python - 如何在 Pandas 中将对象转换为 int 或 float

Question

我在加载的 excel 文件上使用 df.info 方法后得到以下数据

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 30000 entries, 1 to 30000
Data columns (total 25 columns):
 #   Column      Non-Null Count  Dtype 
---  ------      --------------  ----- 
 0   Unnamed: 0  30000 non-null  object
 1   X1          30000 non-null  object
 2   X2          30000 non-null  object
 3   X3          29669 non-null  object
 4   X4          29677 non-null  object
 5   X5          30000 non-null  object
 6   X6          30000 non-null  object
 7   X7          30000 non-null  object
 8   X8          30000 non-null  object
 9   X9          30000 non-null  object
 10  X10         30000 non-null  object
 11  X11         30000 non-null  object
 12  X12         30000 non-null  object
 13  X13         30000 non-null  object
 14  X14         30000 non-null  object
 15  X15         30000 non-null  object
 16  X16         30000 non-null  object
 17  X17         30000 non-null  object
 18  X18         30000 non-null  object
 19  X19         30000 non-null  object
 20  X20         30000 non-null  object
 21  X21         30000 non-null  object
 22  X22         30000 non-null  object
 23  X23         30000 non-null  object
 24  Y           30000 non-null  object
dtypes: object(25)
memory usage: 2.9+ MB

我不知道为什么所有数据类型都是对象，尽管它们中的大多数都有数值如何修复我的数据集的数据类型

Answer 1

让我们尝试to_numeric

df = pd.DataFrame({'1':['1','2'],'2':['a','b']})
df = df.apply(pd.to_numeric,errors='ignore')

检查

df.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 2 entries, 0 to 1
Data columns (total 2 columns):
 #   Column  Non-Null Count  Dtype 
---  ------  --------------  ----- 
 0   1       2 non-null      int64 
 1   2       2 non-null      object
dtypes: int64(1), object(1)
memory usage: 88.0+ bytes