hdx-python-scraper 1.2.8

HDX Python Scraper Library

The HDX Python Scraper Library is designed to enable you to easily develop code that assembles data from one or more sources.

If you have humanitarian-related data, please upload your datasets to HDX.

Usage

The library has detailed API documentation which can be found here: http://ocha-dap.github.io/hdx-python-scraper/. The code for the library is here: https://github.com/ocha-dap/hdx-python-scraper.

To use the optional functions for outputting data from Pandas to JSON, Excel etc., install with:

pip install hdx-python-scraper[pandas]

Scraper Framework Configuration

The following is an example of how the framework is set up:

today = datetime.now()
adminone = AdminOne(configuration)
population_lookup = dict()
headers, columns, sources = get_tabular(configuration, ['AFG'], adminone, 'national', downloader, today=today, scrapers=['population'], population_lookup=population_lookup)
assert headers == [['Population'], ['#population']]
assert columns == [{'AFG': 38041754}]
assert sources == [('#population', '2020-10-01', 'World Bank', 'https://data.humdata.org/organization/world-bank-group')]
results = get_tabular(configuration, ['AFG'], adminone, 'national', downloader, today=today, scrapers=['who'], population_lookup=population_lookup)
assert results['headers'] == [['CasesPer100000', 'DeathsPer100000', 'Cases2Per100000', 'Deaths2Per100000'], ['#affected+infected+per100000', '#affected+killed+per100000', '#affected+infected+2+per100000', '#affected+killed+2+per100000']]
assert results['values']  == [{'AFG': '96.99'}, {'AFG': '3.41'}, {'AFG': '96.99'}, {'AFG': '3.41'}]
assert results['sources'] == [('#affected+infected+per100000', '2020-08-06', 'WHO', 'tests/fixtures/WHO-COVID-19-global-data.csv'), ('#affected+killed+per100000', '2020-08-06', 'WHO', 'tests/fixtures/WHO-COVID-19-global-data.csv'), ('#affected+infected+2+per100000', '2020-08-06', 'WHO', 'tests/fixtures/WHO-COVID-19-global-data.csv'), ('#affected+killed+2+per100000', '2020-08-06', 'WHO', 'tests/fixtures/WHO-COVID-19-global-data.csv')]

The first parameter is a configuration. More on this later.

The second parameter is a list of country iso3s. The third is an AdminOne object. More about this can be found in the HDX Python Country library, but briefly that class accepts a configuration as follows:

country_name_mappings defines the country name overrides we use (ie. where we deviate from the names in the OCHA countries and territories file).

country_name_mappings:
  PSE: "occupied Palestinian territory"
  BOL: "Bolivia"

admin_info defines the admin level 1 names and pcodes.

admin_info:
  - {pcode: AF01, name: Kabul, iso2: AF, iso3: AFG, country: Afghanistan}
  - {pcode: AF02, name: Kapisa, iso2: AF, iso3: AFG, country: Afghanistan}

adm_mappings defines mappings from country name to iso3 and from admin 1 name to pcode.

adm_mappings:
  - "Congo DR": "COD"
    "CAR": "CAF"
    "oPt": "PSE"
  - "Nord-Ouest": "HT09"
    "nord-ouest": "HT09"
...

adm1_name_replacements defines some find and replaces that are done to improve automatic admin1 name matching to pcode.

adm1_name_replacements:
  " urban": ""
  "sud": "south"
  "ouest": "west"
...

adm1_fuzzy_ignore defines admin 1 names to ignore.

adm1_fuzzy_ignore:
  - "nord"
...

The third parameter specifies which scraper set to process corresponding to a name in the configuration beginning scraper_ (but without needing the scraper_ part in the parameter).

The population scraper is a special case, so it is read first so that populations can be used by subsequent scrapers in calculations.

Configuration Parameter

The framework is configured by passing in a configuration. Typically this will come from a yaml file such as config/project_configuration.yml.

The configuration should have a mapping from the internal dictionaries to the tabs in the spreadsheet or keys in the JSON output file(s):

tabs:
  world: "WorldData"
  regional: "RegionalData"
  national: "NationalData"
  subnational: "SubnationalData"
  covid_series: "CovidSeries"
  covid_trend: "CovidTrend"
  sources: "Sources"

Then the location of Google spreadsheets are defined, for prod (production), test and scratch:

googlesheets:
  prod: "https://docs.google.com/spreadsheets/d/SPREADSHEET_KEY_PROD/edit"
  test: "https://docs.google.com/spreadsheets/d/SPREADSHEET_KEY_TEST/edit"
  scratch: "https://docs.google.com/spreadsheets/d/SPREADSHEET_KEY_SCRATCH/edit"

The json outputs are then specified. Under the key “additional”, subsets of the full json can be saved as separate files.

json:
  additional_json:
    - name: "Other"
      source: "Some org"
      source_url: "https://data.humdata.org/organization/world-bank-group"
      format: "json"
      url: "https://raw.githubusercontent.com/mcarans/hdx-python-scraper/master/tests/fixtures/additional_json.json"
      jsonpath: "[*]"
  filepath: "test_tabular_all.json"
  additional:
    - filepath: "test_tabular_population.json"
      tabs:
        - tab: "national"
          key: "cumulative"
          filters:
            "#country+code": "{{countries_to_save}}"
          hxltags:
            - "#country+code"
            - "#country+name"
            - "#population"
    - filepath: "test_tabular_population_2.json"
      tabs:
        - tab: "national"
          key: "cumulative"
          filters:
            "#country+code":
              - "AFG"
          hxltags:
            - "#country+code"
            - "#country+name"
            - "#population"
    - filepath: "test_tabular_other.json"
      remove:
        - "national"

Next comes additional sources. This allows additional HXL hashtags to be associated with a dataset date, source and url. In the ones below, the metadata is obtained from datasets on HDX.

additional_sources:
  - indicator: "#food-prices"
    dataset: "wfp-food-prices"
  - indicator: "#vaccination-campaigns"
    dataset: "immunization-campaigns-impacted"

scraper_tabname defines a set of mini scrapers that use the framework and produce data for the tab tabname eg.

scraper_national:
…

Examples

It is helpful to look at a few example mini scrapers to see how they are configured:

The economicindex mini scraper reads the dataset “covid-19-economic-exposure-index” on HDX, taking from it dataset source, date of dataset and using the url of the dataset in HDX as the source url (used by the DATA link in the visual). The scraper framework finds the first resource that is of format “xlsx”, reads the “economic exposure” sheet and looks for the headers in row 1. Note that it is possible to specify a specific resource name using the key "resource" instead of searching for the first resource of a particular format.

adm_cols defines the column or columns in which to look for admin information. As this is a national level scraper, it uses the “Country” column. input_cols specifies the column(s) to use for values, in this case “Covid 19 Economic exposure index”. output_columns and output_hxltags define the header name(s) and HXL tag(s) to use for the input_cols.

 economicindex:
    format: "xlsx"
    dataset: "covid-19-economic-exposure-index"
    sheet: "economic exposure"
    headers: 1
    adm_cols:
      - "Country"
    input_cols:
      - "Covid 19 Economic exposure index"
    output_columns:
      - "EconomicExposure"
    output_hxltags:
      - "#severity+economic+num"

The population mini scraper configuration directly provides metadata for source, source_url and the download location given by url only taking the source date from the dataset. The scraper pulls subnational data so adm_cols defines both a country column “alpha_3” and an admin 1 pcode column “ADM1_PCODE”.

input_transforms defines operations to be performed on each value in the column. In this case, the value is converted to either an int or float if it is possible.

 population:
    source: "Multiple Sources"
    source_url: "https://data.humdata.org/search?organization=worldpop&q=%22population%20counts%22"
    format: "csv"
    url: "https://docs.google.com/spreadsheets/d/e/2PACX-1vS3_uBOV_uRDSxOggfBus_pkCs6Iw9lm0nAxzwG14YF_frpm13WPKiM1oNnQ9zrUA/pub?gid=1565793974&single=true&output=csv"
    dataset: "global-humanitarian-response-plan-covid-19-administrative-boundaries-and-population-statistics"
    headers: 1
    adm_cols:
      - "alpha_3"
      - "ADM1_PCODE"
    input_cols:
      - "POPULATION"
    input_transforms:
      "POPULATION": "get_numeric_if_possible(POPULATION)"
    output_columns:
      - "Population"
    output_hxltags:
      - "#population"

The travel mini scraper reads values from the “info” and “published” columns of the source. append_cols defines any columns where if the same admin appears again (in this case, the same country), then that data is appended to the existing. keep_cols defines any columns where if the same admin appears again, the existing value is kept rather than replaced.

 travel:
    format: "csv"
    dataset: "covid-19-global-travel-restrictions-and-airline-information"
    headers: 1
    adm_cols:
      - "iso3"
    input_cols:
      - "info"
      - "published"
    append_cols:
      - "info"
    keep_cols:
      - "published"
    output_columns:
      - "TravelRestrictions"
      - "TravelRestrictionsPublished"
    output_hxltags:
      - "#severity+travel"
      - "#severity+date+travel"

The gam mini scraper reads from a spreadsheet that has a multiline header (headers defined as rows 3 and 4). Experimentation is often needed with row numbers since in my experience, they are sometimes offset from the real row numbers seen when opening the spreadsheet. date_col defines a column that contains date information and date_type specifies in what form the date information is held eg. as a date, a year or an int. The scraper will for each admin, obtain the data (in this case the “National Point Estimate”) for the latest date up to the current date (unless ignore_future_date is set to False then future dates will be allowed).

 gam:
    format: "xlsx"
    dataset: "world-global-expanded-database-on-severe-wasting"
    sheet: "Trend"
    headers:
      - 3
      - 4
    adm_cols:
      - "ISO"
    date_col: "Year*"
    date_type: "year"
    input_cols:
      - "National Point Estimate"
    output_columns:
      - "Malnutrition Estimate"
    output_hxltags:
      - "#severity+malnutrition+num+national"

The covidtests mini scraper gets “new_tests” and “new_tests_per_thousand” for the latest date where a date_condition is satisfied which is that “new_tests” is a value greater than zero.

 covidtests:
    format: "xlsx"
    dataset: "total-covid-19-tests-performed-by-country"
    headers: 1
    sheet: 1
    date_col: "date"
    date_type: "date"
    date_condition: "new_tests is not None and new_tests > 0"
    adm_cols:
      - "iso_code"
    input_cols:
      - "new_tests"
      - "new_tests_per_thousand"
    output_columns:
      - "New Tests"
      - "New Tests Per Thousand"
    output_hxltags:
      - "#affected+tested"
      - "#affected+tested+per1000"

The oxcgrt mini scraper reads from a data source that has HXL tags and these can be used instead of the header names provided use_hxl is set to True. By default all the HXLated columns are read with the admin related ones inferred and the rest taken as values except if defined as a date_col.

 oxcgrt:
    format: "csv"
    dataset: "oxford-covid-19-government-response-tracker"
    headers: 1
    use_hxl: True
    date_col: "#date"
    date_type: "date"

In the imperial mini scraper, output_columns and output_hxltags are defined which specify which columns and HXL tags in the HXLated file should be used rather than using all HXLated columns.

 imperial:
    format: "xlsx"
    dataset: "imperial-college-covid-19-projections"
    sheet: 1
    headers: 1
    use_hxl: True
    output_columns:
      - "Imp: Total Cases(min)"
      - "Imp: Total Cases(max)"
      - "Imp: Total Deaths(min)"
      - "Imp: Total Deaths(max)"
    output_hxltags:
      - "#affected+infected+min+imperial"
      - "#affected+infected+max+imperial"
      - "#affected+killed+min+imperial"
      - "#affected+killed+max+imperial"

The idmc mini scraper reads 2 HXLated columns defined in input_cols. In input_transforms, a cast to int is performed if the value is not None or it is set to 0. einput_cols defines new column(s) that can be combinations of the other columns in input_cols. In this case, input_cols specifies a new column which sums the 2 columns in input_cols. That new column is given a header and a HXL tag (in output_columns and output_hxltags).

 idmc:
    format: "csv"
    dataset: "idmc-internally-displaced-persons-idps"
    headers: 1
    use_hxl: True
    date_col: "#date+year"
    date_type: "year"
    input_cols:
      - "#affected+idps+ind+stock+conflict"
      - "#affected+idps+ind+stock+disaster"
    input_transforms:
      "#affected+idps+ind+stock+conflict": "int(#affected+idps+ind+stock+conflict) if #affected+idps+ind+stock+conflict else 0"
      "#affected+idps+ind+stock+disaster": "int(#affected+idps+ind+stock+disaster) if #affected+idps+ind+stock+disaster else 0"
    input_cols:
      - "#affected+idps+ind+stock+conflict + #affected+idps+ind+stock+disaster"
    output_columns:
      - "TotalIDPs"
    output_hxltags:
      - "#affected+displaced"

The needs mini scraper takes data for the latest available date for each country. subsets allows the definition of multiple indicators by way of filters. A filter is defined for each indicator (in this case there is one) which contains one or more filters of the form column=value. The pipe (|) is used as a separator - it means “and” not “or”.

 needs:
    format: "xlsx"
    dataset: "global-humanitarian-overview-2020-figures"
    sheet: "Raw Data"
    headers: 1
    adm_cols:
      - "Country Code"
    date_col: "Year"
    date_type: "year"
    subsets:
      - filter: "Metric=People in need|PiN Value for Dataviz=yes"
        input_cols:
          - "Value"
        output_columns:
          - "PeopleInNeed"
        output_hxltags:
          - "#affected+inneed"

The population mini scraper matches admin names only using exact matching (adm_exact is set to True) rather than the default which tries fuzzy matching in the event of a failure to match exactly. In this case, the fuzzy matching was incorrect. Note: a future fix in HDX Python Country may render this flag unnecessary as it will only fuzzy match if the input has more than 3 characters.

 population:
    source: "World Bank"
    source_url: "https://data.humdata.org/organization/world-bank-group"
    format: "xls"
    url: "http://api.worldbank.org/v2/en/indicator/SP.POP.TOTL?downloadformat=excel&dataformat=list"
    sheet: "Data"
    headers: 3
    adm_cols:
      - "Country Code"
    adm_exact: True
    date_col: "Year"
    date_type: "year"
    date_condition: "Value != ''"
    input_cols:
      - "Value"
    output_columns:
      - "Population"
    output_hxltags:
      - "#population"

The covid tests mini scraper applies a prefilter to the data that only processes rows where the value in the column "new_tests" is not None and is greater than zero.

covidtests:
    source: "Our World in Data"
    format: "xlsx"
    dataset: "total-covid-19-tests-performed-by-country"
    url: "tests/fixtures/owid-covid-data.xlsx"
    headers: 1
    sheet: 1
    prefilter: "new_tests is not None and new_tests > 0"
 ...

The sadd mini scraper reads data from the dataset “covid-19-sex-disaggregated-data-tracker”. It filters that data using data from another file, the url of which is defined in external_filter. Specifically, it cuts down the sadd data to only include countries listed in the “#country+code+v_iso2” column of the external_filter file.

 sadd:
     format: "csv"
     dataset: "covid-19-sex-disaggregated-data-tracker"
     headers: 1
     external_filter:
       url: "https://docs.google.com/spreadsheets/d/e/2PACX-1vR9PhPG7-aH0EkaBGzXYlrO9252gqs-UuKIeDQr9D3pOLBOdQ_AoSwWi21msHsdyT7thnjuhSY6ykSX/pub?gid=434885896&single=true&output=csv"
       hxltags:
         - "#country+code+v_iso2"
 ...

The fsnwg mini scraper first applies a sort to the data it reads. The reverse sort is based on the keys “reference_year” and “reference_code”. adm_cols defines a country column adm0_pcod3 and three admin 1 level columns (“adm1_pcod2”, “adm1_pcod3”, “adm1_name”) which are examined consecutively until a match with the internal admin 1 is made.

date_col is comprised of the amalgamation of two columns “reference_year” and “reference_code” (corresponding to the two columns which were used for sorting earlier). sum_cols is used to sum values in a column. For example, the formula “get_fraction_str(phase3, population)” takes the sum of all phase 3 values for an admin 1 and divides it by the sum of all population values for an admin 1.

mustbepopulated determines if values are included or not. If it is True, then only when all columns in input_cols for the row are populated is the value included. This means that when using multiple summed columns together in a formula, the number of values that were summed in each column will be the same. The last formula uses “#population” which is replaced by the population for the admin (which has been previously obtained by another mini scraper).

 fsnwg:
    format: "xlsx"
    dataset: "cadre-harmonise"
    headers: 1
    sheet: 1
    sort:
      reverse: True
      keys:
        - "reference_year"
        - "reference_code"
    adm_cols:
      - "adm0_pcod3"
      - - "adm1_pcod2"
        - "adm1_pcod3"
        - "adm1_name"
    date_col:
      - "reference_year"
      - "reference_code"
    date_type: "int"
    subsets:
      - filter: "chtype=current"
        input_cols:
          - "phase3"
          - "phase4"
          - "phase5"
          - "phase35"
          - "population"
        input_transforms:
          "phase3": "float(phase3)"
          "phase4": "float(phase4)"
          "phase5": "float(phase5)"
          "phase35": "float(phase35)"
          "population": "float(population)"
        sum_cols:
          - formula: "get_fraction_str(phase3, population)"
            mustbepopulated: True
          - formula: "get_fraction_str(phase4, population)"
            mustbepopulated: True
          - formula: "get_fraction_str(phase5, population)"
            mustbepopulated: True
          - formula: "get_fraction_str(phase35, population)"
            mustbepopulated: True
          - formula: "get_fraction_str(population, #population)"
            mustbepopulated: True
        output_columns:
          - "FoodInsecurityCHP3"
          - "FoodInsecurityCHP4"
          - "FoodInsecurityCHP5"
          - "FoodInsecurityCHP3+"
          - "FoodInsecurityCHAnalysed"
        output_hxltags:
          - "#affected+ch+food+p3+pct"
          - "#affected+ch+food+p4+pct"
          - "#affected+ch+food+p5+pct"
          - "#affected+ch+food+p3plus+pct"
          - "#affected+ch+food+analysed+pct"

The who_subnational mini scraper defines two values in input_ignore_vals which if found are ignored. Since mustbepopulated is True, then only when all columns in input_cols for the row are populated and do not contain either “-2222” or “-4444” is the value included in the sum of any column used in sum_cols.

 who_subnational:
    source: "WHO"
    format: "csv"
    url: "https://docs.google.com/spreadsheets/d/e/2PACX-1vRfjaIXE1hvEIXD66g6cuCbPrGdZkx6vLIgXO_znVbjQ-OgwfaI1kJPhxhgjw2Yg08CmtBuMLAZkTnu/pub?gid=337443769&single=true&output=csv"
    headers: 1
    adm_cols:
      - "iso"
      - "Admin1"
    date_col: "Year"
    date_type: "year"
    subsets:
      - filter: "Vaccine=HepB1"
        input_cols:
          - "Numerator"
          - "Denominator"
        input_ignore_vals:
          - "-2222"
          - "-4444"
        input_transforms:
          Numerator: "float(Numerator)"
          Denominator: "float(Denominator)"
        sum_cols:
          - formula: "get_fraction_str(Numerator, Denominator)"
            mustbepopulated: True
        output_columns:
          - "HepB1 Coverage"
        output_hxltags:
          - "#population+hepb1+pct+vaccinated"

The access mini scraper provides different sources for each HXL tag by providing dictionaries instead of strings in source and source_url. It maps specific HXL tags by key to sources or falls back on a “default_source” and “default_url” for all unspecified HXL tags.

 access:
    source:
      "#access+visas+pct": "OCHA"
      "#access+travel+pct": "OCHA"
      "#event+year+previous+num": "Aid Workers Database"
      "#event+year+todate+num": "Aid Workers Database"
      "#event+year+previous+todate+num": "Aid Workers Database"
      "#activity+cerf+project+insecurity+pct": "OCHA"
      "#activity+cbpf+project+insecurity+pct": "OCHA"
      "#population+education": "UNESCO"
      "default_source": "Multiple sources"
    source_url:
      "#event+year+previous+num": "https://data.humdata.org/dataset/security-incidents-on-aid-workers"
      "#event+year+todate+num": "https://data.humdata.org/dataset/security-incidents-on-aid-workers"
      "#event+year+previous+todate+num": "https://data.humdata.org/dataset/security-incidents-on-aid-workers"
      "default_url": "https://docs.google.com/spreadsheets/d/e/2PACX-1vRSzJzuyVt9i_mkRQ2HbxrUl2Lx2VIhkTHQM-laE8NyhQTy70zQTCuFS3PXbhZGAt1l2bkoA4_dAoAP/pub?gid=1565063847&single=true&output=csv"
    format: "csv"
    url: "https://docs.google.com/spreadsheets/d/e/2PACX-1vRSzJzuyVt9i_mkRQ2HbxrUl2Lx2VIhkTHQM-laE8NyhQTy70zQTCuFS3PXbhZGAt1l2bkoA4_dAoAP/pub?gid=1565063847&single=true&output=csv"
    headers: 1
    use_hxl: True
    input_transforms:
      "#access+visas+pct": "get_numeric_if_possible(#access+visas+pct)"
      "#access+travel+pct": "get_numeric_if_possible(#access+travel+pct)"
      "#activity+cerf+project+insecurity+pct": "get_numeric_if_possible(#activity+cerf+project+insecurity+pct)"
      "#activity+cbpf+project+insecurity+pct": "get_numeric_if_possible(#activity+cbpf+project+insecurity+pct)"
      "#population+education": "get_numeric_if_possible(#population+education)"

The field adm_vals allows overriding the country iso 3 codes and admin 1 pcodes for teh specific mini scraper:

  gam:
    source: "UNICEF"
    format: "xlsx"
    url: "tests/fixtures/unicef_who_wb_global_expanded_databases_severe_wasting.xlsx"
    sheet: "Trend"
    headers:
      - 3
      - 4
    flatten:
      - original: "Region {{1}} Region Name"
        new: "Region Name"
      - original: "Region {{1}} Point Estimate"
        new: "Region Point Estimate"
    adm_cols:
      - "ISO"
      - "Region Name"
    adm_vals:
      - ["AFG"]
      - ["AF09", "AF24"]
    date_col: "Year*"
    date_type: "year"
    input_cols:
      - "Region Point Estimate"
    output_cols:
      - "Malnutrition Estimate"
    output_hxltags:
      - "#severity+malnutrition+num+subnational"

The date_level field enables reading of data containing dates where the latest date must be used at a particular level such as per country or per admin 1. For example, we might want to produce a global value by summing the latest available value per country as shown below. We have specified a date_col and sum_cols. The library will apply a sort by date (since we have not specified one) to ensure correct results. This would also happen if we had used process_cols or append_cols.

  ourworldindata:
    source: "Our World in Data"
    format: "csv"
    url: "tests/fixtures/ourworldindata_vaccinedoses.csv"
    headers: 1
    use_hxl: True
    adm_cols:
      - "#country+code"
    date_col: "#date"
    date_type: "date"
    date_level: "national"
    input_cols:
      - "#total+vaccinations"
    sum_cols:
      - formula: "number_format(#total+vaccinations, format='%.0f')"
        mustbepopulated: False
    output_cols:
      - "TotalDosesAdministered"
    output_hxltags:
      - "#capacity+doses+administered+total"

An example of combining adm_vals, date_col and date_level is getting the latest global value in which global data has been treated as a special case of national data by using OWID_WRL in the #country+code field:

  ourworldindata:
    source: "Our World in Data"
    format: "csv"
    url: "tests/fixtures/ourworldindata_vaccinedoses.csv"
    headers: 1
    use_hxl: True
    adm_cols:
      - "#country+code"
    adm_vals:
      - "OWID_WRL"
    date_col: "#date"
    date_type: "date"
    date_level: "national"
    input_cols:
      - "#total+vaccinations"
    output_cols:
      - "TotalDosesAdministered"
    output_hxltags:
      - "#capacity+doses+administered+total"

If columns need to be summed and the latest date chosen overall not per admin unit, then we can specify single_maxdate as shown below:

  cerf_global:
    format: "csv"
    url: "tests/fixtures/full_pfmb_allocations.csv"
    dataset: "cerf-covid-19-allocations"
    force_date_today: True
    headers: 1
    date_col: "AllocationYear"
    date_type: "year"
    single_maxdate: True
    subsets:
      ...
      - filter: "FundType=CBPF|GenderMarker=0"
        input_cols:
          - "Budget"
        input_transforms:
          Budget: "float(Budget)"
        sum_cols:
          - formula: "Budget"
            mustbepopulated: False
        output_cols:
          - "CBPFFundingGM0"
        output_hxltags:
          - "#value+cbpf+funding+gm0+total+usd"
       ...