Python 从 Pandas DataFrame 中删除非数字列
声明：本页面是StackOverFlow热门问题的中英对照翻译，遵循CC BY-SA 4.0协议，如果您需要使用它，必须同样遵循CC BY-SA许可，注明原文地址和作者信息，同时你必须将它归于原作者（不是我）：StackOverFlow
原文地址: http://stackoverflow.com/questions/12725417/
Warning: these are provided under cc-by-sa 4.0 license. You are free to use/share it, But you must attribute it to the original authors (not me):
StackOverFlow
Drop non-numeric columns from a pandas DataFrame
提问by Einar
In my application I load text files that are structured as follows:
在我的应用程序中，我加载结构如下的文本文件：
- First non numeric column (ID)
- A number of non-numeric columns (strings)
- A number of numeric columns (floats)
- 第一个非数字列 (ID)
- 一些非数字列（字符串）
- 一些数字列（浮点数）
The number of the non-numeric columns is variable. Currently I load the data into a DataFrame like this:
非数字列的数量是可变的。目前我将数据加载到 DataFrame 中，如下所示：
source = pandas.read_table(inputfile, index_col=0)
I would like to drop all non-numeric columns in one fell swoop, without knowing their names or indices, since this could be doable reading their dtype. Is this possible with pandas or do I have to cook up something on my own?
我想一下子删除所有非数字列，而不知道它们的名称或索引，因为这可以读取它们的 dtype。大熊猫可以做到这一点，还是我必须自己做些东西？
采纳答案by sapo_cosmico
To avoid using a private method you can also use select_dtypes, where you can either include or exclude the dtypes you want.
为了避免使用私有方法，您还可以使用select_dtypes，您可以在其中包含或排除所需的 dtypes。
Ran into it on this poston the exact same thing.
在这个帖子上遇到了完全相同的事情。
Or in your case, specifically:source.select_dtypes(['number']) or source.select_dtypes([np.number]
或者在您的情况下，特别是：source.select_dtypes(['number']) or source.select_dtypes([np.number]
回答by Wouter Overmeire
It`s a private method, but it will do the trick: source._get_numeric_data()
这是一个私有方法，但它可以解决问题：source._get_numeric_data()
In [2]: import pandas as pd
In [3]: source = pd.DataFrame({'A': ['foo', 'bar'], 'B': [1, 2], 'C': [(1,2), (3,4)]})
In [4]: source
Out[4]:
A B C
0 foo 1 (1, 2)
1 bar 2 (3, 4)
In [5]: source._get_numeric_data()
Out[5]:
B
0 1
1 2
回答by Luigi Bungaro
I also have another possible solution for dropping the columns with categorical value with 2 lines of code, defining a list with columns of categorical values (1st line) and dropping them with the second line. df is our DataFrame
我还有另一种可能的解决方案，用于用 2 行代码删除具有分类值的列，定义一个包含分类值列的列表（第一行），然后用第二行删除它们。df 是我们的 DataFrame
df before dropping:
df 下降前：
to_be_dropped=pd.DataFrame(df.categorical).columns
df= df.drop(to_be_dropped,axis=1)
df after dropping:
下降后的df：
回答by Thomas Gotwig
This would remove each column which doesn't include float64 numerics.
这将删除不包含 float64 数字的每一列。
df = pd.read_csv('sample.csv', index_col=0)
non_floats = []
for col in df:
if df[col].dtypes != "float64":
non_floats.append(col)
df = df.drop(columns=non_floats)
