在python中将类型从float转换为int

Question

I am trying to change data_df which is type float64 to int .

data_df['grade'] = data_df['grade'].astype(int)

I get the following error.

invalid literal for int() with base 10: '17.44'

Answer 1

I think you need to_numeric first because float cannot be cast to int :

data_df['grade'] = pd.to_numeric(data_df['grade']).astype(int)

Another solution is first cast to float and then to int :

data_df['grade'] = data_df['grade'].astype(float).astype(int)

Sample:

data_df = pd.DataFrame({'grade':['10','20','17.44']})
print (data_df)
   grade
0     10
1     20
2  17.44

data_df['grade'] = pd.to_numeric(data_df['grade']).astype(int)
print (data_df)
   grade
0     10
1     20
2     17

---

data_df['grade'] = data_df['grade'].astype(float).astype(int)
print (data_df)
   grade
0     10
1     20
2     17

---

If some values cannot be converted and after to_numeric get error:

ValueError: Unable to parse string

is possible add parameter errors='coerce' for convert non numeric to NaN .

If NaN values then cast to int is not possible see docs :

data_df = pd.DataFrame({'grade':['10','20','17.44', 'aa']})
print (data_df)
   grade
0     10
1     20
2  17.44
3     aa

data_df['grade'] = pd.to_numeric(data_df['grade'], errors='coerce')
print (data_df)
   grade
0  10.00
1  20.00
2  17.44
3    NaN

If want change NaN to some numeric eg 0 use fillna :

data_df['grade'] = pd.to_numeric(data_df['grade'], errors='coerce')
                     .fillna(0)
                     .astype(int)
print (data_df)
   grade
0     10
1     20
2     17
3      0

Small advice:

Before using errors='coerce' check all rows where is impossible casting to numeric by boolean indexing :

print (data_df[pd.to_numeric(data_df['grade'], errors='coerce').isnull()])
  grade
3    aa

Answer 2

what works is data_df['grade'] = int(pd.to_numeric(data_df['grade'])) The method as_type(int) throws and error because it want's to tell you, that no exact conversion from float to integer is possible and you will lose information. My solution will truncate the integer (ie 1.9 will become 1), so you might want to specifiy in your question wether you want to convert float to integer by truncation or by rounding (ie 1.9 will become 2)

Answer 3

From:

data_df['grade'] = data_df['grade'].astype(int)

Need to change int into 'int'

data_df['grade'] = data_df['grade'].astype('int')

Answer 4

我发现这对我有用，其他先前的答案都对我没有帮助：

data_df['grade'] = data_df['grade'].apply(np.int)