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Anpassung Monatsausgabe

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This commit is contained in:
Beate Trzensiok 2022-09-23 08:34:40 +02:00
parent 086d7782e7
commit d251f7e783
1 changed files with 5 additions and 17 deletions
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20
msp/msp/doctype/verkaufsstatistik_report/verkaufsstatistik_report.py
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@ -103,7 +103,10 @@ class VerkaufsstatistikReport(Document):
df['Kalenderwoche']= df['Date'].dt.isocalendar().week df['Kalenderwoche']= df['Date'].dt.isocalendar().week
#df['Monat']= df['Date'].dt.month #df['Monat']= df['Date'].dt.month
df['Jahr'] = df['Date'].dt.year df['Jahr'] = df['Date'].dt.year
df['Monat'] = df['Date'].dt.strftime("%m.%Y") df['Month'] = df['Date'].dt.month
df['Monat'] = df['Date'].dt.to_period('M')
print(df['Month'].tolist())
#df['Monat'] = df['Date'].dt.strftime("%m.%Y")
from_date_dt = dt. strptime(self.from_date, '%Y-%m-%d') from_date_dt = dt. strptime(self.from_date, '%Y-%m-%d')
from_date_st = from_date_dt.strftime('%d.%m.%Y') from_date_st = from_date_dt.strftime('%d.%m.%Y')
to_date_dt = dt. strptime(self.to_date, '%Y-%m-%d') to_date_dt = dt. strptime(self.to_date, '%Y-%m-%d')
@ -148,21 +151,6 @@ class VerkaufsstatistikReport(Document):
df_pivot =df.pivot_table(index=date, columns="Mitarbeiter", values = values, aggfunc = np.sum, fill_value=0) df_pivot =df.pivot_table(index=date, columns="Mitarbeiter", values = values, aggfunc = np.sum, fill_value=0)
elif self.gruppiert_nach == "Alle": elif self.gruppiert_nach == "Alle":
df_pivot =df.pivot_table(index=date, values = values, aggfunc = np.sum, fill_value=0) df_pivot =df.pivot_table(index=date, values = values, aggfunc = np.sum, fill_value=0)
# df_pivot.columns = df_pivot.columns.droplevel(0) #remove amount
# df_pivot.columns.name = None #remove categories
# df_pivot = df_pivot.reset_index() #index to columns
# print(df_pivot)
# #df_grouped = df.groupby(filters).agg({'Anzahl': ['sum']}).reset_index()
# if self.anzahl == 1:
# df['Item']=df['Item'].map('{} Anzahl'.format)
# df_grouped = df.groupby(filters)['Anzahl'].sum().reset_index()
# df_pivot1 = df_grouped.pivot_table(index= date,columns='Item',values='Anzahl')
# df_pivot1 = df_pivot.fillna(0)
# if self.preis == 1:
# df['Item']=df['Item'].map('{} Preis'.format)
# df_grouped = df.groupby(filters)['Preis'].sum().reset_index()
# df_pivot2 = df_grouped.pivot_table(index= date,columns='Item',values='Preis')
# df_pivot2= df_pivot.fillna(0)
df_pivot = pd.DataFrame(df_pivot.to_records()) df_pivot = pd.DataFrame(df_pivot.to_records())