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" )