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: 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.
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:
{"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"}}
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]}'
{"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"}]}
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]]}'
{"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"]}
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"]}'
{"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]}]}
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]]}'
{"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}}
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]]}'
{"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}]}
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]]]}'
{"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."}
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)"}'
{"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."}
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)])"}'
{"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."}
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"}'
{"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"]}
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')"}'
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优化数据端点返回一个JSON对象包含优化数据结构的详细节省报告 说明原始和优化的大小以及检测到的数据类型
响应中的关键字段包括“优化的”(优化后的数据结构) “节省”(一个包含“原始类型”“优化类型”“原始大小”“优化大小”和“节省”的对象)以及“检测到的类型”(识别的数据类型)
响应数据以JSON格式组织,其中包含一个“status”字段指示成功或失败,后面是一个“result”对象,包含优化后的数据和节省报告
优化数据端点提供有关优化数据结构的信息 原始和优化的大小 节省的内存量以及检测到的数据类型
用户可以通过发送不同的Python数据结构、代码片段或封装在JSON格式中的数据库连接字符串来定制他们的数据请求,从而根据他们的具体需求进行量身定制的优化
在回复中“original_size”表示优化前的数据大小“optimized_size”显示优化后的大小“saved”量化通过优化过程实现的内存减少
典型的用例包括减少Python应用程序的内存使用,优化数据库列类型以降低存储成本,识别内存泄漏,以及压缩稀疏矩阵以用于机器学习任务
数据的准确性通过智能识别和替换低效的数据类型得以保持,同时确保优化后的类型准确地表示原始数据,从而在优化过程中保持其完整性
该API可以优化各种Python数据结构,包括列表、数组、字典和稀疏矩阵。它还支持代码片段和数据库连接字符串,允许多种优化场景
API可以将深度嵌套的列表展平为扁平数组,从而减少复杂性和内存使用。此功能在优化机器学习任务中常见的数据结构时尤为有用
"type_detected"字段表示API在优化前识别的原始数据类型。这有助于用户理解处理的数据类型,并确保他们能够验证优化结果
是的,API支持针对各种数据库系统的优化,包括PostgreSQL、MySQL、SQLite、MongoDB和SQL Server。它为每个系统提供量身定制的建议,以提高数据存储效率
如果输入数据已经优化,API仍会返回一份节省报告,指出不需要进一步优化。这帮助用户确认他们现有数据结构的效率
API智能识别并替换低效的数据类型,同时确保优化后的类型准确表示原始数据。这个过程维护数据完整性并防止信息丢失
将列表转换为集合可以消除重复值,从而节省内存,而使用元组提供不变性,这可以使每个结构减少大约40个字节的内存使用。这两种转换都提高了数据效率
节省报告详细说明了原始大小、优化后的大小和节省的总字节数。用户可以使用这些信息来评估优化的有效性,并就数据管理做出明智的决策
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3,110ms
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233ms
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1,405ms
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472ms
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2,921ms
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20,002ms
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6,655ms