Metadata-Version: 2.1
Name: pycovid
Version: 1.0.0
Summary: Useful tool to access Rami Krispin Novel Corona Dataset
Home-page: https://github.com/sudharshan-ashok/pycovid
Author: Sudharshan Ashok
Author-email: sudharshan93@gmail.com
License: MIT
Download-URL: https://sudharshan-ashok.github.io
Description: # PyCOVID Package
        
        The PyCOVID package provides a Pandas Dataframe of the 2019 Novel Coronavirus COVID-19 (2019-nCoV) epidemic based on Rami Krispin's 'coronavirus' package in R. The raw data pulled from the Johns Hopkins University Center for Systems Science and Engineering (JHU CCSE) Coronavirus
        
        <img src="img/covid.jfif" width="100%" align="center"/></a>
        
        ## Try in a collaboratory iPython notebook
        <a href="https://colab.research.google.com/github/kylemath/pycovid/blob/master/notebooks/PyCovid_Example_Notebook.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg"></a>
        
        ### Quick Installation
        
        ```py
        pip install pycovid
        ```
        ### Importing
        
        ```py
        from pycovid import pycovid
        pycovid.getCovidCases()
        ```
        
        ## Value Addition
        
        The 'coronavirus' R package gets access to data, but the 'pyCOVID' package builts some additional functionality over it.
        
        1. Wide Format for quicker analysis (Wide by case type - Confirmed/Death/Recovered)
        2. Filtering options - By country, timeline, casetype
        3. Cumulative Aggregating options - cumsum parameter to look at the cumulative totals of how the Coronavirus has grown over time
        4. Time Resampling: Converts dataframe to time-indexed, and resamples at required time level (weekly, monthly, etc)
        5. Quick visualization using Plotly: Use the plotCountries() function
        
        
        Usage
        -----
        
        **getCovidCasesWide()** : Get the wide version of the Coronavirus Dataset
        Parameters: 
        1. Countries: List of Countries (Default: All Countries)
        2. start_date and end_date: Use these to set the time window you wish to access
        3. casetype: Python List of Case Types ('confirmed', 'death' and 'recovered' and Default is all) 
        4. cumsum: Gets cumulative sums of cases for each country in list (Default: False)
        
        <img src="img/cumsumwide.PNG" width="100%" align="center"/></a>
        
        **getCovidCases()** : Get the Rami Krispin Coronavirus Dataset in the original format
        Parameters: 
        1. Countries: List of Countries (Default: All Countries)
        2. Provinces: List of Provinces and States (Default: All)
        3. start_date and end_date: Use these to set the time window you wish to access
        4. casetype: Python List of Case Types ('confirmed', 'death' and 'recovered' and Default is all) 
        5. cumsum: gets cummulative sum for each country or province
        5. plotprovinces: default is false, if True it cumsums over provinces instead of countries
        
        <img src="img/long.PNG" width="100%" align="center"/></a>
        
        **plotCountries()**: Plot the country aggregates on world map using Plotly
        Parameters:
        1. df: Pass a wide dataframe to the function with country-wise aggregates on confirmed, death and recovered cases
        2. grouped_date: Boolean to indicate whether dataset has been aggregated at country level or not
        3. metric: Can be 'confirmed' or 'death' or 'recovered'
        
        <img src="img/world.PNG" width="100%" align="center"/></a>
        
        **plot_countries_trend()**: Plot the cummultive trends over time for countries. Currently doesn't work for any countries with provinces/states (US, Canada, Australia, France).
        1. countries - list of country names
        2. start_date
        3. end_date 
        4. casetype as above,
        5. plottype - linear or log
        
        ```py
        from pycovid import pycovid
        
        pycovid.plot_countries_trend(countries=['Iran', 'Italy', 'Spain', 'Portugal', 'Japan', 'Germany', 'Mexico'],
        			casetype=['confirmed'], start_date="2020-01-01", plottype="linear")
        ```
        
        **plotProvinces()**: Plot the values from provinces within a country (tested for australia, US, Canada) over time
        1. countries - just include one
        2. provinces - optional, include names of any states or provinces, otherwise plots all
        3. start_date and end_date: as above
        4. casetype: as above
        5. proportion: default: False, boolean if you want data divided by population
        6. cumulative: default: True, if you want data summed over days
        7. plottype: "log" or "linear"
        
        ```py
        from pycovid import pycovid
        
        pycovid.plot_provinces(contries=['Canada'], 
        			provinces=['Alberta', 'Ontario', 'Quebec', 
        				'Manitoba', 'British Columbia', 
        				'New Brunswick', 'Saskatchewan'], 
        			casetype=['confirmed'], start_date="2020-02-20", plottype="linear")
        ```
        
        <img src="img/province.png" width="100%" align="center"/></a>
        
        **getIntervalData()**: Get resampled dataset of the Coronavirus based on the date (by default Monthly level)
        1. df: Pass a wide dataframe to the function
        2. interval: The time interval you wish to resample the dataset to: 1D = Daily, 1W: Weekly, 1M: Monthly
        
        <img src="img/timeinterval.PNG" width="100%" align="center"/></a>
        
        Installation
        ------------
        ```py
        pip install pycovid
        ```
        ```py
        from pycovid import pycovid
        pycovid.getCovidCases()
        ```
        or with virtual environment
        
        ```bash
        # Configure a virtual environment in project directory
        python3 -m venv venv 
        # Activate the environment (assign paths)
        source venv/bin/activate 
        # Upgrade Pip and install requirements
        pip install --upgrade pip 
        pip install pycovid
        ```
        
        Requirements
        ------------
        Pandas, Numpy and Plotly
        
        Authors
        -------
        PyCOVID was written by Sudharshan Ashok <sudharshan93@gmail.com>
        
        Licence
        -------
        MIT License
        
        
        
Keywords: Coronavirus,COVID
Platform: UNKNOWN
Description-Content-Type: text/markdown
