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XML Data Analyzer

High-Level Overview

The code processes a large XML file to extract and analyze statistical information about its elements and their values. It uses various data structures to maintain statistics for numeric and string values, such as minimum, maximum, average, histogram, and other metrics. The results are stored in a dictionary and can be converted into a pandas DataFrame for further analysis or visualization.

Step-by-Step Explanation

1. Imports and Logging Configuration:

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import xml.etree.ElementTree as ET
import logging
from collections import Counter, defaultdict
from tdigest import TDigest
from probables import CountMinSketch
from datasketch import HyperLogLogPlusPlus
import pandas as pd


# Configure logging

pinatics

RFM Analysis with MySQL

High-Level Overview

The code provided is a series of SQL statements and stored procedures designed to calculate RFM (Recency, Frequency, Monetary) scores for users based on their transaction data. This involves creating temporary tables, calculating percentiles for recency, frequency, and monetary values, and then assigning RFM scores based on these percentiles. Finally, it combines these scores into a single RFM score for each user.

Step-by-Step Explanation

1. Set Maximum Length for Group Concat:

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Quantum Question-and-Answer with Related Content

High-Level Overview

The code is designed to handle HTTP requests, process queries related to quantum computing, and interact with OpenAI's API to generate responses and embeddings. It integrates with a Neo4j graph database and uses environment variables for configuration. The response includes the query embeddings and response embeddings, enabling vectorized representations of the text for further processing or analysis.

Step-by-Step Explanation

1. Imports and Environment Variable Loading:

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MySQL Data Analyzer

High-Level Overview

The code is designed to connect to a MySQL database, extract and process the schema of all tables, and gather detailed information about each table's columns and foreign key constraints. This information is then organized into a pandas DataFrame for easy readability.

Step-by-Step Explanation

1. Imports and Logging Configuration:

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JSON Data Analyzer

High-Level Overview

The code processes a large JSON file containing multiple records to gather and analyze statistical information about the fields in the data. It uses various data structures and algorithms to efficiently compute and update statistics for numeric, boolean, string, dictionary, and list fields. The processed information includes min, max, average, histogram, median, and frequency count for different field types.

Step-by-Step Explanation

1. Imports and Logging Configuration:

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Postgres Data Analyzer

High-Level Overview

The code is designed to extract and process schema information from a PostgreSQL database. It connects to the database, retrieves table definitions, parses the table schemas using DDLParse, and extracts detailed information about table columns, including foreign key constraints. Finally, it presents this information in a pandas DataFrame for easy readability.

Step-by-Step Explanation

1. Imports and Logging Configuration:

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Neo4j Data Analyzer

High-Level Overview

The code is designed to extract schema information from a Neo4j graph database. It performs the following main tasks:

1. Configures logging for informational messages.
2. Defines a Neo4jSchemaExtractor class to interact with the Neo4j database and retrieve schema information about nodes and relationships.
3. Provides methods within the class to get nodes and relationships with their properties and extract detailed schema information.
4. Processes all graph schemas by presenting node and relationship schema details in a tabular format using pandas DataFrame for easy readability.

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Kafka Data Analyzer

High-Level Overview

The code is designed to extract schema information from a Kafka Schema Registry. It performs the following main tasks:

1. Configures logging for informational messages.
2. Defines a KafkaSchemaExtractor class that interacts with the Schema Registry to retrieve schema information.
3. Provides methods within the class to get all subjects, fetch the latest schema for each subject, and extract detailed field information from each schema.
4. Processes all schemas by flattening the schema details and presenting them in a tabular format using pandas DataFrame for easy readability.

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import requests
import logging
import pandas as pd
import json


# Configure logging
logging.basicConfig(level=logging.INFO)