Analyzing covid-19 data with pandas and matplotlib
Blog post from Pybites
In an exploration of data analysis using the pandas library, Cedric delves into the management of large data collections, focusing on Covid-19 statistics sourced from organizations like WHO and ECDC, and stored by OWID on GitHub. The article details the process of retrieving, normalizing, and analyzing data with pandas, using DataFrames to handle complex datasets. Cedric also highlights the utility of Jupyter Notebooks in data analysis, offering a dynamic environment for data exploration with support for pandas and matplotlib. A key focus is the stringency index, a metric reflecting government response to the pandemic, and its relationship with new Covid-19 cases, although the analysis reveals that the correlation is not straightforward. Cedric uses pandas to visualize data patterns, addressing issues such as data irregularities and the impact of non-reporting days, and demonstrates how weekly data grouping and normalization can clarify trends. The exercise illustrates the power of pandas and matplotlib in bridging the gap between raw data and real-world insights, despite Cedric's self-professed lack of formal expertise in data analysis.
No tracked trend matches for this post yet.
Use this post, company, and trend context to find content marketing opportunities, perform competitive analysis, or address product feature gaps via the Plushcap MCP server or the Plushcap API.