How do I find numeric columns in Pandas?

Question

You could use select_dtypes method of DataFrame. It includes two parameters include and exclude. So isNumeric would look like:

numerics = ['int16', 'int32', 'int64', 'float16', 'float32', 'float64']

newdf = df.select_dtypes(include=numerics)

Simple one-line answer to create a new dataframe with only numeric columns:

df.select_dtypes(include=np.number)

If you want the names of numeric columns:

df.select_dtypes(include=np.number).columns.tolist()

Complete code:

import pandas as pd
import numpy as np

df = pd.DataFrame({'A': range(7, 10),
                   'B': np.random.rand(3),
                   'C': ['foo','bar','baz'],
                   'D': ['who','what','when']})
df
#    A         B    C     D
# 0  7  0.704021  foo   who
# 1  8  0.264025  bar  what
# 2  9  0.230671  baz  when

df_numerics_only = df.select_dtypes(include=np.number)
df_numerics_only
#    A         B
# 0  7  0.704021
# 1  8  0.264025
# 2  9  0.230671

colnames_numerics_only = df.select_dtypes(include=np.number).columns.tolist()
colnames_numerics_only
# ['A', 'B']

You can use the undocumented function _get_numeric_data() to filter only numeric columns:

df._get_numeric_data()

Example:

In [32]: data
Out[32]:
   A  B
0  1  s
1  2  s
2  3  s
3  4  s

In [33]: data._get_numeric_data()
Out[33]:
   A
0  1
1  2
2  3
3  4

Note that this is a "private method" (i.e., an implementation detail) and is subject to change or total removal in the future. Use with caution.

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

Update:

df.select_dtypes(include= np.number)

or with new version of panda

 df.select_dtypes('number')

Simple one-liner:

df.select_dtypes('number').columns

Following codes will return list of names of the numeric columns of a data set.

cnames=list(marketing_train.select_dtypes(exclude=['object']).columns)

here marketing_train is my data set and select_dtypes() is function to select data types using exclude and include arguments and columns is used to fetch the column name of data set output of above code will be following:

['custAge',
     'campaign',
     'pdays',
     'previous',
     'emp.var.rate',
     'cons.price.idx',
     'cons.conf.idx',
     'euribor3m',
     'nr.employed',
     'pmonths',
     'pastEmail']