Download and process Uruguayan economic data.
This project aims at simplifying gathering, processing and visualization (in the future) of Uruguay economic statistics. Data is retrieved from (mostly) government sources and can be transformed in several ways (converting to dollars, calculating rolling averages, resampling to other frequencies, etc.).
pip install econuy
git clone https://github.com/rxavier/uy-econ.git python setup.py install
This is the recommended entry point for the package. It allows setting up the common behavior for downloads, and holds the current working dataset.
from econuy.session import Session session = Session(loc_dir="econuy-data", revise_rows="nodup", force_update=False)
Session() object is initialized with the
loc_dircontrols where data will be saved and where it will be looked for when updating. It defaults to "econuy-data", and will create the directory if it doesn't exist.
revise_rowscontrols the updating mechanism. It can be an integer, denoting how many rows from the data held on disk to replace with new data, or a string. In the latter case,
autoindicates that the amount of rows to be replaced will be determined from the inferred data frequency, while
nodupreplaces existing data with new data for each time period found in both.
force_updatecontrols whether whether to redownload data even if existing data in disk was modified recently.
datasetholds the current working dataset and is initialized with an empty Pandas dataframe.
get() downloads the basic datasets. These are basically as provided by official sources, except various Pandas transformations are performed to render nice looking dataframes with appropiate column names, time indexes and properly defined values.
session.get(self, dataset: str, update: bool = True, save: bool = True, override: Optional[str] = None, **kwargs)
Available options for the
dataset argument are "cpi", "fiscal", "nxr", "naccounts", "labor", "rxr_custom", "rxr_official", "commodity_index", "reserves" and "fx_ops". Most are self explanatory but all are explained in the documentation.
override allows setting the CSV's filename to a different one than default (each dataset has a default, for example, "cpi.csv"). If you wanted CPI data:
df = session.get(dataset="cpi").dataset
Note that the previous code block accessed the
dataset attribute in order to get a dataframe. Alternatively, one could also call the
final() method after calling
get_tfm() gives access to predefined data pipelines that output frequently used data. These are based on the datasets provided by
get(), but are transformed to render data that you might find more immediately useful.
session.get_tfm(self, dataset: str, update: bool = True, save: bool = True, override: Optional[str] = None, **kwargs)
session.get_tfm(dataset="inflation") downloads CPI data, calculates annual inflation (pct change from a year ago), monthly inflation, and seasonally adjusted and trend monthly inflation.
Transformation methods take a
Session() object with a valid dataset and allow performing preset transformation pipelines. For example:
df = session.get(dataset="nxr").decompose(flavor="trend", outlier=True, trading=False)
will return a the Session object, with the dataset attribute holding the trend component of nominal exchange rate.
Available transformation methods are
resample()- resample data to a different frequency, taking into account whether data is of stock or flow type.
chg_diff()- calculate percent changes or differences for same period last year, last period or at annual rate.
decompose()- use X13-ARIMA to decompose series into trend and seasonally adjusted components.
convert()- convert to US dollars, constant prices or percent of GDP.
base_index()- set a period or window as 100, scale rest accordingly
rolling()- calculate rolling windows, either average or sum.
X13 ARIMA binary
If you want to use the
decompose() method you will need to supply the X13 binary (or place it somewhere reasonable and set
x13_binary="search"). You can get it from here for Windows or from here for UNIX systems. For macOS you can compile it using the instructions found here (choose the non-html version) or use my version (working under macOS Catalina) from here.
Metadata for each dataset is held in Pandas MultiIndexes with the following:
- Indicator name
- Topic or area
- Current or inflation adjusted
- Base index period(s) (if applicable)
- Seasonal adjustment
- Type (stock or flow)
- Cumulative periods
Word of warning
This project is heavily based on getting data from online sources that could change without notice, causing methods that download data to fail. While I try to stay on my toes and fix these quickly, it helps if you create an issue when you find one of these (or even submit a fix!).
I now realize this project would greatly benefit from OOP and plan to implement it next.
- Handling everything with column multi-indexes really doesn't seem like the best way to go around this.
- Automating data updates.
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