Зміст курсу
Preprocessing Data
Preprocessing Data
Data Types
Let's talk about the types of data that dataframe may contain.
Numerical
Numerical data is presented by int or float values. In the dataframe, it should be stored as int64 or float64 data types value. Use data.info() to check the data types for each column.
Note that some fields in the dataframe may contain numerical values, but are stored using some other data type (object or str). You have to convert it to the int64 or float64, and we’ll explore how to do it later.
Categorical
Categorical data has no numerical representation, it is an item from the list of some groups or categories. For example, column Sex has values Male or Female, or column Season with values Spring, Summer, Fall, and Winter. It requires special conversion and preprocessing. This data has data types: object, bool, str.
Fortunately, the dataset titanic already contains numerical data as int64 and float64.
Завдання
Let's divide the columns into numerical and categorical. Create num_cols as numpy array, including types int and float. Let the cat_cols be all other features except the num_cols.
Перейдіть на комп'ютер для реальної практикиПродовжуйте з того місця, де ви зупинились, використовуючи один з наведених нижче варіантівДякуємо за ваш відгук!
Data Types
Let's talk about the types of data that dataframe may contain.
Numerical
Numerical data is presented by int or float values. In the dataframe, it should be stored as int64 or float64 data types value. Use data.info() to check the data types for each column.
Note that some fields in the dataframe may contain numerical values, but are stored using some other data type (object or str). You have to convert it to the int64 or float64, and we’ll explore how to do it later.
Categorical
Categorical data has no numerical representation, it is an item from the list of some groups or categories. For example, column Sex has values Male or Female, or column Season with values Spring, Summer, Fall, and Winter. It requires special conversion and preprocessing. This data has data types: object, bool, str.
Fortunately, the dataset titanic already contains numerical data as int64 and float64.
Завдання
Let's divide the columns into numerical and categorical. Create num_cols as numpy array, including types int and float. Let the cat_cols be all other features except the num_cols.
Перейдіть на комп'ютер для реальної практикиПродовжуйте з того місця, де ви зупинились, використовуючи один з наведених нижче варіантівДякуємо за ваш відгук!
Data Types
Let's talk about the types of data that dataframe may contain.
Numerical
Numerical data is presented by int or float values. In the dataframe, it should be stored as int64 or float64 data types value. Use data.info() to check the data types for each column.
Note that some fields in the dataframe may contain numerical values, but are stored using some other data type (object or str). You have to convert it to the int64 or float64, and we’ll explore how to do it later.
Categorical
Categorical data has no numerical representation, it is an item from the list of some groups or categories. For example, column Sex has values Male or Female, or column Season with values Spring, Summer, Fall, and Winter. It requires special conversion and preprocessing. This data has data types: object, bool, str.
Fortunately, the dataset titanic already contains numerical data as int64 and float64.
Завдання
Let's divide the columns into numerical and categorical. Create num_cols as numpy array, including types int and float. Let the cat_cols be all other features except the num_cols.
Перейдіть на комп'ютер для реальної практикиПродовжуйте з того місця, де ви зупинились, використовуючи один з наведених нижче варіантівДякуємо за ваш відгук!
Let's talk about the types of data that dataframe may contain.
Numerical
Numerical data is presented by int or float values. In the dataframe, it should be stored as int64 or float64 data types value. Use data.info() to check the data types for each column.
Note that some fields in the dataframe may contain numerical values, but are stored using some other data type (object or str). You have to convert it to the int64 or float64, and we’ll explore how to do it later.
Categorical
Categorical data has no numerical representation, it is an item from the list of some groups or categories. For example, column Sex has values Male or Female, or column Season with values Spring, Summer, Fall, and Winter. It requires special conversion and preprocessing. This data has data types: object, bool, str.
Fortunately, the dataset titanic already contains numerical data as int64 and float64.
Завдання
Let's divide the columns into numerical and categorical. Create num_cols as numpy array, including types int and float. Let the cat_cols be all other features except the num_cols.
Перейдіть на комп'ютер для реальної практикиПродовжуйте з того місця, де ви зупинились, використовуючи один з наведених нижче варіантівПерейдіть на комп'ютер для реальної практикиПродовжуйте з того місця, де ви зупинились, використовуючи один з наведених нижче варіантів