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efueyo
Asked: 2020-04-06 14:40:43 +0800 CST 2020-04-06 14:40:43 +0800 CST

对 DataFrame 列的操作

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我有以下数据框

d = {'Ticker': ["Total_Pond", "Total_Pond", "Total_Pond", "ValorRelativo", "Avantage","Acatis" ],
        'Tipo_Inv': ["FI", "FI", "FI", "FI","FI", "FI"], 'Fact_Pond': [2.313857e+06, 1.342821e+06,3.486820e+06,2.788324e+06, 4.929096e+06, 2.663564e+06],
        'Total_Pond': [-53196.922835, -37853.091155, -114935.107120, -26013.111409,374923.428199, -235590.992103 ]}
df = pd.DataFrame(data=d)
df

我尝试计算每个值的加权平均兴趣，为此我旋转表并将列df_Imp_value 与除以 ["Total_Pond"]的结果 除以df ["Fact_Pond"] 

# Interes medio ponderado de cada valor
df_Imp_valor = pd.pivot_table(df,index=["Ticker"],values=['Fact_Pond', 'Total_Pond' ],aggfunc=np.sum)
df_Imp_valor["Int_Med_Pond_Val"] = df ["Total_Pond"]/  df ["Fact_Pond"]
df_Imp_valor[:3]

结果不正确，因为它将 NaN 添加到该列的值。

          Fact_Pond     Total_Pond     Int_Med_Pond_Val
Ticker          
Acatis    2663564.0     -235590.992103    NaN
Avantage    4929096.0   374923.428199      NaN
Total_Pond  7143498.0   -205985.121110     NaN

我用另一种方式检查，除法的结果是一个数字

a = df ["Total_Pond"]
b = df ["Fact_Pond"] 
a/b

0   -0.022991
1   -0.028189
2   -0.032963
3   -0.009329
4    0.076063
5   -0.088450
dtype: float64

肯定是我着迷了。我犯了什么错误？谢谢您的帮助

pandas

1 Answers

  1. Best Answer

    我了解您想对旋转结果进行除法，而不是对原始表的列进行除法。

    问题在于：

    df_Imp_valor["Int_Med_Pond_Val"] = df ["Total_Pond"]/  df ["Fact_Pond"]

    您从原始数据框中拆分列，df 但将结果分配给具有其他索引的旋转结果数据框。由于索引不匹配，因此拆分结果中没有与股票代码名称对应的行，因此将其填充为NaN.

    这样做：

    df_Imp_valor = pd.pivot_table(df,index=["Ticker"],values=['Fact_Pond', 'Total_Pond' ],aggfunc=np.sum)
df_Imp_valor["Int_Med_Pond_Val"] = df_Imp_valor["Total_Pond"]/  df_Imp_valor["Fact_Pond"]
df_Imp_valor

    结果已经正确：

                  Fact_Pond     Total_Pond  Int_Med_Pond_Val
Ticker                                                   
Acatis         2663564.0 -235590.992103         -0.088450
Avantage       4929096.0  374923.428199          0.076063
Total_Pond     7143498.0 -205985.121110         -0.028835
ValorRelativo  2788324.0  -26013.111409         -0.009329
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