COVID-19 VS Pandas

Data published by: COVID-19 Data Repository by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University Link: https://github.com/CSSEGISandData/COVID-19

I'm checking the statistics regarding new covid cases and was surprised (not in a good way) that there is a sudden increase of new cases based on JHU CSSE COVID-19 Datastatistics

Source: JHU CSSE COVID-19 Data

As far as I understand, the datasets can be found on GitHub: https://github.com/CSSEGISandData/COVID-19/tree/master/csse_covid_19_data/csse_covid_19_daily_reports

Sources: https://www2.census.gov/programs-surveys/popest/datasets/2010-2020/counties/asrh/ https://github.com/CSSEGISandData/COVID-19 https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/VOQCHQ http://www.healthdata.org/us-health/data-download https://www.countyhealthrankings.org/app/alabama/2022/downloads https://raw.githubusercontent.com/camillol/cs424p3/master/data/Population-Density%20By%20County.csv

(data source)

Fonte: https://github.com/CSSEGISandData/COVID-19

I'll also note that despite making claims about the Philippine Statistics Authority's data, they elected not to provide a link to said data. So I tried to figure out what data set they were using - they were comparing "cumulative excess deaths" to "cumulative total vaccine doses", but their graph is cut-off and the right label is missing. Since they're defining excess deaths as "the number of deaths that occurred as a result of the COVID-19 crisis", it's inherently faulty to compare these two - and it makes later statements internally inconsistent, such as "Something is causing the sudden deaths of thousands of Filipinos starting March 2021", because they've defined the something -it's COVID-19! (Source is the Johns Hopkins university COVID-19 data repository, which is publicly available and which I, unlike these scammers, am happy to provide). The entire argument completely falls apart, and it becomes similar to this topic, where they're just counting vaccinated people who died as "possibly because of the COVID vaccine" with absolutely no substantiation.

Dash is a Python framework that enables you to build interactive frontend applications without writing a single line of Javascript. Internally and in projects we like to use it in order to build a quick proof of concept for data driven applications because of the nice integration with Plotly and pandas. For this post, I'm going to assume that you're already familiar with Dash and won't explain that part in detail. Instead, we'll focus on what's necessary to make it run serverless.

there is pull request to integrate in both pydantic extra types and into pandas cose [1]

[1]: https://github.com/pandas-dev/pandas/issues/53999

Last year I worked through the challenges using VisiData, Datasette, and Pandas. I walked through my thought process and solutions in a series of posts.

pandas: A library for data analysis in Python

Data analysis involves scrutinizing datasets for class imbalances or protected features and understanding their correlations and representations. A classical tool like pandas would be my obvious choice for most of the analysis, and I would use OpenCV or Scikit-Image for image-related tasks.

Pandas, a powerful data manipulation library in Python, has become an essential tool for data scientists and analysts. One of its key functions is read_csv(), which allows users to read data from CSV (Comma-Separated Values) files into a Pandas DataFrame. In this tutorial, brought to you by CodesWithPankaj.com, we will explore the intricacies of read_csv() with clear examples to help you harness its full potential.

So, what I'd like to do is write a documentation package in Python to recreate what I've lost. I plan to build upon the fantastic python-docx and docxtpl packages, and I'll probably rely on pandas from much of the tabular stuff. Here are the features I intend to include:

Weirdly most of my time spent with data analysis was in the C layers in pandas.

Explore here.