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- https://eng.libretexts.org/Bookshelves/Computer_Science/Programming_Languages/Python_Programming_(OpenStax)/15%3A_Data_Science/15.04%3A_Exploratory_Data_AnalysisThis page covers exploratory data analysis (EDA), focusing on data inspection, indexing, and methods for handling missing values using Pandas in Python. Key concepts include label-based and integer-ba...This page covers exploratory data analysis (EDA), focusing on data inspection, indexing, and methods for handling missing values using Pandas in Python. Key concepts include label-based and integer-based indexing, along with functions like `isnull()`, `dropna()`, and `fillna()` to maintain data quality. The text emphasizes methods for replacing Null values and correcting common misconceptions about incorrect function usage.
- https://eng.libretexts.org/Bookshelves/Data_Science/Principles_of_Data_Science_(OpenStax)/11%3A_Appendix/11.03%3A_Appendix_D-_Review_of_Python_FunctionsThis page provides an overview of Python functions and machine learning methods. It includes Python functions for data handling, statistical analysis, and visualization, with examples for library usag...This page provides an overview of Python functions and machine learning methods. It includes Python functions for data handling, statistical analysis, and visualization, with examples for library usage. Additionally, it categorizes machine learning techniques, covering algorithms for forecasting, classification, regression, and neural networks, along with tools for model training, evaluation, and data preprocessing.
- https://eng.libretexts.org/Bookshelves/Computer_Science/Programming_Languages/Python_Programming_(OpenStax)/15%3A_Data_Science/15.03%3A_PandasThis page provides an overview of the Pandas library, a powerful Python tool for data cleaning and analysis, detailing its main data structures: Series and DataFrame. It highlights key functions like ...This page provides an overview of the Pandas library, a powerful Python tool for data cleaning and analysis, detailing its main data structures: Series and DataFrame. It highlights key functions like `info()`, `describe()`, `value_counts()`, and `unique()`, which facilitate data exploration and summary. Examples are given for creating DataFrames from various sources. The text also includes practice questions and recommends consulting the Pandas user guide for further learning.
