Summary
This chapter demonstrates how to inspect the data types of pandas DataFrame columns using the `.dtypes` attribute.

General domain of usage
Data analysis

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'`.

import 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**.

Column Types