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

University of Michigan - Ann Arbor

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

https://www.datacamp.com/community/tutorials/pandas-tutorial-dataframe-python#question1

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

Tutorial: Pandas DataFrames

1. Pandas initially has a mild learning slope, but as you go deeper into the library, the learning slope becomes much steeper. The functionality becomes confusing and can cause some problems for beginners. 
2. Pandas can become really tedious with respect to syntax. The syntax of Pandas is different than Python which can cause confusion.
3. One of the biggest drawbacks of Pandas it is not suited to work well with a 3D matrix. It is only good for a 2D matrix. 
4. The Pandas documentation is not the most helpful which can make it hard to learn.

Disadvantages of Pandas Library

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.

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