PYL-W0613 @ 136e5ea • pnijhara / sih-2019

Run summary

4 years ago

f022db2..136e5ea

48 s

Function contains unused argument PYL-W0613

Anti-pattern

Major

28 occurrences in this check

Unused argument 'max_rows'

apps/nv_time_series.py

19labelIds = main_sections
20
21
22def generate_table(dataframe, max_rows=10):23    data = pd.read_excel('data/2018/aggregates-economic-activity/S7.1.xlsx', header = None)
24    df = data[3:]
25    df.columns = df.iloc[0].fillna(value=pd.Series(range(100)))

Unused argument 'dataframe'

apps/nv_time_series.py

19labelIds = main_sections
20
21
22def generate_table(dataframe, max_rows=10):23    data = pd.read_excel('data/2018/aggregates-economic-activity/S7.1.xlsx', header = None)
24    df = data[3:]
25    df.columns = df.iloc[0].fillna(value=pd.Series(range(100)))

Unused argument 'dataframe'

apps/nv_eco.py

 20labelIds = [row.iloc[-2] for row in rows[0:-1]]
 21
 22
 23def generate_table(dataframe, max_rows=10): 24    data = pd.read_excel('data/2018/economic-aggregates/S1.7r.xlsx', header = None)
 25    df = data[6:]
 26    df.columns = df.iloc[0].fillna(value=pd.Series(range(100)))

Unused argument 'category'

apps/nv_eco.py

 62
 63layout=app_layout()
 64
 65def filter(year, category, rows, labels, remove=False): 66    cu_index, co_index = [index for index in years.transpose().index if years[index].iloc[0] == year]
 67    
 68    filtered = rows[0:-1] if remove else rows

Unused argument 'max_rows'

apps/nv_eco.py

 20labelIds = [row.iloc[-2] for row in rows[0:-1]]
 21
 22
 23def generate_table(dataframe, max_rows=10): 24    data = pd.read_excel('data/2018/economic-aggregates/S1.7r.xlsx', header = None)
 25    df = data[6:]
 26    df.columns = df.iloc[0].fillna(value=pd.Series(range(100)))

Unused argument 'dataframe'

apps/household.py

23labelIds = labels.index
24
25
26def generate_table(dataframe, max_rows=10):27    data = pd.read_excel('data/2018/households/S5.2.xlsx', header = None)
28    df = data[3:]
29    df.columns = df.iloc[0].fillna(value=pd.Series(range(100)))

Unused argument 'max_rows'

apps/household.py

23labelIds = labels.index
24
25
26def generate_table(dataframe, max_rows=10):27    data = pd.read_excel('data/2018/households/S5.2.xlsx', header = None)
28    df = data[3:]
29    df.columns = df.iloc[0].fillna(value=pd.Series(range(100)))

Unused argument 'max_rows'

apps/gva_time_series.py

22labelIds = [row.iloc[-2] for row in rows]
23
24
25def generate_table(dataframe, max_rows=10):26    data = pd.read_excel(filename, header = None)
27    df = data[6:]
28    df.columns = df.iloc[0].fillna(value=pd.Series(range(100)))

Unused argument 'dataframe'

apps/gva_time_series.py

22labelIds = [row.iloc[-2] for row in rows]
23
24
25def generate_table(dataframe, max_rows=10):26    data = pd.read_excel(filename, header = None)
27    df = data[6:]
28    df.columns = df.iloc[0].fillna(value=pd.Series(range(100)))

Unused argument 'dataframe'

apps/gva_sectors.py

 52    )
 53
 54
 55def generate_table(dataframe, max_rows=10): 56    data = pd.read_excel(filename, header = None)
 57    df = data[6:]
 58    df.columns = df.iloc[0].fillna(value=pd.Series(range(100)))

Unused argument 'max_rows'

apps/gva_sectors.py

 52    )
 53
 54
 55def generate_table(dataframe, max_rows=10): 56    data = pd.read_excel(filename, header = None)
 57    df = data[6:]
 58    df.columns = df.iloc[0].fillna(value=pd.Series(range(100)))

Unused argument 'category'

apps/gva_sectors.py

 69layout = app_layout()
 70
 71
 72def filter(year, category, rows, labels, remove=False): 73    cu_index, co_index = [index for index in years.transpose().index if years[index].iloc[0] == year]
 74
 75    filtered = rows[0:-1] if remove else rows

Unused argument 'max_rows'

apps/gcf_time_series.py

21print(labelIds[1])
22
23
24def generate_table(dataframe, max_rows=10):25    data = pd.read_excel('data/2018/economic-aggregates/S1.10.xlsx', header = None)
26    df = data[6:]
27    df.columns = df.iloc[0].fillna(value=pd.Series(range(100)))

Unused argument 'dataframe'

apps/gcf_time_series.py

21print(labelIds[1])
22
23
24def generate_table(dataframe, max_rows=10):25    data = pd.read_excel('data/2018/economic-aggregates/S1.10.xlsx', header = None)
26    df = data[6:]
27    df.columns = df.iloc[0].fillna(value=pd.Series(range(100)))

Unused argument 'dataframe'

apps/gcf_sectors.py

 21labelIds = [row.iloc[0] for row in rows[0:-2]]
 22
 23
 24def generate_table(dataframe, max_rows=10): 25    data = pd.read_excel('data/2018/economic-aggregates/S1.10.xlsx', header = None)
 26    df = data[6:]
 27    df.columns = df.iloc[0].fillna(value=pd.Series(range(100)))

Unused argument 'max_rows'

apps/gcf_sectors.py

 21labelIds = [row.iloc[0] for row in rows[0:-2]]
 22
 23
 24def generate_table(dataframe, max_rows=10): 25    data = pd.read_excel('data/2018/economic-aggregates/S1.10.xlsx', header = None)
 26    df = data[6:]
 27    df.columns = df.iloc[0].fillna(value=pd.Series(range(100)))

Unused argument 'category'

apps/gcf_sectors.py

 68layout = app_layout()
 69
 70
 71def filter(year, category, rows, labels, remove=False): 72    cu_index, co_index = [index for index in years.transpose().index if years[index].iloc[0] == year]
 73
 74    filtered = rows[0:-2] if remove else rows

Unused argument 'dataframe'

apps/crop_wise_output.py

19labelIds = main_sections
20
21
22def generate_table(dataframe, max_rows=10):23    data = pd.read_excel('data/2018/disaggregated-statements/S8.1.2.xlsx', header = None)
24    df = data[6:]
25    df.columns = df.iloc[0].fillna(value=pd.Series(range(100)))

Unused argument 'max_rows'

apps/crop_wise_output.py

19labelIds = main_sections
20
21
22def generate_table(dataframe, max_rows=10):23    data = pd.read_excel('data/2018/disaggregated-statements/S8.1.2.xlsx', header = None)
24    df = data[6:]
25    df.columns = df.iloc[0].fillna(value=pd.Series(range(100)))

Unused argument 'max_rows'

apps/cfc_time_series.py

19labelIds = main_sections
20
21
22def generate_table(dataframe, max_rows=10):23    data = pd.read_excel('data/2018/economic-aggregates/S1.8.xlsx', header = None)
24    df = data[6:]
25    df.columns = df.iloc[0].fillna(value=pd.Series(range(100)))

Unused argument 'dataframe'

apps/cfc_time_series.py

19labelIds = main_sections
20
21
22def generate_table(dataframe, max_rows=10):23    data = pd.read_excel('data/2018/economic-aggregates/S1.8.xlsx', header = None)
24    df = data[6:]
25    df.columns = df.iloc[0].fillna(value=pd.Series(range(100)))

Unused argument 'max_rows'

apps/cfc_sectors.py

 50    )
 51
 52
 53def generate_table(dataframe, max_rows=10): 54    data = pd.read_excel('data/2018/economic-aggregates/S1.8.xlsx', header = None)
 55    df = data[6:]
 56    df.columns = df.iloc[0].fillna(value=pd.Series(range(100)))

Unused argument 'category'

apps/cfc_sectors.py

 67layout = app_layout()
 68
 69
 70def filter(year, category, rows, labels, remove=False): 71    cu_index, co_index = [index for index in years.transpose().index if years[index].iloc[0] == year]
 72
 73    filtered = rows[0:-1] if remove else rows

Unused argument 'dataframe'

apps/cfc_sectors.py

 50    )
 51
 52
 53def generate_table(dataframe, max_rows=10): 54    data = pd.read_excel('data/2018/economic-aggregates/S1.8.xlsx', header = None)
 55    df = data[6:]
 56    df.columns = df.iloc[0].fillna(value=pd.Series(range(100)))

Unused argument 'max_rows'

apps/agg_national_accounts.py

 52    )
 53
 54
 55def generate_table(dataframe, max_rows=10): 56    data = pd.read_excel(filename, header = None)
 57    df = data[6:-1]
 58    df.columns = df.iloc[0].fillna(value=pd.Series(range(100)))

Description

An unused argument can lead to confusions. It should be removed. If this variable is necessary, name the variable _ or start the name with unused or _unused.

Bad practice

def square(x, y=1):
    return x * x

class MySubClass(MyClass):
    def __init__(self, number):
        self.value = 42  # argument `number` remains unused

Preferred:

def square(x):
    return x * x

class MySubClass(MyClass):
    def __init__(self, _):
        self.value = 42