def test_query(self):
block = CRC_Query()
df1 = pd.read_sql_query(block.blockwise_percentage, self.connection)
df1 = df1.fillna(0)
block1 = crc_get_data()
df2 = pd.DataFrame.from_records(
block1.dist_wise_crc(config['url']['domain'] +
'crc/allBlockWise')['visits'])
df2.columns = map(str.lower, df2.columns)
df2 = df2[[
'blockid', 'blockname', 'visit_0', 'visit_1_2', 'visit_3_5',
'visit_6_10', 'visit_10_more'
]].fillna(0)
df2['visit_0'] = df2['visit_0'].astype(float)
df2['visit_1_2'] = df2['visit_1_2'].astype(float)
df2['visit_3_5'] = df2['visit_3_5'].astype(float)
df2['visit_6_10'] = df2['visit_6_10'].astype(float)
df2['visit_10_more'] = df2['visit_10_more'].astype(float)
df_diff = pd.concat([df1, df2]).drop_duplicates(keep=False)
assert df_diff.empty, "Found difference between s3 bucket metrics and the metrics generated outside cqube for block wise visits and percentage of schools "
print(
"No Difference between s3 bucket metrics and the metrics generated outside cqube for block wise visits and percentage of schools"
)
def test_query(self):
block = CRC_Query()
df1 = pd.read_sql_query(block.blockwise_crc, self.connection)
block1 = crc_get_data()
df2 = pd.DataFrame.from_records(block1.dist_wise_crc(config['url']['domain']+'crc/allBlockWise')['visits'])
df2.columns = map(str.lower, df2.columns)
df2 = df2[['blockname', 'blockid', 'no_of_schools_per_crc', 'visits_per_school','totalschools']]
df2['no_of_schools_per_crc']=df2['no_of_schools_per_crc'].astype(float)
df2['visits_per_school'] = df2['visits_per_school'].astype(float)
df_diff = pd.concat([df1, df2]).drop_duplicates(keep=False)
assert df_diff.empty, "Found difference between s3 bucket metrics and the metrics generated outside cqube for block wise crc schools "
print("No Difference between s3 bucket metrics and the metrics generated outside cqube for block wise crc schools")
def test_query(self):
cluster = CRC_Query()
df1 = pd.read_sql_query(cluster.clusterwise_visited, self.connection)
cluster1 = crc_get_data()
df2 = pd.DataFrame.from_records(cluster1.dist_wise_crc(config['url']['domain']+'crc/allClusterWise')['visits'])
df2.columns = map(str.lower, df2.columns)
df2 = df2[['clusterid', 'clustername', 'visitedschoolcount', 'totalvisits']]
df1.rename(columns={'visited_schools_count':'visitedschoolcount','total_visits':'totalvisits'},inplace=True)
df2['visitedschoolcount'] = df2['visitedschoolcount'].astype(float)
df2['totalvisits'] = df2['totalvisits'].astype(float)
df_diff = pd.concat([df1, df2]).drop_duplicates(keep=False)
assert df_diff.empty, "Found difference between s3 bucket metrics and the metrics generated outside cqube for cluster wise total visited "
print("No Difference between s3 bucket metrics and the metrics generated outside cqube for cluster wise total visited")
def test_query(self):
school = CRC_Query()
df1 = pd.read_sql_query(school.schoolwise_percentage, self.connection)
school1 = crc_get_data()
df2 = pd.DataFrame.from_records(
school1.dist_wise_crc(config['url']['domain'] +
'crc/allSchoolWise')['visits'])
df2.columns = map(str.lower, df2.columns)
df1 = df1.fillna(0)
df2 = df2[[
'schoolid', 'schoolname', 'visit_0', 'visit_1_2', 'visit_3_5',
'visit_6_10', 'visit_10_more'
]].fillna(0)
df2['visit_0'] = df2['visit_0'].astype(float)
df2['visit_1_2'] = df2['visit_1_2'].astype(float)
df2['visit_3_5'] = df2['visit_3_5'].astype(float)
df2['visit_6_10'] = df2['visit_6_10'].astype(float)
df2['visit_10_more'] = df2['visit_10_more'].astype(float)
df_diff = pd.concat([df1, df2]).drop_duplicates(keep=False)
print(df_diff.sort_values(by='schoolname'))
assert df_diff.empty, "Found difference between s3 bucket metrics and the metrics generated outside cqube for school wise percentage "
print(
"No Difference between s3 bucket metrics and the metrics generated outside cqube for school wise percentage"
)
