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Preprocessing Data
course content

Conteúdo do Curso

Preprocessing Data

Preprocessing Data

1. Data Exploration
Data ExtractingExplore the DatasetData Types
2. Data Cleaning
Missing and Wrong DataWork with NaNsReplace Numerical Missing Data with ValuesDealing with Missing AgesReplace Categorical Missing Data with ValuesReplace Missing Values with InterpolationRemoving DuplicatesRemoving OutliersVisualizing Outliers
3. Data Validation
Types ConversionData Binning
4. Normalization & Standardization
Data NormalizationData Standardization
5. Data Encoding
Label EncodingOne Hot Encoding

bookData 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.

Tarefa

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.

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Como podemos melhorá-lo?

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Seção 1. Capítulo 3
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bookData 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.

Tarefa

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.

Switch to desktopMude para o desktop para praticar no mundo realContinue de onde você está usando uma das opções abaixo
Tudo estava claro?

Como podemos melhorá-lo?

Obrigado pelo seu feedback!

Seção 1. Capítulo 3
toggle bottom row

bookData 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.

Tarefa

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.

Switch to desktopMude para o desktop para praticar no mundo realContinue de onde você está usando uma das opções abaixo
Tudo estava claro?

Como podemos melhorá-lo?

Obrigado pelo seu feedback!

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.

Tarefa

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.

Switch to desktopMude para o desktop para praticar no mundo realContinue de onde você está usando uma das opções abaixo
Seção 1. Capítulo 3
Switch to desktopMude para o desktop para praticar no mundo realContinue de onde você está usando uma das opções abaixo
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