How to drop DataFrame columns based on dtype

问题:

I've got a pandas dataframe and I'm trying to drop all the object fields from so that I'm left with only numeric.

I've been trying to write a for loop to do this task, as I'm likely going to need to do it over and over again with different data.

For some reason I can't get it working. Below is what I've did so far

for cols in data:
    if data.values.type == object:
        numdata = data.drop(axis=1, inplace=True)

The error I get is:

AttributeError Traceback (most recent call last) in () 1 for cols in data: ----> 2 if data.values.type == object: 3 numdata = data.drop(axis=1, inplace=True)

AttributeError: 'numpy.ndarray' object has no attribute 'type'

I am a newb and for some reason I can't get the for loop and if statement logic to stick in my head.

回答1:

You can use select_dtypes to exclude columns of a particular type.

import pandas as pd

df = pd.DataFrame({'x': ['a', 'b', 'c'], 'y': [1, 2, 3], 'z': ['d', 'e', 'f']})

df = df.select_dtypes(exclude=['object'])
print(df)