def __init__(self, cache_dir=utils.default_cache_dir()):
self.cache_dir = cache_dir
self.data = {}
self.data[utils.EN] = self.__do_england()
self.data[utils.WA] = self.__do_wales()
self.data[utils.SC] = self.__do_scotland()
self.data[utils.NI] = self.__do_nireland()
def __init__(self, cache_dir=None):
if cache_dir is None:
cache_dir = utils.default_cache_dir()
self.cache_dir = cache_dir
self.data_api = Api.Nomisweb(self.cache_dir)
# store as a dictionary keyed by year (lazy retrieval)
self.data = {}
def __do_wales(self):
print("Collating SNPP data for Wales...")
cache_dir = utils.default_cache_dir()
wales_raw = cache_dir + "/snpp_w.csv"
if os.path.isfile(wales_raw):
snpp_w = pd.read_csv(wales_raw)
else:
fields = [
'Area_AltCode1', 'Year_Code', 'Data', 'Gender_Code',
'Age_Code', 'Area_Hierarchy', 'Variant_Code'
]
# StatsWales is an OData endpoint, so select fields of interest
url = "http://open.statswales.gov.wales/dataset/popu6010?$select={}".format(
",".join(fields))
# use OData syntax to filter P (persons), AllAges (all ages), Area_Hierarchy 691 (LADs)
url += "&$filter=Gender_Code ne 'P' and Area_Hierarchy gt 690 and Area_Hierarchy lt 694 and Variant_Code eq 'Principal'"
#
data = []
while True:
print(url)
r = requests.get(url)
r_data = r.json()
data += r_data['value']
if "odata.nextLink" in r_data:
url = r_data["odata.nextLink"]
else:
break
snpp_w = pd.DataFrame(data)
# Remove unwanted and rename wanted columns
snpp_w = snpp_w.drop(["Area_Hierarchy", "Variant_Code"], axis=1)
snpp_w = snpp_w.rename(
columns={
"Age_Code": "C_AGE",
"Area_AltCode1": "GEOGRAPHY_CODE",
"Data": "OBS_VALUE",
"Gender_Code": "GENDER",
"Year_Code": "PROJECTED_YEAR_NAME"
})
# Remove all but SYOA and make numeric
snpp_w = snpp_w[(snpp_w.C_AGE != "AllAges")
& (snpp_w.C_AGE != "00To15") &
(snpp_w.C_AGE != "16To64") &
(snpp_w.C_AGE != "65Plus")]
snpp_w.loc[snpp_w.C_AGE == "90Plus", "C_AGE"] = "90"
snpp_w.C_AGE = pd.to_numeric(snpp_w.C_AGE)
# convert gender to census convention 1=M, 2=F
snpp_w.GENDER = snpp_w.GENDER.map({"M": 1, "F": 2})
# assert(len(snpp_w) == 26*2*91*22) # 22 LADs x 91 ages x 2 genders x 26 years
print(wales_raw)
snpp_w.to_csv(wales_raw, index=False)
return snpp_w
def __init__(self, cache_dir = None):
if cache_dir is None:
cache_dir = utils.default_cache_dir()
self.cache_dir = cache_dir
self.data_api = Api.Nomisweb(self.cache_dir)
# map of pandas dataframes keyed by variant code
self.data = {}
# load principal aggressively...
self.data["ppp"] = self.__download_ppp()
def __init__(self, cache_dir=None):
if cache_dir is None:
cache_dir = utils.default_cache_dir()
self.cache_dir = cache_dir
self.data_api = Api.Nomisweb(self.cache_dir)
self.data = {}
self.data[utils.EN] = self.__do_england()
self.data[utils.WA] = self.__do_wales()
self.data[utils.SC] = self.__do_scotland()
self.data[utils.NI] = self.__do_nireland()
def register_custom_projection(name, data,
cache_dir=utils.default_cache_dir()):
# check data is compatible
required_colnames = [
"GEOGRAPHY_CODE", "OBS_VALUE", "GENDER", "C_AGE", "PROJECTED_YEAR_NAME"
]
for col in required_colnames:
if not col in data.columns.values:
raise ValueError("Custom SNPP dataset must contain a %s column" %
col)
if not (data.GENDER.unique() == [1, 2]).all():
raise ValueError(
"GENDER column must only contain 1 (male) and 2 (female)")
if min(data.C_AGE.unique()) != 0 or max(data.C_AGE.unique()) != 90:
raise ValueError("C_AGE column must range from 0 to 90 (inclusive)")
filename = _custom_snpp_filename(name, cache_dir)
print("Writing custom SNPP %s to %s" % (name, filename))
data.to_csv(filename, index=False)
def __init__(self, name, cache_dir=utils.default_cache_dir()):
self.name = name
self.cache_dir = cache_dir
filename = _custom_snpp_filename(name, self.cache_dir)
self.data = pd.read_csv(filename)
def list_custom_projections(cache_dir=utils.default_cache_dir()):
files = glob.glob(os.path.join(cache_dir,
"ukpopulation_custom_snpp_*.csv"))
projs = [os.path.basename(file)[25:-4] for file in files]
return projs