In this shot, we will discuss how we can select columns based on their data types in pandas.
Let’s first create a DataFrame.
import pandas as pddrinks = pd.read_csv('http://bit.ly/drinksbycountry')drinks = drinks[["country","beer_servings","wine_servings","continent"]]print("Datatypes of the columns:\n", drinks.dtypes)
Explanation
drinks
.Now, we will use some data types to filter the columns.
number_cols = drinks.select_dtypes(include='number').head()print("Number Columns:\n",number_cols)object_cols = drinks.select_dtypes(include='object').head()print("\nObject Columns:\n",object_cols)multi_cols = drinks.select_dtypes(include=['number', 'object']).head()print("\nMulti-Datatype Columns:\n", multi_cols)exclude_cols = drinks.select_dtypes(exclude='number').head()print("\nExcluded Datatype Columns:\n",exclude_cols)
Explanation
select_dtypes()
and pass the data type as number
, which will filter the columns with their data type as number.select_dtypes()
, but this time we pass the data type as object.select_dtypes()
function, but we pass a list of data types that we want to use for filtering the columns.exclude = number
).