## Serialization/Deserialization for Python native and NumPy/Pandas objects.
Created to be used in a project, this package is published to github for ease of management and installation across different modules.
### Installation
Install from `PyPi`
```bash
pip install serializor
```
Install from `github`
```bash
pip install git+https://github.com/AresJef/Serializor.git
```
### Requirements
- Python 3.10 or higher.
### Features
This package is designed to serialize most of Python objects into <'bytes'>, and then deserializes them back to the original (or compatiable) object.
- Python natives:
- Ser <'str'> -> Des <'str'>
- Ser <'int'> -> Des <'int'>
- Ser <'float'> -> Des <'float'>
- Ser <'bool'> -> Des <'bool'>
- Ser <'datetime.datetime'> -> Des <'datetime.datetime'> `[Supports Timezone]`
- Ser <'datetime.date'> -> Des <'datetime.date'>
- Ser <'datetime.time'> -> Des <'datetime.time'> `[Supports Timezone]`
- Ser <'datetime.timedelta'> -> Des <'datetime.timedelta'>
- Ser <'time.struct_time'> -> Des <'time.struct_time'>
- Ser <'decimal.Decimal'> -> Des <'decimal.Decimal'>
- Ser <'complex'> -> Des <'comples'>
- Ser <'bytes'> -> Des <'bytes'>
- Ser <'bytearray'> -> Des <'bytearray'>
- Ser <'memoryview'> -> Des <'bytes'>
- Ser <'list'> -> Des <'list'>
- Ser <'tuple'> -> Des <'tuple'>
- Ser <'set'> -> Des <'set'>
- Ser <'frozenset'> -> Des <'frozenset'>
- Ser <'range'> -> Des <'range'>
- Ser <'deque'> -> Des <'list'>
- Ser <'dict'> -> Des <'dict'>
- NumPy objects:
- Ser <'np.str\_'> -> Des <'str'>
- Ser <'np.int\*'> -> Des <'int'>
- Ser <'np.uint\*'> -> Des <'int'>
- Ser <'np.float\*'> -> Des <'float'>
- Ser <'np.bool\_'> -> Des <'bool'>
- Ser <'np.datetime64'> -> Des <'np.datetime64'>
- Ser <'np.timedelta64'> -> Des <'np.timedelta64'>
- Ser <'np.complex\*'> -> Des <'complex'>
- Ser <'np.bytes\_'> -> Des <'bytes'>
- Ser <'np.ndarray'> -> Des <'np.ndarray'> `[1-4 dimemsional]`
- Pandas objects:
- Ser <'pd.Timestamp'> -> Des <'pd.Timestamp'> `[Supports Timezone]`
- Ser <'pd.Timedelta'> -> Des <'pd.Timedelta'>
- Ser <'pd.Series'> -> Des <'pd.Series'>
- Ser <'pd.DatetimeIndex'> -> Des <'pd.DatetimeIndex'> `[Supports Timezone]`
- Ser <'pd.TimedeltaIndex'> -> Des <'pd.TimedeltaIndex'>
- Ser <'pd.DataFrame'> -> Des <'pd.DataFrame'>
### Benchmark
The following result comes from [benchmark](./src/benchmark.py):
- Device: MacbookPro M1Pro(2E8P) 32GB
- Python: 3.11.6
```
method description rounds ser_time des_time total_time
serializor datetime.date 1,000,000 0.111606 0.054381 0.165986
serializor datetime.datetime 1,000,000 0.156439 0.058737 0.215176
serializor datetime.datetime.tz 1,000,000 0.279386 0.146563 0.425948
serializor datetime.time 1,000,000 0.130660 0.053886 0.184547
serializor datetime.timedelta 1,000,000 0.137054 0.058629 0.195683
serializor decimal.Decimal 1,000,000 0.151619 0.164718 0.316336
serializor native.None 1,000,000 0.029402 0.032072 0.061473
serializor native.bool 1,000,000 0.029300 0.032557 0.061856
serializor native.bytearray 1,000,000 0.163795 0.204271 0.368066
serializor native.bytes.ascci 1,000,000 0.090823 0.059227 0.150049
serializor native.bytes.utf-8 1,000,000 0.086925 0.063321 0.150246
serializor native.complex 1,000,000 0.340286 0.109108 0.449394
serializor native.float 1,000,000 0.148515 0.075095 0.223610
serializor native.int 1,000,000 0.126915 0.046609 0.173524
serializor native.memoryview 1,000,000 0.116604 0.062738 0.179342
serializor native.str.ascii 1,000,000 0.103631 0.065440 0.169071
serializor native.str.utf-8 1,000,000 0.119890 0.103121 0.223011
serializor np.bool_ 1,000,000 0.038970 0.032415 0.071385
serializor np.bytes_ 1,000,000 0.085287 0.056964 0.142251
serializor np.complex128 1,000,000 0.346779 0.104836 0.451615
serializor np.datetime64 1,000,000 0.108729 0.131430 0.240160
serializor np.float64 1,000,000 0.175410 0.076764 0.252173
serializor np.int64 1,000,000 0.144910 0.046478 0.191387
serializor np.int64 1,000,000 0.149947 0.062591 0.212537
serializor np.str_ 1,000,000 0.113398 0.066752 0.180150
serializor np.timedelta64 1,000,000 0.108617 0.120959 0.229576
serializor pd.Timedelta 1,000,000 0.102173 2.273358 2.375531
serializor pd.Timestamp 1,000,000 0.143575 1.037143 1.180718
serializor pd.Timestamp.tz 1,000,000 0.243033 2.138342 2.381375
serializor time.struct_time 1,000,000 1.443414 1.181920 2.625334
serializor native.dict[mixed] 100,000 0.166442 0.095385 0.261826
serializor native.frozenset[mixed] 100,000 0.086782 0.105518 0.192300
serializor native.list[mixed] 100,000 0.086708 0.049511 0.136219
serializor native.range 100,000 0.044731 0.018418 0.063149
serializor native.set[mixed] 100,000 0.088746 0.078731 0.167477
serializor native.tuple[mixed] 100,000 0.084207 0.050072 0.134279
serializor np.ndarray.bool.1dim 100,000 0.059088 0.038294 0.097381
serializor np.ndarray.bool.2dim 100,000 0.095351 0.055688 0.151039
serializor np.ndarray.bytes.1dim 100,000 0.067917 0.042592 0.110509
serializor np.ndarray.bytes.2dim 100,000 0.125669 0.061103 0.186772
serializor np.ndarray.complex128.1dim 100,000 0.101098 0.066359 0.167458
serializor np.ndarray.complex128.2dim 100,000 0.182839 0.114994 0.297833
serializor np.ndarray.datetime64.1dim 100,000 0.077447 0.062867 0.140315
serializor np.ndarray.datetime64.2dim 100,000 0.117511 0.081945 0.199457
serializor np.ndarray.float64.1dim 100,000 0.079962 0.043448 0.123410
serializor np.ndarray.float64.2dim 100,000 0.151705 0.062965 0.214670
serializor np.ndarray.int64.1dim 100,000 0.040866 0.040583 0.081449
serializor np.ndarray.int64.2dim 100,000 0.075535 0.057880 0.133414
serializor np.ndarray.object.1dim 100,000 0.120009 0.035457 0.155466
serializor np.ndarray.object.2dim 100,000 0.228742 0.055797 0.284538
serializor np.ndarray.timedelta64.1dim 100,000 0.078657 0.060962 0.139619
serializor np.ndarray.timedelta64.2dim 100,000 0.118687 0.079886 0.198573
serializor np.ndarray.unicode.1dim 100,000 0.032552 0.076028 0.108581
serializor np.ndarray.unicode.2dim 100,000 0.071057 0.116673 0.187730
serializor pd.DataFrame 10,000 0.241303 2.152159 2.393463
serializor pd.DatetimeIndex 10,000 0.012195 0.078967 0.091162
serializor pd.Series.bool 10,000 0.012649 0.107103 0.119751
serializor pd.Series.bytes 10,000 0.012066 0.097440 0.109506
serializor pd.Series.complex128 10,000 0.015063 0.112606 0.127668
serializor pd.Series.datetime64 10,000 0.172289 0.193961 0.366250
serializor pd.Series.float64 10,000 0.013266 0.180588 0.193854
serializor pd.Series.int64 10,000 0.009387 0.109114 0.118501
serializor pd.Series.object 10,000 0.017119 0.107831 0.124949
serializor pd.Series.timedelta64 10,000 0.013114 0.170046 0.183160
serializor pd.Series.unicode 10,000 0.013292 0.126546 0.139838
serializor pd.TimedeltaIndex 10,000 0.010394 0.060776 0.071169
```
### Usage
#### Serialize & Deserialize
```python
import datetime
from decimal import Decimal
from zoneinfo import ZoneInfo
import numpy as np
import serializor
obj = {
"str": "Hello World!\n中国\n한국어\nにほんご\nEspañol",
"int": 1234567890,
"float": 3.141592653589793,
"bool": True,
"none": None,
"datetime": datetime.datetime(2012, 1, 2, 3, 4, 5, 6),
"datetime.tz1": datetime.datetime.now(ZoneInfo("CET")),
"datetime.tz2": datetime.datetime.now(
datetime.timezone(datetime.timedelta(hours=9))
),
"date": datetime.date(2012, 1, 2),
"time": datetime.time(3, 4, 5, 6),
"timedelta": datetime.timedelta(1, 2, 3),
"decimal": Decimal("3.1234"),
"complex": 2.12345 + 3.12345j,
"bytes": "Hello World!\n中国\n한국어\nにほんご\nEspañol".encode("utf-8"),
"datetime64": np.datetime64("2012-06-30 12:00:00.000000010"),
"timedelta64": np.timedelta64(-datetime.timedelta(1, 2, 3)),
"complex64": np.complex64(1 + 1j),
"complex128": np.complex128(-1 + -1j),
}
```
```python
se = serializor.serialize(obj)
print(se)
```
```
b'D\x12s\x03strs3Hello World!\n\xe4\xb8\xad\xe5\x9b\xbd\n\xed\x95\x9c\xea\xb5\xad\xec\x96\xb4\n\xe3\x81\xab\xe3\x81\xbb\xe3\x82\x93\xe3\x81\x94\nEspa\xc3\xb1ols\x03inti\n1234567890s\x05floatf\x113.141592653589793s\x04boolo\x01s\x04nonens\x08datetimez\x00\xdc\x07\x01\x02\x03\x04\x05\x06\x00\x00\x00s\x0cdatetime.tz1z\x01\xe8\x07\x08\x1f\x05/\x15\x11t\x0e\x00\x03CETs\x0cdatetime.tz2z\x02\xe8\x07\x08\x1f\x0c/\x15\x1at\x0e\x00\x00\x00\x00\x00\x90~\x00\x00\x00\x00\x00\x00s\x04dated\xdc\x07\x01\x02s\x04timet\x00\x03\x04\x05\x06\x00\x00\x00s\ttimedeltal\x01\x00\x00\x00\x02\x00\x00\x00\x03\x00\x00\x00s\x07decimale\x063.1234s\x07complexc\x072.12345\x073.12345s\x05bytesb3Hello World!\n\xe4\xb8\xad\xe5\x9b\xbd\n\xed\x95\x9c\xea\xb5\xad\xec\x96\xb4\n\xe3\x81\xab\xe3\x81\xbb\xe3\x82\x93\xe3\x81\x94\nEspa\xc3\xb1ols\ndatetime64M\n\n\x80V/\xc8d\x9c\x12s\x0btimedelta64m\t}\x1b\n\xe2\xeb\xff\xff\xffs\tcomplex64c\x031.0\x031.0s\ncomplex128c\x04-1.0\x04-1.0'
```
```python
de = serializor.deserialize(se)
assert obj == de
print(de)
```
```
{'str': 'Hello World!\n中国\n한국어\nにほんご\nEspañol', 'int': 1234567890, 'float': 3.141592653589793, 'bool': True, 'none': None, 'datetime': datetime.datetime(2012, 1, 2, 3, 4, 5, 6), 'datetime.tz1': datetime.datetime(2024, 8, 31, 5, 47, 21, 947217, tzinfo=zoneinfo.ZoneInfo(key='CET')), 'datetime.tz2': datetime.datetime(2024, 8, 31, 12, 47, 21, 947226, tzinfo=datetime.timezone(datetime.timedelta(seconds=32400))), 'date': datetime.date(2012, 1, 2), 'time': datetime.time(3, 4, 5, 6), 'timedelta': datetime.timedelta(days=1, seconds=2, microseconds=3), 'decimal': Decimal('3.1234'), 'complex': (2.12345+3.12345j), 'bytes': b'Hello World!\n\xe4\xb8\xad\xe5\x9b\xbd\n\xed\x95\x9c\xea\xb5\xad\xec\x96\xb4\n\xe3\x81\xab\xe3\x81\xbb\xe3\x82\x93\xe3\x81\x94\nEspa\xc3\xb1ol', 'datetime64': numpy.datetime64('2012-06-30T12:00:00.000000010'), 'timedelta64': numpy.timedelta64(-86402000003,'us'), 'complex64': (1+1j), 'complex128': (-1-1j)}
```
### Acknowledgements
SQLCyCli is based on the following open-source repositories:
- [cryptography](https://github.com/pyca/cryptography)
- [numpy](https://github.com/numpy/numpy)
- [orjson](https://github.com/ijl/orjson)
- [pandas](https://github.com/pandas-dev/pandas)
Raw data
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"keywords": "serialize, serialization, numpy, pandas, python, deserialize, deserialization",
"author": "Jiefu Chen",
"author_email": "keppa1991@163.com",
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"description": "## Serialization/Deserialization for Python native and NumPy/Pandas objects.\n\nCreated to be used in a project, this package is published to github for ease of management and installation across different modules.\n\n### Installation\n\nInstall from `PyPi`\n\n```bash\npip install serializor\n```\n\nInstall from `github`\n\n```bash\npip install git+https://github.com/AresJef/Serializor.git\n```\n\n### Requirements\n\n- Python 3.10 or higher.\n\n### Features\n\nThis package is designed to serialize most of Python objects into <'bytes'>, and then deserializes them back to the original (or compatiable) object.\n\n- Python natives:\n - Ser <'str'> -> Des <'str'>\n - Ser <'int'> -> Des <'int'>\n - Ser <'float'> -> Des <'float'>\n - Ser <'bool'> -> Des <'bool'>\n - Ser <'datetime.datetime'> -> Des <'datetime.datetime'> `[Supports Timezone]`\n - Ser <'datetime.date'> -> Des <'datetime.date'>\n - Ser <'datetime.time'> -> Des <'datetime.time'> `[Supports Timezone]`\n - Ser <'datetime.timedelta'> -> Des <'datetime.timedelta'>\n - Ser <'time.struct_time'> -> Des <'time.struct_time'>\n - Ser <'decimal.Decimal'> -> Des <'decimal.Decimal'>\n - Ser <'complex'> -> Des <'comples'>\n - Ser <'bytes'> -> Des <'bytes'>\n - Ser <'bytearray'> -> Des <'bytearray'>\n - Ser <'memoryview'> -> Des <'bytes'>\n - Ser <'list'> -> Des <'list'>\n - Ser <'tuple'> -> Des <'tuple'>\n - Ser <'set'> -> Des <'set'>\n - Ser <'frozenset'> -> Des <'frozenset'>\n - Ser <'range'> -> Des <'range'>\n - Ser <'deque'> -> Des <'list'>\n - Ser <'dict'> -> Des <'dict'>\n- NumPy objects:\n - Ser <'np.str\\_'> -> Des <'str'>\n - Ser <'np.int\\*'> -> Des <'int'>\n - Ser <'np.uint\\*'> -> Des <'int'>\n - Ser <'np.float\\*'> -> Des <'float'>\n - Ser <'np.bool\\_'> -> Des <'bool'>\n - Ser <'np.datetime64'> -> Des <'np.datetime64'>\n - Ser <'np.timedelta64'> -> Des <'np.timedelta64'>\n - Ser <'np.complex\\*'> -> Des <'complex'>\n - Ser <'np.bytes\\_'> -> Des <'bytes'>\n - Ser <'np.ndarray'> -> Des <'np.ndarray'> `[1-4 dimemsional]`\n- Pandas objects:\n - Ser <'pd.Timestamp'> -> Des <'pd.Timestamp'> `[Supports Timezone]`\n - Ser <'pd.Timedelta'> -> Des <'pd.Timedelta'>\n - Ser <'pd.Series'> -> Des <'pd.Series'>\n - Ser <'pd.DatetimeIndex'> -> Des <'pd.DatetimeIndex'> `[Supports Timezone]`\n - Ser <'pd.TimedeltaIndex'> -> Des <'pd.TimedeltaIndex'>\n - Ser <'pd.DataFrame'> -> Des <'pd.DataFrame'>\n\n### Benchmark\n\nThe following result comes from [benchmark](./src/benchmark.py):\n\n- Device: MacbookPro M1Pro(2E8P) 32GB\n- Python: 3.11.6\n\n```\nmethod description rounds ser_time des_time total_time\nserializor datetime.date 1,000,000 0.111606 0.054381 0.165986\nserializor datetime.datetime 1,000,000 0.156439 0.058737 0.215176\nserializor datetime.datetime.tz 1,000,000 0.279386 0.146563 0.425948\nserializor datetime.time 1,000,000 0.130660 0.053886 0.184547\nserializor datetime.timedelta 1,000,000 0.137054 0.058629 0.195683\nserializor decimal.Decimal 1,000,000 0.151619 0.164718 0.316336\nserializor native.None 1,000,000 0.029402 0.032072 0.061473\nserializor native.bool 1,000,000 0.029300 0.032557 0.061856\nserializor native.bytearray 1,000,000 0.163795 0.204271 0.368066\nserializor native.bytes.ascci 1,000,000 0.090823 0.059227 0.150049\nserializor native.bytes.utf-8 1,000,000 0.086925 0.063321 0.150246\nserializor native.complex 1,000,000 0.340286 0.109108 0.449394\nserializor native.float 1,000,000 0.148515 0.075095 0.223610\nserializor native.int 1,000,000 0.126915 0.046609 0.173524\nserializor native.memoryview 1,000,000 0.116604 0.062738 0.179342\nserializor native.str.ascii 1,000,000 0.103631 0.065440 0.169071\nserializor native.str.utf-8 1,000,000 0.119890 0.103121 0.223011\nserializor np.bool_ 1,000,000 0.038970 0.032415 0.071385\nserializor np.bytes_ 1,000,000 0.085287 0.056964 0.142251\nserializor np.complex128 1,000,000 0.346779 0.104836 0.451615\nserializor np.datetime64 1,000,000 0.108729 0.131430 0.240160\nserializor np.float64 1,000,000 0.175410 0.076764 0.252173\nserializor np.int64 1,000,000 0.144910 0.046478 0.191387\nserializor np.int64 1,000,000 0.149947 0.062591 0.212537\nserializor np.str_ 1,000,000 0.113398 0.066752 0.180150\nserializor np.timedelta64 1,000,000 0.108617 0.120959 0.229576\nserializor pd.Timedelta 1,000,000 0.102173 2.273358 2.375531\nserializor pd.Timestamp 1,000,000 0.143575 1.037143 1.180718\nserializor pd.Timestamp.tz 1,000,000 0.243033 2.138342 2.381375\nserializor time.struct_time 1,000,000 1.443414 1.181920 2.625334\nserializor native.dict[mixed] 100,000 0.166442 0.095385 0.261826\nserializor native.frozenset[mixed] 100,000 0.086782 0.105518 0.192300\nserializor native.list[mixed] 100,000 0.086708 0.049511 0.136219\nserializor native.range 100,000 0.044731 0.018418 0.063149\nserializor native.set[mixed] 100,000 0.088746 0.078731 0.167477\nserializor native.tuple[mixed] 100,000 0.084207 0.050072 0.134279\nserializor np.ndarray.bool.1dim 100,000 0.059088 0.038294 0.097381\nserializor np.ndarray.bool.2dim 100,000 0.095351 0.055688 0.151039\nserializor np.ndarray.bytes.1dim 100,000 0.067917 0.042592 0.110509\nserializor np.ndarray.bytes.2dim 100,000 0.125669 0.061103 0.186772\nserializor np.ndarray.complex128.1dim 100,000 0.101098 0.066359 0.167458\nserializor np.ndarray.complex128.2dim 100,000 0.182839 0.114994 0.297833\nserializor np.ndarray.datetime64.1dim 100,000 0.077447 0.062867 0.140315\nserializor np.ndarray.datetime64.2dim 100,000 0.117511 0.081945 0.199457\nserializor np.ndarray.float64.1dim 100,000 0.079962 0.043448 0.123410\nserializor np.ndarray.float64.2dim 100,000 0.151705 0.062965 0.214670\nserializor np.ndarray.int64.1dim 100,000 0.040866 0.040583 0.081449\nserializor np.ndarray.int64.2dim 100,000 0.075535 0.057880 0.133414\nserializor np.ndarray.object.1dim 100,000 0.120009 0.035457 0.155466\nserializor np.ndarray.object.2dim 100,000 0.228742 0.055797 0.284538\nserializor np.ndarray.timedelta64.1dim 100,000 0.078657 0.060962 0.139619\nserializor np.ndarray.timedelta64.2dim 100,000 0.118687 0.079886 0.198573\nserializor np.ndarray.unicode.1dim 100,000 0.032552 0.076028 0.108581\nserializor np.ndarray.unicode.2dim 100,000 0.071057 0.116673 0.187730\nserializor pd.DataFrame 10,000 0.241303 2.152159 2.393463\nserializor pd.DatetimeIndex 10,000 0.012195 0.078967 0.091162\nserializor pd.Series.bool 10,000 0.012649 0.107103 0.119751\nserializor pd.Series.bytes 10,000 0.012066 0.097440 0.109506\nserializor pd.Series.complex128 10,000 0.015063 0.112606 0.127668\nserializor pd.Series.datetime64 10,000 0.172289 0.193961 0.366250\nserializor pd.Series.float64 10,000 0.013266 0.180588 0.193854\nserializor pd.Series.int64 10,000 0.009387 0.109114 0.118501\nserializor pd.Series.object 10,000 0.017119 0.107831 0.124949\nserializor pd.Series.timedelta64 10,000 0.013114 0.170046 0.183160\nserializor pd.Series.unicode 10,000 0.013292 0.126546 0.139838\nserializor pd.TimedeltaIndex 10,000 0.010394 0.060776 0.071169\n```\n\n### Usage\n\n#### Serialize & Deserialize\n\n```python\nimport datetime\nfrom decimal import Decimal\nfrom zoneinfo import ZoneInfo\nimport numpy as np\nimport serializor\n\nobj = {\n \"str\": \"Hello World!\\n\u4e2d\u56fd\\n\ud55c\uad6d\uc5b4\\n\u306b\u307b\u3093\u3054\\nEspa\u00f1ol\",\n \"int\": 1234567890,\n \"float\": 3.141592653589793,\n \"bool\": True,\n \"none\": None,\n \"datetime\": datetime.datetime(2012, 1, 2, 3, 4, 5, 6),\n \"datetime.tz1\": datetime.datetime.now(ZoneInfo(\"CET\")),\n \"datetime.tz2\": datetime.datetime.now(\n datetime.timezone(datetime.timedelta(hours=9))\n ),\n \"date\": datetime.date(2012, 1, 2),\n \"time\": datetime.time(3, 4, 5, 6),\n \"timedelta\": datetime.timedelta(1, 2, 3),\n \"decimal\": Decimal(\"3.1234\"),\n \"complex\": 2.12345 + 3.12345j,\n \"bytes\": \"Hello World!\\n\u4e2d\u56fd\\n\ud55c\uad6d\uc5b4\\n\u306b\u307b\u3093\u3054\\nEspa\u00f1ol\".encode(\"utf-8\"),\n \"datetime64\": np.datetime64(\"2012-06-30 12:00:00.000000010\"),\n \"timedelta64\": np.timedelta64(-datetime.timedelta(1, 2, 3)),\n \"complex64\": np.complex64(1 + 1j),\n \"complex128\": np.complex128(-1 + -1j),\n}\n```\n\n```python\nse = serializor.serialize(obj)\nprint(se)\n```\n\n```\nb'D\\x12s\\x03strs3Hello World!\\n\\xe4\\xb8\\xad\\xe5\\x9b\\xbd\\n\\xed\\x95\\x9c\\xea\\xb5\\xad\\xec\\x96\\xb4\\n\\xe3\\x81\\xab\\xe3\\x81\\xbb\\xe3\\x82\\x93\\xe3\\x81\\x94\\nEspa\\xc3\\xb1ols\\x03inti\\n1234567890s\\x05floatf\\x113.141592653589793s\\x04boolo\\x01s\\x04nonens\\x08datetimez\\x00\\xdc\\x07\\x01\\x02\\x03\\x04\\x05\\x06\\x00\\x00\\x00s\\x0cdatetime.tz1z\\x01\\xe8\\x07\\x08\\x1f\\x05/\\x15\\x11t\\x0e\\x00\\x03CETs\\x0cdatetime.tz2z\\x02\\xe8\\x07\\x08\\x1f\\x0c/\\x15\\x1at\\x0e\\x00\\x00\\x00\\x00\\x00\\x90~\\x00\\x00\\x00\\x00\\x00\\x00s\\x04dated\\xdc\\x07\\x01\\x02s\\x04timet\\x00\\x03\\x04\\x05\\x06\\x00\\x00\\x00s\\ttimedeltal\\x01\\x00\\x00\\x00\\x02\\x00\\x00\\x00\\x03\\x00\\x00\\x00s\\x07decimale\\x063.1234s\\x07complexc\\x072.12345\\x073.12345s\\x05bytesb3Hello World!\\n\\xe4\\xb8\\xad\\xe5\\x9b\\xbd\\n\\xed\\x95\\x9c\\xea\\xb5\\xad\\xec\\x96\\xb4\\n\\xe3\\x81\\xab\\xe3\\x81\\xbb\\xe3\\x82\\x93\\xe3\\x81\\x94\\nEspa\\xc3\\xb1ols\\ndatetime64M\\n\\n\\x80V/\\xc8d\\x9c\\x12s\\x0btimedelta64m\\t}\\x1b\\n\\xe2\\xeb\\xff\\xff\\xffs\\tcomplex64c\\x031.0\\x031.0s\\ncomplex128c\\x04-1.0\\x04-1.0'\n```\n\n```python\nde = serializor.deserialize(se)\nassert obj == de\nprint(de)\n```\n\n```\n{'str': 'Hello World!\\n\u4e2d\u56fd\\n\ud55c\uad6d\uc5b4\\n\u306b\u307b\u3093\u3054\\nEspa\u00f1ol', 'int': 1234567890, 'float': 3.141592653589793, 'bool': True, 'none': None, 'datetime': datetime.datetime(2012, 1, 2, 3, 4, 5, 6), 'datetime.tz1': datetime.datetime(2024, 8, 31, 5, 47, 21, 947217, tzinfo=zoneinfo.ZoneInfo(key='CET')), 'datetime.tz2': datetime.datetime(2024, 8, 31, 12, 47, 21, 947226, tzinfo=datetime.timezone(datetime.timedelta(seconds=32400))), 'date': datetime.date(2012, 1, 2), 'time': datetime.time(3, 4, 5, 6), 'timedelta': datetime.timedelta(days=1, seconds=2, microseconds=3), 'decimal': Decimal('3.1234'), 'complex': (2.12345+3.12345j), 'bytes': b'Hello World!\\n\\xe4\\xb8\\xad\\xe5\\x9b\\xbd\\n\\xed\\x95\\x9c\\xea\\xb5\\xad\\xec\\x96\\xb4\\n\\xe3\\x81\\xab\\xe3\\x81\\xbb\\xe3\\x82\\x93\\xe3\\x81\\x94\\nEspa\\xc3\\xb1ol', 'datetime64': numpy.datetime64('2012-06-30T12:00:00.000000010'), 'timedelta64': numpy.timedelta64(-86402000003,'us'), 'complex64': (1+1j), 'complex128': (-1-1j)}\n```\n\n### Acknowledgements\n\nSQLCyCli is based on the following open-source repositories:\n\n- [cryptography](https://github.com/pyca/cryptography)\n- [numpy](https://github.com/numpy/numpy)\n- [orjson](https://github.com/ijl/orjson)\n- [pandas](https://github.com/pandas-dev/pandas)\n",
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