def parse_globaldata_power_extract(
filepath,
columns_name_list
):
"""Read a table from read the global data powerplants file."""
# Find the header row
raw_data = pd.read_excel(
io=filepath,
sheet_name=None,
header=None
)
if len(raw_data.keys()) != 1:
raise Exception(f"""Multiple sheets found in excel file,
but only one expected: {raw_data.keys()}""")
raw_data = raw_data[list(raw_data.keys())[0]]
df = dfu.clean_df(
df=raw_data,
columns_name_list=columns_name_list
)
# Table specificecific renames
if "company_type_(private/_public..)" in df.columns:
df = df.rename(
mapper={"company_type_(private/_public..)": "company_type"},
axis='columns',
errors='raise'
)
if "parent/subsidiary" in df.columns:
df = df.rename(
mapper={"parent/subsidiary": "parent_subsidiary"},
axis='columns',
errors='raise'
)
return df
def parse_globaldata_power_purchase_agreements(
filepath,
columns_name_list
):
"""Read a table from read the global data powerplants file."""
# Find the header row
raw_data = pd.read_excel(
io=filepath,
sheet_name=None,
header=None
)
raw_data = raw_data['Power Purchase Agreements']
df = dfu.clean_df(
df=raw_data,
columns_name_list=columns_name_list
)
# Table specificecific renames
if "associated_plant_capacity_(mw)" in df.columns:
df['associated_plant_capacity_unit'] = 'MW'
df = df.rename(
mapper={
"associated_plant_capacity_(mw)": "associated_plant_capacity"
},
axis='columns',
errors='raise'
)
return df
def parse_globaldata_power_plants(
filepath,
columns_name_list
):
"""Read a table from read the global data powerplants file."""
raw_data = pd.read_excel(
io=filepath,
sheet_name=None,
header=None
)
if len(raw_data.keys()) != 1:
raise Exception(f"""Multiple sheets found in excel file,
but only one expected: {raw_data.keys()}""")
raw_data = raw_data[list(raw_data.keys())[0]]
df = dfu.clean_df(
df=raw_data,
columns_name_list=columns_name_list
)
# Table specific rename
if "total_capacity_(mw)" in df.columns:
df['total_capacity_unit'] = 'MW'
df = df.rename(
mapper={"total_capacity_(mw)": "total_capacity"},
axis='columns',
errors='raise'
)
if "active_capacity_(mw)" in df.columns:
df['active_capacity_unit'] = 'MW'
df = df.rename(
mapper={"active_capacity_(mw)": "active_capacity"},
axis='columns',
errors='raise'
)
if "pipeline_capacity_(mw)" in df.columns:
df['pipeline_capacity_unit'] = 'MW'
df = df.rename(
mapper={"pipeline_capacity_(mw)": "pipeline_capacity"},
axis='columns',
errors='raise'
)
if "discontinued_capacity_(mw)" in df.columns:
df['discontinued_capacity_unit'] = 'MW'
df = df.rename(
mapper={"discontinued_capacity_(mw)": "discontinued_capacity"},
axis='columns',
errors='raise'
)
if "owner_stake_(%)" in df.columns:
df = df.rename(
mapper={"owner_stake_(%)": "owner_stake_percentage"},
axis='columns',
errors='raise'
)
if "capex_usd_(million)" in df.columns:
df['capex_usd'] = df['capex_usd_(million)'] * 1e6
df = df.drop("capex_usd_(million)", axis='columns')
if "efficiency_(%)" in df.columns:
df = df.rename(
mapper={"efficiency_(%)": "efficiency_percentage"},
axis='columns',
errors='raise'
)
if "decommissioning_year_(actual/estimated)" in df.columns:
df = df.rename(
mapper={
"decommissioning_year_(actual/estimated)":
"decommissioning_year_status"
},
axis='columns',
errors='raise'
)
return df
def test_clean_df_empty_header_footer():
clean_df = dfu.clean_df(df=df_test_small_empty_header_small_footer,
columns_name_list=df_test_names)
npt.assert_array_equal(clean_df, df_test_simple_no_header)
npt.assert_array_equal(clean_df.columns, clean_column_names)
def test_clean_df_empty_middle_column_header():
clean_df = dfu.clean_df(df=df_test_empty_middle_column_header,
columns_name_list=df_test_names,
rows_to_search=25)
npt.assert_array_equal(clean_df, df_test_simple_no_header)
npt.assert_array_equal(clean_df.columns, clean_column_names)
def test_clean_df_long_footer_value():
clean_df = dfu.clean_df(df=df_test_long_footer,
columns_name_list=df_test_names,
rows_to_search=25)
npt.assert_array_equal(clean_df, df_test_simple_no_header)
npt.assert_array_equal(clean_df.columns, clean_column_names)
def test_clean_df_simple_footer_mixed_nan():
clean_df = dfu.clean_df(df=df_test_footer_mixed,
columns_name_list=df_test_names)
npt.assert_array_equal(clean_df, df_test_simple_no_header)
npt.assert_array_equal(clean_df.columns, clean_column_names)
def test_clean_df_long_header_rows_none():
clean_df = dfu.clean_df(df=df_test_small_header,
columns_name_list=df_test_names,
rows_to_search=None)
npt.assert_array_equal(clean_df, df_test_simple_no_header)
npt.assert_array_equal(clean_df.columns, clean_column_names)
