def apiEconomy(self):
gdp_india = {}
for record in self.data['records']:
gdp = {}
# taking out yearly GDP value from records
gdp['GDP_in_rs_cr'] = int(
record['gross_domestic_product_in_rs_cr_at_2004_05_prices'])
gdp_india[record['financial_year']] = gdp
gdp_india_yrs = list(gdp_india)
for i in range(len(gdp_india_yrs)):
if i == 0:
pass
else:
key = 'GDP_Growth_' + gdp_india_yrs[i]
# calculating GDP growth on yearly basis
gdp_india[gdp_india_yrs[i]][key] = round(
((gdp_india[gdp_india_yrs[i]]['GDP_in_rs_cr'] -
gdp_india[gdp_india_yrs[i - 1]]['GDP_in_rs_cr']) /
gdp_india[gdp_india_yrs[i - 1]]['GDP_in_rs_cr']) * 100, 2)
# connection to mongo db
mongoDB_obj = MongoDB(urllib.parse.quote_plus('root'),
urllib.parse.quote_plus('password'), 'host',
'GDP')
# Insert Data into MongoDB
mongoDB_obj.insert_into_db(gdp_india, 'India_GDP')
def apiEconomy(self):
gdp_india = {}
for record in self.data['records']:
gdp={}
gdp['GDP_in_rs_cr'] = int(record['gross_domestic_product_in_rs_cr_at_2004_05_prices'])
gdp_india[record['financial_year']] = gdp
gdp_india_yrs = list(gdp_india)
for i in range(len(gdp_india_yrs)):
if i == 0:
pass
else:
key = 'GDP_Growth_' + gdp_india_yrs[i]
gdp_india[gdp_india_yrs[i]][key] = round(((gdp_india[gdp_india_yrs[i]]['GDP_in_rs_cr'] -gdp_india[gdp_india_yrs[i-1]]['GDP_in_rs_cr'])/gdp_india[gdp_india_yrs[i-1]]['GDP_in_rs_cr'])*100,2)
print(gdp_india)
# connection to mongo db
mongoDB_obj = MongoDB(urllib.parse.quote_plus('root'), urllib.parse.quote_plus('password'), 'host', 'GDP')
# Insert Data into MongoDB
mongoDB_obj.insert_into_db(gdp_india, 'India_GDP')
def apiPollution(self):
air_data = self.data['results']
# Converting nested data into linear structure
air_list = []
for data in air_data:
for measurement in data['measurements']:
air_dict = {}
air_dict['city'] = data['city']
air_dict['country'] = data['country']
air_dict['parameter'] = measurement['parameter']
air_dict['value'] = measurement['value']
air_dict['unit'] = measurement['unit']
air_list.append(air_dict)
# Convert list of dict into pandas df
df = pd.DataFrame(air_list, columns=air_dict.keys())
# connection to mongo db
mongoDB_obj = MongoDB(urllib.parse.quote_plus('root'),
urllib.parse.quote_plus('password'), 'host',
'Pollution_Data')
# Insert Data into MongoDB
mongoDB_obj.insert_into_db(df, 'Air_Quality_India')
def apiPollution(self):
air_data = self.data['results']
# Converting nested data into linear structure
air_list = []
for data in air_data:
for measurement in data['measurements']:
air_dict = {}
air_dict['location'] = data['location']
air_dict['city'] = data['city']
air_dict['country'] = data['country']
air_dict['parameter'] = measurement['parameter']
air_dict['value'] = measurement['value']
air_dict['lastUpdated'] = measurement['lastUpdated']
air_dict['unit'] = measurement['unit']
air_dict['sourceName'] = measurement['sourceName']
air_list.append(air_dict)
print('len', len(air_list))
# Convert list of dict into pandas df
df = pd.DataFrame(air_list, columns=air_dict.keys())
print(df.size)
# connection to mongo db
mongoDB_obj = MongoDB(urllib.quote_plus('root'), urllib.quote_plus('password'), 'host', 'Pollution_Data')
# Insert Data into MongoDB
mongoDB_obj.insert_into_db(df, 'Air_Quality_India')