pandas 如何在熊猫数据框中获取数字列名称
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how to get numeric column names in pandas dataframe
提问by Neil
I have pandas dataframe which has object,int64,float64datatypes. I want to get column names for int64 and float64columns. I am using following command in pandas,but it does not seem to work
我有具有object,int64,float64数据类型的Pandas数据框。我想获取列的列名int64 and float64。我在Pandas中使用以下命令，但它似乎不起作用
cat_num_prv_app = [num for num in list(df.columns) if isinstance(num, (np.int64,np.float64))]
Following are my datatypes
以下是我的数据类型
df.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1670214 entries, 0 to 1670213
Data columns (total 37 columns):
ID 1670214 non-null int64
NAME 1670214 non-null object
ANNUITY 1297979 non-null float64
AMOUNT 1670214 non-null float64
CREDIT 1670213 non-null float64
I want to store column names ID,ANNUITY,AMOUNT and CREDITin a variable,which I can use later to subset the dataframe.
我想将列名存储ID,ANNUITY,AMOUNT and CREDIT在一个变量中，稍后我可以使用它来对数据帧进行子集化。
回答by jezrael
Use select_dtypeswith np.numberfor select all numeric columns:
使用select_dtypes与np.number用于选择所有数字列：
df = pd.DataFrame({'A':list('abcdef'),
'B':[4.5,5,4,5,5,4],
'C':[7.4,8,9,4,2,3],
'D':[1,3,5,7,1,0],
'E':list('aaabbb')})
print (df)
A B C D E
0 a 4.5 7.4 1 a
1 b 5.0 8.0 3 a
2 c 4.0 9.0 5 a
3 d 5.0 4.0 7 b
4 e 5.0 2.0 1 b
5 f 4.0 3.0 0 b
print (df.dtypes)
A object
B float64
C float64
D int64
E object
dtype: object
cols = df.select_dtypes([np.number]).columns
print (cols)
Index(['B', 'C', 'D'], dtype='object')
Here is possible specify float64and int64:
这里可以指定float64和int64：
df = pd.DataFrame({'A':list('abcdef'),
'B':[4.5,5,4,5,5,4],
'C':[7,8,9,4,2,3],
'D':[1,3,5,7,1,0],
'E':list('aaabbb')})
df['D'] = df['D'].astype(np.int32)
print (df.dtypes)
A object
B float64
C int64
D int32
E object
dtype: object
cols = df.select_dtypes([np.int64,np.float64]).columns
print (cols)
Index(['B', 'C'], dtype='object')
回答by Teoretic
Alternative solution using "np.where"
(uglier than approved answer though)
使用“np.where”的替代解决方案
（虽然比批准的答案更丑）
df.iloc[:, (np.where((df.dtypes == np.int64) | (df.dtypes == np.float64)))[0]].columns
df.iloc[:, (np.where((df.dtypes == np.int64) | (df.dtypes == np.float64)))[0]].columns
Sample code:
示例代码：
import pandas as pd
import numpy as np
df = pd.DataFrame({"A": [1, 2, 3], "B": [1.0, 2.0, 3.0], "C": ["a", "b", "c"]})
print(df.iloc[:, (np.where((df.dtypes == np.int64) |
(df.dtypes == np.float64)))[0]].columns)
> Index(['A', 'B'], dtype='object')
