每行用一个随机值替换 NaN

Question

我有一个包含列 'Self_Employed' 的数据集。这些列中有值 'Yes'、'No' 和 'NaN。我想用在 calc() 中计算的值替换 NaN 值。我尝试了一些在此处找到的方法，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，但是我有一个单独的集合，其中只有以前的缺失值，但我想在整个数据集中填充缺失值。对于最后一行我收到错误，我链接到它的屏幕截图。你明白我的问题吗？如果明白，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

df['Self_Employed'] = df['Self_Employed'].fillna(calc())呢？

Answer 2

确保 SEno+SEyes != null 使用.loc方法设置Self_Employed为空时的值

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())

Answer 3

您可以先确定 NaN 的位置，例如

na_loc = df.index[df['Self_Employed'].isnull()]

计算您的列中 NaN 的数量，例如

num_nas = len(na_loc)

然后生成相应数量的随机数，轻松索引和设置

fill_values = pd.DataFrame({'Self_Employed': [random.randint(0,100) for i in range(num_nas)]}, index = na_loc)

最后在您的数据框中替换这些值

df.loc[na_loc]['Self_Employed'] = fill_values

每行用一个随机值替换 NaN

Replace NaN with a random value every row

python

pandas

data-cleaning