Don't let your data become a psychological thriller. Data can be gracefully and effortlessly compartmentalised by pandas: the powerful, flexible and easy way to use open source analytics. Avoid losing the plot. Sensible subsets and elegant pivot tables can bring calm in a sea of madness. Melodrama is best left to be reveled in - at the opera. Leave the complex emotional turmoil behind in Edmea's head.
pandas is a fast, powerful, flexible and easy to use open source data analysis and manipulation tool - built on top of the Python programming language. pandas supply a practical yet elegant grammar for data transformation. These open source tools are highly effective when putting into effect data manipulation - central to gleaning deep insights into science and business intelligence. These tools elevate research beyond the purely naked eyeballing of relentless rows and columns. Here palmerpenguins is explored using the python library. The former has been introduced by Dr. Kristen Gorman and subsequently by Allison Horst, Alison Hill, and Kristen Gorman who made it publicly available as an R package. A pip install for python is available. The palmerpenguins package contains two datasets that can either be downloaded from the original GitHub link or can be accessed from Kaggle or loaded using seaborn using:
import seaborn as sns
penguins = sns.load_dataset(“penguins”)
See below an explanatory video and google colab python notebook for same:
Please see here.
