Python Pandas - 按年份显示在一行中的数据帧总数

Question

I have the following code that summarizes data into the table below listed under Output

df['Customer ID'] = df['Ship To Customer'] + df['ZipCleaned']

df.Date = pd.to_datetime(df.Date)

min_dates = df.groupby(['Customer ID'])['Date'].min()
df['First_Purchase_Date'] = df.apply(lambda row: min_dates.loc[row['Customer ID']], axis=1) 
df['New Customer'] = df['Date'] <= df['First_Purchase_Date']
df['Existing Customer'] = df['Date'] > df['First_Purchase_Date']
df['Total Customers'] = (df['New Customer']==True).value_counts() + (df['Existing Customer']==True).value_counts() 

df['revenue'] = pd.to_numeric(df['revenue']) 
df['Date'] = pd.to_datetime(df['Date'], unit='s')
df['Year'] = df['Date'].dt.year
df['Month'] = df['Date'].dt.month
df['First_Purchase_Date'] = pd.to_datetime(df['First_Purchase_Date'], unit='s')

FPRANGE = df.First_Purchase_Date.between('2014-01-01','2019-12-03') #customer first purchase dates we want to include in the dataset

Table = df.loc[FPRANGE].groupby(df['Year'])[['New Customer', 'Existing Customer', 'Total Customers','revenue']].sum() #with date filter on first purchase date

print(Table)

OUTPUT

      New Customer  Existing Customer  Total Customers   revenue
Year
2014          7.00               2.00           156.00 11,869.47
2015          1.00               3.00             0.00  9,853.93
2016          5.00               3.00             0.00  4,058.53
2017          9.00               3.00             0.00  8,056.37
2018         12.00               7.00             0.00 22,031.23
2019         16.00              10.00             0.00 97,142.42

Notice that the total customers column has an amount under 2014 and not the rest of the rows by year. I want to be able to see the total number of customers for each year (ie New Customer + Existing Customer). I have tried several different approaches but can't seem to get this right.

Answer 1

上面建议的解决方案有效

Table['Total Customers'] = Table['New Customer'] + Table['Existing Customer']