pandas 如何根据 dtype 删除 DataFrame 列
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How to drop DataFrame columns based on dtype
提问by Nick Duddy
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.
我有一个 Pandas 数据框，我试图从中删除所有对象字段，这样我就只剩下数字了。
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.
我一直在尝试编写一个 for 循环来完成此任务，因为我可能需要使用不同的数据一遍又一遍地执行此操作。
For some reason I can't get it working. Below is what I've did so far
出于某种原因，我无法让它工作。以下是我到目前为止所做的
for cols in data:
if data.values.type == object:
numdata = data.drop(axis=1, inplace=True)
The error I get is:
我得到的错误是：
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'
AttributeError Traceback（最近一次调用最后一次） 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'
I am a newb and for some reason I can't get the for loop and if statement logic to stick in my head.
我是一个新手，出于某种原因，我无法将 for 循环和 if 语句逻辑留在我的脑海中。
回答by roganjosh
You can use select_dtypesto exclude columns of a particular type.
您可以使用select_dtypes排除特定类型的列。
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)
