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Source channel @devilsbelow · Post #445 · Jan 13
🌐Weekly News Digest [ January 5 – January 11 ] That was the first full-fledged week of the new year of 2026, during which rulers forgave those who polluted their land, dismissed those who were managing their oil. 💡Here are the key highlights: 🇧🇼 Botswana — Botswana Invites Russia to Invest in Its Mining Sector 🇨🇩 DR Congo — Congolese clergy speaks against the US-DRC agreement. — The government allows processing units to accept ore from artisanal miners amid protests 🇬🇶 Equatorial Guinea — Equatorial Guinea moves its capital to a brand new city built on oil revenues 🇬🇭 Ghana — A Ghanaian prophet predicts the discovery of major onshore oil deposits in Ghana — Ghana hopes to keep its oil fields viable until 2040 🇲🇱 Mali — JNIM militants attack a gold mine in southeastern Mali 🇳🇪 Niger — Niger replaces its oil minister 🇳🇬 Nigeria — President reshuffles the country's oil sector management 🇸🇩 Sudan — Sudan’s central bank and Sudanese Mineral Resources Company set up a joint commission to curb illegal gold exports. 🇺🇬 Uganda — Uganda to start its first oil exports by October, despite environmental concerns 🇿🇲 Zambia — First report on the toxic pollution caused by a Chinese company designates 160 people as victims — Zambia is concerned over the safety of its workers in southern DRC #NewsDigest ➡️ Follow to stay informed - @devilsbelow
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@djangoproject · Post #316 · 04/28/2017, 06:09 AM
https://github.com/blissnd/easyxls Convert any #spreadsheet into a Python internal #dict/#array data structure, for easy processing. Can also handle pivot tables. For pivot table usage, header_row_start & header_col_start need to be set equal to the top left corner of the pivot table => header_row_start=8, header_col_start='c' in the included example. Column IDs must always be lowercase chars in quotes, e.g. 'a'.
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@djangoproject · Post #129 · 08/31/2016, 03:36 PM
https://pypi.python.org/pypi/numpy #NumPy is a general-purpose #array-processing package designed to efficiently manipulate large #multi-dimensional arrays of arbitrary records without sacrificing too much speed for small multi-dimensional #arrays. NumPy is built on the #Numeric code base and adds features introduced by #numarray as well as an extended #C-API and the ability to create arrays of arbitrary type which also makes NumPy suitable for interfacing with general-purpose #data-base applications.