def __process_ex_data(cls, original_pkl_path, root_path, pkl_file_name):
data_frame = DataFrameFunction.get_data_frame_from_pkl(
original_pkl_path)
data_frame['company'] = cls.COMPANY_NAME
# DateとTimeで分割されているので結合した項目を作る。
DataFrameFunction.generate_data_time_field(data_frame)
data_frame.set_index('date_time')
# 2,500みたいなデータがあるので取り除く。
data_frame['demand'] = data_frame['demand'].astype(str).str.replace(
',', '').astype(int)
data_frame['total_supply_capacity'] = data_frame[
'total_supply_capacity'].astype(str).str.replace(',',
'').astype(int)
# 後続で計算できないのでfloatに変換している。
data_frame['thermal'] = data_frame['thermal'].astype(str).str.replace(
',', '').astype(float)
# 他の電力に合わせて万kwからMWhに揃える。
DataFrameFunction.to_mwh(data_frame)
processed_pkl_path = FileFunction.get_processed_pkl_path(
root_path, cls.COMPANY_NAME, pkl_file_name)
FileFunction.create_pkl_file(processed_pkl_path, data_frame)
return processed_pkl_path
def __process_ex_data(cls, original_pkl_path, root_path, pkl_file_name):
data_frame = DataFrameFunction.get_data_frame_from_pkl(
original_pkl_path)
data_frame['company'] = cls.COMPANY_NAME
# DateとTimeで分割されているので結合した項目を作る。
DataFrameFunction.generate_data_time_field(data_frame)
data_frame.set_index('date_time')
# TOTAL算出 Total Supply Capacity
data_frame[
'total_supply_capacity'] = DataFrameFunction.get_total_supply_capacity(
data_frame)
# 後続で計算できないのでfloatに変換している。
data_frame['solar_output_control'] = data_frame[
'solar_output_control'].astype(str).str.replace(',', '')
data_frame['solar_output_control'] = data_frame[
'solar_output_control'].astype(str).str.replace('−', '0')
data_frame['solar_output_control'] = data_frame[
'solar_output_control'].astype(float)
data_frame['wind_output_control'] = data_frame[
'wind_output_control'].astype(str).str.replace(',', '')
data_frame['wind_output_control'] = data_frame[
'wind_output_control'].astype(str).str.replace('−', '0')
data_frame['wind_output_control'] = data_frame[
'wind_output_control'].astype(float)
processed_pkl_path = FileFunction.get_processed_pkl_path(
root_path, cls.COMPANY_NAME, pkl_file_name)
FileFunction.create_pkl_file(processed_pkl_path, data_frame)
return processed_pkl_path
def __correct_ex_data(cls, root_path, pkl_file_name, url, reflesh):
original_pkl_path = FileFunction.get_original_pkl_path(root_path, cls.COMPANY_NAME, pkl_file_name)
if reflesh or not os.path.exists(original_pkl_path):
decoded_data = FileFunction.get_decoded_data(url)
data_frame = cls.__parse(decoded_data)
FileFunction.create_pkl_file(original_pkl_path, data_frame)
return original_pkl_path
def __correct_ex_data(cls, root_path, pkl_file_name, url, reflesh):
original_pkl_path = FileFunction.get_original_pkl_path(root_path, cls.COMPANY_NAME, pkl_file_name)
if reflesh or not os.path.exists(original_pkl_path):
decoded_data = FileFunction.get_decoded_data(url)
# 「2016/10/1 1:00」とか余計な半角が存在するデータがあるので加工する。
tohokuepco_csv = cls.__get_tohokuepco_csv(decoded_data)
data_frame = cls.__parse(tohokuepco_csv)
FileFunction.create_pkl_file(original_pkl_path, data_frame)
return original_pkl_path
def __process_ex_data(cls, original_pkl_path, root_path, pkl_file_name):
data_frame = DataFrameFunction.get_data_frame_from_pkl(
original_pkl_path)
data_frame['company'] = cls.COMPANY_NAME
# DateとTimeで分割されているので結合した項目を作る。
DataFrameFunction.generate_data_time_field(data_frame)
processed_pkl_path = FileFunction.get_processed_pkl_path(
root_path, cls.COMPANY_NAME, pkl_file_name)
FileFunction.create_pkl_file(processed_pkl_path, data_frame)
return processed_pkl_path
def __correct_ex_data(cls, root_path, pkl_file_name, url, reflesh):
original_pkl_path = FileFunction.get_original_pkl_path(root_path, cls.COMPANY_NAME, pkl_file_name)
if reflesh or not os.path.exists(original_pkl_path):
decoded_data = FileFunction.get_decoded_data(url)
# okidenは、空行が入っていたりするので、csv読み込み前にデータ補正が必要。
okiden_csv = cls.__get_okiden_csv(decoded_data)
data_frame = cls.__parse(okiden_csv)
data_frame.reset_index()
data_frame.reset_index()
FileFunction.create_pkl_file(original_pkl_path, data_frame)
return original_pkl_path
def __correct_ex_data(cls, root_path, pkl_file_name, url, reflesh):
original_pkl_path = FileFunction.get_original_pkl_path(
root_path, cls.COMPANY_NAME, pkl_file_name)
if reflesh or not os.path.exists(original_pkl_path):
decoded_data = FileFunction.get_decoded_data(url)
# 末尾に余計な「,」が入っていることがあるので除去する。
# スキップすべき行が違うこともあるので対応する。
rikuden_csv = cls.__get_rikuden_csv(decoded_data)
data_frame = cls.__parse(rikuden_csv)
data_frame.reset_index()
FileFunction.create_pkl_file(original_pkl_path, data_frame)
return original_pkl_path
def __process_ex_data(cls, original_pkl_path, root_path, pkl_file_name):
data_frame = DataFrameFunction.get_data_frame_from_pkl(original_pkl_path)
data_frame['company'] = cls.COMPANY_NAME
# tohokuepcoは、日時で持っているのでTepcoに合わせて分割する。
DataFrameFunction.create_date_and_time_from_datetime(data_frame)
# Date型に変換しておく。
data_frame['date_time'] = pandas.to_datetime(data_frame['date_time'], format='%Y/%m/%d %H:%M')
# TOTAL算出 Total Supply Capacity
data_frame['total_supply_capacity'] = DataFrameFunction.get_total_supply_capacity(data_frame)
processed_pkl_path = FileFunction.get_processed_pkl_path(root_path, cls.COMPANY_NAME, pkl_file_name)
FileFunction.create_pkl_file(processed_pkl_path, data_frame)
return processed_pkl_path
def __process_ex_data(cls, original_pkl_path, root_path, pkl_file_name):
data_frame = DataFrameFunction.get_data_frame_from_pkl(original_pkl_path)
data_frame['company'] = cls.COMPANY_NAME
# DateとTimeで分割されているので結合した項目を作る。
DataFrameFunction.generate_data_time_field(data_frame)
data_frame.set_index('date_time')
# 沖縄にない電力項目は0で埋める。
data_frame['nuclear'] = 0
data_frame['geothermal'] = 0
data_frame['pumping'] = 0
data_frame['interconnection'] = 0
processed_pkl_path = FileFunction.get_processed_pkl_path(root_path, cls.COMPANY_NAME, pkl_file_name)
FileFunction.create_pkl_file(processed_pkl_path, data_frame)
return processed_pkl_path
def __process_ex_data(cls, original_pkl_path, root_path, pkl_file_name):
data_frame = DataFrameFunction.get_data_frame_from_pkl(
original_pkl_path)
data_frame['company'] = cls.COMPANY_NAME
# DateとTimeで分割されているので結合した項目を作る。
DataFrameFunction.generate_data_time_field(data_frame)
data_frame.set_index('date_time')
# TOTAL算出 Total Supply Capacity
data_frame[
'total_supply_capacity'] = DataFrameFunction.get_total_supply_capacity(
data_frame)
processed_pkl_path = FileFunction.get_processed_pkl_path(
root_path, cls.COMPANY_NAME, pkl_file_name)
FileFunction.create_pkl_file(processed_pkl_path, data_frame)
return processed_pkl_path
def __correct_ex_data(cls, root_path, pkl_file_name, url, reflesh):
original_pkl_path = FileFunction.get_original_pkl_path(
root_path, cls.COMPANY_NAME, pkl_file_name)
if reflesh or not os.path.exists(original_pkl_path):
data_frame_from_xls = pandas.read_excel(
url,
header=None,
index_col=None,
skiprows=[0, 1, 2, 3, 4, 5, 6, 7, 8])
# 最終行は不要なので削除する。
data_frame_from_xls.drop(data_frame_from_xls.tail(1).index,
inplace=True)
# Excelから読み込みやすいフォーマットに加工する。
yonden_csv = cls.__create_yonden_csv_from_xls(
data_frame_from_xls.to_csv(index=False))
data_frame = cls.__parse(yonden_csv)
print(data_frame)
FileFunction.create_pkl_file(original_pkl_path, data_frame)
return original_pkl_path
def __process_ex_data(cls, original_pkl_path, root_path, pkl_file_name):
data_frame = DataFrameFunction.get_data_frame_from_pkl(
original_pkl_path)
data_frame['company'] = cls.COMPANY_NAME
# DateとTimeで分割されているので結合した項目を作る。
DataFrameFunction.generate_data_time_field(data_frame)
data_frame.set_index('date_time')
# 後続で計算できないのでfloatに変換している。
# 地熱がハイフンなので0扱いにする。
data_frame['geothermal'] = data_frame['geothermal'].astype(
str).str.replace('－', '0').astype(float)
# 他の電力に合わせて万kwからMWhに揃える。
DataFrameFunction.to_mwh(data_frame)
processed_pkl_path = FileFunction.get_processed_pkl_path(
root_path, cls.COMPANY_NAME, pkl_file_name)
FileFunction.create_pkl_file(processed_pkl_path, data_frame)
return processed_pkl_path
def __correct_ex_data(cls, root_path, pkl_file_name, url, reflesh):
original_pkl_path = FileFunction.get_original_pkl_path(
root_path, cls.COMPANY_NAME, pkl_file_name)
if reflesh or not os.path.exists(original_pkl_path):
if '.xls' in url:
data_frame_from_xls = pandas.read_excel(url,
header=None,
index_col=None,
skiprows=[0, 1, 2, 3])
hepco_csv = cls.__create_hepco_csv_from_xls(
data_frame_from_xls.to_csv(index=False))
data_frame = cls.__parse_csv_from_xls(hepco_csv)
FileFunction.create_pkl_file(original_pkl_path, data_frame)
else:
decoded_data = FileFunction.get_decoded_data(url)
# hepcoは、日時周りのフォーマットが他と違うので、csv読み込み前にデータ補正が必要。
hepco_csv = cls.__get_hepco_csv(decoded_data)
data_frame = cls.__parse(hepco_csv)
FileFunction.create_pkl_file(original_pkl_path, data_frame)
return original_pkl_path
def __process_ex_data(cls, original_pkl_path, root_path, pkl_file_name):
data_frame = DataFrameFunction.get_data_frame_from_pkl(original_pkl_path)
data_frame['company'] = cls.COMPANY_NAME
# Kyudenは、日時で持っているのでTepcoに合わせて分割する。
DataFrameFunction.create_date_and_time_from_datetime(data_frame)
# 2,500みたいなデータがあるので取り除く。
data_frame['demand'] = data_frame['demand'].astype(str).str.replace(',', '').astype(float)
# 後続で計算できないのでfloatに変換している。
data_frame['nuclear'] = data_frame['nuclear'].astype(str).str.replace(',', '').astype(float)
data_frame['thermal'] = data_frame['thermal'].astype(str).str.replace(',', '').astype(float)
data_frame['solar_output_control'] = data_frame['solar_output_control'].astype(str).str.replace('None', '0')
data_frame['solar_output_control'] = data_frame['solar_output_control'].str.replace('nan', '0')
data_frame['solar_output_control'] = data_frame['solar_output_control'].str.replace(',', '')
data_frame['solar_output_control'] = data_frame['solar_output_control'].astype(float)
data_frame['pumping'] = data_frame['pumping'].astype(str).str.replace('None', '0')
data_frame['pumping'] = data_frame['pumping'].str.replace('nan', '0')
data_frame['pumping'] = data_frame['pumping'].str.replace(',', '')
data_frame['pumping'] = data_frame['pumping'].astype(float)
data_frame['interconnection'] = data_frame['interconnection'].astype(str).str.replace('None', '0')
data_frame['interconnection'] = data_frame['interconnection'].str.replace('nan', '0')
data_frame['interconnection'] = data_frame['interconnection'].str.replace(',', '')
data_frame['interconnection'] = data_frame['interconnection'].astype(float)
# Date型に変換しておく。
data_frame['date_time'] = pandas.to_datetime(data_frame['date_time'], format='%Y/%m/%d %H:%M')
# TOTAL算出 Total Supply Capacity
data_frame['total_supply_capacity'] = DataFrameFunction.get_total_supply_capacity(data_frame)
processed_pkl_path = FileFunction.get_processed_pkl_path(root_path, cls.COMPANY_NAME, pkl_file_name)
FileFunction.create_pkl_file(processed_pkl_path, data_frame)
return processed_pkl_path
