Checking the Column Type
If you can come across the column 'Fare', the numbers here are separated with the - sign. It looks weird, doesn't it? We used to use . as the separator, and Python can understand numbers separated only with dots. Let's check the type of this column. You can do so using the attribute .dtypes. Look at the example with the column 'Age'.
123import pandas as pd data = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/4bf24830-59ba-4418-969b-aaf8117d522e/titanic3.csv', index_col = 0) print(data['Age'].dtypes)
Explanation:
The .dtypes syntax is simple; you just apply it to the column or to the whole data set. In our case, the type is float64.
¡Gracias por tus comentarios!
Pregunte a AI
Pregunte a AI
Pregunte lo que quiera o pruebe una de las preguntas sugeridas para comenzar nuestra charla
Awesome!
Completion rate improved to 3.03Checking the Column Type
Desliza para mostrar el menú
If you can come across the column 'Fare', the numbers here are separated with the - sign. It looks weird, doesn't it? We used to use . as the separator, and Python can understand numbers separated only with dots. Let's check the type of this column. You can do so using the attribute .dtypes. Look at the example with the column 'Age'.
123import pandas as pd data = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/4bf24830-59ba-4418-969b-aaf8117d522e/titanic3.csv', index_col = 0) print(data['Age'].dtypes)
Explanation:
The .dtypes syntax is simple; you just apply it to the column or to the whole data set. In our case, the type is float64.
¡Gracias por tus comentarios!
