python - 每行用随机值替换 NaN

Question

我有一个包含“Self_Employed”列的数据集。这些列中有值“Yes”、“No”和“NaN”。我想用 calc() 中计算的值替换 NaN 值。我尝试了在这里找到的一些方法，但找不到适合我的方法。这是我的代码，我把我尝试过的东西放在评论中。:

    # Handling missing data - Self_employed
SEyes = (df['Self_Employed']=='Yes').sum()
SEno = (df['Self_Employed']=='No').sum()

def calc():
    rand_SE = randint(0,(SEno+SEyes))
    if rand_SE > 81:
        return 'No'
    else:
        return 'Yes'


> # df['Self_Employed'] = df['Self_Employed'].fillna(randint(0,100))
> #df['Self_Employed'].isnull().apply(lambda v: calc())
> 
> 
> # df[df['Self_Employed'].isnull()] = df[df['Self_Employed'].isnull()].apply(lambda v: calc())  
> # df[df['Self_Employed']]
> 
> # df_nan['Self_Employed'] = df_nan['Self_Employed'].isnull().apply(lambda v: calc())
> # df_nan
> 
> #  for i in range(df['Self_Employed'].isnull().sum()):
> #      print(df.Self_Employed[i]


df[df['Self_Employed'].isnull()] = df[df['Self_Employed'].isnull()].apply(lambda v: calc())
df

现在我用 df_nan 尝试的行似乎可以工作，但后来我有一个单独的集合，其中仅包含以前的缺失值，但我想填充整个数据集中的缺失值。对于最后一行，我收到一个错误，我链接到它的屏幕截图。你明白我的问题吗？如果明白的话，你能帮忙吗？

This is the set with only the rows where Self_Employed is NaN

This is the original dataset

This is the error

Answer 1

确保 SEno+SEyes != null当Self_Employed为空时，使用.loc方法设置其值

SEyes = (df['Self_Employed']=='Yes').sum() + 1
SEno = (df['Self_Employed']=='No').sum()

def calc():
    rand_SE = np.random.randint(0,(SEno+SEyes))
    if(rand_SE >= 81):
        return 'No'
    else:
        return 'Yes'

df.loc[df['Self_Employed'].isna(), 'Self_Employed'] = df.loc[df['Self_Employed'].isna(), 'Self_Employed'].apply(lambda x: calc())