Python 中的条件数据插补 [英] Conditional data imputation in Python

问题描述

我正在尝试有条件地估算数据集中的值.

I am trying to impute values in my dataset conditionally.

假设我有三列，如果第 1 列为 1，则第 2 列为 0，第 3 列为 0；如果第 1 列是 2，则第 2 列是 Mean ()，第 3 列是 Mean().

Say I have three columns, If Column 1 is 1 then Column 2 is 0 and Column 3 is 0; If column 1 is 2 then Column 2 is Mean () and Column 3 is Mean().

我尝试使用函数 any() 运行 if 语句并单独定义条件.

I tried running an if statement with the function any() and defined the conditions separately.

但是根据条件没有满足条件，我要么得到所有平均值，要么全部为零.

However the conditions are not being fulfilled based on conditions, I am either getting all mean values or all zeroes.

具体代码如下:

if (df['Retention_Term'] == 6):
    df.cl_tot_calls_term_seq_1.replace(999, np.nan,inplace = True)
df['cl_tot_calls_term_seq_1'].fillna(df['cl_tot_calls_term_seq_1'].median(),inplace= True)

ValueError: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().

推荐答案

试试这个.

mask1 = df['Retention_Term']==6
mask2 = df['cl_tot_calls_term_seq_1'] == 999 
df.loc[mask1 & mask2, 'cl_tot_calls_term_seq_1'] = np.nan

那么剩下的应该没问题.

Then the rest should be ok.

df['cl_tot_calls_term_seq_1'].fillna(df['cl_tot_calls_term_seq_1'].median(), inplace= True)

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