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.
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How can I check the data type of the 'Fare' column?
Why is the 'Fare' column using '-' instead of '.' as a separator?
What should I do if the 'Fare' column is not recognized as a numeric type?
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Checking the Column Type
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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!