如何基于dtype删除DataFrame列 [英] How to drop DataFrame columns based on dtype

问题描述

我有一个pandas数据框，我试图从中删除所有对象字段，以便只剩下数字.

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.

我一直在尝试编写一个for循环来完成此任务，因为我可能需要一遍又一遍地对不同的数据进行处理.

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.

由于某种原因，我无法使其正常运行.以下是我到目前为止所做的事情

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)

我得到的错误是:

AttributeError跟踪(最近的调用) 最后)在() 数据中的cols为1: ----> 2如果data.values.type == object: 3 numdata = data.drop(axis = 1，inplace = True)

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'对象没有属性'type'

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

我是个新手，由于某种原因，我无法获得for循环，而if语句逻辑一直困扰我.

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

推荐答案

您可以使用

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)

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