error using astype when NaN exists in a dataframe

Question

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.extra         


        

Answer 1

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