I have several raw financial datasets that need to be turned into a single, reliable source of truth and then explored for key insights. Everything will be handled in Python, working mainly with Pandas (feel free to bring in NumPy, Jupyter, matplotlib or seaborn for visuals where useful). Here’s what I need from you: • Clean each file: handle missing values, inconsistent formats, duplicated rows, and any obvious outliers. • Merge the cleaned tables into one well-structured DataFrame, joining on the appropriate keys and keeping a clear data dictionary so I know exactly how everything lines up. • Perform an initial exploratory analysis: summary statistics, trend identification, and any red-flag anomalies you uncover. I’m especially interested in cash-flow patterns over time, variance across departments, and anything that jumps out once the data is tidy. • Package the results: deliver the cleaned & merged CSV (or Parquet), the reproducible Python script or notebook, and a short written summary of findings. I’ll share the raw files and my current schema notes once we start, and I’m happy to clarify objectives as you dig in. Clean, well-commented code and clear documentation are the key success factors.