The table below summarizes some of the main differences between these three data structures.

Difference between a Python list, NumPy arrays, and Pandas DataFrames

Unlike the arrays supported by NumPy, the columns in a DataFrame can be of all different types. You can have one column holding string values, another column holding a date, another column holding a floating point number and so on.

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In NumPy, there are two objects that are crucial to understanding how this library works. Those are:

1. N-dimensional array object (ndarray)

2. Universal function object (ufunc)

However, keep in mind that there are other objects that are built upon these that can be incorporated into whatever you're doing.

Fundamental Objects in NumPy

The key data structure pandas supports, which is basically like a spreadsheet table with rows and named columns.

It is also called a 2 dimensional data structure

Pandas Dataframe

Pandas Documentation

Pandas Dataframe vs. numpy.array

Consider the two following random arrays "a" and "b" below: what will be the shape of "c"?

In an n-dimensional array there are two characteristics that define it:
- Shape of the array
- type of element the array is composed of

Essentially, ndarrays describes the collection of items of the same  data type. Since that's the case, then every item will take up the same size block of memory. Each of those blocks are interpreted in the same way.

Ndarray Object

Essentially, there are NumPy functions that operate on ndarrays. All (ufuncs) wrap a core function that takes scalar inputs and produces scalar outputs.

Why would this be useful?
- Say for example a user wants to sum individual elements in a list with the same corresponding elements in another list. One could iterate through both lists and add them as it goes through the iteration. Or, if the user uses a ufunc called "add(x,y)"  then it does the same thing - but saves times.

Universal Function Object

Which of the following are correct methods that can be used to create a pandas DataFrame? 

Creating a Pandas DataFrame in Python

For example, lets create some data:

import pandas as pd

data = {"Trees": [['Blue','Brown','Red']], "Age": [[3,6,10]]}

- The next step is to put this data in to the Data frame object:

frame = pd.DataFrame(data)

print(frame)





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