python - 在 Pandas 中将 float64 列转换为 int64

Question

我试图从数据类型 float64 转换列至int64使用:

df['column name'].astype(int64)

但出现错误:

NameError: name 'int64' is not defined

该列包含人数，但格式为 7500000.0 ，知道如何简单地更改此float64进入 int64 ?

Answer 1

pandas 0.24+ 用于转换缺失值的数值的解决方案:

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

---

我认为您需要转换为 numpy.int64 :

df['column name'].astype(np.int64)

示例:

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 需要用 fillna 将它们替换为某些 int(例如 0) , 因为 NaN 的 type 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

同时检查 documentation - missing data casting rules

编辑:

用 NaNs 转换值是有问题的:

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