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maj fossil_reserves_OPEC #22
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75
data-preparation/src/data_processing/FOSSIL_RESERVES_opec_fossil_with_zones_prod_2023.py
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,75 @@ | ||
| #Fichier Python permettant de générer : | ||
| #__FOSSIL_RESERVES_bp_fossil_with_zones_prod.csv__# | ||
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| import pandas as pd | ||
| import numpy as np | ||
| from utils.translation import CountryTranslatorFrenchToEnglish | ||
| from transformation.demographic.countries import StatisticsPerCountriesAndZonesJoiner | ||
| from utils.format import StatisticsDataframeFormatter | ||
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| # Charger donnée "country_groups.csv" | ||
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| # Charger données OPEC -> pas API disponible | ||
| # site source Maj annuelle : https://publications.opec.org/asb | ||
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| class OpecFossilProvenReservesCleaner: | ||
| def __init__(self) : | ||
| pass | ||
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| def rename_column(self, df) : | ||
| return df.rename(columns={'Unnamed: 0': 'country'}) | ||
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| def drop_unnecessary_lines(self, df) : | ||
| """ | ||
| Drop footnotes not necessary for data processing | ||
| """ | ||
| df.set_index('country', inplace=True) | ||
| df = df.loc[:"Total World", :] | ||
| df.drop(['Africa', 'Latin America', 'Other Asia', 'Other Eurasia', 'Middle East', 'OECD Europe', 'OECD Asia Pacific', 'OECD Americas', 'Others', 'Other Europe', 'Total World'], axis = 0, inplace = True) | ||
| return df | ||
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| def mb_to_gb_scale(self, df) : | ||
| df = df.apply(lambda x: round(pd.to_numeric(x, errors='coerce') / 1000, 3)) | ||
| return df | ||
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| def translate_country(self, df) : | ||
| df = df.reset_index() | ||
| df["country"] = CountryTranslatorFrenchToEnglish().run(df["country"], raise_errors=False) | ||
| return df | ||
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| def column_to_line(self, df) : | ||
| df = pd.melt(df, | ||
| id_vars = "country", | ||
| var_name = "year", | ||
| value_name = "proven_reserves") | ||
| df.dropna(inplace = True) | ||
| return df | ||
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| def stat_per_countries_zones_join(self, df, country) : | ||
| list_col_group_by = ['group_type', 'group_name', 'year'] | ||
| dict_agg = {"proven_reserves" : "sum"} | ||
| df = StatisticsPerCountriesAndZonesJoiner().run(df, country, list_col_group_by, dict_agg) | ||
| return df | ||
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| def stat_df_formatter(self, df) : | ||
| col_statistics = "proven_reserves" | ||
| df = StatisticsDataframeFormatter().select_and_sort_values(df, col_statistics) | ||
| df["energy_source"] = "Oil" | ||
| df["proven_reserves_unit"] = "Gb" | ||
| df = df[["group_type", "group_name", "energy_source", "year", "proven_reserves", "proven_reserves_unit"]] | ||
| return df | ||
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| def clean_data(self, df, country): | ||
| df = self.rename_column(df) | ||
| df = self.drop_unnecessary_lines(df) | ||
| df = self.mb_to_gb_scale(df) | ||
| df = self.translate_country(df) | ||
| df = self.column_to_line(df) | ||
| df = self.stat_per_countries_zones_join(df, country) | ||
| df = self.stat_df_formatter(df) | ||
| return df | ||
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| # Application class | ||
| cleaner = OpecFossilProvenReservesCleaner() | ||
| df_new = cleaner.clean_data(df, country) | ||
| df_new | ||
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J'ajouterais aussi un commentaire pour dire où se trouve ces lignes vides et les notes de bas de page. Et dans la docstring, j'expliquerais aussi pourquoi on exclut certains continents et certains groupements de pays, car sur le site on peut filtrer sur ce type de zones géographiques. Ça nous évitera aussi de nous reposer la question dans quelques mois :)
Ça donnerait quelque chose comme ça :
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C'est plus clair tu as raison merci