In this tutorial, we’ll try to have a look at different methods used to check if the dtype of a column in a pandas dataframe is numeric or not.

## How to check if a column datatype is numeric?

To check if a specific datatype of a column in the pandas dataframe is numeric or not we can use the following methods –

- Using
`is_numeric_dtype()`

method from the pandas module - Using
`np.issubdtype()`

method from the numpy module - Using
`.dtype.kind`

attribute of the pandas dataframe object

### Method – 1: ** Using pandas **`is_numeric_dtype()`

`is_numeric_dtype()`

Sample Code:

import pandas as pd from pandas.api.types import is_numeric_dtype is_numeric_dtype(df['Col'])

In this method, we use the `is_numeric_dtype()`

function available in the `pandas.api.types`

module which returns a boolean value of True if the datatype of elements in the column is numeric and False otherwise. Pass the column as an argument.

### Method – 2: ** Using **`np.issubdtype()`

`np.issubdtype()`

Sample Code:

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np.issubdtype(df['Col'].dtype, np.number) # where df['X'] is a pandas Series

In this method, use the `issubdtype()`

function available in the `numpy`

module and check if the data type of elements in the column is `np.number`

as the `issubdtype()`

function returns True if the datatype of elements in the column is Numeric and False otherwise.

### Method – 3: ** Using .dtype.kind **

Sample Code:

df['Col'].dtype.kind in 'biufc'

In this method, we use the `.dtype.kind`

attribute to check if the column datatype is in `biufc`

where `biufc`

: `b`

bool, `i`

int (signed), `u`

unsigned int, `f`

float, `c`

complex.

## Examples

Now, Let us look at examples of Each method discussed above to check if specific column elements are of Numeric datatype or not.

**Note: **In case, you don’t have a pandas Dataframe, use the below simple method to create one. we use this dataframe for all the examples mentioned below

import pandas as pd df = pd.DataFrame({'Col1': ['a', 'b', 'c'], 'Col2': [1.0, 2.0, 3.0]}) df

Output:

**Using **`is_numeric_dtype()`

`is_numeric_dtype()`

#### Example – 1

In this method, we can simply import the **is_numeric_dtype ** function from **pandas.api.types** module and directly pass the specific column to the function as a parameter that returns the boolean value of True or False.** Code: **

is_numeric_dtype(df['Col2'])

** Output: **

True

#### Example – 2

** Code: **

is_numeric_dtype(df['Col1'])

** Output: **

False

**Using **`np.issubdtype()`

`np.issubdtype()`

#### Example – 1

In this method, we can simply import the** numpy **module and directly use the np.issubdtype function by passing the specific column to the function as a parameter along with the np.number which returns the boolean value of True or False.** Code: **

import numpy as np np.issubdtype(df['Col2'].dtype, np.number)

** Output: **

True

#### Example – 2

** Code: **

import numpy as np np.issubdtype(df['Col1'].dtype, np.number)

** Output: **

False

**Using .dtype.kind**

#### Example – 1

In this method, we don’t need to import any functions instead use the built-in property of the pandas dataframe object called **.dtype.kind **which directly returns the boolean value of True or False.** Code: **

df['Col2'].dtype.kind in 'biufc'

** Output: **

True

#### Example – 2

** Code: **

df['Col1'].dtype.kind in 'biufc'

** Output: **

False

## Summary

In this tutorial, we looked at 3 different ways to check if the elements in a specific column of a pandas dataframe are of numeric dtype or not.

They are:

- Using
`is_numeric_dtype()`

method from the pandas module. - Using
`np.issubdtype()`

method from the numpy module. - Using
`.dtype.kind`

attribute of the pandas dataframe object.

You might also be interested in –

- Pandas – Select Columns of a Specific Type
- Pandas – Change Column Type to Category
- Pandas – Get Columns with Missing Values
- Pandas – Get dataframe summary with info()

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