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
456
Mole
9781837634743
15 a 20 dias
Descrição não disponível.
Table of Contents
Data Ingestion Techniques
Importance of Data Quality
Data Profiling - Understanding Data Structure, Quality, and Distribution
Cleaning Messy Data and Data Manipulation
Data Transformation - Merging and Concatenating
Data Grouping, Aggregation, Filtering, and Applying Functions
Data Sinks
Detecting and Handling Missing Values and Outliers
Normalization and Standardization
Handling Categorical Features
Consuming Time Series Data
Text Preprocessing in the Era of LLMs
Image and Audio Preprocessing with LLMs
Data Ingestion Techniques
Importance of Data Quality
Data Profiling - Understanding Data Structure, Quality, and Distribution
Cleaning Messy Data and Data Manipulation
Data Transformation - Merging and Concatenating
Data Grouping, Aggregation, Filtering, and Applying Functions
Data Sinks
Detecting and Handling Missing Values and Outliers
Normalization and Standardization
Handling Categorical Features
Consuming Time Series Data
Text Preprocessing in the Era of LLMs
Image and Audio Preprocessing with LLMs
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
Table of Contents
Data Ingestion Techniques
Importance of Data Quality
Data Profiling - Understanding Data Structure, Quality, and Distribution
Cleaning Messy Data and Data Manipulation
Data Transformation - Merging and Concatenating
Data Grouping, Aggregation, Filtering, and Applying Functions
Data Sinks
Detecting and Handling Missing Values and Outliers
Normalization and Standardization
Handling Categorical Features
Consuming Time Series Data
Text Preprocessing in the Era of LLMs
Image and Audio Preprocessing with LLMs
Data Ingestion Techniques
Importance of Data Quality
Data Profiling - Understanding Data Structure, Quality, and Distribution
Cleaning Messy Data and Data Manipulation
Data Transformation - Merging and Concatenating
Data Grouping, Aggregation, Filtering, and Applying Functions
Data Sinks
Detecting and Handling Missing Values and Outliers
Normalization and Standardization
Handling Categorical Features
Consuming Time Series Data
Text Preprocessing in the Era of LLMs
Image and Audio Preprocessing with LLMs
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
