API documentation

Pipeline class

class econuy.core.Pipeline(location: str | PathLike | Engine | Connection | None = None, download: bool = True, always_save: bool = True, read_fmt: str = 'csv', read_header: str | None = 'included', save_fmt: str = 'csv', save_header: str | None = 'included', errors: str = 'raise')[source]

Bases: object

Main class to access download and transformation methods.

location

Either Path or path-like string pointing to a directory where to find a CSV for updating and saving, SQLAlchemy connection or engine object, or None, don’t save or update.

Type:: str, os.PathLike, SQLAlchemy Connection or Engine, or None, default None

download

If False the get method will only try to retrieve data on disk.

Type:: bool, default True

always_Save

If True, save every retrieved dataset to the specified location.

Type:: bool, default True

read_fmt

File format of previously downloaded data. Ignored if location points to a SQL object.

Type:: {‘csv’, ‘xls’, ‘xlsx’}

save_fmt

File format for saving. Ignored if location points to a SQL object.

Type:: {‘csv’, ‘xls’, ‘xlsx’}

read_header

Location of dataset metadata headers. ‘included’ means they are in the first 9 rows of the dataset. ‘separate’ means they are in a separate Excel sheet (if read_fmt='csv', headers are discarded). None means there are no metadata headers.

Type:: {‘included’, ‘separate’, None}

save_header

Location of dataset metadata headers. ‘included’ means they will be set as the first 9 rows of the dataset. ‘separate’ means they will be saved in a separate Excel sheet (if save_fmt='csv', headers are discarded). None discards any headers.

Type:: {‘included’, ‘separate’, None}

errors

How to handle errors that arise from transformations. raise will raise a ValueError, coerce will force the data into np.nan and ignore will leave the input data as is.

Type:: {‘raise’, ‘coerce’, ‘ignore’}

property dataset: DataFrame: Get dataset.

property dataset_flat: DataFrame: Get dataset with no metadata in its column names.

property name: str: Get dataset name.

property description: str: Get dataset description.

property available_datasets: Dict

Get a dictionary with all available datasets.

The dictionary is separated by original and custom keys, which denote whether the dataset has been modified in some way or if its as provided by the source

copy(deep: bool = False) → Pipeline[source]

Copy or deepcopy a Pipeline object.

Parameters:: deep (bool, default True) – If True, deepcopy.
Return type:: Pipeline

get(name: str) → Pipeline[source]

Main download method.

Parameters:: name (str) – Dataset to download, see available options in available_datasets.
Raises:: ValueError – If an invalid string is given to the name argument.

resample(rule: DateOffset | Timedelta | str, operation: str = 'sum', interpolation: str = 'linear', warn: bool = False) → Pipeline[source]

Wrapper for the resample method in Pandas that integrates with econuy dataframes’ metadata.

Trim partial bins, i.e. do not calculate the resampled period if it is not complete, unless the input dataframe has no defined frequency, in which case no trimming is done.

Parameters:

rule (pd.DateOffset, pd.Timedelta or str) – Target frequency to resample to. See Pandas offset aliases
operation ({'sum', 'mean', 'last', 'upsample'}) – Operation to use for resampling.
interpolation (str, default 'linear') – Method to use when missing data are produced as a result of resampling, for example when upsampling to a higher frequency. See Pandas interpolation methods
warn (bool, default False) – If False, don’t raise warnings with incomplete time-range bins.

Return type:

None

Raises:

ValueError – If operation is not one of available options.
ValueError – If the input dataframe’s columns do not have the appropiate levels.

Warns:

UserWarning – If input frequencies cannot be assigned a numeric value, preventing incomplete bin trimming.

chg_diff(operation: str = 'chg', period: str = 'last') → Pipeline[source]

Wrapper for the pct_change and diff Pandas methods.

Calculate percentage change or difference for dataframes. The period argument takes into account the frequency of the dataframe, i.e., inter (for interannual) will calculate pct change/differences with periods=4 for quarterly frequency, but periods=12 for monthly frequency.

Parameters:

df (pd.DataFrame) – Input dataframe.
operation ({'chg', 'diff'}) – chg for percent change or diff for differences.
period ({'last', 'inter', 'annual'}) – Period with which to calculate change or difference. last for previous period (last month for monthly data), inter for same period last year, annual for same period last year but taking annual sums.

Return type:

None

Raises:

ValueError – If the dataframe is not of frequency M (month), Q or Q-DEC (quarter), or A or A-DEC (year).
ValueError – If the operation parameter does not have a valid argument.
ValueError – If the period parameter does not have a valid argument.
ValueError – If the input dataframe’s columns do not have the appropiate levels.

decompose(component: str = 'seas', method: str = 'x13', force_x13: bool = False, fallback: str = 'loess', trading: bool = True, outlier: bool = True, x13_binary: str | PathLike = 'search', search_parents: int = 0, ignore_warnings: bool = True, **kwargs) → Pipeline[source]

Apply seasonal decomposition.

Decompose the series in a Pandas dataframe using either X13 ARIMA, Loess or moving averages. X13 can be forced in case of failure by alternating the underlying function’s parameters. If not, it will fall back to one of the other methods. If the X13 method is chosen, the X13 binary has to be provided. Please refer to the README for instructions on where to get this binary.

Parameters:

component ({'seas', 'trend'}) – Return seasonally adjusted or trend component.
method ({'x13', 'loess', 'ma'}) – Decomposition method. X13 refers to X13 ARIMA from the US Census, loess refers to Loess decomposition and ma refers to moving average decomposition, in all cases as implemented by statsmodels.
force_x13 (bool, default False) – Whether to try different outlier and trading parameters in statsmodels’ x13 arima analysis for each series that fails. If False, jump to the fallback method for the whole dataframe at the first error.
fallback ({'loess', 'ma'}) – Decomposition method to fall back to if method="x13" fails and force_x13=False.
trading (bool, default True) – Whether to automatically detect trading days in X13 ARIMA.
outlier (bool, default True) – Whether to automatically detect outliers in X13 ARIMA.
x13_binary (str, os.PathLike or None, default 'search') – Location of the X13 binary. If search is used, will attempt to find the binary in the project structure. If None, statsmodels will handle it.
search_parents (int, default 0) – If x13_binary=search, this parameter controls how many parent directories to go up before recursively searching for the binary.
ignore_warnings (bool, default True) – Whether to suppress X13Warnings from statsmodels.
kwargs – Keyword arguments passed to statsmodels’ x13_arima_analysis, STL and seasonal_decompose.

Return type:

None

Raises:

ValueError – If the method parameter does not have a valid argument.
ValueError – If the component parameter does not have a valid argument.
ValueError – If the fallback parameter does not have a valid argument.
ValueError – If the errors parameter does not have a valid argument.
FileNotFoundError – If the path provided for the X13 binary does not point to a file and method='x13'.

convert(flavor: str, start_date: str | datetime | None = None, end_date: str | datetime | None = None) → Pipeline[source]

Convert dataframe from UYU to USD, from UYU to real UYU or from UYU/USD to % GDP.

flavor=usd: Convert a dataframe’s columns from Uruguayan pesos to US dollars. Call the get function to obtain nominal exchange rates, and take into account whether the input dataframe’s Type, as defined by its multiindex, is flow or stock, in order to ` choose end of period or monthly average NXR. Also take into account the input dataframe’s frequency and whether columns represent rolling averages or sums.

flavor=real: Convert a dataframe’s columns to real prices. Call the get method to obtain the consumer price index. take into account the input dataframe’s frequency and whether columns represent rolling averages or sums. Allow choosing a single period, a range of dates or no period as a base (i.e., period for which the average/sum of input dataframe and output dataframe is the same).

flavor=gdp: Convert a dataframe’s columns to percentage of GDP. Call the the get method to obtain UYU and USD quarterly GDP series. Take into account the input dataframe’s currency for chossing UYU or USD GDP. If frequency of input dataframe is higher than quarterly, GDP will be upsampled and linear interpolation will be performed to complete missing data. If input dataframe’s “Acum.” level is not 12 for monthly frequency or 4 for quarterly frequency, calculate rolling input dataframe.

In all cases, if input dataframe’s frequency is higher than monthly (daily, business, etc.), resample to monthly frequency.

Parameters:

pipeline (econuy.core.Pipeline or None, default None) – An instance of the econuy Pipeline class.
start_date (str, datetime.date or None, default None) – Only used if flavor=real. If set to a date-like string or a date, and end_date is None, the base period will be start_date.
end_date (str, datetime.date or None, default None) – Only used if flavor=real. If start_date is set, calculate so that the data is in constant prices of start_date-end_date.
errors ({'raise', 'coerce', 'ignore'}) – What to do when a column in the input dataframe is not expressed in Uruguayan pesos. raise will raise a ValueError, coerce will force the entire column into np.nan and ignore will leave the input column as is.

Return type:

None

Raises:

ValueError – If the errors parameter does not have a valid argument.
ValueError – If the input dataframe’s columns do not have the appropiate levels.

rebase(start_date: str | datetime, end_date: str | datetime | None = None, base: float | int = 100.0) → Pipeline[source]

Rebase all dataframe columns to a date or range of dates.

Parameters:

start_date (string or datetime.datetime) – Date to which series will be rebased.
end_date (string or datetime.datetime, default None) – If specified, series will be rebased to the average between start_date and end_date.
base (float, default 100) – Float for which start_date == base or average between start_date and end_date == base.

Return type:

None

rolling(window: int | None = None, operation: str = 'sum') → Pipeline[source]

Wrapper for the rolling method in Pandas that integrates with econuy dataframes’ metadata.

If periods is None, try to infer the frequency and set periods according to the following logic: {'A': 1, 'Q-DEC': 4, 'M': 12}, that is, each period will be calculated as the sum or mean of the last year.

Parameters:

window (int, default None) – How many periods the window should cover.
operation ({'sum', 'mean'}) – Operation used to calculate rolling windows.

Return type:

None

Raises:

ValueError – If operation is not one of available options.
ValueError – If the input dataframe’s columns do not have the appropiate levels.

Warns:

UserWarning – If the input dataframe is a stock time series, for which rolling operations are not recommended.

save()[source]

Write held dataset.

Raises:: ValueError – If dataset is an empty DataFrame or self.location is None.

Session class

class econuy.session.Session(location: str | PathLike | Engine | Connection | None = None, download: bool = True, always_save: bool = True, read_fmt: str = 'csv', read_header: str | None = 'included', save_fmt: str = 'csv', save_header: str | None = 'included', errors: str = 'raise', log: int | str = 1, logger: Logger | None = None, max_retries: int = 3)[source]

Bases: object

A download and transformation session that creates a Pipeline object and simplifies working with multiple datasets.

Alternatively, can be created directly from a Pipeline by using the from_pipeline class method.

location

Either Path or path-like string pointing to a directory where to find a CSV for updating and saving, SQLAlchemy connection or engine object, or None, don’t save or update.

Type:: str, os.PathLike, SQLAlchemy Connection or Engine, or None, default None

download

If False the get method will only try to retrieve data on disk.

Type:: bool, default True

always_Save

If True, save every retrieved dataset to the specified location.

Type:: bool, default True

read_fmt

File format of previously downloaded data. Ignored if location points to a SQL object.

Type:: {‘csv’, ‘xls’, ‘xlsx’}

save_fmt

File format for saving. Ignored if location points to a SQL object.

Type:: {‘csv’, ‘xls’, ‘xlsx’}

read_header

Location of dataset metadata headers. ‘included’ means they are in the first 9 rows of the dataset. ‘separate’ means they are in a separate Excel sheet (if read_fmt='csv', headers are discarded). None means there are no metadata headers.

Type:: {‘included’, ‘separate’, None}

save_header

Location of dataset metadata headers. ‘included’ means they will be set as the first 9 rows of the dataset. ‘separate’ means they will be saved in a separate Excel sheet (if save_fmt='csv', headers are discarded). None discards any headers.

Type:: {‘included’, ‘separate’, None}

errors

How to handle errors that arise from transformations. raise will raise a ValueError, coerce will force the data into np.nan and ignore will leave the input data as is.

Type:: {‘raise’, ‘coerce’, ‘ignore’}

log

Controls how logging works. 0: don’t log; 1: log to console; 2: log to console and file with default file; str: log to console and file with filename=str

Type:: {str, 0, 1, 2}

logger

Logger object. For most cases this attribute should be None, allowing log to control how logging works.

Type:: logging.Logger, default None

max_retries

Number of retries for get in case any of the selected datasets cannot be retrieved.

Type:: int, default 3

classmethod from_pipeline(pipeline: Pipeline) → Session[source]

property pipeline: Pipeline

property datasets: Dict[str, DataFrame]

Holds retrieved datasets.

Returns:: Datasets
Return type:: Dict[str, pd.DataFrame]

property datasets_flat: Dict[str, DataFrame]

Holds retrieved datasets.

Returns:: Datasets
Return type:: Dict[str, pd.DataFrame]

copy(deep: bool = False) → Session[source]

Copy or deepcopy a Session object.

Parameters:: deep (bool, default True) – If True, deepcopy.
Return type:: Session

property available_datasets: Dict[str, Dict]

Return available dataset arguments for use in get.

Returns:: Dataset
Return type:: Dict[str, Dict]

get(names: str | Sequence[str]) → Session[source]

Main download method.

Parameters:: names (Union[str, Sequence[str]]) – Dataset to download, see available options in available_datasets. Either a string representing a dataset name or a sequence of strings in order to download several datasets.
Raises:: ValueError – If an invalid string is found in the names argument.

get_bulk(names: str) → Session[source]

Get datasets in bulk.

Parameters:: names ({'all', 'original', 'custom', 'economic_activity', 'prices', 'fiscal_accounts', 'labor', 'external_sector', 'financial_sector', 'income', 'international', 'regional'}) – Type of data to download. all gets all available datasets, original gets all original datatsets and custom gets all custom datasets. The remaining options get all datasets for that area.
Raises:: ValueError – If an invalid string is given to the names argument.

resample(rule: DateOffset | Timedelta | str | List, operation: str | List = 'sum', interpolation: str | List = 'linear', warn: bool | List = False, select: str | int | Sequence[str] | Sequence[int] = 'all') → Session[source]: Resample to target frequencies.

See also

resample

chg_diff(operation: str | List = 'chg', period: str | List = 'last', select: str | int | Sequence[str] | Sequence[int] = 'all') → Session[source]: Calculate pct change or difference.

See also

chg_diff

decompose(component: str | List = 'seas', method: str | List = 'x13', force_x13: bool | List = False, fallback: str | List = 'loess', trading: bool | List = True, outlier: bool | List = True, x13_binary: str | PathLike | List = 'search', search_parents: int | List = 0, ignore_warnings: bool | List = True, select: str | int | Sequence[str] | Sequence[int] = 'all', **kwargs) → Session[source]: Apply seasonal decomposition.

See also

decompose

convert(flavor: str | List, start_date: str | datetime | None | List = None, end_date: str | datetime | None | List = None, select: str | int | Sequence[str] | Sequence[int] = 'all') → Session[source]: Convert to other units.

See also

convert

rebase(start_date: str | datetime | List, end_date: str | datetime | None | List = None, base: float | List = 100.0, select: str | int | Sequence[str] | Sequence[int] = 'all') → Session[source]: Scale to a period or range of periods.

See also

rebase

rolling(window: int | List | None = None, operation: str | List = 'sum', select: str | int | Sequence[str] | Sequence[int] = 'all') → Session[source]: Calculate rolling averages or sums.

See also

rolling

concat(select: str | int | Sequence[str] | Sequence[int] = 'all', concat_name: str | None = None, force_suffix: bool = False) → Session[source]

Concatenate datasets in datasets and add as a new dataset.

Resample to lowest frequency of selected datasets.

Parameters:

select (str, int, Sequence[str] or Sequence[int], default "all") – Datasets to concatenate.
concat_name (Optional[str], default None) – Name used as a key for the output dataset. The default None sets the name to “concat_{dataset_1_name}_…_{dataset_n_name}”.
force_suffix (bool, default False) – Whether to include each dataset’s full name as a prefix in all indicator columns.

save(select: str | int | Sequence[str] | Sequence[int] = 'all')[source]

Write datasets.

Parameters:: select (str, int, Sequence[str] or Sequence[int], default "all") – Datasets to save.
Raises:: ValueError – If self.location is None.

Data retrieval functions

econuy.retrieval.activity.monthly_gdp() → DataFrame[source]

Get the monthly indicator for economic activity.

Returns:: Monthly GDP
Return type:: pd.DataFrame

econuy.retrieval.activity.national_accounts_supply_constant_nsa() → DataFrame[source]

Get supply-side national accounts data in NSA constant prices, 2005-.

Returns:: National accounts, supply side, constant prices, NSA
Return type:: pd.DataFrame

econuy.retrieval.activity.national_accounts_demand_constant_nsa() → DataFrame[source]

Get demand-side national accounts data in NSA constant prices, 2005-.

Returns:: National accounts, demand side, constant prices, NSA
Return type:: pd.DataFrame

econuy.retrieval.activity.national_accounts_demand_current_nsa() → DataFrame[source]

Get demand-side national accounts data in NSA current prices.

Returns:: National accounts, demand side, current prices, NSA
Return type:: pd.DataFrame

econuy.retrieval.activity.national_accounts_supply_current_nsa() → DataFrame[source]

Get supply-side national accounts data in NSA current prices, 2005-.

Returns:: National accounts, supply side, current prices, NSA
Return type:: pd.DataFrame

econuy.retrieval.activity.gdp_index_constant_sa() → DataFrame[source]

Get supply-side national accounts data in SA real index, 1997-.

Returns:: National accounts, supply side, real index, SA
Return type:: pd.DataFrame

econuy.retrieval.activity.national_accounts_supply_constant_nsa_extended(pipeline: Pipeline = None) → DataFrame[source]

Get supply-side national accounts data in NSA constant prices, 1988-.

Three datasets with different base years, 1983, 2005 and 2016, are spliced in order to get to the result DataFrame.

Returns:: National accounts, supply side, constant prices, NSA
Return type:: pd.DataFrame

econuy.retrieval.activity.national_accounts_demand_constant_nsa_extended(pipeline: Pipeline = None) → DataFrame[source]

Get demand-side national accounts data in NSA constant prices, 1988-.

Three datasets with different base years, 1983, 2005 and 2016, are spliced in order to get to the result DataFrame.

Returns:: National accounts, demand side, constant prices, NSA
Return type:: pd.DataFrame

econuy.retrieval.activity.gdp_index_constant_sa_extended(pipeline: Pipeline = None) → DataFrame[source]

Get GDP data in SA constant prices, 1988-.

Three datasets with different base years, 1983, 2005 and 2016, are spliced in order to get to the result DataFrame.

Returns:: GDP, constant prices, SA
Return type:: pd.DataFrame

econuy.retrieval.activity.gdp_constant_nsa_extended(pipeline: Pipeline = None) → DataFrame[source]

Get GDP data in NSA constant prices, 1988-.

Three datasets with two different base years, 1983 and 2016, are spliced in order to get to the result DataFrame. It uses the BCU’s working paper for retropolated GDP in current and constant prices for 1997-2015.

Returns:: GDP, constant prices, NSA
Return type:: pd.DataFrame

econuy.retrieval.activity.gdp_current_nsa_extended(pipeline: Pipeline = None) → DataFrame[source]

Get GDP data in NSA current prices, 1997-.

It uses the BCU’s working paper for retropolated GDP in current and constant prices for 1997-2015.

Returns:: GDP, current prices, NSA
Return type:: pd.DataFrame

econuy.retrieval.activity.industrial_production() → DataFrame[source]

Get industrial production data.

Returns:: Monthly industrial production index
Return type:: pd.DataFrame

econuy.retrieval.activity.core_industrial_production(pipeline: Pipeline | None = None) → DataFrame[source]

Get total industrial production, industrial production excluding oil refinery and core industrial production.

Parameters:: pipeline (econuy.core.Pipeline or None, default None) – An instance of the econuy Pipeline class.
Returns:: Measures of industrial production
Return type:: pd.DataFrame

econuy.retrieval.activity.cattle_slaughter() → DataFrame[source]

Get weekly cattle slaughter data.

Returns:: Weekly cattle slaughter
Return type:: pd.DataFrame

econuy.retrieval.activity.milk_shipments() → DataFrame[source]

Get monthly milk shipments from farms data.

Returns:: Monhtly milk shipments from farms
Return type:: pd.DataFrame

econuy.retrieval.activity.diesel_sales() → DataFrame[source]

Get diesel sales by department data.

This retrieval function requires the unrar binaries to be found in your system.

Returns:: Monthly diesel dales
Return type:: pd.DataFrame

econuy.retrieval.activity.gasoline_sales() → DataFrame[source]

Get gasoline sales by department data.

This retrieval function requires the unrar binaries to be found in your system.

Returns:: Monthly gasoline dales
Return type:: pd.DataFrame

econuy.retrieval.activity.electricity_sales() → DataFrame[source]

Get electricity sales by sector data.

This retrieval function requires the unrar binaries to be found in your system.

Returns:: Monthly electricity dales
Return type:: pd.DataFrame

econuy.retrieval.prices.cpi() → DataFrame[source]

Get CPI data.

Returns:: Monthly CPI
Return type:: pd.DataFrame

econuy.retrieval.prices.cpi_divisions() → DataFrame[source]

Get CPI data by division.

Returns:: Monthly CPI by division
Return type:: pd.DataFrame

econuy.retrieval.prices.inflation_expectations() → DataFrame[source]

Get data for the BCU inflation expectations survey.

Returns:: Monthly inflation expectations
Return type:: pd.DataFrame

econuy.retrieval.prices.ppi() → DataFrame[source]

Get PPI data.

Returns:: Monthly PPI
Return type:: pd.DataFrame

econuy.retrieval.prices.nxr_monthly(pipeline: Pipeline | None = None) → DataFrame[source]

Get monthly nominal exchange rate data.

Parameters:: pipeline (econuy.core.Pipeline or None, default None) – An instance of the econuy Pipeline class.
Returns:: Monthly nominal exchange rates – Sell rate, monthly average and end of period.
Return type:: pd.DataFrame

econuy.retrieval.prices.nxr_daily() → DataFrame[source]

Get daily nominal exchange rate data.

Returns:: Daily nominal exchange rates – Sell rate.
Return type:: pd.DataFrame

econuy.retrieval.fiscal.fiscal_balance_global_public_sector() → DataFrame[source]

Get fiscal balance data for the consolidated public sector.

Returns:: Monthly fiscal balance for the consolidated public sector
Return type:: pd.DataFrame

econuy.retrieval.fiscal.fiscal_balance_nonfinancial_public_sector() → DataFrame[source]

Get fiscal balance data for the non-financial public sector.

Returns:: Monthly fiscal balance for the non-financial public sector
Return type:: pd.DataFrame

econuy.retrieval.fiscal.fiscal_balance_central_government() → DataFrame[source]

Get fiscal balance data for the central government + BPS.

Returns:: Monthly fiscal balance for the central government + BPS
Return type:: pd.DataFrame

econuy.retrieval.fiscal.fiscal_balance_soe() → DataFrame[source]

Get fiscal balance data for public enterprises.

Returns:: Monthly fiscal balance for public enterprises
Return type:: pd.DataFrame

econuy.retrieval.fiscal.fiscal_balance_ancap() → DataFrame[source]

Get fiscal balance data for ANCAP.

Returns:: Monthly fiscal balance for ANCAP
Return type:: pd.DataFrame

econuy.retrieval.fiscal.fiscal_balance_ute() → DataFrame[source]

Get fiscal balance data for UTE.

Returns:: Monthly fiscal balance for UTE
Return type:: pd.DataFrame

econuy.retrieval.fiscal.fiscal_balance_antel() → DataFrame[source]

Get fiscal balance data for ANTEL.

Returns:: Monthly fiscal balance for ANTEL
Return type:: pd.DataFrame

econuy.retrieval.fiscal.fiscal_balance_ose() → DataFrame[source]

Get fiscal balance data for OSE.

Returns:: Monthly fiscal balance for OSE
Return type:: pd.DataFrame

econuy.retrieval.fiscal.tax_revenue() → DataFrame[source]

Get tax revenues data.

This retrieval function requires that Ghostscript and Tkinter be found in your system.

Returns:: Monthly tax revenues
Return type:: pd.DataFrame

econuy.retrieval.fiscal.public_debt_global_public_sector() → DataFrame[source]

Get public debt data for the consolidated public sector.

Returns:: Quarterly public debt data for the consolidated public sector
Return type:: pd.DataFrame

econuy.retrieval.fiscal.public_debt_nonfinancial_public_sector() → DataFrame[source]

Get public debt data for the non-financial public sector.

Returns:: Quarterly public debt data for the non-financial public sector
Return type:: pd.DataFrame

econuy.retrieval.fiscal.public_debt_central_bank() → DataFrame[source]

Get public debt data for the central bank

Returns:: Quarterly public debt data for the central bank
Return type:: pd.DataFrame

econuy.retrieval.fiscal.public_assets() → DataFrame[source]

Get public sector assets data.

Returns:: Quarterly public sector assets
Return type:: pd.DataFrame

econuy.retrieval.fiscal.net_public_debt_global_public_sector(pipeline: Pipeline | None = None) → DataFrame[source]

Get net public debt excluding deposits at the central bank.

Parameters:: pipeline (econuy.core.Pipeline or None, default None) – An instance of the econuy Pipeline class.
Returns:: Net public debt excl. deposits at the central bank
Return type:: pd.DataFrame

econuy.retrieval.fiscal.fiscal_balance_summary(pipeline: Pipeline | None = None) → DataFrame[source]

Get the summary fiscal balance table found in the Budget Law. Includes adjustments for the Social Security Fund.

Parameters:: pipeline (econuy.core.Pipeline or None, default None) – An instance of the econuy Pipeline class.
Returns:: Summary fiscal balance table
Return type:: pd.DataFrame

econuy.retrieval.external.trade_exports_sector_value() → DataFrame[source]

Get export values by product.

Returns:: Export values by product
Return type:: pd.DataFrame

econuy.retrieval.external.trade_exports_sector_volume() → DataFrame[source]

Get export volumes by product.

Returns:: Export volumes by product
Return type:: pd.DataFrame

econuy.retrieval.external.trade_exports_sector_price() → DataFrame[source]

Get export prices by product.

Returns:: Export prices by product
Return type:: pd.DataFrame

econuy.retrieval.external.trade_exports_destination_value() → DataFrame[source]

Get export values by destination.

Returns:: Export values by destination
Return type:: pd.DataFrame

econuy.retrieval.external.trade_exports_destination_volume() → DataFrame[source]

Get export volumes by destination.

Returns:: Export volumes by destination
Return type:: pd.DataFrame

econuy.retrieval.external.trade_exports_destination_price() → DataFrame[source]

Get export prices by destination.

Returns:: Export prices by destination
Return type:: pd.DataFrame

econuy.retrieval.external.trade_imports_category_value() → DataFrame[source]

Get import values by sector.

Returns:: Import values by sector
Return type:: pd.DataFrame

econuy.retrieval.external.trade_imports_category_volume() → DataFrame[source]

Get import volumes by sector.

Returns:: Import volumes by sector
Return type:: pd.DataFrame

econuy.retrieval.external.trade_imports_category_price() → DataFrame[source]

Get import prices by sector.

Returns:: Import prices by sector
Return type:: pd.DataFrame

econuy.retrieval.external.trade_imports_origin_value() → DataFrame[source]

Get import values by origin.

Returns:: Import values by origin
Return type:: pd.DataFrame

econuy.retrieval.external.trade_imports_origin_volume() → DataFrame[source]

Get import volumes by origin.

Returns:: Import volumes by origin
Return type:: pd.DataFrame

econuy.retrieval.external.trade_imports_origin_price() → DataFrame[source]

Get import prices by origin.

Returns:: Import prices by origin
Return type:: pd.DataFrame

econuy.retrieval.external.trade_balance(pipeline: Pipeline | None = None) → DataFrame[source]

Get net trade balance data by country/region.

Parameters:: pipeline (econuy.core.Pipeline or None, default None) – An instance of the econuy Pipeline class.
Returns:: Net trade balance value by region/country
Return type:: pd.DataFrame

econuy.retrieval.external.terms_of_trade(pipeline: Pipeline | None = None) → DataFrame[source]

Get terms of trade.

Parameters:: pipeline (econuy.core.Pipeline or None, default None) – An instance of the econuy Pipeline class.
Returns:: Terms of trade (exports/imports)
Return type:: pd.DataFrame

econuy.retrieval.external.commodity_prices() → DataFrame[source]

Get commodity prices for Uruguay.

Returns:: Commodity prices – Prices and price indexes of relevant commodities for Uruguay.
Return type:: pd.DataFrame

econuy.retrieval.external.commodity_index(pipeline: Pipeline | None = None) → DataFrame[source]

Get export-weighted commodity price index for Uruguay.

Parameters:: pipeline (econuy.core.Pipeline or None, default None) – An instance of the econuy Pipeline class.
Returns:: Monthly export-weighted commodity index – Export-weighted average of commodity prices relevant to Uruguay.
Return type:: pd.DataFrame

econuy.retrieval.external.rxr() → DataFrame[source]

Get official (BCU) real exchange rates.

Returns:: Monthly real exchange rates vs select countries/regions – Available: global, regional, extraregional, Argentina, Brazil, US.
Return type:: pd.DataFrame

econuy.retrieval.external.rxr_custom(pipeline: Pipeline | None = None) → DataFrame[source]

Get custom real exchange rates vis-à-vis the US, Argentina and Brazil.

Parameters:: pipeline (econuy.core.Pipeline or None, default None) – An instance of the econuy Pipeline class.
Returns:: Monthly real exchange rates vs select countries – Available: Argentina, Brazil, US.
Return type:: pd.DataFrame

econuy.retrieval.external.balance_of_payments() → DataFrame[source]

Get balance of payments.

Returns:: Quarterly balance of payments
Return type:: pd.DataFrame

econuy.retrieval.external.balance_of_payments_summary(pipeline: Pipeline | None = None) → DataFrame[source]

Get a balance of payments summary and capital flows calculations.

Returns:: Quarterly balance of payments summary
Return type:: pd.DataFrame

econuy.retrieval.external.international_reserves() → DataFrame[source]

Get international reserves data.

Returns:: Daily international reserves
Return type:: pd.DataFrame

econuy.retrieval.external.international_reserves_changes(pipeline: ~econuy.core.Pipeline | None = None, previous_data: ~pandas.core.frame.DataFrame = Empty DataFrame Columns: [] Index: []) → DataFrame[source]

Get international reserves changes data.

Parameters:

pipeline (econuy.core.Pipeline or None, default None) – An instance of the econuy Pipeline class.
previous_data (pd.DataFrame) – A DataFrame representing this dataset used to extract last available dates.

Returns:

Monthly international reserves changes

Return type:

pd.DataFrame

econuy.retrieval.labor.labor_rates() → DataFrame[source]

Get labor market data (LFPR, employment and unemployment).

Returns:: Monthly participation, employment and unemployment rates
Return type:: pd.DataFrame

econuy.retrieval.labor.nominal_wages() → DataFrame[source]

Get nominal general, public and private sector wages data

Returns:: Monthly wages separated by public and private sector
Return type:: pd.DataFrame

econuy.retrieval.labor.hours_worked() → DataFrame[source]

Get average hours worked data.

Returns:: Monthly hours worked
Return type:: pd.DataFrame

econuy.retrieval.labor.labor_rates_persons(pipeline: Pipeline | None = None) → DataFrame[source]

Get labor data, both rates and persons. Extends national data between 1991 and 2005 with data for jurisdictions with more than 5,000 inhabitants.

Parameters:: pipeline (econuy.core.Pipeline or None, default None) – An instance of the econuy Pipeline class.
Returns:: Labor market data
Return type:: pd.DataFrame

econuy.retrieval.labor.real_wages(pipeline: Pipeline | None = None) → DataFrame[source]

Get real wages.

Parameters:: pipeline (econuy.core.Pipeline or None, default None) – An instance of the econuy Pipeline class.
Returns:: Real wages data
Return type:: pd.DataFrame

econuy.retrieval.financial.bank_credit() → DataFrame[source]

Get bank credit data.

Returns:: Monthly credit
Return type:: pd.DataFrame

econuy.retrieval.financial.bank_deposits() → DataFrame[source]

Get bank deposits data.

Returns:: Monthly deposits
Return type:: pd.DataFrame

econuy.retrieval.financial.bank_interest_rates() → DataFrame[source]

Get interest rates data.

Returns:: Monthly interest rates
Return type:: pd.DataFrame

econuy.retrieval.financial.sovereign_risk_index() → DataFrame[source]

Get Uruguayan Bond Index (sovereign risk spreads) data.

Returns:: Uruguayan Bond Index
Return type:: pd.DataFrame

econuy.retrieval.financial.call_rate(driver: WebDriver | None = None) → DataFrame[source]

Get 1-day call interest rate data.

This function requires a Selenium webdriver. It can be provided in the driver parameter, or it will attempt to configure a Chrome webdriver.

Parameters:: driver (selenium.webdriver.chrome.webdriver.WebDriver, default None) – Selenium webdriver for scraping. If None, build a Chrome webdriver.
Returns:: Daily call rate
Return type:: pd.DataFrame

econuy.retrieval.financial.sovereign_bond_yields(driver: WebDriver | None = None) → DataFrame[source]

Get interest rate yield for Uruguayan US-denominated bonds, inflation-linked bonds and peso bonds.

This function requires a Selenium webdriver. It can be provided in the driver parameter, or it will attempt to configure a Chrome webdriver.

Parameters:: driver (selenium.webdriver.chrome.webdriver.WebDriver, default None) – Selenium webdriver for scraping. If None, build a Chrome webdriver.
Returns:: Daily bond yields in basis points
Return type:: pd.DataFrame

econuy.retrieval.income.income_household() → DataFrame[source]

Get average household income.

Returns:: Monthly average household income
Return type:: pd.DataFrame

econuy.retrieval.income.income_capita() → DataFrame[source]

Get average per capita income.

Returns:: Monthly average per capita income
Return type:: pd.DataFrame

econuy.retrieval.global_.global_gdp() → DataFrame[source]

Get seasonally adjusted real quarterly GDP for select countries.

Countries/aggregates are US, EU-27, Japan and China.

Returns:: Quarterly real GDP in seasonally adjusted terms
Return type:: pd.DataFrame

econuy.retrieval.global_.global_stock_markets() → DataFrame[source]

Get stock market index data.

Indexes selected are S&P 500, Euronext 100, Nikkei 225 and Shanghai Composite.

Returns:: Daily stock market index in USD
Return type:: pd.DataFrame

econuy.retrieval.global_.global_policy_rates() → DataFrame[source]

Get central bank policy interest rates data.

Countries/aggregates selected are US, Euro Area, Japan and China.

Returns:: Daily policy interest rates
Return type:: pd.DataFrame

econuy.retrieval.global_.global_nxr() → DataFrame[source]

Get currencies data.

Selected currencies are the US dollar index, USDEUR, USDJPY and USDCNY.

Returns:: Daily currencies
Return type:: pd.DataFrame

econuy.retrieval.regional.regional_gdp(driver: WebDriver = None) → DataFrame[source]

Get seasonally adjusted real GDP for Argentina and Brazil.

This function requires a Selenium webdriver. It can be provided in the driver parameter, or it will attempt to configure a Chrome webdriver.

Parameters:: driver (selenium.webdriver.chrome.webdriver.WebDriver, default None) – Selenium webdriver for scraping. If None, build a Chrome webdriver.
Returns:: Quarterly real GDP
Return type:: pd.DataFrame

econuy.retrieval.regional.regional_monthly_gdp() → DataFrame[source]

Get monthly GDP data.

Countries/aggregates selected are Argentina and Brazil.

Returns:: Monthly GDP
Return type:: pd.DataFrame

econuy.retrieval.regional.regional_cpi() → DataFrame[source]

Get consumer price index for Argentina and Brazil.

Returns:: Monthly CPI
Return type:: pd.DataFrame

econuy.retrieval.regional.regional_embi_spreads() → DataFrame[source]

Get EMBI spread for Argentina, Brazil and the EMBI Global.

Returns:: Daily 10-year government bond spreads
Return type:: pd.DataFrame

econuy.retrieval.regional.regional_embi_yields(pipeline: Pipeline | None = None) → DataFrame[source]

Get EMBI yields for Argentina, Brazil and the EMBI Global.

Yields are calculated by adding EMBI spreads to the 10-year US Treasury bond rate.

Parameters:: pipeline (econuy.core.Pipeline or None, default None) – An instance of the econuy Pipeline class.
Returns:: Daily 10-year government bonds interest rates
Return type:: pd.DataFrame

econuy.retrieval.regional.regional_nxr() → DataFrame[source]

Get USDARS and USDBRL.

Returns:: Daily exchange rates
Return type:: pd.DataFrame

econuy.retrieval.regional.regional_policy_rates() → DataFrame[source]

Get central bank policy interest rates data.

Countries/aggregates selected are Argentina and Brazil.

Returns:: Daily policy interest rates
Return type:: pd.DataFrame

econuy.retrieval.regional.regional_stock_markets() → DataFrame[source]

Get stock market index data in USD terms.

Indexes selected are MERVAL and BOVESPA.

Parameters:: pipeline (econuy.core.Pipeline or None, default None) – An instance of the econuy Pipeline class.
Returns:: Daily stock market index in USD terms
Return type:: pd.DataFrame

econuy.retrieval.regional.regional_rxr(pipeline: Pipeline | None = None) → DataFrame[source]

Get real exchange rates vis-á-vis the US dollar for Argentina and Brasil .

Returns:: Monthly real exchange rate
Return type:: pd.DataFrame

Transformation functions

econuy.transform.resample(df: DataFrame, rule: DateOffset | Timedelta | str, operation: str = 'sum', interpolation: str = 'linear', warn: bool = False) → DataFrame[source]: Resample to target frequencies.

See also

resample

econuy.transform.rolling(df: DataFrame, window: int | None = None, operation: str = 'sum') → DataFrame[source]: Calculate rolling averages or sums.

See also

rolling

econuy.transform.rebase(df: DataFrame, start_date: str | datetime, end_date: str | datetime | None = None, base: int | float = 100.0) → DataFrame[source]: Scale to a period or range of periods.

See also

rebase

econuy.transform.decompose(df: DataFrame, component: str = 'both', method: str = 'x13', force_x13: bool = False, fallback: str = 'loess', outlier: bool = True, trading: bool = True, x13_binary: str | PathLike | None = 'search', search_parents: int = 0, ignore_warnings: bool = True, errors: str = 'raise', **kwargs) → Dict[str, DataFrame] | DataFrame[source]

Apply seasonal decomposition.

By default returns both trend and seasonally adjusted components, unlike the class method referred below.

Utility functions

econuy.utils.operations.load_datasets_info() → Dict[source]

econuy.utils.operations.get_name_from_function() → str[source]

econuy.utils.operations.get_download_sources(name: str) → Dict[source]

Convenience wrapper around pandas.read_sql_query.

Deals with multiindex column names.

Parameters:

con (sqlalchemy.engine.base.Connection) – Connection to SQL database.
command (str, sqlalchemy.sql.Selectable or None, default None) –
Command to pass to pandas.read_sql_query. If this parameter is not None, table, cols, start_date and end_date will be ignored.
table_name (str or None, default None) – String representing which table should be retrieved from the database.
cols (str, iterable or None, default None) – Column(s) to retrieve. By default, gets all all columns.
start_date (str or None, default None) – Dates to filter. Inclusive.
end_date (str or None, default None) – Dates to filter. Inclusive.
**kwargs –
Keyword arguments passed to pandas.read_sql_query.

Returns:

SQL queried table

Return type:

pd.DataFrame

econuy.utils.sql.df_to_sql(df: DataFrame, name: str, con: Connection, if_exists: str = 'replace') → None[source]: Flatten MultiIndex index columns before creating SQL table from dataframe.

econuy.utils.sql.insert_csvs(con: Connection, directory: str | Path | PathLike) → None[source]: Insert all CSV files in data directory into a SQL database.