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

Question

In the dataframe, I am trying to find numeric data columns which has dtype as "object". I want to do it automated way rather then looking into actual data within the dataframe.

I tried this, but it didn't work:

for obj_feature in df.select_dtypes(include="object").columns:
    if df[obj_feature].str.isalpha == False:
        print("Numeric data columns", obj_feature)

DDL to generate Dataframe:

import pandas as pd

df = pd.DataFrame({'id': [1, 2, 3],
                  'A': ['Month', 'Year', 'Quater'],
                  'B' : ['29.85', '85.43', '33.87'],
                  'C' : [45, 22, 33.4]})

Sorry forgot to add this: Expected Output: Pick Dataframe columns, B since it has numeric data values, but it has 'object' dtype.

Thanks!

mozway · Accepted Answer · 2022-01-05 09:45:06Z

3

You can use pandas.api.types.is_numeric_dtype:

from pandas.api.types import is_numeric_dtype
{c: is_numeric_dtype(df[c]) for c in df}

output:

{'id': True, 'A': False, 'B': False, 'C': True}

selecting the numeric columns:

Here use select_dtype:

df.select_dtypes('number')

output:

   id     C
0   1  45.0
1   2  22.0
2   3  33.4

answered Jan 5, 2022 at 9:45

mozway

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4 Comments

Anku Over a year ago

Thanks @mozway for your answer, but I am expecting different output. Edited my question, Sorry I should have done that earlier.

mozway Over a year ago

not sure why I got downvoted, the question was clarified after I answered

Anku Over a year ago

I have not done that.

mozway Over a year ago

@Anku I didn't say it was you ;)

wwnde · Accepted Answer · 2022-01-05 10:25:25Z

2

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

edited Jan 5, 2022 at 10:25

answered Jan 5, 2022 at 9:55

wwnde

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5 Comments

wwnde Over a year ago

Please see my edits.

Anku Over a year ago

Thanks @wwnde. This is what that I need as expected output.

Anku Over a year ago

What if I have NaN available in that column B. I suppose dropna(axis=1) will not work in that case. Am I right ?

Anku Over a year ago

I figure it out, then I will use this code: df.select_dtypes('object').apply(lambda x: pd.to_numeric(x, errors = 'coerce')).dropna(axis=1, how='all')

wwnde Over a year ago

Dropna (how='all', axis=1)?

Daweo · Accepted Answer · 2022-01-05 09:45:24Z

0

You might use pandas.api.types.is_numeric_dtype, consider following example

import pandas as pd
df = pd.DataFrame({'id': [1, 2, 3],
                  'A': ['Month', 'Year', 'Quater'],
                  'B' : ['29.85', '85.43', '33.87'],
                  'C' : [45, 22, 33.4]})
for colname in df.columns:
    print(colname,pd.api.types.is_numeric_dtype(df[colname]))

output

id True
A False
B False
C True

answered Jan 5, 2022 at 9:45

Daweo

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1 Comment

Anku Over a year ago

Thanks @Daweo for your answer, but I am expecting different output. Edited my question, Sorry I should have done that earlier.

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