Welcome to OGeek Q&A Community for programmer and developer-Open, Learning and Share
Welcome To Ask or Share your Answers For Others

Categories

0 votes
1.3k views
in Technique[技术] by (71.8m points)

pandas - error using astype when NaN exists in a dataframe

df
     A     B  
0   a=10   b=20.10
1   a=20   NaN
2   NaN    b=30.10
3   a=40   b=40.10

I tried :

df['A'] = df['A'].str.extract('(d+)').astype(int)
df['B'] = df['B'].str.extract('(d+)').astype(float)

But I get the following error:

ValueError: cannot convert float NaN to integer

And:

AttributeError: Can only use .str accessor with string values, which use np.object_ dtype in pandas

How do I fix this ?

See Question&Answers more detail:os

与恶龙缠斗过久,自身亦成为恶龙;凝视深渊过久,深渊将回以凝视…
Welcome To Ask or Share your Answers For Others

1 Reply

0 votes
by (71.8m points)

If some values in column are missing (NaN) and then converted to numeric, always dtype is float. You cannot convert values to int. Only to float, because type of NaN is float.

print (type(np.nan))
<class 'float'>

See docs how convert values if at least one NaN:

integer > cast to float64

If need int values you need replace NaN to some int, e.g. 0 by fillna and then it works perfectly:

df['A'] = df['A'].str.extract('(d+)', expand=False)
df['B'] = df['B'].str.extract('(d+)', expand=False)
print (df)
     A    B
0   10   20
1   20  NaN
2  NaN   30
3   40   40

df1 = df.fillna(0).astype(int)
print (df1)
    A   B
0  10  20
1  20   0
2   0  30
3  40  40

print (df1.dtypes)
A    int32
B    int32
dtype: object

与恶龙缠斗过久,自身亦成为恶龙;凝视深渊过久,深渊将回以凝视…
OGeek|极客中国-欢迎来到极客的世界,一个免费开放的程序员编程交流平台!开放,进步,分享!让技术改变生活,让极客改变未来! Welcome to OGeek Q&A Community for programmer and developer-Open, Learning and Share
Click Here to Ask a Question

...