計算不等於（！=）的float64或int64的頻率

Question

我知道有很多帖子，但這並不能解決我的問題。

我的數據框是這樣的：

df1 = [{"Customer Number": "AFIMBN01000BCA17030001177", "Account Name": "Sunarto","Debit/Credit Indicator" : "k","Money" : 100},
    {"Customer Number": "AFIMBN01000BCA17030001177", "Account Name": "Sunarto","Debit/Credit Indicator": "k","Money" : 200},
    {"Customer Number": "AFIMBN01000BCA17030001177", "Account Name": "Sunarto","Debit/Credit Indicator" : "D", "Money" : 0}]
df1 = pd.DataFrame(df1)
df1

Account Name    Customer Number           Debit/Credit Indicator         Money
Sunarto      AFIMBN01000BCA17030001177       k                            100
Sunarto      AFIMBN01000BCA17030001177       k                            200
Sunarto      AFIMBN01000BCA17030001177       D                             0

Account Name              object
Customer Number           object
Debit/Credit Indicator    object
Money                      int64 (or let's say float64)

我想根據“金錢”來計算頻率

如果 Money為0，則不計算在內。

我已經嘗試過df1["Money"].value_counts()不起作用

df1.loc[df1["Money"] != 0, "Per item"] = df1["Money"].value_counts()
df1

Account Name    Customer Number           Debit/Credit Indicator         Money   Per item
Sunarto      AFIMBN01000BCA17030001177       k                            100     1
Sunarto      AFIMBN01000BCA17030001177       k                            200    NaN
Sunarto      AFIMBN01000BCA17030001177       D                             0   NaN

但我的期望是

Account Name    Customer Number           Debit/Credit Indicator         Money   Per item
Sunarto      AFIMBN01000BCA17030001177       k                            100     1
Sunarto      AFIMBN01000BCA17030001177       k                            200    1
Sunarto      AFIMBN01000BCA17030001177       D                             0   0

因此，當我在數據透視中應用時，我的期望是，我可以獲得具有“金錢”價值的商品

我的期望

gdf = pd.pivot_table(df1, index = ["Account Name","Customer Number"],values = ["Money", "Per item"],aggfunc = np.sum)

gdf.head()

                                                Money              Per item
Account Name      Customer Number
Sunarto           AFIMBN01000BCA17030001177     300                2.0

Answer 1

您需要為每個條件分配1 ：

df1.loc[df1["Money"] != 0, "Per item"] = 1

或將布爾值掩碼轉換為整數：

df1["Per item"] = (df1["Money"] != 0).astype(int)

不帶數據pivot_table另一種解決方案：

gdf = (df1.groupby(["Account Name","Customer Number"])['Money']
          .agg([('Money','sum'), ('Per item', lambda x: x.ne(0).sum())]))
print (gdf)
                                        Money  Per item
Account Name Customer Number                           
Sunarto      AFIMBN01000BCA17030001177    300         2

編輯：

我可以知道為什么我的代碼不起作用嗎？

問題是Series.value_counts返回帶有計數器值的Series，但是索引值是由原始Series值創建的，此處為100, 200 。 因此索引不匹配並獲得缺失值。 解決方法是使用Series.map ：

df1.loc[df1["Money"] != 0, "Per item"] = df1["Money"].map(df1["Money"].value_counts())
print (df1)
  Account Name            Customer Number Debit/Credit Indicator  Money  \
0      Sunarto  AFIMBN01000BCA17030001177                      k    100   
1      Sunarto  AFIMBN01000BCA17030001177                      k    200   
2      Sunarto  AFIMBN01000BCA17030001177                      D      0   

   Per item  
0       1.0  
1       1.0  
2       NaN  

但是，如果有多個重復的值，那么這不是問題，而是沒有分配1而是計數器值並得到錯誤的輸出，這里，將200值加倍會錯誤地返回4值，而不是2 ：

df1 = [{"Customer Number": "AFIMBN01000BCA17030001177", "Account Name": "Sunarto","Debit/Credit Indicator" : "k","Money" : 200},
    {"Customer Number": "AFIMBN01000BCA17030001177", "Account Name": "Sunarto","Debit/Credit Indicator": "k","Money" : 200},
    {"Customer Number": "AFIMBN01000BCA17030001177", "Account Name": "Sunarto","Debit/Credit Indicator" : "D", "Money" : 0}]
df1 = pd.DataFrame(df1)


df1.loc[df1["Money"] != 0, "Per item"] = df1["Money"].map(df1["Money"].value_counts())
print (df1)
  Account Name            Customer Number Debit/Credit Indicator  Money  \
0      Sunarto  AFIMBN01000BCA17030001177                      k    200   
1      Sunarto  AFIMBN01000BCA17030001177                      k    200   
2      Sunarto  AFIMBN01000BCA17030001177                      D      0   

   Per item  
0       2.0  
1       2.0  
2       NaN  

gdf = pd.pivot_table(df1, index = ["Account Name","Customer Number"],values = ["Money", "Per item"],aggfunc = np.sum)

print (gdf)
                                        Money  Per item
Account Name Customer Number                           
Sunarto      AFIMBN01000BCA17030001177    400       4.0