Pandas DataFrames present two notable challenges for learners. Advanced operations can involve a steeper learning curve because Pandas introduces many library-specific APIs and conventions. In addition, a DataFrame is a two-dimensional structure organized into rows and columns, so data naturally organized in three dimensions does not fit directly into a single DataFrame.

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A DataFrame is a 2-dimensional data structure, in which the data is organized in rows and columns of any type. Each column has a label, and the index of a DataFrame are the row labels. To create a DataFrame, the data can be passed in several ways: as a Series, a record or structured ndarray, a 2-D ndarray, or as a dictionary of Series, lists, or dictionaries. 

Pandas DataFrame

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https://www.tutorialspoint.com/python_pandas/python_pandas_dataframe.htm

Tutorial: Pandas DataFrames

Why Python Pandas is Popular:

1. Pandas provide extremely streamlined forms of data representation.

2. Less writing and more work done.

3. Pandas is really powerful. It provides a huge set of important commands and features which are used to easily analyze your data.

4. Wes McKinney, the creator of Pandas, made the python library to mainly handle large datasets efficiently. 

5. Pandas provide a huge feature set to apply on the data you have so that you can customize, edit and pivot it according to your own will and desire. 

6. It is made for Python.

Adavantages of Pandas Library

Below is a diagram that shows the basic structure, funcitons, and capabiiliities of DataFrames in Python.

• https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html

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