Hello, students! Today, we're exploring the pandas DataFrame, a powerhouse structure in data analysis with Python. We'll contrast it with NumPy arrays and teach you how to build a DataFrame. Additionally, we'll delve into its integral parts and data types.

The pandas library is Python's solution for tabular data operations, packing more punch for data analysis than NumPy, which is skewed towards numerical computations. Pandas houses two fundamental data structures: the Series (1D) and the DataFrame (2D). Often, the DataFrame is the go-to choice. Let's start by importing pandas:

Here, pd serves as a standard alias for pandas.

Building a DataFrame in pandas is straightforward. It can be created from a dictionary, list, or NumPy array. Here's an example of creating a DataFrame from a dictionary:

In the student_data dictionary, each (key, value) pair becomes a DataFrame column. The DataFrame automatically assigns an index (0-2) to each row, but we can also specify our own if we choose to do so.