Give a substantive comment on this post: Pandas is a Python library used for working with data sets. It has functions for analyzing, cleaning, exploring, and manipulating data. The name "Pandas" has a reference to both "Panel Data", and "Python Data Analysis" and was created by Wes McKinney in 2008. Pandas module provides a bridge between Scikit-Learn's machine learning methods and pandas-style Data Frames. In particular, it provides a way to map DataFrame columns to transformations, which are later recombined into features. Pandas and Scikit-learn are two Python libraries commonly used in data science for data manipulation and analysis and for machine learning, respectively. Pandas can be used for data cleaning, exploration, and wrangling, while Scikit-learn can be used for classification, regression, clustering, and dimensionality reduction. Matplotlib is a library for creating visualizations, and Tensorflow is a library for building and training complex machine learning models. Just like Pandas and Numpy, it's a Python library, but SciKit more specific for Machine Learning. SciKit Learn includes everything from dataset manipulation to processing metrics. Scikit Learn requires Python and NumPy. For plotting (functions that start with "plot_") you'll first need to import Matplotlib. The great thing about Numpy, Pandas and Scikit Learn is that they all work together. A default thing to do is to load/clean/manipulate the data using Pandas. Translate the Pandas DataFrame into a Numpy array and fed it to Scikit Learn function(s). Often this happens automatically so you won't need to worry about this process. Reference Bernard, J., & Bernard, J. (2016). Python data analysis with pandas. Python Recipes Handbook: A Problem-Solution Approach, 37-48. McKinney, W., & Team, P. D. (2015). Pandas-Powerful python data analysis toolkit. PandasPowerful Python Data Analysis Toolkit, 1625