Section 2: In-Depth Look at Python Libraries for the Analysis of Financial Datasets

This section will deep dive into the core Python libraries NumPy and pandas, which are used for the analysis and manipulation of large DataFrames. We will also cover the visualization library Matplotlib, which is closely linked to pandas. Finally, we will look at the statsmodels and scikit-learn libraries, which allow more advanced analysis of financial datasets.

This section comprises the following chapters:

• Chapter 2, Exploratory Data Analysis in Python
• Chapter 3, High-Speed Scientific Computing Using NumPy
• Chapter 4, Data Manipulation and Analysis with Pandas
• Chapter 5, Data Visualization Using Matplotlib
• Chapter 6, Statistical Estimation, Inference, and Prediction