def get_property_inflation_values(start, end):
collection = jsonstat.from_url('https://statbank.cso.ie/StatbankServices/StatbankServices.svc/jsonservice/responseinstance/HPM06', 'tttt.json')
df = collection.dataset(0).to_data_frame()
con = df['Type of Residential Property'] == 'Dublin - all residential properties'
con1 = df['Month'] == start
con2 = df.Statistic == 'Residential Property Price Index (Base Jan 2005 = 100)'
s_dub = df[con & con1 & con2]['Value'].values[0]
con = df['Type of Residential Property'] == 'National excluding Dublin - all residential properties'
con1 = df['Month'] == start
con2 = df.Statistic == 'Residential Property Price Index (Base Jan 2005 = 100)'
s_out = df[con & con1 & con2]['Value'].values[0]
con = df['Type of Residential Property'] == 'Dublin - all residential properties'
con1 = df['Month'] == end
con2 = df.Statistic == 'Residential Property Price Index (Base Jan 2005 = 100)'
e_dub = df[con & con1 & con2]['Value'].values[0]
con = df['Type of Residential Property'] == 'National excluding Dublin - all residential properties'
con1 = df['Month'] == end
con2 = df.Statistic == 'Residential Property Price Index (Base Jan 2005 = 100)'
e_out = df[con & con1 & con2]['Value'].values[0]
return s_dub, e_dub, s_out, e_out
def test(uri, filename):
print("downloading data from '{}'".format(uri))
# extract collection
collection = jsonstat.from_url(uri, filename)
print(collection)
# extract dataset contained into collection
ds = collection.dataset('nama_gdp_c')
print(ds)
# show some values
v = ds.data(geo="IT", time="2011")
print("IT gdp in 2011 was {}".format(v))
def test(uri, filename):
# extract collection
print("downloading data from '{}'".format(uri))
collection = jsonstat.from_url(uri, filename)
print(collection)
# extract dataset contained into collection
ds = collection.dataset(0)
print(ds)
# show some values
v = ds.data(0)
lcat = ds.idx_as_lcat(0)
print("{} -> {}".format(lcat, v.value))
def test(uri, filename):
print("downloading data from '{}'".format(uri))
# extract collection
collection = jsonstat.from_url(uri, filename)
print(collection)
# extract dataset contained into collection
ds = collection.dataset('nama_gdp_c')
print(ds)
# show some values
v = ds.data(geo="IT", time="2011")
print("IT gdp in 2011 was {}".format(v))
def test(uri, filename):
# extract collection
print("downloading data from '{}'".format(uri))
collection = jsonstat.from_url(uri, filename)
print(collection)
# extract dataset contained into collection
ds = collection.dataset(0)
print(ds)
# show some values
v = ds.data(0)
lcat = ds.idx_as_lcat(0)
print("{} -> {}".format(lcat, v.value))
def info(cache_dir, args):
if len(args) == 0:
args = ['http://json-stat.org/samples/oecd-canada-col.json']
d = jsonstat.cache_dir(cache_dir)
print("downloaded file(s) are stored into '{}'\n".format(d))
for arg in args:
if arg.startswith("http"):
print("download '{}'".format(arg))
o = jsonstat.from_url(arg)
else:
print("reading '{}'".format(arg))
o = jsonstat.from_file(arg)
print(o)
if isinstance(o, jsonstat.JsonStatCollection):
print("\nfirst dataset:\n")
print(o.dataset(0))
def info(cache_dir, args):
if len(args) == 0:
args = ['http://json-stat.org/samples/oecd-canada-col.json']
d = jsonstat.cache_dir(cache_dir)
print("downloaded file(s) are stored into '{}'\n".format(d))
for arg in args:
if arg.startswith("http"):
print("download '{}'".format(arg))
o = jsonstat.from_url(arg)
else:
print("reading '{}'".format(arg))
o = jsonstat.from_file(arg)
print(o)
if isinstance(o, jsonstat.JsonStatCollection):
print("\nfirst dataset:\n")
print(o.dataset(0))
import pandas as pd
import jsonstat
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
url = (
'http://www.cso.ie/StatbankServices/StatbankServices.svc/jsonservice/responseinstance/TEM20'
)
r = requests.get(url)
json_data = r.json()
for key, value in json_data.items():
print(key + ':', value)
print(json_data.keys())
data = jsonstat.from_url(url)
print(data)
mcar = data.dataset(0)
print(mcar)
df = mcar.to_data_frame()
print(df.head(10))
print(df.tail(10))
print(df.dtypes)
print(df.sort_values("Make and Model", ascending=False))
Car = df['Make and Model']
print(Car)
print(max(Car))
print(min(Car))
print(list(Car))
df1 = df['Make and Model'].value_counts()
print(df.shape)
import requests
import pandas as pd
import jsonstat
import os
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
url = url = 'https://ws.cso.ie/public/api.restful/PxStat.Data.Cube_API.ReadDataset/TEM22/JSON-stat/1.0/'
collection = jsonstat.from_url(url)
#print(collection)
mcar = collection.dataset(0)
#print(mcar)
for d in mcar.dimensions():
print(d)
#print(mcar.value)
#print(mcar.value(C02437V02947= "Ford"))
df = mcar.to_data_frame()
#print(df)
#print(df.head())
#print(df.dtypes)
Car = df['Car Make']
print(Car.head)
print()rint(max(Car))
print(min(Car))
print(list(Car))
finalcarList=np.unique(Car).tolist()
print(finalcarList)
