In Pandas, a Series is a data structure which is a one-dimensional labeled array. The labels of a Series are known as the index. A Series can hold any data type.

Pandas Series

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

Pandas includes two main data structures: 
- Series
- DataFrame

University of Michigan - Ann Arbor

In computer programming, pandas is a software library written for the Python programming language for data manipulation and analysis. It is primarily used for data cleaning and preparation through the use of dataframes and series. In particular, it offers data structures and operations for manipulating numerical tables and time series. It is a free software anyone can download.

Pandas

https://pandas.pydata.org/docs/index.html

Pandas documentation

All references relating to the pandas library of Python.

Pandas References

Pandas Data Structures

Pandas DataFrame is two-dimensional size-mutable, potentially heterogeneous tabular data structure with labeled axes (rows and columns). A Data frame is a two-dimensional data structure, i.e., data is aligned in a tabular fashion in rows and columns.

The to_numpy() method in pandas is used to convert a DataFrame into a NumPy array. By passing the dtype argument, such as dtype=float, you can ensure that all extracted numerical data is uniformly formatted as floating-point numbers. This is a common preprocessing step before loading tabular data into deep learning tensors.

pandas.DataFrame.to_numpy()

In data science, missing values (often represented programmatically as `NaN`) occur when entries in a dataset are empty or undefined, resulting from missing data collection or parsing errors. These incomplete records are a persistent challenge in data preprocessing and must be deliberately handled before training a model, typically through either imputation (estimating replacement values) or deletion (discarding the incomplete rows or columns).

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