Python for Data Analysis: Comprehensive Mastery Notes for Finance Majors

Flashcards covering key concepts and techniques for using Python in financial data analysis, based on provided lecture notes.

What is pandas?

Core Python library for loading, cleaning, and initial data analysis. Provides powerful structures like Series (1-dimensional) and DataFrames (2-dimensional).

Why should you always use .head()?

To quickly preview datasets, ensuring clarity about the data you're analyzing.

Why should you utilize .shape?

To instantly see data dimensions, helping you gauge dataset size and complexity.

What is IPython?

Interactive shell providing efficient coding through auto-completion and easy debugging.

What are Jupyter Notebooks?

User-friendly interface for combining code, output, and documentation clearly.

What does %run do?

Quickly run external Python scripts within your interactive session.

What does %timeit do?

Measure the execution speed of code blocks, essential for performance optimization.

What are NumPy arrays (ndarray)?

Crucial for numerical operations, significantly faster than Python lists.

What does the .reshape() method do?

Adjust array shapes for different mathematical or analytical purposes.

What are .loc[] and .iloc[] used for?

Efficiently select rows and columns by labels or integer positions.

What does .isnull().sum() do?

Quickly identify and quantify missing data.

What does .describe() do?

Rapid statistical summaries of your data.

What does pandas excel at in terms of data loading and storage?

Reading and writing data in formats such as CSV, Excel, JSON, and SQL databases.

Why should you always use index=False in .to_csv()?

To avoid extra columns in your data.

Why check data integrity and types post-loading with .info()?

Check data integrity and types post-loading for accurate analyses.

What does .drop_duplicates() do?

Effectively cleans datasets by removing redundant entries.

What does .fillna() do?

Strategic handling of missing data, often filling with mean or median.

What do .pivot() and .melt() do?

Reshaping and restructuring data for analytical flexibility.

What does .merge() do?

Enables precise and efficient data integration from multiple sources.

What do pivot tables (.pivot_table()) do?

Summarize data powerfully, crucial for financial insights.

Why should you always convert date columns with pd.to_datetime()?

For accurate temporal analysis.

Why calculate returns using .pct_change()?

Understand asset performance effectively.

Why utilize rolling windows (.rolling())?

Advanced statistical insights such as volatility and moving averages.

What is .groupby() used for?

Powerful and insightful data aggregations, vital in financial contexts.