From 65154d6bc9ac01a3218b3cb03708b4c004e37e37 Mon Sep 17 00:00:00 2001 From: zelima Date: Sat, 7 Mar 2020 16:56:33 +0400 Subject: [PATCH] [All]: Use dataflows to process and normilize data --- README.md | 19 +- data/confirmed.csv | 46 - data/deaths.csv | 46 - data/recovered.csv | 46 - data/regions.csv | 200 - datapackage.json | 3140 +--------- fetch_process_data.py | 36 - process.py | 33 + time_series_19-covid-Confirmed.csv | 8956 ++++++++++++++++++++++++++++ time_series_19-covid-Deaths.csv | 8956 ++++++++++++++++++++++++++++ time_series_19-covid-Recovered.csv | 8956 ++++++++++++++++++++++++++++ 11 files changed, 26983 insertions(+), 3451 deletions(-) delete mode 100644 data/confirmed.csv delete mode 100644 data/deaths.csv delete mode 100644 data/recovered.csv delete mode 100644 data/regions.csv delete mode 100644 fetch_process_data.py create mode 100644 process.py create mode 100644 time_series_19-covid-Confirmed.csv create mode 100644 time_series_19-covid-Deaths.csv create mode 100644 time_series_19-covid-Recovered.csv diff --git a/README.md b/README.md index 516d672..e8fc6dc 100644 --- a/README.md +++ b/README.md @@ -2,9 +2,20 @@ The new strain of Coronavirus has had a worldwide effect. It has effected people from different countries. The dataset provides, a time series data tracking the number of people effected by the virus, how many deaths has the virus caused and the number of reported people who have recovered. -## Running the generator script +## Run the script -The script requires importing the of following packages +We use [dataflows](https://github.com/datahq/dataflows) to process and normalize the data -* pandas -* datapackage +``` +pip install dataflows +``` + +Run the sript + +``` +python process.py +``` + +## Data + +Data is coming from https://githab.com/CSSEGISandData/COVID-19 updated daily. We have normalized data a bit - unpivoted and transfered dates to be more machine readable. diff --git a/data/confirmed.csv b/data/confirmed.csv deleted file mode 100644 index 45be638..0000000 --- a/data/confirmed.csv +++ /dev/null @@ -1,46 +0,0 @@ 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-1007,Guizhou,Mainland China,26.8154,106.8748 -1008,Hainan,Mainland China,19.1959,109.7453 -1009,Hebei,Mainland China,38.0428,114.5149 -1010,Heilongjiang,Mainland China,47.861999999999995,127.7615 -1011,Henan,Mainland China,33.882020000000004,113.61399999999999 -1012,Hubei,Mainland China,30.9756,112.2707 -1013,Hunan,Mainland China,27.6104,111.7088 -1014,Inner Mongolia,Mainland China,44.0935,113.9448 -1015,Jiangsu,Mainland China,32.9711,119.455 -1016,Jiangxi,Mainland China,27.614,115.7221 -1017,Jilin,Mainland China,43.6661,126.1923 -1018,Liaoning,Mainland China,41.2956,122.6085 -1019,Ningxia,Mainland China,37.2692,106.1655 -1020,Qinghai,Mainland China,35.7452,95.9956 -1021,Shaanxi,Mainland China,35.1917,108.8701 -1022,Shandong,Mainland China,36.3427,118.1498 -1023,Shanghai,Mainland China,31.201999999999998,121.4491 -1024,Shanxi,Mainland China,37.5777,112.2922 -1025,Sichuan,Mainland China,30.6171,102.7103 -1026,Tianjin,Mainland China,39.3054,117.323 -1027,Tibet,Mainland China,31.6927,88.0924 -1028,Xinjiang,Mainland China,41.1129,85.2401 -1029,Yunnan,Mainland China,24.974,101.48700000000001 -1030,Zhejiang,Mainland China,29.1832,120.0934 -1031,,Thailand,15.0,101.0 -1032,,Japan,36.0,138.0 -1033,,South Korea,36.0,128.0 -1034,Taiwan,Taiwan,23.7,121.0 -1035,"King County, WA",US,47.6062,-122.3321 -1036,"Cook County, IL",US,41.7377,-87.6976 -1037,"Tempe, AZ",US,33.4255,-111.94 -1038,Macau,Macau,22.1667,113.55 -1039,Hong Kong,Hong Kong,22.3,114.2 -1040,,Singapore,1.2833,103.8333 -1041,,Vietnam,16.0,108.0 -1042,,France,47.0,2.0 -1043,,Nepal,28.1667,84.25 -1044,,Malaysia,2.5,112.5 -1045,"Toronto, ON",Canada,43.6532,-79.3832 -1046,British Columbia,Canada,49.2827,-123.1207 -1047,"Los Angeles, CA",US,34.0522,-118.2437 -1048,New South Wales,Australia,-33.8688,151.2093 -1049,Victoria,Australia,-37.8136,144.9631 -1050,Queensland,Australia,-28.0167,153.4 -1051,,Cambodia,11.55,104.9167 -1052,,Sri Lanka,7.0,81.0 -1053,,Germany,51.0,9.0 -1054,,Finland,64.0,26.0 -1055,,United Arab Emirates,24.0,54.0 -1056,,Philippines,13.0,122.0 -1057,,India,21.0,78.0 -1058,"London, ON",Canada,42.9849,-81.2453 -1059,,Italy,43.0,12.0 -1060,,UK,55.0,-3.0 -1061,,Russia,60.0,90.0 -1062,,Sweden,63.0,16.0 -1063,"Santa Clara, CA",US,37.3541,-121.9552 -1064,,Spain,40.0,-4.0 -1065,South Australia,Australia,-34.9285,138.6007 -1066,"San Benito, CA",US,36.5761,-120.9876 -1067,,Belgium,50.8333,4.0 -1068,"Madison, WI",US,43.0731,-89.4012 -1069,Diamond Princess cruise ship,Others,35.4437,139.638 -1070,"San Diego County, CA",US,32.7157,-117.1611 -1071,"San Antonio, TX",US,29.4241,-98.4936 -1072,,Egypt,26.0,30.0 -1073,,Iran,32.0,53.0 -1074,"Omaha, NE (From Diamond Princess)",US,41.2545,-95.9758 -1075,"Travis, CA (From Diamond Princess)",US,38.2721,-121.9399 -1076,From Diamond Princess,Australia,35.4437,139.638 -1077,"Lackland, TX (From Diamond Princess)",US,29.3829,-98.6134 -1078,,Lebanon,33.8547,35.8623 -1079,"Humboldt County, CA",US,40.745,-123.8695 -1080,"Sacramento County, 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Australia,Australia,-31.9505,115.8605 -1111,,Ireland,53.1424,-7.6921 -1112,,Luxembourg,49.8153,6.1296 -1113,,Monaco,43.7333,7.4167 -1114,,Qatar,25.3548,51.1839 -1115,"Snohomish County, WA",US,48.033,-121.8339 -1116,,Ecuador,-1.8312,-78.1834 -1117,,Azerbaijan,40.1431,47.5769 -1118,,Czech Republic,49.8175,15.472999999999999 -1119,,Armenia,40.0691,45.0382 -1120,,Dominican Republic,18.7357,-70.1627 -1121,"Providence, RI",US,41.824,-71.4128 -1122,,Indonesia,-0.7893,113.9213 -1123,,Portugal,39.3999,-8.2245 -1124,,Andorra,42.5063,1.5218 -1125,Tasmania,Australia,-41.4545,145.9707 -1126,,Latvia,56.8796,24.6032 -1127,,Morocco,31.7917,-7.0926 -1128,,Saudi Arabia,24.0,45.0 -1129,,Senegal,14.4974,-14.4524 -1130,"Grafton County, NH",US,43.9088,-71.82600000000001 -1131,"Hillsborough, FL",US,27.9904,-82.3018 -1132,"Placer County, CA",US,39.0916,-120.8039 -1133,"San Mateo, CA",US,37.563,-122.3255 -1134,"Sarasota, FL",US,27.3364,-82.5307 -1135,"Sonoma County, CA",US,38.578,-122.9888 -1136,"Umatilla, 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NJ",US,40.9263,-74.077 -1162,"Harris County, TX",US,29.7752,-95.3103 -1163,"San Francisco County, CA",US,37.7749,-122.4194 -1164,,South Africa,-30.5595,22.9375 -1165,"Clark County, NV",US,36.0796,-115.094 -1166,"Fort Bend County, TX",US,29.5693,-95.8143 -1167,"Grant County, WA",US,47.1981,-119.3732 -1168,"Santa Rosa County, FL",US,30.769000000000002,-86.9824 -1169,"Williamson County, TN",US,35.9179,-86.8622 -1170,"New York County, NY",US,40.7128,-74.006 -1171,"Unassigned Location, WA",US,47.7511,-120.7401 -1172,"Montgomery County, MD",US,39.1547,-77.2405 -1173,"Suffolk County, MA",US,42.3601,-71.0589 -1174,"Denver County, CO",US,39.7392,-104.9903 -1175,"Summit County, CO",US,39.5912,-106.064 -1176,,Bhutan,27.5142,90.4336 -1177,,Cameroon,3.8480000000000003,11.5021 -1178,"Calgary, Alberta",Canada,51.0447,-114.0719 -1179,,Colombia,4.5709,-74.2973 -1180,,Costa Rica,9.7489,-83.7534 -1181,,Peru,-9.19,-75.0152 -1182,,Serbia,44.0165,21.0059 -1183,,Slovakia,48.669,19.699 -1184,,Togo,8.6195,0.8248 -1185,"Chatham County, NC",US,35.7211,-79.1781 -1186,"Delaware County, PA",US,39.9078,-75.3879 -1187,"Douglas County, NE",US,41.3148,-96.1951 -1188,"Fayette County, KY",US,38.0606,-84.4803 -1189,"Floyd County, GA",US,34.2829,-85.2308 -1190,"Marion County, IN",US,39.8362,-86.1752 -1191,"Middlesex County, MA",US,42.4672,-71.2874 -1192,"Nassau County, NY",US,40.6546,-73.5594 -1193,"Norwell County, MA",US,42.1615,-70.7928 -1194,"Ramsey County, MN",US,44.9964,-93.0616 -1195,"Washoe County, NV",US,40.5608,-119.6035 -1196,"Wayne County, PA",US,41.6739,-75.2479 -1197,"Yolo County, CA",US,38.7646,-121.9018 -1198,,Vatican City,41.9029,12.4534 diff --git a/datapackage.json b/datapackage.json index baee9fe..066065c 100644 --- a/datapackage.json +++ b/datapackage.json @@ -1,3075 +1,69 @@ { - "profile": "tabular-data-package", - "resources": [ - { - "path": "data\\confirmed.csv", - "profile": "tabular-data-resource", - "name": "confirmed", - "format": "csv", - "mediatype": 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} - ], - "missingValues": [ - "" - ] - } - } - ] + "bytes": 419065, + "count_of_rows": 8955, + "hash": "e96470246df7974be1ac04dc880b3799", + "profile": "data-package", + "resources": [ + { + "bytes": 419065, + "dialect": { + "caseSensitiveHeader": false, + "delimiter": ",", + "doubleQuote": true, + "header": true, + "lineTerminator": "\r\n", + "quoteChar": "\"", + "skipInitialSpace": false + }, + "encoding": "utf-8", + "format": "csv", + "hash": "8697a80eb83feaf11c7041b2724eaa91", + "name": "time_series_19-covid-Recovered", + "path": "time_series_19-covid-Recovered.csv", + "profile": "tabular-data-resource", + "schema": { + "fields": [ + { + "format": "default", + "name": "Province/State", + "type": "string" + }, + { + "format": "default", + "name": "Country/Region", + "type": "string" + }, + { + "decimalChar": ".", + "format": "default", + "groupChar": "", + "name": "Lat", + "type": "number" + }, + { + "decimalChar": ".", + "format": "default", + "groupChar": "", + "name": "Long", + "type": "number" + }, + { + "format": "%Y-%m-%d", + "name": "date", + "type": "date" + }, + { + "decimalChar": ".", + "format": "default", + "groupChar": "", + "name": "case", + "type": "number" + } + ], + "missingValues": [ + "" + ] + } + } + ] } \ No newline at end of file diff --git a/fetch_process_data.py b/fetch_process_data.py deleted file mode 100644 index 6a21b15..0000000 --- a/fetch_process_data.py +++ /dev/null @@ -1,36 +0,0 @@ -import csv -import pandas as pd -import datapackage - -URL = 'https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/' -CONFIRMED = 'time_series_19-covid-Confirmed.csv' -DEATH = 'time_series_19-covid-Deaths.csv' -RECOVERED = 'time_series_19-covid-Recovered.csv' - -confirmed_cases = pd.read_csv(URL+CONFIRMED) -total_deaths = pd.read_csv(URL+DEATH) -total_recovered = pd.read_csv(URL+RECOVERED) - -def generate_region_file(): - subset = confirmed_cases.loc[:,'Province/State':'Long'] - subset.insert(0, 'ID', range(1000, 1000 + len(subset))) - subset.to_csv('data/regions.csv', index = False) - -def process_data(input_df, output_filename): - output = 'data/' + output_filename - subset = input_df.loc[:,'1/22/20':] - subset.insert(0, 'ID', range(1000, 1000 + len(subset))) - subset = subset.set_index('ID').T - subset = subset.rename(columns = {'ID':'Date'}) - subset.to_csv(output, index = False) - -def generate_data_package(): - package = datapackage.Package() - package.infer('data/*.csv') - package.save('datapackage.json') - -generate_region_file() -process_data(confirmed_cases, 'confirmed.csv') -process_data(total_deaths, 'deaths.csv') -process_data(total_recovered, 'recovered.csv') -generate_data_package() diff --git a/process.py b/process.py new file mode 100644 index 0000000..04e7673 --- /dev/null +++ b/process.py @@ -0,0 +1,33 @@ +import csv +from dataflows import Flow, load, unpivot, find_replace, set_type, dump_to_path +import datapackage + +BASE_URL = 'https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/' +CONFIRMED = 'time_series_19-covid-Confirmed.csv' +DEATH = 'time_series_19-covid-Deaths.csv' +RECOVERED = 'time_series_19-covid-Recovered.csv' + +def to_normal_date(row): + old_date = row['date'] + month, day, year = row['date'].split('-') + day = f'0{day}' if len(day) == 1 else day + month = f'0{month}' if len(month) == 1 else month + row['date'] = '-'.join([day, month, year]) + +unpivoting_fields = [ + { 'name': '([0-9]+\/[0-9]+\/[0-9]+)', 'keys': {'date': r'\1'} } +] + +extra_keys = [{'name': 'date', 'type': 'string'} ] +extra_value = {'name': 'case', 'type': 'string'} + +for case in [CONFIRMED, DEATH, RECOVERED]: + Flow( + load(f'{BASE_URL}{case}'), + unpivot(unpivoting_fields, extra_keys, extra_value), + find_replace([{'name': 'date', 'patterns': [{'find': '/', 'replace': '-'}]}]), + to_normal_date, + set_type('date', type='date', format='%d-%m-%y'), + set_type('case', type='number'), + dump_to_path() + ).results()[0] diff --git a/time_series_19-covid-Confirmed.csv b/time_series_19-covid-Confirmed.csv new file mode 100644 index 0000000..381434a --- /dev/null +++ b/time_series_19-covid-Confirmed.csv @@ -0,0 +1,8956 @@ +Province/State,Country/Region,Lat,Long,date,case +Anhui,Mainland China,31.8257,117.2264,2020-01-22,1 +Anhui,Mainland China,31.8257,117.2264,2020-01-23,9 +Anhui,Mainland China,31.8257,117.2264,2020-01-24,15 +Anhui,Mainland China,31.8257,117.2264,2020-01-25,39 +Anhui,Mainland China,31.8257,117.2264,2020-01-26,60 +Anhui,Mainland China,31.8257,117.2264,2020-01-27,70 +Anhui,Mainland China,31.8257,117.2264,2020-01-28,106 +Anhui,Mainland China,31.8257,117.2264,2020-01-29,152 +Anhui,Mainland China,31.8257,117.2264,2020-01-30,200 +Anhui,Mainland China,31.8257,117.2264,2020-01-31,237 +Anhui,Mainland China,31.8257,117.2264,2020-02-01,297 +Anhui,Mainland China,31.8257,117.2264,2020-02-02,340 +Anhui,Mainland China,31.8257,117.2264,2020-02-03,408 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+"Floyd County, GA",US,34.2829,-85.2308,2020-03-03,0 +"Floyd County, GA",US,34.2829,-85.2308,2020-03-04,0 +"Floyd County, GA",US,34.2829,-85.2308,2020-03-05,0 +"Floyd County, GA",US,34.2829,-85.2308,2020-03-06,0 +"Marion County, IN",US,39.8362,-86.1752,2020-01-22,0 +"Marion County, IN",US,39.8362,-86.1752,2020-01-23,0 +"Marion County, IN",US,39.8362,-86.1752,2020-01-24,0 +"Marion County, IN",US,39.8362,-86.1752,2020-01-25,0 +"Marion County, IN",US,39.8362,-86.1752,2020-01-26,0 +"Marion County, IN",US,39.8362,-86.1752,2020-01-27,0 +"Marion County, IN",US,39.8362,-86.1752,2020-01-28,0 +"Marion County, IN",US,39.8362,-86.1752,2020-01-29,0 +"Marion County, IN",US,39.8362,-86.1752,2020-01-30,0 +"Marion County, IN",US,39.8362,-86.1752,2020-01-31,0 +"Marion County, IN",US,39.8362,-86.1752,2020-02-01,0 +"Marion County, IN",US,39.8362,-86.1752,2020-02-02,0 +"Marion County, IN",US,39.8362,-86.1752,2020-02-03,0 +"Marion County, IN",US,39.8362,-86.1752,2020-02-04,0 +"Marion County, 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