Pandas 将字符串列和 NaN（浮点数）转换为整数，保持 NaN
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Pandas converting column of strings and NaN (floats) to integers, keeping the NaN
提问by mik.ferrucci
I have problems in converting a column which contains both numbers of 2 digits in string format (type: str) and NaN (type: float64). I want to obtain a new column made this way: NaN where there was NaN and integer numbers where there was a number of 2 digits in string format. As an example: I want to obtain column Yearbirth2 from column YearBirth1 like this:
我在转换包含字符串格式（类型：str）和 NaN（类型：float64）的 2 位数的列时遇到问题。我想以这种方式获得一个新列：NaN，其中有 NaN 和整数，其中有字符串格式的 2 位数字。例如：我想从 YearBirth1 列中获取 Yearbirth2 列，如下所示：
YearBirth1 #numbers here are formatted as strings: type(YearBirth1[0])=str
34 # and NaN are floats: type(YearBirth1[2])=float64.
76
Nan
09
Nan
91
YearBirth2 #numbers here are formatted as integers: type(YearBirth2[0])=int
34 #NaN can remain floats as they were.
76
Nan
9
Nan
91
I have tried this:
我试过这个：
csv['YearBirth2'] = (csv['YearBirth1']).astype(int)
And as I expected i got this error:
正如我所料，我收到了这个错误：
ValueError: cannot convert float NaN to integer
So I tried this:
所以我试过这个：
csv['YearBirth2'] = (csv['YearBirth1']!=NaN).astype(int)
And got this error:
并得到这个错误：
NameError: name 'NaN' is not defined
Finally I have tried this:
最后我试过这个：
csv['YearBirth2'] = (csv['YearBirth1']!='NaN').astype(int)
NO error, but when I checked the column YearBirth2, this was the result:
没有错误，但是当我检查 YearBirth2 列时，结果如下：
YearBirth2:
1
1
1
1
1
1
Very bad.. I think the idea is right but there is a problem to make Python able to understand what I mean for NaN.. Or maybe the method I tried is wrong..
非常糟糕.. 我认为这个想法是正确的，但有一个问题让 Python 能够理解我对 NaN 的意思.. 或者我尝试的方法可能是错误的..
I also used pd.to_numeric() method, but this way i obtain floats, not integers..
我还使用了 pd.to_numeric() 方法，但这样我获得了浮点数，而不是整数..
Any help?! Thanks to everyone!
有什么帮助吗？！谢谢大家！
P.S: csv is the name of my DataFrame; Sorry if I am not so clear, I am on improving with English language!
PS：csv是我的DataFrame的名字；对不起，如果我不太清楚，我正在提高英语水平！
回答by jezrael
You can use to_numeric, but is impossible get intwith NaNvalues - they are always converted to float: see na type promotions.
您可以使用to_numeric，但无法int使用NaN值获取- 它们始终转换为float：请参阅 na 类型促销。
df['YearBirth2'] = pd.to_numeric(df.YearBirth1, errors='coerce')
print (df)
YearBirth1 YearBirth2
0 34 34.0
1 76 76.0
2 Nan NaN
3 09 9.0
4 Nan NaN
5 91 91.0
