在python中调用Nan值并更改为数字

我有一个数据帧，比如说

df

，它看起来像这样：

id            property_type1  property_type  pro
1               Condominium              2    2
2                      Farm             14   14
3                     House              7    7
4                 Lots/Land             15   15
5  Mobile/Manufactured Home             13   13
6              Multi-Family              8    8
7                 Townhouse             11   11
8             Single Family             10   10
9                 Apt/Condo              1    1
10                      Home             7    7
11                       NaN             29  NaN

现在，我需要

pro

列具有与

property\u type

列相同的值，只要

property\u type1

列具有

NaN

值。应该是这样的：

id            property_type1  property_type  pro
1               Condominium              2    2
2                      Farm             14   14
3                     House              7    7
4                 Lots/Land             15   15
5  Mobile/Manufactured Home             13   13
6              Multi-Family              8    8
7                 Townhouse             11   11
8             Single Family             10   10
9                 Apt/Condo              1    1
10                      Home             7    7
11                       NaN             29  29

---

也就是说，在第11行中，

property\u type 1

是

NaN

，

pro

列的值变为29，这是

property\u type

的值。如何执行此操作？

查找

property\u type 1

列为

NaN

的行，对于这些行：将

property\u type

值分配给

pro

列

df.ix[df.property_type1.isnull(), 'pro'] = df.ix[df.property_type1.isnull(), 'property_type']

---

ix

已弃用，请勿使用

选项1
我会用

np.where

-

df = df.assign(pro=np.where(df.pro.isnull(), df.property_type, df.pro))
df
    id            property_type1  property_type   pro
0    1               Condominium              2   2.0
1    2                      Farm             14  14.0
2    3                     House              7   7.0
3    4                 Lots/Land             15  15.0
4    5  Mobile/Manufactured Home             13  13.0
5    6              Multi-Family              8   8.0
6    7                 Townhouse             11  11.0
7    8             Single Family             10  10.0
8    9                 Apt/Condo              1   1.0
9   10                      Home              7   7.0
10  11                       NaN             29  29.0

m = df.pro.isnull()
df.loc[m, 'pro'] = df.loc[m, 'property_type']
df

    id            property_type1  property_type   pro
0    1               Condominium              2   2.0
1    2                      Farm             14  14.0
2    3                     House              7   7.0
3    4                 Lots/Land             15  15.0
4    5  Mobile/Manufactured Home             13  13.0
5    6              Multi-Family              8   8.0
6    7                 Townhouse             11  11.0
7    8             Single Family             10  10.0
8    9                 Apt/Condo              1   1.0
9   10                      Home              7   7.0
10  11                       NaN             29  29.0

---

选项2
如果要执行就地分配，请使用

loc

-

df = df.assign(pro=np.where(df.pro.isnull(), df.property_type, df.pro))
df
    id            property_type1  property_type   pro
0    1               Condominium              2   2.0
1    2                      Farm             14  14.0
2    3                     House              7   7.0
3    4                 Lots/Land             15  15.0
4    5  Mobile/Manufactured Home             13  13.0
5    6              Multi-Family              8   8.0
6    7                 Townhouse             11  11.0
7    8             Single Family             10  10.0
8    9                 Apt/Condo              1   1.0
9   10                      Home              7   7.0
10  11                       NaN             29  29.0

m = df.pro.isnull()
df.loc[m, 'pro'] = df.loc[m, 'property_type']
df

    id            property_type1  property_type   pro
0    1               Condominium              2   2.0
1    2                      Farm             14  14.0
2    3                     House              7   7.0
3    4                 Lots/Land             15  15.0
4    5  Mobile/Manufactured Home             13  13.0
5    6              Multi-Family              8   8.0
6    7                 Townhouse             11  11.0
7    8             Single Family             10  10.0
8    9                 Apt/Condo              1   1.0
9   10                      Home              7   7.0
10  11                       NaN             29  29.0

---

只计算一次掩码，然后使用它多次索引，这应该比计算两次更有效。

1。不要使用

ix

，它已被弃用。2.不要计算掩码两次，效率很低。没问题@cᴏʟᴅsᴘᴇᴇᴅ, 你是对的。我不是最新的

ix

，双重输入掩码是我的错误。只要你明白，那太棒了。我不知道是谁投了反对票，但我已经投了一张赞成票。祝你好运