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summaryStats_res.py
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summaryStats_res.py
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import pandas as pd
#thie code looks up the total customer country from the annnoymous summary and then goes through the directory of load profiles and computes the total for
#each sub-category..and summarizes in the summary file
if __name__ == "__main__":
folder = 'anonymized_1in10_actual_actual_2014'
# print "folder", folder
summary_file = folder + "/" + folder + "_cluster_summary.csv"
# print "file1", summary_file
summary_df = pd.read_csv(summary_file)
# print "Begin", summary_df.columns
summary_df = summary_df.drop(['cluster_oldname', 'weather_station', 'bev_count', 'bev_work_count','phev_count', 'phev_work_count' ], axis =1)
#summary_df['kwh_ann_tot'] = summary_df['kwh_ann_tot'].apply(lambda x: int(x.strip().replace(',', '')))
summary_df['customer_count'] = summary_df['customer_count'].apply(lambda x: int(x.strip().replace(',', '')))
# print(summary_df.dtypes)
sectors = ['res']
car = ['Care', 'nonCare']
utils = ['pge', 'sce', 'sdge']
#kwbin =['0.5_0.6' ] # , '0.0_0.1', '0.1_0.2', '0.2_0.3', '0.3_0.4', '0.4_0.5']
query_df = summary_df[
(summary_df['sector'].isin(sectors)) & #if sectors name is in 'sector'
(summary_df['care'].isin(car)) &
# (summary_df['kwh_bin'].isin(kwbin)) &
(summary_df['util'].isin(utils)) &
# (summary_df['kwh_bin'].isin(bins)) &
(True) # for ease of adding/removing conditions above
]
for i, row in query_df.iterrows():
# print "test", i, "+cluster", folder
# print "cluster", row["cluster"]
clust = row["cluster"]
filename = folder + "/" + clust + ".csv"
# print "filename", i, filename
cluster_df = pd.read_csv(filename) #for every file
#print "cluster_df", cluster_df
for col in cluster_df.columns: #read the relevant columns
# sum_cols.add(col_sum)
# print "col", col
query_df.loc[i, col + "_sum"] = cluster_df[col].sum()
# hourly_demand = cluster_df[col]
# print "test1", query_df.head(5)
query_df = query_df.drop(query_df.columns[query_df.columns.str.contains('Unnamed',case = False)],axis = 1)
query_df = query_df.drop([ 'poolpump_penetration','battery_kwh_per_customer', 'hour_ending_sum', 'poolpump_sum'], axis =1)
query_df.to_csv("WH_Output" + "/" + 'res_summary_Feb4.csv')