How to Find Numeric Columns in Pandas

how to get numeric column names in pandas dataframe

Use select_dtypes with np.number for select all numeric columns:

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 float64 and 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')

Identify pandas dataframe columns containing both numeric and string

You can def your own function

def findchrandnum(x):
    try :
        return all(x.str.isalnum() & ~x.str.isalpha() & ~x.str.isdigit())
    except:
        return False
df.apply(findchrandnum)
Out[66]: 
ltv         False
age         False
job         False
UniqueID     True
dtype: bool

Pandas way to do arithmetic operations on only numeric columns

You can find the numeric columns using select_dtypes:

s = df.select_dtypes("number").columns
df[s] *= 100

print (df)

  name1  val1 name2  val21  val22
0     a   100    aa    100    500
1     b   200    bb    200    600
2     c   300    cc    300    700

How to check if a pandas dataframe contains only numeric values column-wise?

You can check that using to_numeric and coercing errors:

pd.to_numeric(df['column'], errors='coerce').notnull().all()

For all columns, you can iterate through columns or just use apply

df.apply(lambda s: pd.to_numeric(s, errors='coerce').notnull().all())

---

E.g.

df = pd.DataFrame({'col' : [1,2, 10, np.nan, 'a'], 
                   'col2': ['a', 10, 30, 40 ,50],
                   'col3': [1,2,3,4,5.0]})

Outputs

col     False
col2    False
col3     True
dtype: bool

How to get only numeric type columns from dataframe

The following works

import numpy as np
import pandas as pd 

df = pd.DataFrame({'float_col': [1.0],
                   'int_col': [1],
                   'datetime_col': [pd.Timestamp('20180310')],
                   'string_col': ['foo']})
df_int = df[df.columns[df.dtypes == np.int64]]

In this case, df is the data frame

   float_col  int_col datetime_col string_col
0        1.0        1   2018-03-10        foo

and df_int is the data frame

   int_col
0        1

Python Pandas: How to find in dataframe object type columns which has numeric data?

Not straight forward, the following is a wilcard and is all weather though

First select dtypes='object'
Second attempt to coerce them to numeric, setting errors='coerce', what that will do is if alphanumeric, it will output them as NaN giving you the privilege to leverage dropna() and remain with only numeric/object dtypes

Code below

 df.select_dtypes('object').apply(lambda x: pd.to_numeric(x,errors='coerce')).dropna(axis=1)

Outcome

    B
0  29.85
1  85.43
2  33.87

find numeric column names in Pandas

Try

df.ids = df.ids.astype('object')    
new_df = df.select_dtypes([np.number])

    0       1       2       3       4       
0   17.0    18.0    16.0    15.0    15.0    
1   18.0    16.0    15.0    15.0    16.0    
2   16.0    15.0    15.0    16.0    15.0    
3   15.0    15.0    16.0    15.0    17.0    
4   15.0    16.0    15.0    17.0    NaN     

EDIT:
If you are interested in selecting column names that are numeric, here is something that you can do.

df = pd.DataFrame({0: [1,2], '1': [3,4], 'blah': [5,6], 2: [7,8]})
df.columns = pd.to_numeric(df.columns, errors = 'coerce')
df[df.columns.dropna()]

You get

    0.0 1.0 2.0
0   1   3   7
1   2   4   8

---

Related Topics