Question

给定具有相似列的数据帧，其间具有空值。如何动态填充来自其他列的非空值的列中的空值，而不显式声明其他列名称的名称，例如选择第一列category1并使用来自相同行的其他列的值填充空行？

data = {'year': [2010, 2011, 2012, 2013, 2014, 2015, 2016,2017, 2018, 2019],
        'category1': [None, 21, None, 10, None, 30, 31,45, 23, 56],
        'category2': [10, 21, 20, 10, None, 30, None,45, 23, 56],
        'category3': [10, 21, 20, 10, None, 30, 31,45, 23, 56],}


df = pd.DataFrame(data)
df = df.set_index('year')
df

    category1   category2   category3
year            
2010    NaN 10  10
2011    21  21  21
2012    NaN 20  20
2013    10  10  10
2014    NaN NaN NaN
2015    30  30  NaN
2016    31  NaN 31
2017    45  45  45
2018    23  23  23
2019    56  56  56

填写category1后：

category1   category2   category3
year            
2010    10  10  10
2011    21  21  21
2012    20  20  20
2013    10  10  10
2014    NaN NaN NaN
2015    30  30  NaN
2016    31  NaN 31
2017    45  45  45
2018    23  23  23
2019    56  56  56

Answer 1

IIUC你可以这样做：

In [369]: df['category1'] = df['category1'].fillna(df['category2'])

In [370]: df
Out[370]:
      category1  category2  category3
year
2010       10.0       10.0       10.0
2011       21.0       21.0       21.0
2012       20.0       20.0       20.0
2013       10.0       10.0       10.0
2014        NaN        NaN        NaN
2015       30.0       30.0       30.0
2016       31.0        NaN       31.0
2017       45.0       45.0       45.0
2018       23.0       23.0       23.0
2019       56.0       56.0       56.0

Answer 2

如果所有值均为NaN，则可以first_valid_index使用条件：

def f(x):
    if x.first_valid_index() is None:
        return None
    else:
        return x[x.first_valid_index()]

df['a'] = df.apply(f, axis=1)

print (df)
      category1  category2  category3     a
year                                       
2010        NaN       10.0       10.0  10.0
2011       21.0       21.0       21.0  21.0
2012        NaN       20.0       20.0  20.0
2013       10.0       10.0       10.0  10.0
2014        NaN        NaN        NaN   NaN
2015       30.0       30.0       30.0  30.0
2016       31.0        NaN       31.0  31.0
2017       45.0       45.0       45.0  45.0
2018       23.0       23.0       23.0  23.0
2019       56.0       56.0       56.0  56.0

Answer 3

试试这个：

df['category1']= df['category1'].fillna(df.median(axis=1))

使用其他列中的非空值填充空值

3 个答案: