, while melting reshaped the dataframe for easier plotting and analysis. A custom age-bucketing function grouped individuals as Young, Adult, or Senior. Together, these steps illustrate how Pandas and NumPy streamline data wrangling for accurate and efficient data science workflows.

[Straight-Data-Science]

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[Straight-Data-Science]

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