Python 在 Pandas 中查找类型为 float 或特定类型的所有数据框列？
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Find all columns of dataframe in Pandas whose type is float, or a particular type?
提问by Yu Shen
I have a dataframe, df, that has some columns of type float64, while the others are of object. Due to the mixed nature, I cannot use
我有一个数据框 df，它有一些 float64 类型的列，而其他列是对象。由于混合性质，我不能使用
df.fillna('unknown') #getting error "ValueError: could not convert string to float:"
as the error happened with the columns whose type is float64 (what a misleading error message!)
因为错误发生在类型为 float64 的列上（多么具有误导性的错误消息！）
so I'd wish that I could do something like
所以我希望我能做类似的事情
for col in df.columns[<dtype == object>]:
df[col] = df[col].fillna("unknown")
So my question is if there is any such filter expression that I can use with df.columns?
所以我的问题是，是否有任何此类过滤器表达式可以与 df.columns 一起使用？
I guess alternatively, less elegantly, I could do:
我想或者，不那么优雅，我可以这样做：
for col in df.columns:
if (df[col].dtype == dtype('O')): # for object type
df[col] = df[col].fillna('')
# still puzzled, only empty string works as replacement, 'unknown' would not work for certain value leading to error of "ValueError: Error parsing datetime string "unknown" at position 0"
I also would like to know why in the above code replacing '' with 'unknown' the code would work for certain cells but failed with a cell with the error of "ValueError: Error parsing datetime string "unknown" at position 0"
我还想知道为什么在上面的代码中用 'unknown' 替换 '' 代码对某些单元格有效，但由于单元格失败，错误为“ValueError: Error parsing datetime string “unknown” at position 0”
Thanks a lot!
非常感谢！
Yu
于
采纳答案by Andy Hayden
You can see what the dtype is for all the columns using the dtypes attribute:
您可以使用 dtypes 属性查看所有列的 dtype：
In [11]: df = pd.DataFrame([[1, 'a', 2.]])
In [12]: df
Out[12]:
0 1 2
0 1 a 2
In [13]: df.dtypes
Out[13]:
0 int64
1 object
2 float64
dtype: object
In [14]: df.dtypes == object
Out[14]:
0 False
1 True
2 False
dtype: bool
To access the object columns:
要访问对象列：
In [15]: df.loc[:, df.dtypes == object]
Out[15]:
1
0 a
I think it's most explicit to use (I'm not surethat inplace would work here):
我认为使用它是最明确的（我不确定就地是否可以在这里工作）：
In [16]: df.loc[:, df.dtypes == object] = df.loc[:, df.dtypes == object].fillna('')
Saying that, I recommend you use NaN for missing data.
回答by RNA
This is conciser:
这更简洁：
# select the float columns
df_num = df.select_dtypes(include=[np.float])
# select non-numeric columns
df_num = df.select_dtypes(exclude=[np.number])
回答by Jaroslav Bezděk
As @RNA said, you can use pandas.DataFrame.select_dtypes. The code using your example from a question would look like this:
正如@RNA 所说，您可以使用pandas.DataFrame.select_dtypes。使用您的问题示例的代码如下所示：
for col in df.select_dtypes(include=['object']).columns:
df[col] = df[col].fillna('unknown')
