All references relating to the pandas library of Python.

Pandas References

Pandas includes two main data structures: 
- Series
- DataFrame

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.

Pandas DataFrame

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).

Missing Values

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.

University of Michigan - Ann Arbor

Claude

Data Processing and Modeling

https://www.dataquest.io/blog/15-python-libraries-for-data-science/

Python Libraries for Data Science

Dive into Deep Learning

Pandas

NumPy, which stands for Numerical Python, is a Python library that contains n-dimensional arrays and tools for processing those arrays.

NumPy

The SciPy library is one of the core packages that make up the SciPy stack. It provides many user-friendly and efficient numerical routines, such as routines for numerical integration, interpolation, optimization, linear algebra, and statistics. SciPy is NumPy extension of Python.

-Interacting with NumPy
-Linear Algebra with SciPy

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