Python Data Optimizer API API ID: 12866

Your ultimate solution for reducing memory usage, cutting storage costs, and enhancing application performance—all without the need to rewrite your existing business logic. With a single API call, you receive a comprehensive savings report detailing the optimization in bytes, KB, MB, or GB.
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Unlock Significant Real-World Savings

  • Integer List: 80 bytes reduced to 20 bytes (75% reduction)
  • Float List: 40 bytes reduced to 20 bytes (50% reduction)
  • Sparse Matrix (88% zeros): 200 bytes reduced to 48 bytes (76% reduction)
  • Six Boolean Variables: 168 bytes reduced to 28 bytes (83% reduction)
  • Large 1GB File: Memory optimized to accessed bytes only (up to 99% reduction)
  • Cloud Costs: Monthly bill reduced from $100 to $25-$70 (30-75% savings)

About the Python Data Optimizer API

The Python Data Optimizer API, developed by Winter Hazel Inc., intelligently identifies inefficient data types within your Python code and databases, replacing them with the most compact, efficient types that accurately retain your data.

Input and Output Specifications

Input: Send Python data structures, code snippets, or database connection strings encapsulated in JSON format via an API call.

Output: Receive optimized data along with a detailed savings report showcasing the original size, optimized size, and total bytes saved.

Core Optimization Features: 12 Capabilities

  1. Downcast numeric types: e.g., convert float64 to float32, int64 to int8—saving up to 75% memory.
  2. Transform object columns to categorical: converting pandas object columns to category types can save 50-90% memory.
  3. Enforce specialized containers: optimally converts lists to deque and Counter for faster operations and reduced memory.
  4. Utilize native arrays for large lists: converting Python lists to typed arrays can save up to 75% memory.
  5. Convert strings to numbers: automatically detects and converts numeric strings to int or float.
  6. Transform lists to sets: eliminate duplicate storage on large lists exceeding 100 items.
  7. Convert lists to tuples: using immutable tuples can save approximately 40 bytes per structure.
  8. Automatically remove duplicates: maintains original order while removing duplicate values.
  9. Flatten nested structures: efficiently reduces deeply nested lists into flat arrays.
  10. Map loose variables to key-value pairs: organize scattered variables into structured dictionaries.
  11. Lock fixed data as immutable: identify constant data, such as country codes, and secure as tuples.
  12. Detect empty containers: eliminate empty lists, dicts, and sets that unnecessarily consume memory.

Advanced Analysis Features: 5 Capabilities

  1. Memory Leak Detector: scans your Python code to identify global structures that increase indefinitely, reporting line numbers and severity.
  2. Sparse Matrix Optimizer: compresses matrices with over 70% zeros into CSR format; for example, a 1000x1000 matrix can shrink from 8MB to under 1MB.
  3. Generator Expression Advisor: identifies list comprehensions loading entire datasets into memory, potentially saving up to 80MB on datasets with 10 million items.
  4. Bitwise Flag Optimizer: replaces six boolean variables (168 bytes) with one integer (28 bytes), achieving an 83% reduction.
  5. Memory Mapping Advisor: recommends techniques like mmap, numpy.memmap, and pandas chunksize for handling large files, saving up to 990MB on 1GB files.

Database Schema Advisor

This feature connects directly to your database and scans tables and columns for oversized data types, returning precise recommendations along with bytes saved per row:

  • PostgreSQL: Replace BIGINT with TINYINT, SMALLINT.
  • MySQL: Use INT, FLOAT, VARCHAR instead of BIGINT, DOUBLE, LONGTEXT.
  • SQLite: Opt for BLOB instead of TEXT for storing binary data.
  • MongoDB: Use Float instead of Double, TINYINT instead of Int64.
  • SQL Server: Implement INT, VARCHAR instead of BIGINT, NVARCHAR.
  • Amazon RDS: Supports both MySQL and PostgreSQL engines.
  • Google BigQuery: Use INT32, FLOAT32 instead of INT64, FLOAT64.

Common Use Cases

  1. Reducing monthly expenses on AWS and Google Cloud by optimizing database column types.
  2. Enhancing the performance of Python applications through numeric data type downcasting.
  3. Identifying potential memory leaks before they lead to application failures.
  4. Compressing sparse matrices effectively in machine learning tasks.
  5. Optimizing the processing of large files with memory mapping techniques.

API Documentation

Endpoints


Optimizes a single Python data structure and returns a full savings report showing original size, optimized size, and bytes saved.



                                                                            
POST https://zylalabs.com/api/12866/python+data+optimizer+api/25666/optimize+data
                                                                            
                                                                        

Optimize Data - Endpoint Features

Object Description
data [Required] Python data structure to optimize in JSON format
Request Body [Required] Json

Free test requests remaining: 3 of 3.


INPUT PARAMETERS

data

API EXAMPLE RESPONSE

{"status":"success","result":{"optimized":[1000,2000,3000,4000,5000],"savings":{"original_type":"list","optimized_type":"array.h","original_size":40,"optimized_size":10,"saved":"30 bytes"},"type_detected":"list"}}

Optimize Data - CODE SNIPPETS


curl --location --request POST 'https://zylalabs.com/api/12866/python+data+optimizer+api/25666/optimize+data' --header 'Authorization: Bearer YOUR_API_KEY' 

--data-raw '{"data": [1000, 2000, 3000, 4000, 5000]}'

    

Optimizes multiple Python data structures in a single API call. Send a list of data structures and receive a savings report for each one showing original size, optimized size, and bytes saved.



                                                                            
POST https://zylalabs.com/api/12866/python+data+optimizer+api/25670/optimize+batch
                                                                            
                                                                        

Optimize Batch - Endpoint Features

Object Description
Request Body [Required] Json

Free test requests remaining: 3 of 3.


INPUT PARAMETERS

request_body

API EXAMPLE RESPONSE

{"status":"success","results":[{"optimized":[1000,2000,3000],"savings":{"original_type":"list","optimized_type":"array.h","original_size":24,"optimized_size":6,"saved":"18 bytes"},"type_detected":"list"},{"optimized":[1.5,2.5,3.5],"savings":{"original_type":"list","optimized_type":"array.f","original_size":24,"optimized_size":12,"saved":"12 bytes"},"type_detected":"list"}]}

Optimize Batch - CODE SNIPPETS


curl --location --request POST 'https://zylalabs.com/api/12866/python+data+optimizer+api/25670/optimize+batch' --header 'Authorization: Bearer YOUR_API_KEY' 

--data-raw '{"data_list": [[1000, 2000, 3000], [1.5, 2.5, 3.5]]}'

    

 Optimizes structural patterns in Python data. Detects duplicates, converts lists to tuples, removes empty containers, and flattens nested structures to reduce memory usage.



                                                                            
POST https://zylalabs.com/api/12866/python+data+optimizer+api/25671/optimize+structural
                                                                            
                                                                        

Optimize Structural - Endpoint Features

Object Description
Request Body [Required] Json

Free test requests remaining: 3 of 3.


INPUT PARAMETERS

request_body

API EXAMPLE RESPONSE

{"original_type":"list","optimized_type":"list","pattern_applied":"duplicates_removed — list preserved","original_size":88,"optimized_size":88,"saved":"0 bytes","optimized":["hello","world","python"]}

Optimize Structural - CODE SNIPPETS


curl --location --request POST 'https://zylalabs.com/api/12866/python+data+optimizer+api/25671/optimize+structural' --header 'Authorization: Bearer YOUR_API_KEY' 

--data-raw '{"data": ["hello", "world", "hello", "python"]}'

    

Optimizes multiple Python data structures for structural patterns in a single API call. Detects duplicates, converts lists to tuples, removes empty containers and flattens nested structures.



                                                                            
POST https://zylalabs.com/api/12866/python+data+optimizer+api/25672/optimize+structural+batch
                                                                            
                                                                        

Optimize Structural Batch - Endpoint Features

Object Description
Request Body [Required] Json

Free test requests remaining: 3 of 3.


INPUT PARAMETERS

request_body

API EXAMPLE RESPONSE

{"status":"success","results":[{"original_type":"list","optimized_type":"list","pattern_applied":"duplicates_removed — list preserved","original_size":88,"optimized_size":88,"saved":"0 bytes","optimized":["hello","world"]},{"original_type":"list","optimized_type":"tuple","pattern_applied":"list_to_tuple — immutable conversion","original_size":120,"optimized_size":80,"saved":"40 bytes","optimized":[1,2,3,4,5]}]}

Optimize Structural Batch - CODE SNIPPETS


curl --location --request POST 'https://zylalabs.com/api/12866/python+data+optimizer+api/25672/optimize+structural+batch' --header 'Authorization: Bearer YOUR_API_KEY' 

--data-raw '{"data_list": [["hello", "world", "hello"], [1, 2, 3, 4, 5]]}'

    

 Detects sparse matrices with over 70% zero values and compresses them to CSR format. A 1000x1000 matrix shrinks from 8MB to under 1MB saving significant memory in machine learning applications.



                                                                            
POST https://zylalabs.com/api/12866/python+data+optimizer+api/25673/optimize+sparse+matrix
                                                                            
                                                                        

Optimize Sparse Matrix - Endpoint Features

Object Description
Request Body [Required] Json

Free test requests remaining: 3 of 3.


INPUT PARAMETERS

request_body

API EXAMPLE RESPONSE

{"status":"optimized","sparsity_ratio":87.5,"original_format":"dense matrix","optimized_format":"CSR sparse matrix","original_size":128,"optimized_size":32,"saved":"96 bytes","non_zero_elements":2,"total_elements":16,"compressed_data":{"format":"CSR","shape":[4,4],"rows":[0,2],"cols":[3,1],"values":[5,3],"total_elements":16,"stored_elements":2}}

Optimize Sparse Matrix - CODE SNIPPETS


curl --location --request POST 'https://zylalabs.com/api/12866/python+data+optimizer+api/25673/optimize+sparse+matrix' --header 'Authorization: Bearer YOUR_API_KEY' 

--data-raw '{"matrix": [[0,0,0,5],[0,0,0,0],[0,3,0,0],[0,0,0,0]]}'

    

Optimizes multiple sparse matrices in a single API call. Detects matrices with over 70% zero values and compresses them to CSR format saving significant memory in machine learning applications.



                                                                            
POST https://zylalabs.com/api/12866/python+data+optimizer+api/25674/optimize+sparse+matrix+batch
                                                                            
                                                                        

Optimize Sparse Matrix Batch - Endpoint Features

Object Description
Request Body [Required] Json

Free test requests remaining: 3 of 3.


INPUT PARAMETERS

request_body

API EXAMPLE RESPONSE

{"status":"success","results":[{"status":"optimized","sparsity_ratio":87.5,"original_format":"dense matrix","optimized_format":"CSR sparse matrix","original_size":128,"optimized_size":32,"saved":"96 bytes","non_zero_elements":2,"total_elements":16,"compressed_data":{"format":"CSR","shape":[4,4],"rows":[0,2],"cols":[3,1],"values":[5,3],"total_elements":16,"stored_elements":2}},{"status":"not_sparse","sparsity_ratio":0.0,"message":"Matrix is only 0.0% sparse. Optimization not needed.","original_size":32}]}

Optimize Sparse Matrix Batch - CODE SNIPPETS


curl --location --request POST 'https://zylalabs.com/api/12866/python+data+optimizer+api/25674/optimize+sparse+matrix+batch' --header 'Authorization: Bearer YOUR_API_KEY' 

--data-raw '{"matrices": [[[0,0,0,5],[0,0,0,0],[0,3,0,0],[0,0,0,0]],[[1,2],[3,4]]]}'

    

Scans Python code and detects memory leaks from global structures that grow indefinitely. Reports line numbers and severity levels — high, medium, and low — with recommendations to fix each leak.



                                                                            
POST https://zylalabs.com/api/12866/python+data+optimizer+api/25691/analyze+memory+leak
                                                                            
                                                                        

Analyze Memory Leak - Endpoint Features

Object Description
Request Body [Required] Json

Free test requests remaining: 3 of 3.


INPUT PARAMETERS

request_body

API EXAMPLE RESPONSE

{"status":"success","total_leaks_found":2,"severity":{"high":1,"medium":1,"low":0},"leaks":[{"type":"global_list","variable":"logs","line":3,"description":"Global list that grows indefinitely","recommendation":"Move 'logs' inside a function or class. Clear it periodically."},{"type":"large_global","variable":"logs","line":1,"description":"Global list 'logs' detected","recommendation":"Consider moving 'logs' inside a function or use weak references."}],"summary":"Found 2 potential memory leaks. 1 high severity, 1 medium severity, 0 low severity."}

Analyze Memory Leak - CODE SNIPPETS


curl --location --request POST 'https://zylalabs.com/api/12866/python+data+optimizer+api/25691/analyze+memory+leak' --header 'Authorization: Bearer YOUR_API_KEY' 

--data-raw '{"code": "logs = []\ndef add_log(msg):\n    logs.append(msg)"}'

    

Scans Python code for list comprehensions that load entire datasets into memory and recommends generator expressions. Saves up to 80MB on 10 million item datasets by processing data one item at a time.



                                                                            
POST https://zylalabs.com/api/12866/python+data+optimizer+api/25692/generator+advisor
                                                                            
                                                                        

Generator Advisor - Endpoint Features

Object Description
Request Body [Required] Json

Free test requests remaining: 3 of 3.


INPUT PARAMETERS

request_body

API EXAMPLE RESPONSE

{"status":"success","total_recommendations":3,"recommendations":[{"type":"list_comprehension","line":2,"description":"List comprehension detected — loads all data into memory at once.","recommendation":"Convert to generator expression by replacing [] with ().","example_before":"[x for x in data]","example_after":"(x for x in data)","memory_impact":"High — saves memory proportional to data size"},{"type":"function_with_list","line":3,"function":"sum","description":"List comprehension passed to sum() — unnecessary memory allocation.","recommendation":"Replace list comprehension with generator inside sum().","example_before":"sum([x for x in data])","example_after":"sum(x for x in data)","memory_impact":"Very High — generator never builds the full list in memory"},{"type":"list_comprehension","line":3,"description":"List comprehension detected — loads all data into memory at once.","recommendation":"Convert to generator expression by replacing [] with ().","example_before":"[x for x in data]","example_after":"(x for x in data)","memory_impact":"High — saves memory proportional to data size"}],"summary":"Found 3 opportunities to use generator expressions.","benefit":"Generator expressions process data one item at a time — saving significant memory on large datasets."}

Generator Advisor - CODE SNIPPETS


curl --location --request POST 'https://zylalabs.com/api/12866/python+data+optimizer+api/25692/generator+advisor' --header 'Authorization: Bearer YOUR_API_KEY' 

--data-raw '{"code": "numbers = [1,2,3,4,5]\nsquares = [x * x for x in numbers]\ntotal = sum([x * x for x in range(1000000)])"}'

    

Detects multiple boolean variables in Python code and recommends combining them into a single integer using bitwise flags. Replaces 6 booleans using 168 bytes with 1 integer using 28 bytes — 83% reduction.



                                                                            
POST https://zylalabs.com/api/12866/python+data+optimizer+api/25693/bitwise+optimizer
                                                                            
                                                                        

Bitwise Optimizer - Endpoint Features

Object Description
Request Body [Required] Json

Free test requests remaining: 3 of 3.


INPUT PARAMETERS

request_body

API EXAMPLE RESPONSE

{"status":"success","total_recommendations":1,"recommendations":[{"type":"multiple_booleans","boolean_variables":["is_active","is_admin","is_verified","is_premium","has_access","can_edit"],"count":6,"description":"Found 6 boolean variables that can be combined into one integer.","recommendation":"Replace multiple boolean variables with a single bitwise integer flag.","example_before":"is_active = True\nis_admin = True\nis_verified = True","example_after":"\nclass Flags:\n    IS_ACTIVE = 1\n    IS_ADMIN = 2\n    IS_VERIFIED = 4\n    IS_PREMIUM = 8\n    HAS_ACCESS = 16\n    CAN_EDIT = 32\n\n# Set flags\nflags = 0\nflags |= Flags.IS_ACTIVE\nflags |= Flags.IS_ADMIN\n\n# Check flags\nis_set = bool(flags & Flags.IS_ACTIVE)\n","memory_impact":"Saves 140 bytes — replacing 6 booleans with 1 integer"}],"savings":{"boolean_count":6,"original_size":168,"optimized_size":28,"saved":"140 bytes"},"summary":"Found 1 opportunities to use bitwise flags.","benefit":"Bitwise flags store multiple boolean states in one integer — saving memory and improving performance."}

Bitwise Optimizer - CODE SNIPPETS


curl --location --request POST 'https://zylalabs.com/api/12866/python+data+optimizer+api/25693/bitwise+optimizer' --header 'Authorization: Bearer YOUR_API_KEY' 

--data-raw '{"code": "is_active = True\nis_admin = False\nis_verified = True\nis_premium = False\nhas_access = True\ncan_edit = False"}'

    

Analyzes Python code for large file operations and recommends memory mapping techniques. Recommends mmap, numpy.memmap, and pandas chunksize to save up to 990MB on 1GB files.



                                                                            
POST https://zylalabs.com/api/12866/python+data+optimizer+api/25694/memory+mapper
                                                                            
                                                                        

Memory Mapper - Endpoint Features

Object Description
Request Body [Required] Json

Free test requests remaining: 3 of 3.


INPUT PARAMETERS

request_body

API EXAMPLE RESPONSE

{"status":"success","total_recommendations":3,"recommendations":[{"type":"file_open","line":3,"description":"File opened with standard open() — loads entire file into memory.","recommendation":"Use mmap for large files to avoid loading entire file into memory.","example_before":"\nwith open('large_file.bin', 'rb') as f:\n    data = f.read()\n","example_after":"\nimport mmap\nwith open('large_file.bin', 'rb') as f:\n    with mmap.mmap(f.fileno(), 0, access=mmap.ACCESS_READ) as mm:\n        data = mm[0:1024]  # Read only what you need\n","memory_impact":"High — reads only required bytes instead of entire file"},{"type":"numpy_file_load","line":5,"description":"numpy.loadtxt() loads entire file into memory.","recommendation":"Use numpy.memmap for large arrays to avoid full file loading.","example_before":"\nimport numpy as np\ndata = np.loadtxt('large_array.txt')\n","example_after":"\nimport numpy as np\ndata = np.memmap('large_array.bin', dtype='float32', mode='r')\n","memory_impact":"Very High — numpy memmap reads only accessed portions"},{"type":"pandas_file_load","line":6,"description":"pandas.read_csv() loads entire file into memory.","recommendation":"Use chunksize parameter to process file in smaller pieces.","example_before":"\nimport pandas as pd\ndf = pd.read_csv('large_file.csv')\n","example_after":"\nimport pandas as pd\nfor chunk in pd.read_csv('large_file.csv', chunksize=10000):\n    process(chunk)  # Process one chunk at a time\n","memory_impact":"Very High — processes file in chunks instead of loading all at once"}],"summary":"Found 3 opportunities to use memory mapping.","benefit":"Memory mapping reads only the parts of a file you need — saving significant memory on large files.","best_for":["Files larger than 100 MB","Binary data files","Large numpy arrays","Large CSV or data files"]}

Memory Mapper - CODE SNIPPETS


curl --location --request POST 'https://zylalabs.com/api/12866/python+data+optimizer+api/25694/memory+mapper' --header 'Authorization: Bearer YOUR_API_KEY' 

--data-raw '{"code": "import numpy as np\nimport pandas as pd\nfile = open('large_data.bin', 'rb')\ndata = file.read()\narray = np.loadtxt('large_array.txt')\ndf = pd.read_csv('large_file.csv')"}'

    

API Access Key & Authentication

After signing up, every developer is assigned a personal API access key, a unique combination of letters and digits provided to access to our API endpoint. To authenticate with the Python Data Optimizer API simply include your bearer token in the Authorization header.
Headers
Header Description
Authorization [Required] Should be Bearer access_key. See "Your API Access Key" above when you are subscribed.

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Python Data Optimizer API FAQs

The Optimize Data endpoint returns a JSON object containing the optimized data structure, a savings report detailing the original and optimized sizes, and the type of data detected.

The key fields in the response include "optimized" (the optimized data structure), "savings" (an object with "original_type," "optimized_type," "original_size," "optimized_size," and "saved"), and "type_detected" (the data type identified).

The response data is organized in a JSON format with a "status" field indicating success or failure, followed by a "result" object that contains the optimized data and savings report.

The Optimize Data endpoint provides information on the optimized data structure, the original and optimized sizes, the amount of memory saved, and the detected data type.

Users can customize their data requests by sending different Python data structures, code snippets, or database connection strings encapsulated in JSON format, allowing for tailored optimization based on their specific needs.

In the response, "original_size" indicates the size of the data before optimization, "optimized_size" shows the size after optimization, and "saved" quantifies the memory reduction achieved through the optimization process.

Typical use cases include reducing memory usage in Python applications, optimizing database column types to lower storage costs, identifying memory leaks, and compressing sparse matrices for machine learning tasks.

Data accuracy is maintained by intelligently identifying and replacing inefficient data types while ensuring that the optimized types accurately represent the original data, thus preserving its integrity during the optimization process.

The API can optimize various Python data structures, including lists, arrays, dictionaries, and sparse matrices. It also supports code snippets and database connection strings, allowing for a wide range of optimization scenarios.

The API can flatten deeply nested lists into flat arrays, which reduces complexity and memory usage. This feature is particularly useful for optimizing data structures commonly found in machine learning tasks.

The "type_detected" field indicates the original data type identified by the API before optimization. This helps users understand what type of data was processed and ensures they can verify the optimization results.

Yes, the API supports optimization for various database systems, including PostgreSQL, MySQL, SQLite, MongoDB, and SQL Server. It provides tailored recommendations for each system to enhance data storage efficiency.

If the input data is already optimized, the API will still return a savings report indicating that no further optimization is necessary. This helps users confirm the efficiency of their existing data structures.

The API intelligently identifies and replaces inefficient data types while ensuring that the optimized types accurately represent the original data. This process maintains data integrity and prevents loss of information.

Converting lists to sets eliminates duplicate values, saving memory, while using tuples provides immutability, which can reduce memory usage by approximately 40 bytes per structure. Both conversions enhance data efficiency.

The savings report details the original size, optimized size, and total bytes saved. Users can use this information to assess the effectiveness of the optimization and make informed decisions about data management.

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