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Showing posts with the label Python IF Condtions

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14 Top Data Pipeline Key Terms Explained

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 Here are some key terms commonly used in data pipelines 1. Data Sources Definition: Points where data originates (e.g., databases, APIs, files, IoT devices). Examples: Relational databases (PostgreSQL, MySQL), APIs, cloud storage (S3), streaming data (Kafka), and on-premise systems. 2. Data Ingestion Definition: The process of importing or collecting raw data from various sources into a system for processing or storage. Methods: Batch ingestion, real-time/streaming ingestion. 3. Data Transformation Definition: Modifying, cleaning, or enriching data to make it usable for analysis or storage. Examples: Data cleaning (removing duplicates, fixing missing values). Data enrichment (joining with other data sources). ETL (Extract, Transform, Load). ELT (Extract, Load, Transform). 4. Data Storage Definition: Locations where data is stored after ingestion and transformation. Types: Data Lakes: Store raw, unstructured, or semi-structured data (e.g., S3, Azure Data Lake). Data Warehous...
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How to Check If Statement Multiple Conditions in Python and Ensure Tidy Code

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Here're examples for Python multiple if conditions (statements). These are useful for interviews and projects. Many programmers confuse to write IF logic in Python. Below examples useful for your quick reference. Multiple IF Conditions IF, IF IF 'ELSE' IF 'or' IF 'and' Nested IF IF 'continue' IF 'break' In Python, the decision-making logic you can write with IF condition. You can write multiple IF conditions (Single way decision). At the same time, you can write IF and ELSE conditions (Two-way decision). Multiple IF conditions the best example. def main():         celsius = float(input("What is the Celsius temperature? "))         fahrenheit = 9/5 * celsius + 32         print("The temperature is", fahrenheit, "degrees Fahrenheit.")  # Print warnings for extreme temps         i f fahrenheit > 90:                print("It's rea...
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