This workshop will introduce participants to Python’s Pandas. We’ll start with a brief explanation of Anaconda and the Jupyter notebook environment (although not required for the participant, the instructor will be using these tools). After a brief introduction to main Python’s standard data types as well as Pandas data structures, we’ll demonstrate how to retrieve information from Pandas Series and DataFrames. We’ll also demonstrate basic input/output, selection, dropping, sorting, ranking, grouping and apply operations. Although not required, we recommend all participants to have a basic knowledge of Python.
This workshop introduces participants to Python’s NumPy, Pandas DataFrames, Matplotlib and StatsModels using an advertising dataset. Participants will use these tools to model (OLS) associations between advertising expenditures and product sales in example data. We will start with an introductory explanation of Anaconda and the Jupyter notebook environment (although not required for the participant, the instructor will be using these tools). We will proceed with topics including: reading data files; creation, indexing and slicing of Pandas DataFrames; creation and handling of Matplotlib objects; and creation and interpretation of models using Python’s StatsModels. Although not required, we recommend that participants have a basic knowledge of Python.
Pandas aims to be the fundamental high-level building block for doing practical, real-world data analysis in Python. Topics will include how to read various dat formats (csv, excel, databases, etc), clean, manipulate, analyze, graph and write results to an output file. Real world data will be used. The workshop is intended for users with basic Python knowledge. Anaconda Python 3.5 will be used.