使用Pandas DataFrame中的列值逐行填充字符串

Question

假設您有一個數據框：

import pandas as pd

sales = [{'account': 'Jones LLC', 'nuts': 150, 'bolts': 200, 'totalval': 140, 'Cur': 'pesos'},
         {'account': 'Alpha Co',  'nuts': 200, 'bolts': 210, 'totalval': 215, 'Cur': 'euros'},
         {'account': 'Blue Inc',  'nuts': 50,  'bolts': 90,  'totalval': 95 , 'Cur': 'pounds'}]

mydf = pd.DataFrame(sales)

您想生成一個慶祝當月利潤的字符串。 例如：

"We made 140 pesos from Jones LLC. Wahoo!"

解決此問題的第一個嘗試是使用帶有占位符的模板字符串，並逐行逐月格式化其格式。 請注意，這些月度數字是整數，而不是字符串。

celebstring = "We made amt Cur from Jones LLC. Woohoo!"

def createpr(inputdf):
    for index, row in inputdf.iterrows():
        filledstring = celebstring.replace("amt","{0}".format(str(row["totalval"]))).replace('Cur','{0}'.format(str(row['Cur'])))
        inputdf['fullstring'] = filledstring
    return inputdf

df2 = createpr(mydf)

但是，當您運行此代碼時，所有行的“ fullstring”字段僅填充最后一行的值。 數據框看起來像這樣（請注意，為了便於閱讀，我刪除了兩列）：

sales = [{'account': 'Jones LLC','totalval': 140, 'Cur': 'pesos', 'fullstring': 'We got an order totaling 95 pounds selling parts wahoo!'},
         {'account': 'Alpha Co','totalval': 215, 'Cur': 'euros', 'fullstring': 'We got an order totaling 95 pounds selling parts wahoo!'},
         {'account': 'Blue Inc','totalval': 95 , 'Cur': 'pounds','fullstring': 'We got an order totaling 95 pounds selling parts wahoo!'}]

如何獲得根據每行中的相應值替換值的函數？

Answer 1

使用format_map更容易

In [40]: mydf.apply('We made {totalval} pesos from {account}. Woohoo!'.format_map, axis=1)
Out[40]:
0    We made 140 pesos from Jones LLC. Woohoo!
1     We made 215 pesos from Alpha Co. Woohoo!
2      We made 95 pesos from Blue Inc. Woohoo!
dtype: object

分配給

In [46]: mydf.assign(fullstring=mydf.apply(
          'We made {totalval} pesos from {account}. Woohoo!'.format_map, axis=1))
Out[46]:
      Cur    account  bolts  nuts  totalval  \
0   pesos  Jones LLC    200   150       140
1   euros   Alpha Co    210   200       215
2  pounds   Blue Inc     90    50        95

                                  fullstring
0  We made 140 pesos from Jones LLC. Woohoo!
1   We made 215 pesos from Alpha Co. Woohoo!
2    We made 95 pesos from Blue Inc. Woohoo!

對於dict您可以使用to_dict

In [48]: mydf.assign(fullstring=mydf.apply(
              'We made {totalval} pesos from {account}. Woohoo!'.format_map, axis=1)
             ).to_dict(orient='r')
Out[48]:
[{'Cur': 'pesos',
  'account': 'Jones LLC',
  'bolts': 200,
  'nuts': 150,
  'totalval': 140,
  'fullstring': 'We made 140 pesos from Jones LLC. Woohoo!'},
 {'Cur': 'euros',
  'account': 'Alpha Co',
  'bolts': 210,
  'nuts': 200,
  'totalval': 215,
  'fullstring': 'We made 215 pesos from Alpha Co. Woohoo!'},
 {'Cur': 'pounds',
  'account': 'Blue Inc',
  'bolts': 90,
  'nuts': 50,
  'totalval': 95,
  'fullstring': 'We made 95 pesos from Blue Inc. Woohoo!'}]