def match_category(df):
"""compare data colected from matching file (match_df) with grainger and gws data pulls and create a column to tell analysts
whether attributes from the two systems have been matched"""
df['Matching'] = 'no'
for row in df.itertuples():
grainger_string = str(row.Grainger_Attribute_Name)
gws_string = str(row.Gamut_Attribute_Name)
# gws_string = str(row.GWS_Attribute_Name)
if (grainger_string) == (gws_string):
df.at[row.Index, 'Matching'] = 'Match'
elif (grainger_string) in (gws_string):
df.at[row.Index, 'Matching'] = 'Potential Match'
elif (gws_string) in (grainger_string):
df.at[row.Index, 'Matching'] = 'Potential Match'
elif process.isBlank(row.Grainger_Attribute_Name) == False:
# if process.isBlank(row.GWS_Attribute_Name) == True:
if process.isBlank(row.Gamut_Attribute_Name) == True:
df.at[row.Index, 'Matching'] = 'Grainger only'
elif process.isBlank(row.Grainger_Attribute_Name) == True:
# if process.isBlank(row.GWS_Attribute_Name) == False:
if process.isBlank(row.Gamut_Attribute_Name) == False:
df.at[row.Index, 'Matching'] = 'GWS only'
return df
def choose_definition(df):
"""pick the definition to upload to GWS based on 1. Cat specific, 2. Attribute level, 3. old Gamut definition"""
df['Definition'] = ''
df['Definition Source'] = ''
for row in df.itertuples():
cat_def = str(row.Grainger_Category_Specific_Definition)
attr_def = str(row.Grainger_Attribute_Definition)
gamut_def = str(row.Gamut_Attribute_Definition)
if process.isBlank(row.Grainger_Category_Specific_Definition) == False:
df.at[row.Index, 'Definition'] = cat_def
df.at[row.Index,
'Definition Source'] = 'Grainger Category Specific'
elif process.isBlank(row.Grainger_Attribute_Definition) == False:
df.at[row.Index, 'Definition'] = attr_def
df.at[row.Index,
'Definition Source'] = 'Grainger Attribute Definition'
elif process.isBlank(row.Gamut_Attribute_Definition) == False:
df.at[row.Index, 'Definition'] = gamut_def
df.at[row.Index, 'Definition Source'] = 'Gamut Definition'
if df.empty == False:
df = df.drop([
'Grainger_Attribute_Definition',
'Grainger_Category_Specific_Definition',
'Gamut_Attribute_Definition'
],
axis=1) #remove unneeded columns
return df
def match_category(df):
"""compare data colected from matching file (match_df) with grainger and gamut data pulls and create a column to tell analysts
whether attributes from the two systems have been matched"""
for row in df.itertuples():
if (row.Index,
row.Grainger_Attribute_Name) == (row.Index,
row.Gamut_Attribute_Name):
df.at[row.Index, 'Matching'] = 'Match'
elif process.isBlank(row.Grainger_Attribute_Name) == False:
if process.isBlank(row.Gamut_Attribute_Name) == True:
df.at[row.Index, 'Matching'] = 'Grainger only'
elif process.isBlank(row.Grainger_Attribute_Name) == True:
if process.isBlank(row.Gamut_Attribute_Name) == False:
df.at[row.Index, 'Matching'] = 'Gamut only'
return df
