Convert some data into Panda DataFrames
Project description
Convert some data into Panda DataFrames
British Petroleum (BP)
It parse sheet like Primary Energy Consumption
(not like Primary Energy - Cons by fuel
).
Open: http://www.bp.com/statisticalreview or https://www.bp.com/en/global/corporate/energy-economics/statistical-review-of-world-energy.html
Download Statistical Review of World Energy – all data
.
Use:
from shifter_pandas.bp import UNITS_ENERGY, BPDatasource
shifter_ds = BPDatasource("bp-stats-review-2021-all-data.xlsx")
df = shifter_ds.datasource(units_filter=UNITS_ENERGY, regions_filter=["Switzerland"])
df
Swiss Office Federal of Statistics (OFS)
From https://www.bfs.admin.ch/bfs/fr/home/services/recherche/stat-tab-donnees-interactives.html create a stat table.
Click on À propos du tableau
Click on Rendez ce tableau disponible dans votre application
Use:
from shifter_pandas.ofs import OFSDatasource
shifter_ds = OFSDatasource("<URL>")
df = shifter_ds.datasource(<Requête Json>)
df
And replace <URL>
and <Requête Json>
with the content of the fields of the OFS web page.
Interesting sources
- Parc de motocycles par caractéristiques techniques et émissions
- Bilan démographique selon l'âge et le canton
Our World in Data
Select a publication.
Click Download
.
Click Full data (CSV)
.
Use:
import pandas as pd
from shifter_pandas.wikidata_ import WikidataDatasource
df_owid = pd.read_csv("<file name>")
wdds = WikidataDatasource()
df_wd = wdds.datasource_code(wikidata_id=True, wikidata_name=True, wikidata_type=True)
df = pd.merge(df_owid, df_wd, how="inner", left_on='iso_code', right_on='Code')
df
Interesting sources
World Bank
Open https://data.worldbank.org/
Find a chart
In Download
click CSV
Use:
from shifter_pandas.worldbank import wbDatasource
df = wbDatasource("<file name>")
df
Interesting sources
Wikidata
By providing the wikidata_*
parameters, you can ass some data from WikiData.
Careful, the WikiData is relatively slow then the first time you run it il will be slow. We use a cache to make it fast the next times.
You can also get the country list with population and ISO 2 code with:
from shifter_pandas.wikidata_ import (
ELEMENT_COUNTRY,
PROPERTY_ISO_3166_1_ALPHA_2,
PROPERTY_POPULATION,
WikidataDatasource,
)
shifter_ds = WikidataDatasource()
df = shifter_ds.datasource(
instance_of=ELEMENT_COUNTRY,
with_id=True,
with_name=True,
properties=[PROPERTY_ISO_3166_1_ALPHA_2, PROPERTY_POPULATION],
limit=1000,
)
df
Contributing
Install the pre-commit hooks:
pip install pre-commit
pre-commit install --allow-missing-config
Project details
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