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clean.py
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# type: ignore
"""Cleans WDI metadata and data points in preparation for MySQL insert.
Usage:
python -m worldbank_wdi.clean
"""
import os
import re
import simplejson as json
import shutil
from typing import List, Tuple, Dict, Optional
import pandas as pd
from pandas.api.types import is_numeric_dtype
from tqdm import tqdm
from dotenv import load_dotenv
from utils import camel_case2snake_case
from worldbank_wdi import (
DATASET_NAME,
DATASET_AUTHORS,
DATASET_VERSION,
DATASET_LINK,
DATASET_RETRIEVED_DATE,
CONFIGPATH,
INPATH,
OUTPATH,
)
import logging
logging.basicConfig()
logger = logging.getLogger(__name__)
logger.setLevel(logging.INFO)
load_dotenv()
# KEEP_PATHS: Names of files in `{DATASET_DIR}/output` that you do NOT
# want deleted in the beginning of this script.
KEEP_PATHS = ["variables_to_clean.json"]
# Max length of source name.
MAX_SOURCE_NAME_LEN = 256
def main() -> None:
delete_output(KEEP_PATHS)
mk_output_dir()
# loads variables to be cleaned and uploaded.
variables_to_clean = load_variables_to_clean()
var_code2meta = {ind["code"]: ind for ind in variables_to_clean}
assert all([pd.notnull(c) for c in var_code2meta.keys()])
# loads mapping of "{UNSTANDARDIZED_ENTITY_CODE}" -> "{STANDARDIZED_OWID_NAME}"
# i.e. {"AFG": "Afghanistan", "SSF": "Sub-Saharan Africa", ...}
entity2owid_name = (
pd.read_csv(os.path.join(CONFIGPATH, "standardized_entity_names.csv"))
.set_index("country_code")
.squeeze()
.to_dict()
)
# cleans datasets, datapoints, variables, and sources.
df_datasets = clean_datasets()
var_code2meta_temp = clean_and_create_datapoints(
variable_codes=list(var_code2meta.keys()), entity2owid_name=entity2owid_name
)
# updates variable metadata with metadata constructed during data point
# creation and removes variables from cleaning that do not have any data
# values associated with them.
remove = []
for var_code, meta in var_code2meta.items():
if var_code in var_code2meta_temp:
meta.update(var_code2meta_temp[var_code])
else: # no data values were constructed for this variable
remove.append(var_code)
for var_code in remove:
del var_code2meta[var_code]
assert (
df_datasets.shape[0] == 1
), f"Only expected one dataset in {os.path.join(OUTPATH, 'datasets.csv')}."
df_sources, var_code2source_id = clean_sources(
dataset_id=df_datasets["id"].iloc[0],
dataset_name=df_datasets["name"].iloc[0],
variable_codes=list(var_code2meta.keys()),
)
for var_code, source_id in var_code2source_id.items():
var_code2meta[var_code]["source_id"] = source_id
df_variables = clean_variables(
dataset_id=df_datasets["id"].iloc[0],
variables=[var for var in var_code2meta.values()],
)
assert df_sources["id"].isin(df_variables["source_id"].unique()).all()
df_distinct_entities = pd.DataFrame(get_distinct_entities(), columns=["name"])
# saves datasets, sources, variables, and distinct entities to disk.
df_datasets.to_csv(os.path.join(OUTPATH, "datasets.csv"), index=False)
df_sources.to_csv(os.path.join(OUTPATH, "sources.csv"), index=False)
df_variables.to_csv(os.path.join(OUTPATH, "variables.csv"), index=False)
df_distinct_entities.to_csv(
os.path.join(OUTPATH, "distinct_countries_standardized.csv"), index=False
)
def load_variables_to_clean() -> List[dict]:
"""loads the array of variables to clean."""
try:
with open(os.path.join(CONFIGPATH, "variables_to_clean.json"), "r") as f:
variables = json.load(f)["variables"]
except: # noqa
with open(os.path.join(OUTPATH, "variables_to_clean.json"), "r") as f:
variables = json.load(f)["variables"]
return variables
def delete_output(keep_paths: List[str]) -> None:
"""deletes all files in `{DATASET_DIR}/output` EXCEPT for any file
names in `keep_paths`.
Arguments:
keep_paths: List[str]. List of subpaths in `{DATASET_DIR}/output` that
you do NOT want deleted. They will be temporarily move to `{DATASET_DIR}`
and then back into `{DATASET_DIR}/output` after everything else in
`{DATASET_DIR}/output` has been deleted.
Returns:
None.
"""
# temporarily moves some files out of the output directory so that they
# are not deleted.
for path in keep_paths:
if os.path.exists(os.path.join(OUTPATH, path)):
os.rename(os.path.join(OUTPATH, path), os.path.join(OUTPATH, "..", path))
# deletes all remaining output files
if os.path.exists(OUTPATH):
shutil.rmtree(OUTPATH)
os.makedirs(OUTPATH)
# moves the exception files back into the output directory.
for path in keep_paths:
if os.path.exists(os.path.join(OUTPATH, "..", path)):
os.rename(os.path.join(OUTPATH, "..", path), os.path.join(OUTPATH, path))
def mk_output_dir() -> None:
"""creates output directory, if it does not already exist."""
if not os.path.exists(OUTPATH):
os.makedirs(OUTPATH)
def clean_datasets():
"""Constructs a dataframe where each row represents a dataset to be upserted."""
data = [
{"id": 0, "name": f"{DATASET_NAME} - {DATASET_AUTHORS} ({DATASET_VERSION})"}
]
df = pd.DataFrame(data)
return df
def clean_and_create_datapoints(
variable_codes: List[str], entity2owid_name: dict
) -> Dict[str, dict]:
"""Cleans all entity-variable-year data observations and saves all
data points to csv in the `{OUTPATH}/datapoints` directory.
The data for each variable is saved as a separate csv file.
Arguments:
variable_codes: List[str]. List of World Bank WDI variable codes to
clean. Example::
["EG.ELC.ACCS.ZS", ...]
entity2owid_name: dict. Dict of "{UNSTANDARDIZED_ENTITY_CODE}" -> "{STANDARDIZED_OWID_NAME}"
mappings. Example::
{"AFG": "Afghanistan", "SSF": "Sub-Saharan Africa", ...}
Returns:
var_code2meta: Dict[str, dict]. Dictionary that maps each var code to
a dict of metadata including a temporary id and any metadata that
is derived from datapoints (e.g. timespan). Example::
{"EG.ELC.ACCS.ZS": {"id": 0, "timespan": "1960-2019"}}
"""
# loads data
df_data = pd.read_csv(os.path.join(INPATH, "WDIData.csv.zip"), compression="gzip")
df_data.columns = df_data.columns.str.lower().str.replace(
r"[\s/-]+", "_", regex=True
)
df_data["indicator_code"] = df_data["indicator_code"].str.upper()
years = (
df_data.columns[df_data.columns.str.contains(r"^\d{4}$")].sort_values().tolist()
)
df_data.dropna(subset=years, how="all", inplace=True)
# standardizes entity names.
df_data["country"] = df_data["country_code"].apply(lambda x: entity2owid_name[x])
assert (
df_data.groupby("indicator_code")["indicator_name"].apply(
lambda gp: gp.nunique()
)
== 1
).all(), "A variable code in `WDIData.csv` has multiple variable names."
uniq_codes = df_data["indicator_code"].unique().tolist()
for code in variable_codes:
if code not in uniq_codes:
logger.warning(
f'Variable code "{code}" is not a valid World Bank WDI '
"variable code."
)
df_data = df_data[df_data["indicator_code"].isin(variable_codes)]
# compares indicator names in WDIData.csv to WDISeries.csv
# df_variables = pd.read_csv(os.path.join(DATASET_DIR, 'input', 'WDISeries.csv'))
# df_variables.columns = df_variables.columns.str.lower().str.replace(r'[\s/-]+', '_', regex=True)
# d = df_variables.set_index('series_code')[['indicator_name']].rename(columns={'indicator_name': 'series'}).to_dict(orient='index')
# for code, name in df_data.set_index('indicator_code')['indicator_name'].to_dict().items():
# d[code]['data'] = name
# for k, subd in d.items():
# if subd['series'] != subd['data']:
# print(f"{k:20s}: '{str(subd['series'])}' ; '{str(subd['data'])}'")
# cleans each variable and saves it to csv.
out_path = os.path.join(OUTPATH, "datapoints")
if not os.path.exists(out_path):
os.makedirs(out_path)
i = 0
ignored_var_codes = set({})
kept_var_codes = set({})
var_code2meta = {}
grouped = df_data.groupby("indicator_code")
logger.info("Saving data points for each variable to csv...")
for var_code, gp in tqdm(grouped, total=len(grouped)):
gp_long = (
gp.set_index("country")[years]
.stack()
.sort_index()
.reset_index()
.rename(columns={"level_1": "year", 0: "value"})
)
gp_long["year"] = gp_long["year"].astype(int)
assert not gp_long.duplicated(subset=["country", "year"]).any()
assert is_numeric_dtype(gp_long["value"])
assert is_numeric_dtype(gp_long["year"])
assert gp_long.notnull().all().all()
if gp_long.shape[0] == 0:
ignored_var_codes.add(var_code)
else:
kept_var_codes.add(var_code)
assert var_code not in var_code2meta
timespan = f"{int(gp_long['year'].min())}-{int(gp_long['year'].max())}"
var_code2meta[var_code] = {"id": i, "timespan": timespan}
fpath = os.path.join(out_path, f"datapoints_{i}.csv")
assert not os.path.exists(fpath), (
f"{fpath} already exists. This should not be possible, because "
"each variable is supposed to be assigned its own unique "
"file name."
)
gp_long.to_csv(fpath, index=False)
i += 1
logger.info(
f"Saved data points to csv for {i} variables. Excluded {len(ignored_var_codes)} variables."
)
# df_variables = df_data[['indicator_code', 'indicator_name']].drop_duplicates()
# df_variables = df_variables[df_variables['indicator_code'].isin(kept_var_codes)]
# df_variables['id'] = df_variables['indicator_code'].apply(lambda x: var_code2temp_id[x])
# kept_variables = df_variables.to_dict(orient='records')
return var_code2meta
def clean_sources(
dataset_id: int, dataset_name: str, variable_codes: List[str]
) -> Tuple[pd.DataFrame, Dict[str, int]]:
"""Cleans a dataframe of data sources in preparation for uploading the
sources to the `sources` database table.
Arguments:
dataset_name: str. Dataset name.
dataset_id: int. Temporary dataset id.
variables_codes: List[str]. List of variable codes for which to clean
source metadata.
Returns:
df_sources: pd.DataFrame. Cleaned dataframe of data sources
to be uploaded.
var_code2source_id: Dict[str, int]. Dict of "{var_code}" ->
"{source_id}" pairings. Example::
{
'SH.MED.SAOP.P5': 0,
'NY.ADJ.DPEM.GN.ZS': 1,
'NY.ADJ.DPEM.CD': 1,
...
}
"""
with open(os.path.join(CONFIGPATH, "standardized_source_names.json"), "r") as f:
df_source_names = pd.DataFrame(json.load(f))
assert df_source_names["rawName"].duplicated().sum() == 0
df_variables = _load_variables(variable_codes)
df_source_names = (
df_source_names.merge(
df_variables.groupby("source")
.apply(lambda gp: gp["indicator_code"].unique().tolist())
.reset_index()
.rename(columns={0: "variable_codes"}),
left_on="rawName",
right_on="source",
how="left",
validate="1:1",
)
.drop("source", axis=1)
.dropna(subset=["variable_codes"])
)
sources = []
var_code2source_id = {}
for i, ((name, data_publisher_source), gp) in enumerate(
df_source_names.groupby(["name", "dataPublisherSource"])
):
var_codes = [c for codes in gp["variable_codes"] for c in codes]
source = {
"id": i,
"dataset_id": dataset_id,
"name": name,
"description": json.dumps(
{
"link": DATASET_LINK,
"retrievedDate": DATASET_RETRIEVED_DATE,
"additionalInfo": None,
"dataPublishedBy": dataset_name,
"dataPublisherSource": data_publisher_source,
},
ignore_nan=True,
),
}
sources.append(source)
for c in var_codes:
assert c not in var_code2source_id
var_code2source_id[c] = i
df_sources = pd.DataFrame(sources)
missing_var_codes = [c for c in variable_codes if c not in var_code2source_id]
assert len(missing_var_codes) == 0, (
"All variable codes must have a source ID, but the following variables "
f"do not: {missing_var_codes}. Are the source names for these variables "
"missing from `standardized_source_names.json`?"
)
return df_sources, var_code2source_id
def clean_variables(dataset_id: int, variables: List[dict]) -> pd.DataFrame:
"""Cleans a dataframe of variables in preparation for uploading the
variables to the `variables` database table.
Arguments:
dataset_id: int. Integer representing the dataset id for all variables.
variables: List[dict]. List of variables with metadata. Contains some
metadata for each variable that was constructed during the
`clean_and_create_datapoints` step and the `clean_sources` step.
Also contains variable metadata that is present in
`variable_to_clean.json`. Example:
[
{
"name": "Under-five mortality rate (probability of dying by age 5 per 1000 live births)",
"code": "MDG_0000000007",
"old": {
"unit": "%",
"shortUnit": "%",
"display": {"name": "Child mortality rate", "unit": "%", "shortUnit": "%", "numDecimalPlaces": 2},
}
"id": 0,
"timespan": "2000-2019",
"source_id": 0
},
...
]
Returns:
df_variables: pd.DataFrame. Cleaned dataframe of variables
to be uploaded.
"""
assert all(
[pd.notnull(var["code"]) for var in variables]
), "One or more variables has a null `code` field."
df_variables = pd.DataFrame(variables)
# fetches description for each variable
df_variables["description"] = _load_description_many_variables(
df_variables.code.tolist()
)
# cleans name column
df_variables["name"] = df_variables["name"].str.replace(r"\s+", " ", regex=True)
# cleans display column
if "old" in df_variables.columns:
displays = []
for _, row in df_variables.iterrows():
display = row["old"].get("display") if pd.notnull(row["old"]) else None
if display:
year_in_name_regex = re.search(r"\b([1-2]\d{3})\b", row["name"])
if year_in_name_regex:
for k in ["name", "unit"]:
if k in display:
year = year_in_name_regex.groups()[0]
year_in_val_regex = re.search(
r"\b([1-2]\d{3})\b", display[k]
)
if year_in_val_regex:
val_year = year_in_val_regex.groups()[0]
if year != val_year:
new_val = re.sub(
r"\b([1-2]\d{3})\b", year, display[k]
)
logger.warning(
f'The "display.{k}" field for variable "{row["name"]}" '
f'contains a different year ("{display[k]}"). The {k} '
f'year is being replaced to become: "{new_val}"'
)
val = new_val
display[k] = val
display = json.dumps(display, ignore_nan=True)
displays.append(display)
df_variables["display"] = displays
# cleans shortUnit column
if "old" in df_variables.columns:
df_variables["shortUnit"] = df_variables["old"].apply(
lambda x: x.get("shortUnit") if pd.notnull(x) else None
)
# cleans unit column
if "old" in df_variables.columns:
units = []
for _, row in df_variables.iterrows():
unit = row["old"].get("unit") if pd.notnull(row["old"]) else None
if unit:
year_in_name_regex = re.search(r"\b([1-2]\d{3})\b", row["name"])
if year_in_name_regex:
year = year_in_name_regex.groups()[0]
year_in_unit_regex = re.search(r"\b([1-2]\d{3})\b", unit)
if year_in_unit_regex:
unit_year = year_in_unit_regex.groups()[0]
if year != unit_year:
new_unit = re.sub(r"\b([1-2]\d{3})\b", year, unit)
logger.warning(
f'The unit field for variable "{row["name"]}" '
f'contains a different year ("{unit}"). The unit '
f'year is being replaced to become: "{new_unit}"'
)
unit = new_unit
units.append(unit)
df_variables["unit"] = units
# cleans originalMetadata column
if "old" in df_variables.columns:
df_variables["originalMetadata"] = df_variables["old"].apply(
lambda x: x.get("originalMetadata") if pd.notnull(x) else None
)
df_variables["originalMetadata"] = df_variables["originalMetadata"].apply(
lambda x: json.dumps(x, ignore_nan=True) if pd.notnull(x) else None
)
df_variables["dataset_id"] = dataset_id
# converts column names to snake case b/c this is what is expected in the
# `standard_importer.import_dataset` module.
df_variables.columns = df_variables.columns.map(camel_case2snake_case)
if "old" in df_variables.columns:
df_variables.drop("old", axis=1, inplace=True)
required_fields = ["id", "name", "dataset_id", "source_id"]
for field in required_fields:
assert field in df_variables.columns, f"`{field}` does not exist."
assert df_variables[field].notnull().all(), (
f"The following variables have a null `{field}` field:\n"
f"{df_variables.loc[df_variables[field].isnull(), required_fields]}"
)
df_variables = df_variables.set_index(["id", "name"]).reset_index()
return df_variables
def get_distinct_entities() -> List[str]:
"""retrieves a list of all distinct entities that contain at least
on non-null data point that was saved to disk from the
`clean_and_create_datapoints()` method.
Returns:
entities: List[str]. List of distinct entity names.
"""
fnames = [
fname
for fname in os.listdir(os.path.join(OUTPATH, "datapoints"))
if fname.endswith(".csv")
]
entities = set({})
for fname in fnames:
df_temp = pd.read_csv(os.path.join(OUTPATH, "datapoints", fname))
entities.update(df_temp["country"].unique().tolist())
entity_list = sorted(entities)
assert pd.notnull(entity_list).all(), (
"All entities should be non-null. Something went wrong in "
"`clean_and_create_datapoints()`."
)
return entity_list
def _load_variables(codes: List[str]) -> pd.DataFrame:
df_variables = pd.read_csv(
os.path.join(INPATH, "WDISeries.csv.zip"), compression="gzip"
)
df_variables.columns = df_variables.columns.str.lower().str.replace(
r"[\s/-]+", "_", regex=True
)
df_variables.rename(columns={"series_code": "indicator_code"}, inplace=True)
df_variables["indicator_code"] = df_variables["indicator_code"].str.upper()
df_variables = df_variables[df_variables["indicator_code"].isin(codes)]
return df_variables
def _load_description_many_variables(codes: List[str]) -> List[Optional[str]]:
df_variables = _load_variables(codes)
# creates `description` column
descriptions: List[Optional[str]] = []
for _, var in df_variables.iterrows():
desc = ""
if (
pd.notnull(var["long_definition"])
and len(var["long_definition"].strip()) > 0
):
desc += var["long_definition"]
elif (
pd.notnull(var["short_definition"])
and len(var["short_definition"].strip()) > 0
):
desc += var["short_definition"]
if (
pd.notnull(var["limitations_and_exceptions"])
and len(var["limitations_and_exceptions"].strip()) > 0
):
desc += (
f'\n\nLimitations and exceptions: {var["limitations_and_exceptions"]}'
)
if (
pd.notnull(var["statistical_concept_and_methodology"])
and len(var["statistical_concept_and_methodology"].strip()) > 0
):
desc += f'\n\nStatistical concept and methodology: {var["statistical_concept_and_methodology"]}'
# retrieves additional source info, if it exists.
if (
pd.notnull(var["notes_from_original_source"])
and len(var["notes_from_original_source"].strip()) > 0
):
desc += (
f'\n\nNotes from original source: {var["notes_from_original_source"]}'
)
desc = re.sub(r" *(\n+) *", r"\1", re.sub(r"[ \t]+", " ", desc)).strip()
if len(desc) == 0:
descriptions.append(None)
else:
descriptions.append(desc)
df_variables.loc[:, "description"] = descriptions
if df_variables["description"].isnull().any():
logger.warning(
"The `description` column (i.e. variable definition) is null for the "
"following variables:\n"
f"{json.dumps(df_variables.loc[df_variables['description'].isnull(), 'indicator_name'].tolist(), indent=2)}\n"
"These variables will not have a variable description."
)
var_code2desc = df_variables.set_index("indicator_code")["description"].to_dict()
descriptions = [var_code2desc[c] for c in codes]
return descriptions
if __name__ == "__main__":
main()