Python Data Cleaning and Preparation Best Practices
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Python Data Cleaning and Preparation Best Practices
A practical guide to organizing and handling data from various sources and formats using Python
Zervou, Maria
Packt Publishing Limited
09/2024
306
Mole
9781837634743
Pré-lançamento - envio 15 a 20 dias após a sua edição
Descrição não disponível.
Table of Contents
Data Ingestion Sources
Importance of Data Quality
Data Profiling and Validation
Cleaning of Messy data and Data Manipulation
Data Transformation, Aggregation and Grouping
Data Destination/Sinks
Detecting and Handle Missing Values and Outliers
Feature Scaling and Normalization
Handling Categorical Features
Consume Times Series Data
From Raw Text to Full Tokenization
From clean Tokens to Text Structuring and Vector Models
From Image Preprocessing to Video Handling
Data Ingestion Sources
Importance of Data Quality
Data Profiling and Validation
Cleaning of Messy data and Data Manipulation
Data Transformation, Aggregation and Grouping
Data Destination/Sinks
Detecting and Handle Missing Values and Outliers
Feature Scaling and Normalization
Handling Categorical Features
Consume Times Series Data
From Raw Text to Full Tokenization
From clean Tokens to Text Structuring and Vector Models
From Image Preprocessing to Video Handling
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
Table of Contents
Data Ingestion Sources
Importance of Data Quality
Data Profiling and Validation
Cleaning of Messy data and Data Manipulation
Data Transformation, Aggregation and Grouping
Data Destination/Sinks
Detecting and Handle Missing Values and Outliers
Feature Scaling and Normalization
Handling Categorical Features
Consume Times Series Data
From Raw Text to Full Tokenization
From clean Tokens to Text Structuring and Vector Models
From Image Preprocessing to Video Handling
Data Ingestion Sources
Importance of Data Quality
Data Profiling and Validation
Cleaning of Messy data and Data Manipulation
Data Transformation, Aggregation and Grouping
Data Destination/Sinks
Detecting and Handle Missing Values and Outliers
Feature Scaling and Normalization
Handling Categorical Features
Consume Times Series Data
From Raw Text to Full Tokenization
From clean Tokens to Text Structuring and Vector Models
From Image Preprocessing to Video Handling
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
