STRLCPY/451-CorporateRiskMiner

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sanctions_and_peps/README.md

1		-	## Folder with Sanctions and PEP lists parsers
	1	+	# Folder with Sanctions and PEP lists parsers
2	2
3		-	`source` directory has raw sanction lists (e.g UN and OFAC)
	3	+	`source` directory has raw sanction lists
4	4
5	5		`parsed` directory contains parced data
6	6
7		-	### How to use
	7	+	## How to use
8	8
9		-	UN sanctions:
	9	+	### UN sanctions:
	10	+
	11	+	[UN list](https://www.un.org/securitycouncil/content/un-sc-consolidated-list)
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11	13		```
12	14		python sanctions/un_parser.py -i "/sanctions_and_peps/source/un.xml" -o "/sanctions_and_peps/parsed/un_parsed.csv"
13	15		```
14	16
15		-	RU BL PEPs:
	17	+	### RU BL PEPs:
16	18
17	19		The data are scraped from [RuPEP](https://rupep.org/en/persons_list/)
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20	22		python sanctions_and_peps/ru_bl_peps_parser.py -o /sanctions_and_peps/parsed/ru_bl_peps_parsed.csv
21	23		```
22	24
23		-	All politicians:
	25	+	### All politicians:
24	26
25	27		The data are scraped from [GitHub](https://raw.githubusercontent.com/everypolitician/everypolitician-data/master/countries.json)
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31	33		The script will automatically scrape the data from the links in the original file what will approximately take 150mb on your disk
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33		-	Navalny list:
	35	+	### Navalny list:
34	36
35	37		The parsed data are taken from [OCCRP](https://www.occrp.org/en/daily/16253-navalny-s-foundation-lists-putin-s-6-000-bribe-takers-and-warmongers)
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