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我尝试使用以下命令将数据类型为float64的列转换为int64:
df['column name'].astype(int64)
但得到一个错误:
NameError: name ‘int64’ is not defined
该列有多少人,但格式为7500000.0,任何想法我怎么可以简单地将这个float64更改为int64?
最佳答案
pandas 0.24的解决方案,用于转换带缺失值的数字:
df = pd.DataFrame({'column name':[7500000.0,7500000.0, np.nan]})
print (df['column name'])
0 7500000.0
1 7500000.0
2 NaN
Name: column name, dtype: float64
df['column name'] = df['column name'].astype(np.int64)
ValueError: Cannot convert non-finite values (NA or inf) to integer
#http://pandas.pydata.org/pandas-docs/stable/user_guide/integer_na.html
df['column name'] = df['column name'].astype('Int64')
print (df['column name'])
0 7500000
1 7500000
2 NaN
Name: column name, dtype: Int64
我认为你需要施展到numpy.int64
:
df['column name'].astype(np.int64)
样品:
df = pd.DataFrame({'column name':[7500000.0,7500000.0]})
print (df['column name'])
0 7500000.0
1 7500000.0
Name: column name, dtype: float64
df['column name'] = df['column name'].astype(np.int64)
#same as
#df['column name'] = df['column name'].astype(pd.np.int64)
print (df['column name'])
0 7500000
1 7500000
Name: column name, dtype: int64
如果列中的某些NaN需要在fillna
之前将它们替换为某个int(例如0),因为NaN的类型是float:
df = pd.DataFrame({'column name':[7500000.0,np.nan]})
df['column name'] = df['column name'].fillna(0).astype(np.int64)
print (df['column name'])
0 7500000
1 0
Name: column name, dtype: int64
另请查看documentation – missing data casting rules
编辑:
使用NaN转换值是错误的:
df = pd.DataFrame({'column name':[7500000.0,np.nan]})
df['column name'] = df['column name'].values.astype(np.int64)
print (df['column name'])
0 7500000
1 -9223372036854775808
Name: column name, dtype: int64
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