Find all columns of dataframe in Pandas whose type is float, or a particular type?
This is conciser:
# select the float columnsdf_num = df.select_dtypes(include=[np.float])# select non-numeric columnsdf_num = df.select_dtypes(exclude=[np.number])
You can see what the dtype is for all the columns using the dtypes attribute:
In [11]: df = pd.DataFrame([[1, 'a', 2.]])In [12]: dfOut[12]: 0 1 20 1 a 2In [13]: df.dtypesOut[13]: 0 int641 object2 float64dtype: objectIn [14]: df.dtypes == objectOut[14]: 0 False1 True2 Falsedtype: boolTo access the object columns:
In [15]: df.loc[:, df.dtypes == object]Out[15]: 10 aI think it's most explicit to use (I'm not sure that inplace would work here):
In [16]: df.loc[:, df.dtypes == object] = df.loc[:, df.dtypes == object].fillna('')Saying that, I recommend you use NaN for missing data.
As @RNA said, you can use pandas.DataFrame.select_dtypes. The code using your example from a question would look like this:
for col in df.select_dtypes(include=['object']).columns: df[col] = df[col].fillna('unknown')