david-andrew

Search: tariff
--------------------------------- text results: ---------------------------------
>>>>>>>>>>>>>>>>>>>>>>>>>>>>
score: 324.95238095238096
name: TM.TAX.MRCH.IP.ZS;
display name: Share of tariff lines with international peaks, all products (%);
description: Share of tariff lines with international peaks is the share of lines in the tariff schedule with tariff rates that exceed 15 percent. It provides an indication of how selectively tariffs are applied.;
unit: %;
unit description: %;
<<<<<<<<<<<<<<<<<<<<<<<<<<<<

david-andrew

{
    "Node(name='wm', examples=())": {
        "text": [
            [
                "name: TM.TAX.MRCH.WM.AR.ZS;\ndisplay name: Tariff rate, applied, weighted mean, all products (%);\ndescription: Weighted mean applied tariff is the average of effectively applied rates weighted by the product import shares corresponding to each partner country. Data are classified using the Harmonized System of trade at the six- or eight-digit level. Tariff line data were matched to Standard International Trade Classification (SITC) revision 3 codes to define commodity groups and import weights. To the extent possible, specific rates have been converted to their ad valorem equivalent rates and have been included in the calculation of weighted mean tariffs. Import weights were calculated using the United Nations Statistics Division's Commodity Trade (Comtrade) database. Effectively applied tariff rates at the six- and eight-digit product level are averaged for products in each commodity group. When the effectively applied 

david-andrew

---------------------------------------------------------
data/transition_reports/cb4410en.pdf
(page [0]) Food and Agriculture Organization
(page [0]) of the United Nations
(page [0]) FAOSTAT ANALYTICAL BRIEF 19
(page [0]) Temperature change statistics
(page [0]) 1961–2020
(page [0]) Global, regional and country trends
(page [0]) ISSN 2709-006X [Print] ISSN 2709-0078 [Online]
(page [1]) Temperature change statistics 1961 –2020 - Global, regional and country trends

david-andrew

(chatty) david@blade15:~/dev/askem/data-service$ sudo docker-compose logs
Attaching to data-service-api, data-service_graphdb_1, data-service-rdb, data-service_minio_1
data-service-api | Skipping virtualenv creation, as specified in config file.
data-service-api | 
data-service-api | tds is not a package.
data-service-api | Skipping virtualenv creation, as specified in config file.
data-service-api | 
data-service-api | tds is not a package.
data-service-api | Skipping virtualenv creation, as specified in config file.
data-service-api | 

david-andrew

from typing import TypeVar
from typing_extensions import ParamSpec
_R_co = TypeVar("_R_co", covariant=True)
_P = ParamSpec("_P")

# decorated functions will mantain identical typing to their original form
def myDecorator(func: Callable[_P, _R_co]) -> Callable[_P, _R_co]:
    def wrapper(*args, **kwargs):
        # do wrapper stuff
        return func(*args, **kwargs)

david-andrew

43.19   Building wheel for GDAL (setup.py): started
45.54   Building wheel for GDAL (setup.py): finished with status 'error'
45.60   error: subprocess-exited-with-error
45.60   
45.60   × python setup.py bdist_wheel did not run successfully.
45.60   │ exit code: 1
45.60   ╰─> [782 lines of output]
45.60       running bdist_wheel
45.60       running build
45.60       running build_py

david-andrew

>>> how is climate change expected to affect flooding in ethiopia?
|||| Context free query: climate change impact on flooding in Ethiopia

Answer: Climate change is expected to affect flooding in Ethiopia in several ways. Primarily, it will likely lead to an increased frequency and intensity of extreme hydrologic events, causing more pronounced disastrous floods which can negatively impact the economy and society [0][7]. The country is susceptible to floods, and past events have shown considerable loss of life and property [1]. The median temperature increase for Africa is predicted to be 3-4°C by the end of the 21st century, possibly intensifying evapotranspiration, which may negate any benefits from increased rainfall, thus exacerbating drought and flood conditions [1].

Moreover, variability in rainfall is expected to increase due to climate change, resulting in more frequent droughts and floods [9]. These changes threaten the stability and transformation of Ethiopia's agricultural sector, which is heavily

david-andrew

Meta(path=PosixPath('datasets/mock_aqi.csv'), name='Air Quality Index', description='This dataset represents daily air quality observations collected from various monitoring stations across different cities worldwide. Each row corresponds to a single observation with details about the date, time, and location of the observation, along with specific air quality metrics and conditions.')
LLM identified column "year" as a DATE
LLM identified column "month" as a DATE
LLM identified column "day" as a DATE
LLM identified column "time" as a DATE
LLM identified column "lat" as a GEO
LLM identified column "lon" as a GEO
LLM identified column "country" as a GEO
LLM identified column "admin1" as a GEO
LLM identified column "admin2" as a GEO

david-andrew

geo=[GeoAnnotation(name='iso', display_name=None, description='The values in the dataset represent the ISO 3166-1 numeric country codes, which are internationally recognized codes assigned to each country and certain territories. In this context, the number 854 corresponds to Burkina Faso. These codes are used for data exchange and to increase clarity and ensure unambiguity when identifying countries on a global scale.', type=<ColumnType.GEO: 'geo'>, geo_type=<GeoType.COUNTRY: 'country'>, primary_geo=None, resolve_to_gadm=None, is_geo_pair=None, coord_format=None, qualifies=None, aliases={}, gadm_level=None), GeoAnnotation(name='region', display_name=None, description='This dataset categorizes events based on their geographic location within the continent of Africa, specifically focusing on the sub-region of Western Africa. This area includes countries along the Atlantic coast, from the Sahara Desert in the north to the Gulf of Guinea in the south.', type=<ColumnType.GEO: 'geo'>, geo_type=<GeoType.COUNTRY: 'c

david-andrew

Meta(path=PosixPath('datasets/mock_aqi.csv'), name='Air Quality Index', description='This dataset represents daily air quality observations collected from various monitoring stations across different cities worldwide. Each row corresponds to a single observation with details about the date, time, and location of the observation, along with specific air quality metrics and conditions.')
LLM identified column "year" as a DATE
LLM identified column "month" as a DATE
LLM identified column "day" as a DATE
LLM identified column "time" as a DATE
LLM identified column "lat" as a GEO
LLM identified column "lon" as a GEO
LLM identified column "country" as a GEO
LLM identified column "admin1" as a GEO
LLM identified column "admin2" as a GEO