Metadata-Version: 2.1
Name: weo
Version: 0.1.2020
Summary: Python client to read IMF WEO dataset as pandas dataframe
Home-page: http://github.com/epogrebnyak/weo-reader
Author: Evgeniy Pogrebnyak
Author-email: e.pogrebnyak@gmail.com
License: MIT
Description: # weo-reader
        
        
        
        1. The program uses Python 3.6. To install `weo` as a python package use:
        
        
        
        `pip install weo`
        
           
        
           
        
        2. You need the data saved as a local file.  Download latest WEO country data file from IMF web site (for example as `weo.csv`). 
        
        
        
        You can do it manually in a command line with `curl` command:
        
        
        
        ```
        
        curl -o weo.csv https://www.imf.org/external/pubs/ft/weo/2019/02/weodata/WEOOct2019all.xls
        
        ```
        
        
        
        Please note `WEOOct2019all.xls` is in fact a tab-delimited CSV file. 
        
        
        
        Alternatively, can use `weo.download()` function:
        
        
        
        ```python 
        
        from weo import download
        
        
        
        download(year=2019, period=2, path='weo_2019_2.csv', overwrite=True)
        
        ```
        
        
        
        2. Use `WEO` class from `weo` package or `weo.py` to view and extract data. `WEO` is a wrapper around a by-country pandas dataframe that ensures proper data import and easier access to it.
        
        
        
        3. Things to try in a REPL, by line:
        
        
        
        ```python
        
        from weo import WEO
        
        
        
        w = WEO("weo_2019_2.csv")
        
        
        
        # What is inside?
        
        w.variables()
        
        w.units()
        
        w.units("Gross domestic product, current prices")
        
        w.codes
        
        w.from_code("LUR")
        
        
        
        # Countries
        
        w.countries("United")      # Dataframe with United Arab Emirates, United Kingdom
        
                                   # and United States
        
        w.iso_code3("Netherlands") # 'NLD'
        
        
        
        # Get some data
        
        w.get("General government gross debt", "Percent of GDP")
        
        w.gdp_usd(2024).head(20).sort_values().plot.barh(title="GDP by country, USD bln (2024)")
        
        w.country("DEU", 2018)
        
        ```
        
        
        
        4.  Try [Google Colab Notebook](https://colab.research.google.com/drive/1euKYK0hdKREC0HQZt6SfHqBGtSbu45eL#scrollTo=BQkkZrcw7a1V)
        
        
        
        ## Dev notes
        
        
        
        -  `WEOOct2019all.xls` file from the web site is really a CSV file.
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Intended Audience :: Financial and Insurance Industry
Classifier: Topic :: Office/Business :: Financial
Description-Content-Type: text/markdown
