Python’s Pandas library is a well-liked tool for handling and analyzing data. It offers several ways to handle and work with different types of data, including numerical data. It can be helpful to identify the numerical columns in a dataframe when working with a large dataset.

In this article, we will see how to find numerical columns in pandas.

First, let’s create a sample dataframe:

```
import pandas as pd
import numpy as np
df = pd.DataFrame({'A': [1, 2, 3, 4, 5],
'B': [6, 7, 8, 9, 10],
'C': ['a', 'b', 'c', 'd', 'e'],
'D': [0.1, 0.2, 0.3, 0.4, 0.5]})
```

The sample dataframe `df`

will look like this:

```
A B C D
0 1 6 a 0.1
1 2 7 b 0.2
2 3 8 c 0.3
3 4 9 d 0.4
4 5 10 e 0.5
```

To find the numeric columns, we can use the `select_dtypes`

method and pass in the argument `include='number'`

:

`numeric_cols = df.select_dtypes(include='number').columns`

In this example, the method `select_dtypes`

is used to select the columns that have numerical data types (i.e., `int`

and `float`

). The resulting `numeric_cols`

will be a list of the numeric column names:

`Index(['A', 'B', 'D'], dtype='object')`

You can also use the`.apply`

method and the `.isnumeric`

method to find numeric columns.

`numeric_cols = df.columns[df.apply(lambda x: x.str.isnumeric().all())]`

In this example, the `.apply`

method is used to apply the `.isnumeric`

method to each column in the dataframe. The `.isnumeric`

method returns a Boolean value indicating whether or not all elements in the column are numeric. The `.columns`

property is used to get the column names.

The resulting `numeric_cols`

will be a list of the numeric column names:

`Index(['A', 'B'], dtype='object')`