Python for Data Engineering role ๐
โ List Comprehensions and Dict Comprehensions
โณ Optimize iteration with one-liners
โณ Fast filtering and transformations
โณ O(n) time complexity
โ Lambda Functions
โณ Anonymous functions for concise operations
โณ Used in map(), filter(), and sort()
โณ Key for functional programming
โ Functional Programming (map, filter, reduce)
โณ Apply transformations efficiently
โณ Reduce dataset size dynamically
โณ Avoid unnecessary loops
โ Iterators and Generators
โณ Efficient memory handling with yield
โณ Streaming large datasets
โณ Lazy evaluation for performance
โ Error Handling with Try-Except
โณ Graceful failure handling
โณ Preventing crashes in pipelines
โณ Custom exception classes
โ Regex for Data Cleaning
โณ Extract structured data from unstructured text
โณ Pattern matching for text processing
โณ Optimized with re.compile()
โ File Handling (CSV, JSON, Parquet)
โณ Read and write structured data efficiently
โณ pandas.read_csv(), json.load(), pyarrow
โณ Handling large files in chunks
โ Handling Missing Data
โณ .fillna(), .dropna(), .interpolate()
โณ Imputing missing values
โณ Reducing nulls for better analytics
โ Pandas Operations
โณ DataFrame filtering and aggregations
โณ .groupby(), .pivot_table(), .merge()
โณ Handling large structured datasets
โ SQL Queries in Python
โณ Using sqlalchemy and pandas.read_sql()
โณ Writing optimized queries
โณ Connecting to databases
โซ Working with APIs
โณ Fetching data with requests and httpx
โณ Handling rate limits and retries
โณ Parsing JSON/XML responses
โฌ Cloud Data Handling (AWS S3, Google Cloud, Azure)
โณ Upload/download data from cloud storage
โณ boto3, gcsfs, azure-storage
โณ Handling large-scale data ingestion
๐๐ก๐ ๐๐๐ฌ๐ญ ๐ฐ๐๐ฒ ๐ญ๐จ ๐ฅ๐๐๐ซ๐ง ๐๐ฒ๐ญ๐ก๐จ๐ง ๐ข๐ฌ ๐ง๐จ๐ญ ๐ฃ๐ฎ๐ฌ๐ญ ๐๐ฒ ๐ฌ๐ญ๐ฎ๐๐ฒ๐ข๐ง๐ , ๐๐ฎ๐ญ ๐๐ฒ ๐ข๐ฆ๐ฉ๐ฅ๐๐ฆ๐๐ง๐ญ๐ข๐ง๐ ๐ข๐ญ
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Post #427
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