{
  "trx_id": "e5ab6e436d904d0295a1c832cff7ecd6a4381a25",
  "block": 106152954,
  "trx_in_block": 0,
  "op_in_trx": 0,
  "virtual_op": 0,
  "timestamp": "2026-05-18T08:12:27",
  "op": [
    "delegate_vesting_shares",
    {
      "delegator": "steem",
      "delegatee": "whytin",
      "vesting_shares": "7117.090451 VESTS"
    }
  ]
}

{
  "trx_id": "c0c64ebbadfdec734588cb41614f8c374c120930",
  "block": 106014392,
  "trx_in_block": 1,
  "op_in_trx": 0,
  "virtual_op": 0,
  "timestamp": "2026-05-13T12:08:57",
  "op": [
    "delegate_vesting_shares",
    {
      "delegator": "steem",
      "delegatee": "whytin",
      "vesting_shares": "4404.880046 VESTS"
    }
  ]
}

{
  "trx_id": "7fce6d69769df0148af2e893ccf6771e6d1f60b4",
  "block": 105520387,
  "trx_in_block": 0,
  "op_in_trx": 0,
  "virtual_op": 0,
  "timestamp": "2026-04-26T07:21:36",
  "op": [
    "delegate_vesting_shares",
    {
      "delegator": "steem",
      "delegatee": "whytin",
      "vesting_shares": "7129.606207 VESTS"
    }
  ]
}

{
  "trx_id": "fcc45b5b43996680c92aa250e0b79e228b69c04f",
  "block": 102877424,
  "trx_in_block": 2,
  "op_in_trx": 0,
  "virtual_op": 0,
  "timestamp": "2026-01-24T05:11:30",
  "op": [
    "delegate_vesting_shares",
    {
      "delegator": "steem",
      "delegatee": "whytin",
      "vesting_shares": "4446.426865 VESTS"
    }
  ]
}

{
  "trx_id": "55df1ee50da21c8b84893a253087142dee16ef92",
  "block": 91323620,
  "trx_in_block": 0,
  "op_in_trx": 0,
  "virtual_op": 0,
  "timestamp": "2024-12-18T00:20:18",
  "op": [
    "delegate_vesting_shares",
    {
      "delegator": "steem",
      "delegatee": "whytin",
      "vesting_shares": "4610.646062 VESTS"
    }
  ]
}

{
  "trx_id": "1facca1bd2e011920b03e825bb14a01b2679e3bb",
  "block": 79877711,
  "trx_in_block": 7,
  "op_in_trx": 0,
  "virtual_op": 0,
  "timestamp": "2023-11-14T15:59:09",
  "op": [
    "delegate_vesting_shares",
    {
      "delegator": "steem",
      "delegatee": "whytin",
      "vesting_shares": "4779.779594 VESTS"
    }
  ]
}

{
  "trx_id": "669189de06106e61bbc34f826c591932fdeabea7",
  "block": 78365582,
  "trx_in_block": 1,
  "op_in_trx": 0,
  "virtual_op": 0,
  "timestamp": "2023-09-22T12:39:45",
  "op": [
    "delegate_vesting_shares",
    {
      "delegator": "steem",
      "delegatee": "whytin",
      "vesting_shares": "7716.688380 VESTS"
    }
  ]
}

{
  "trx_id": "fb132392fb92840736e3f7fa81ae6aa97ee11847",
  "block": 69122980,
  "trx_in_block": 1,
  "op_in_trx": 0,
  "virtual_op": 0,
  "timestamp": "2022-11-03T19:50:21",
  "op": [
    "delegate_vesting_shares",
    {
      "delegator": "steem",
      "delegatee": "whytin",
      "vesting_shares": "7938.739818 VESTS"
    }
  ]
}

{
  "trx_id": "ca4f3569ea5b9a2ec563c34bbf2181ed8e9ceb0e",
  "block": 60826004,
  "trx_in_block": 18,
  "op_in_trx": 0,
  "virtual_op": 0,
  "timestamp": "2022-01-18T00:51:03",
  "op": [
    "delegate_vesting_shares",
    {
      "delegator": "steem",
      "delegatee": "whytin",
      "vesting_shares": "8158.847419 VESTS"
    }
  ]
}

{
  "trx_id": "e732fef4fc25c8a92fc04d24bb5794eab8e77a90",
  "block": 54616225,
  "trx_in_block": 10,
  "op_in_trx": 0,
  "virtual_op": 0,
  "timestamp": "2021-06-14T07:57:09",
  "op": [
    "delegate_vesting_shares",
    {
      "delegator": "steem",
      "delegatee": "whytin",
      "vesting_shares": "8343.041707 VESTS"
    }
  ]
}

{
  "trx_id": "8e517ef5c719931e3e8b5d843adc4fea774ad334",
  "block": 49363425,
  "trx_in_block": 16,
  "op_in_trx": 0,
  "virtual_op": 0,
  "timestamp": "2020-12-11T18:07:27",
  "op": [
    "delegate_vesting_shares",
    {
      "delegator": "steem",
      "delegatee": "whytin",
      "vesting_shares": "8530.463681 VESTS"
    }
  ]
}

{
  "trx_id": "30dbff3fd19859f39abeb0a8f4197937d6a99a47",
  "block": 49214939,
  "trx_in_block": 1,
  "op_in_trx": 0,
  "virtual_op": 0,
  "timestamp": "2020-12-06T11:42:30",
  "op": [
    "delegate_vesting_shares",
    {
      "delegator": "steem",
      "delegatee": "whytin",
      "vesting_shares": "1912.543513 VESTS"
    }
  ]
}

{
  "trx_id": "f2f66f16ac88a1fb5babc3287b72768cec01c3f2",
  "block": 49198512,
  "trx_in_block": 0,
  "op_in_trx": 0,
  "virtual_op": 0,
  "timestamp": "2020-12-05T21:45:21",
  "op": [
    "delegate_vesting_shares",
    {
      "delegator": "steem",
      "delegatee": "whytin",
      "vesting_shares": "8536.671535 VESTS"
    }
  ]
}

{
  "trx_id": "080e54f3d4a4a01814fa58b83800dc5d2b136d71",
  "block": 48274901,
  "trx_in_block": 0,
  "op_in_trx": 0,
  "virtual_op": 0,
  "timestamp": "2020-11-03T06:09:42",
  "op": [
    "delegate_vesting_shares",
    {
      "delegator": "steem",
      "delegatee": "whytin",
      "vesting_shares": "1920.017158 VESTS"
    }
  ]
}

{
  "trx_id": "1a9bfadc9e9ad34d3987f2885d9b75ce556e23a6",
  "block": 43225296,
  "trx_in_block": 24,
  "op_in_trx": 0,
  "virtual_op": 0,
  "timestamp": "2020-05-09T12:47:24",
  "op": [
    "delegate_vesting_shares",
    {
      "delegator": "steem",
      "delegatee": "whytin",
      "vesting_shares": "8739.476894 VESTS"
    }
  ]
}

{
  "trx_id": "f883bdf55197faa558cc4d4aeb2e984b871ea055",
  "block": 43202635,
  "trx_in_block": 16,
  "op_in_trx": 0,
  "virtual_op": 0,
  "timestamp": "2020-05-08T17:27:09",
  "op": [
    "delegate_vesting_shares",
    {
      "delegator": "steem",
      "delegatee": "whytin",
      "vesting_shares": "1953.311140 VESTS"
    }
  ]
}

{
  "trx_id": "8b1f6422e71bdf49c327238f01d277289ed1d426",
  "block": 42570387,
  "trx_in_block": 26,
  "op_in_trx": 0,
  "virtual_op": 0,
  "timestamp": "2020-04-16T04:21:24",
  "op": [
    "delegate_vesting_shares",
    {
      "delegator": "steem",
      "delegatee": "whytin",
      "vesting_shares": "8752.364342 VESTS"
    }
  ]
}

{
  "trx_id": "88c7e204e73b342177e5d2c54d5adc31700debb3",
  "block": 38431561,
  "trx_in_block": 9,
  "op_in_trx": 0,
  "virtual_op": 0,
  "timestamp": "2019-11-23T17:01:09",
  "op": [
    "comment",
    {
      "parent_author": "whytin",
      "parent_permlink": "python-python",
      "author": "steemitboard",
      "permlink": "steemitboard-notify-whytin-20191123t170108000z",
      "title": "",
      "body": "Congratulations @whytin! You received a personal award!\n\n<table><tr><td>https://steemitimages.com/70x70/http://steemitboard.com/@whytin/birthday2.png</td><td>Happy Birthday! - You are on the Steem blockchain for 2 years!</td></tr></table>\n\n<sub>_You can view [your badges on your Steem Board](https://steemitboard.com/@whytin) and compare to others on the [Steem Ranking](https://steemitboard.com/ranking/index.php?name=whytin)_</sub>\n\n\n###### [Vote for @Steemitboard as a witness](https://v2.steemconnect.com/sign/account-witness-vote?witness=steemitboard&approve=1) to get one more award and increased upvotes!",
      "json_metadata": "{\"image\":[\"https://steemitboard.com/img/notify.png\"]}"
    }
  ]
}

{
  "trx_id": "64a958f0d08133b51751d1f1077f8aff28bb80c4",
  "block": 32853383,
  "trx_in_block": 44,
  "op_in_trx": 0,
  "virtual_op": 0,
  "timestamp": "2019-05-12T21:29:12",
  "op": [
    "delegate_vesting_shares",
    {
      "delegator": "steem",
      "delegatee": "whytin",
      "vesting_shares": "8947.981155 VESTS"
    }
  ]
}

{
  "trx_id": "63adb94e2b86d294b259765ba95e9c939e41e5f0",
  "block": 27954824,
  "trx_in_block": 2,
  "op_in_trx": 0,
  "virtual_op": 0,
  "timestamp": "2018-11-23T15:10:48",
  "op": [
    "comment",
    {
      "parent_author": "whytin",
      "parent_permlink": "python-python",
      "author": "steemitboard",
      "permlink": "steemitboard-notify-whytin-20181123t151047000z",
      "title": "",
      "body": "Congratulations @whytin! You received a personal award!\n\n<table><tr><td>https://steemitimages.com/70x70/http://steemitboard.com/@whytin/birthday1.png</td><td>1 Year on Steemit</td></tr></table>\n\n<sub>_[Click here to view your Board of Honor](https://steemitboard.com/@whytin)_</sub>\n\n\n**Do not miss the last post from @steemitboard:**\n<table><tr><td><a href=\"https://steemit.com/steemfest/@steemitboard/meet-the-steemians-contest-the-results-the-winners-and-the-prizes\"><img src=\"https://steemitimages.com/64x128/https://cdn.steemitimages.com/DQmeLukvNFRsa7RURqsFpiLGEZZD49MiU52JtWmjS5S2wtW/image.png\"></a></td><td><a href=\"https://steemit.com/steemfest/@steemitboard/meet-the-steemians-contest-the-results-the-winners-and-the-prizes\">Meet the Steemians Contest - The results, the winners and the prizes</a></td></tr><tr><td><a href=\"https://steemit.com/steemfest/@steemitboard/meet-the-steemians-contest-special-attendees-revealed\"><img src=\"https://steemitimages.com/64x128/https://cdn.steemitimages.com/DQmeLukvNFRsa7RURqsFpiLGEZZD49MiU52JtWmjS5S2wtW/image.png\"></a></td><td><a href=\"https://steemit.com/steemfest/@steemitboard/meet-the-steemians-contest-special-attendees-revealed\">Meet the Steemians Contest - Special attendees revealed</a></td></tr><tr><td><a href=\"https://steemit.com/steemfest/@steemitboard/meet-the-steemians-contest-intermediate-results\"><img src=\"https://steemitimages.com/64x128/https://cdn.steemitimages.com/DQmeLukvNFRsa7RURqsFpiLGEZZD49MiU52JtWmjS5S2wtW/image.png\"></a></td><td><a href=\"https://steemit.com/steemfest/@steemitboard/meet-the-steemians-contest-intermediate-results\">Meet the Steemians Contest - Intermediate results</a></td></tr></table>\n\n> Support [SteemitBoard's project](https://steemit.com/@steemitboard)! **[Vote for its witness](https://v2.steemconnect.com/sign/account-witness-vote?witness=steemitboard&approve=1)** and **get one more award**!",
      "json_metadata": "{\"image\":[\"https://steemitboard.com/img/notify.png\"]}"
    }
  ]
}

{
  "trx_id": "b6eabbd0b11529cfee6ea650c1d020c2509237fa",
  "block": 22498772,
  "trx_in_block": 19,
  "op_in_trx": 0,
  "virtual_op": 0,
  "timestamp": "2018-05-17T03:43:42",
  "op": [
    "delegate_vesting_shares",
    {
      "delegator": "steem",
      "delegatee": "whytin",
      "vesting_shares": "9147.496247 VESTS"
    }
  ]
}

{
  "trx_id": "2268676a0c0bab05b558334af64a05fc068b8211",
  "block": 21771393,
  "trx_in_block": 25,
  "op_in_trx": 0,
  "virtual_op": 0,
  "timestamp": "2018-04-21T20:55:18",
  "op": [
    "delegate_vesting_shares",
    {
      "delegator": "steem",
      "delegatee": "whytin",
      "vesting_shares": "29535.598593 VESTS"
    }
  ]
}

{
  "trx_id": "b1181a2a3c39cf3a9b917a824d200a2d68a3c668",
  "block": 19739463,
  "trx_in_block": 26,
  "op_in_trx": 0,
  "virtual_op": 0,
  "timestamp": "2018-02-10T06:00:36",
  "op": [
    "comment",
    {
      "parent_author": "whytin",
      "parent_permlink": "python-python",
      "author": "dtubix",
      "permlink": "re-python-python-140",
      "title": "",
      "body": "Nice post! I will follow you from now on.  <p><img src=\"https://preview.ibb.co/hJ5ArH/up2.png\" width=\"200\"/></p>",
      "json_metadata": ""
    }
  ]
}

{
  "trx_id": "c947645a6b4e7d97bed7ccb2f8e5d909a39f7939",
  "block": 19739431,
  "trx_in_block": 55,
  "op_in_trx": 0,
  "virtual_op": 0,
  "timestamp": "2018-02-10T05:59:00",
  "op": [
    "vote",
    {
      "voter": "dtubix",
      "author": "whytin",
      "permlink": "python-python",
      "weight": 5000
    }
  ]
}

{
  "trx_id": "0000000000000000000000000000000000000000",
  "block": 18415212,
  "trx_in_block": 4294967295,
  "op_in_trx": 0,
  "virtual_op": 29,
  "timestamp": "2017-12-26T05:19:15",
  "op": [
    "author_reward",
    {
      "author": "whytin",
      "permlink": "python-python",
      "sbd_payout": "0.124 SBD",
      "steem_payout": "0.000 STEEM",
      "vesting_payout": "79.941237 VESTS"
    }
  ]
}

{
  "trx_id": "b5742bf8c1528bfbca353f0c61c35feca4926020",
  "block": 18214342,
  "trx_in_block": 28,
  "op_in_trx": 0,
  "virtual_op": 0,
  "timestamp": "2017-12-19T05:50:12",
  "op": [
    "vote",
    {
      "voter": "hr1",
      "author": "whytin",
      "permlink": "python-python",
      "weight": 50
    }
  ]
}

{
  "trx_id": "e8415716d45df6bc98cc526df231254773436407",
  "block": 18213728,
  "trx_in_block": 10,
  "op_in_trx": 0,
  "virtual_op": 0,
  "timestamp": "2017-12-19T05:19:27",
  "op": [
    "vote",
    {
      "voter": "whytin",
      "author": "whytin",
      "permlink": "python-python",
      "weight": 10000
    }
  ]
}

{
  "trx_id": "fe29108f45415f43d9600a0017d368b7c51feba9",
  "block": 18213724,
  "trx_in_block": 28,
  "op_in_trx": 0,
  "virtual_op": 0,
  "timestamp": "2017-12-19T05:19:15",
  "op": [
    "comment",
    {
      "parent_author": "",
      "parent_permlink": "python",
      "author": "whytin",
      "permlink": "python-python",
      "title": "Python教程：进击机器学习（二）--认识Python",
      "body": "Self-intro: I am a graduate student at an unnamed institution in China :) The main focus is Computer vision using deep learning. I will update some of notes about deep learning at steemit. Hope that like-minded friends follow me, and we discuss and support with each other. Thanks for reading! @whytin\n\n如果有Python基础或者有学习过的，可以直接跳过本教程。\n本教程旨在快速认识和了解Python，作为机器学习的前调\\坏笑。\n## 了解Python\nPython作为一个高级编程语言，它有哪一些特性:\n* 不同于C语言（编译型语言），Python是一种解释型语言。就是在执行一段Python代码之前并不需要对其编译。\n* Python是开源的、免费的\n* 跨平台，Linux/Unix、Windows、MacOS X。\n* 没有冗长的语法，可读性很强\n* 兼容并支持其他语言\n\n## Hello Python\n打开Ipython shell，类似Matlab，简单的Hello world就是这样。\n```python\n>>> print(\"Hello, world!\")\nHello, world! \n```\nPython简单的语法体现在，不需要声明变量和函数类型。\n```python\n>>> a = 3\n>>> type(a)   \n<type 'int'>\n```\n如果是C语言：\n```c\nint a = 3;\n```\n再来感受一下复数计算：\n```python\n>>> a = 1.5 + 0.5j\n>>> a.real\n1.5\n>>> a.imag\n0.5\n```\nPython的基础我就不讲太多，如果之前没接触过编程的可以推荐参考：http://www.runoob.com/python/python-tutorial.html\n书籍的话推荐《Python基础教程 (第2版) 》下载地址：[Beginning.Python.From.Novice.to.Professional,2nd.Edition_chs](http://61.135.203.11/club/it/dev/prog/Beginning.Python.From.Novice.to.Professional,2nd.Edition_chs.zip)\n\n### 数组\n定义数组不用声明类型，也不限制数组里面的类型。\n```python\n>>> colors = [3, -200, 'hello']\n```\n数组索引是从0开始，跟C语言一样，跟Matlab有区别（从1开始）\n```python\n>>> colors[0], colors[2]\n(3, 'hello')\n```\nPython关于数组的也有很多内置函数，方便我们操作函数。\n例如添加值list.append(x)，插入list.insert(i, x)，排序list.sort(key=None, reverse=False）。更多数组的内置函数参考：\nhttps://docs.python.org/3/tutorial/datastructures.html#more-on-lists\n\n### 字符串\nPython对String的操作类似List：\n```python\n>>> a = \"hello\"\n>>> a[0]\n'h'\n>>> a[-1]\n'o'\n```\n字符串分割：\n```python\n>>> a = \"hello, world!\"\n>>> a[2:10:2]               # 参数: a[开始:结束:步长]\n'lo o'\n```\n字符串格式化：\n```python\n>>> 'An integer: %i; a float: %f; another string: %s' % (1, 0.1, 'string')\n'An integer: 1; a float: 0.100000; another string: string'\n```\n更多关于Python字符串参考：[https://docs.python.org/tutorial/introduction.html#unicode-strings](https://docs.python.org/tutorial/introduction.html#unicode-strings)\n\n### 字典\nPython字典有一个特点，无序。\n定义一个字典：\n```python\n>>> tel = {'emmanuelle': 5752, 'sebastian': 5578}\n```\n添加键值对：\n```python\ntel['francis'] = 5915\n```\n然后我们再来输出一下这个字典\n```python\n>>> tel     \n{'sebastian': 5578, 'francis': 5915, 'emmanuelle': 5752}\n```\n可以看到我们插入的键值对被放在中间，Python的字典是不按任何规则来排序的。\n我们可以调用这个字典键和值通过以下两个函数：\n```python\n>>> tel.keys()  \n['sebastian', 'francis', 'emmanuelle']\n>>> tel.values() \n[5578, 5915, 5752]\n```\n关于字典更高级的操作参考：[https://docs.python.org/tutorial/datastructures.html#dictionaries](https://docs.python.org/tutorial/datastructures.html#dictionaries) \n除了这几个常用的容器类型，还有元组（Tuples）和集合（Sets），因为在实际开发中比较少用，所以就不展开讲了。\n\n需要注意的一点是Python对变量分配地址的机制，不像其他语言，Python是对对象分配地址，同一个对象只分配一个地址，但是可以有多个变量名：\n```python\nIn   [1]:  a = [1, 2, 3]\nIn   [2]:  b = a\nIn   [3]:  a\nOut[3]:  [1, 2, 3]\nIn   [4]:  b\nOut[4]:  [1, 2, 3]\nIn   [5]:  a is b\nOut[5]:  True\nIn   [6]:  b[1] = 'hi!'\nIn   [7]:  a\nOut[7]:  [1, 'hi!', 3]\n```\n### 控制语句\n在C语言中，在同一个控制语句下的需要加｛｝来表示。而在Python中只需要通过缩进来保持控制块，在同一个控制块的缩进要一致。\n```python\n>>> a = 10\n>>> if a == 1:\n...　　print(1)\n... elif a == 2:\n...　　print(2)\n... else:\n...　　print('A lot')\nA lot\n```\nfor语句的用法：\n```python\n>>> for i in range(4):\n...　　print(i)\n0\n1\n2\n3\n```\nwhile语句的用法：\n```python\n>>> a = [1, 0, 2, 4]\n>>> for element in a:\n...　　if element == 0:\n...　　　　continue\n...　　print(1. / element)\n1.0\n0.5\n0.25\n```\n### 函数定义\nPython的函数定义也只需要def 函数名(参数)：\n（同样需要缩进）\n```python\nIn [81]: def double_it(x):\n....:　 　　　return x * 2\nIn [82]: double_it(3)\nOut[82]: 6\n```\n### 脚本和模块\n我们已经把最基础的数据类型、容器、控制语句和函数简单了解一下，接下来我们就是把它们写到程序里面。我们把代码写进文件的目的就是重复使用这些代码，就跟为什么我们要用函数类似。我们一般用Python编辑器（Windows下IDE可以用Pycharm来写Python程序）来创建.py文件。\n\n比如我们构建一个测试文件test.py\n```python\nmessage = \"Hello how are you?\"\nfor word in message.split():\n　　print word\n```\n在IPython里面我们可以用%run test.py来运行程序\n```python\nIn [1]: %run test.py\nHello\nhow\nare\nyou?\n```\n在Windows或者Linux的命令行就使用 python test.py 来运行程序\n```python\n$ python test.py\nHello\nhow\nare\nyou?\n```\n但是我们有时候又不需要执行一个程序，只是希望把一些函数写到文件里面，需要的时候再拿出来用，有点类似C++的头文件。这个时候就需要用到导入模块（我们可以用来导入我们自己写的文件，也可以导入所需要的函数库）：\n假设我们需要导入Numpy库来对0-10进行采样，我们可以这样使用：\n```python\n>>>import numpy as np\n>>>np.linspace(0, 10, 6)\narray([0, 2, 4, 6, 8, 10])\n```\nas可以对导入的文件设置别名，当我们在调用模块里面的函数时只需在用一个.来链接（module.function()）\n\n关于Python的介绍就先讲到这里，接下来是Python数学计算库Numpy。\n\nRef：http://www.scipy-lectures.org/intro/language/python_language.html",
      "json_metadata": "{\"tags\":[\"python\"],\"users\":[\"whytin\"],\"links\":[\"http://www.runoob.com/python/python-tutorial.html\",\"http://61.135.203.11/club/it/dev/prog/Beginning.Python.From.Novice.to.Professional,2nd.Edition_chs.zip\",\"https://docs.python.org/3/tutorial/datastructures.html#more-on-lists\",\"https://docs.python.org/tutorial/introduction.html#unicode-strings\",\"https://docs.python.org/tutorial/datastructures.html#dictionaries\",\"http://www.scipy-lectures.org/intro/language/python_language.html\"],\"app\":\"steemit/0.1\",\"format\":\"markdown\"}"
    }
  ]
}

{
  "trx_id": "3b99873f6a58eee88b5c9db1a9f4bc3b1444f9bf",
  "block": 18032608,
  "trx_in_block": 16,
  "op_in_trx": 0,
  "virtual_op": 0,
  "timestamp": "2017-12-12T22:21:15",
  "op": [
    "delegate_vesting_shares",
    {
      "delegator": "steem",
      "delegatee": "whytin",
      "vesting_shares": "29739.430645 VESTS"
    }
  ]
}

{
  "trx_id": "2f50b84c3f61dbbb3a28c6c3b91e1ac96d158791",
  "block": 17991901,
  "trx_in_block": 11,
  "op_in_trx": 0,
  "virtual_op": 0,
  "timestamp": "2017-12-11T12:25:09",
  "op": [
    "vote",
    {
      "voter": "whytin",
      "author": "whytin",
      "permlink": "python",
      "weight": 10000
    }
  ]
}

{
  "trx_id": "070b2bcd7166f529f72268b2aca0404b24bd40cd",
  "block": 17991898,
  "trx_in_block": 0,
  "op_in_trx": 0,
  "virtual_op": 0,
  "timestamp": "2017-12-11T12:25:00",
  "op": [
    "comment",
    {
      "parent_author": "",
      "parent_permlink": "deeplearning",
      "author": "whytin",
      "permlink": "python",
      "title": "Python笔记：进击机器学习（一）--概述",
      "body": "**Self-intro:** I am a graduate student at an unnamed institution in China :) The main focus is Computer vision using deep learning. I will update some of notes about deep learning at steemit. Hope that like-minded friends follow me, and we discuss and support with each other. Thanks for reading! [@whytin](https://steemit.com/@whytin)\n\n# 1 开启Python科学之旅\n本章介绍了Python在机器学习中常用的库和工具，包括数学计算和绘图。\n## 1.1 Python数据科学生态\n### 1.1.1 为什么选择Python？\n先献上IEEE Spectrum Magazine 2017 编程排行图一张\n\n![IEEE Spectrum 2017 编程语言 Top 10 ](http://upload-images.jianshu.io/upload_images/5027777-a1f418f12b9937d0.png?imageMogr2/auto-orient/strip%7CimageView2/2/w/1240)\n\nPython 的排名从去年开始就在持续上升，并跃至第一。\n\n抛开这骄人的成绩，我们讲一讲做数据科学的刚需：\n* 获取数据\n* 操作数据\n* 可视化数据\n\n那么Python的优势又是什么：\n1. Python存在丰富的数学计算、绘图和数据处理的工具。我们不需要复杂的编程去绘制一条曲线、计算傅里叶变换和一个基础的算法。用老外的一句话来形容就特别贴切，Don’t reinvent the wheel!\n2. Python相比起其他高级语言来说是容易上手的，没有那么多复杂的格式，只需用缩进来规整格式。相比起C、C++这些开发语言来说，高级语言的优势就明显了，Easy to learn！\n3. 当你用上了Python之后，就会对它爱不释手，Python的程序可读性并不是其他语言可以相比的，简单的语法、没有一些奇怪的符号和长长的参数。不管是阅读别人的代码还是别人参考你的代码都变得容易，Easy communication！\n4. Python高效的计算能力，虽然Python的执行效率不可能比得上C、C++这种底层的语言，但是从开发时间来说，Python可是不会辜负快速开发这个称号，不然Python也不会这么受欢迎。\n5. 还有重要的一点是，Python可以用于解决各种各样的问题，不用为了解决某个问题而去学习新的语言和工具。Python不仅可以作为脚本语言、开发网站和软件、科学计算、爬虫等。\n\n当然说到机器学习用的工具，除了Python不得不说的就是Matlab。Matlab具有和Python一样的易用性，在仿真和实验上比Python要好，是学术学者最喜欢用的工具，做理论研究的必备。但是在应用开发上，Matlab就显得捉襟见肘了。\n\n虽然说Python和Matlab走的是两个不同的路子，但是如果要选一个的话，就只能是Python了，在现在学术界和产业界的联系越来越紧密，应用的生产周期越来越短的时代，Python的快速开发性就顺应了这个潮流。\n\n讲了这么多Python的废话，还是来点干的。\n\n# 1.1.2 Python机器学习库\n\nPython在科学计算领域，有两个重要的扩展模块：[Numpy](http://www.numpy.org/)和[Scipy](http://www.scipy.org/)。其中Numpy是一个用python实现的科学计算包。包括：\n\n* 一个强大的N维数组对象Array；\n* 比较成熟的（广播）函数库；\n* 用于整合C/C++代码的工具包；\n* 实用的线性代数、傅里叶变换和随机数生成函数。\n\nSciPy是一个开源的Python算法库和数学工具包，SciPy包含的模块有最优化、线性代数、积分、插值、特殊函数、快速傅里叶变换、信号处理和图像处理、常微分方程求解和其他科学与工程中常用的计算。其功能与软件MATLAB、Scilab和GNU Octave类似。\n\nNumpy的项目主页http://www.numpy.org/\nScipy的项目主页http://www.scipy.org/\n这两个计算库在接下来的博客也有详细介绍和教程\n\n而Python大多数机器学习库都依赖于这两个模块，主流的机器学习库有：\n1. **scikit-learn**\nscikit-learn 是一个基于SciPy和Numpy的开源机器学习模块，包括分类、回归、聚类系列算法，主要算法有SVM、逻辑回归、朴素贝叶斯、Kmeans、DBSCAN等，目前由INRI 资助，偶尔Google也资助一点。\n　　项目主页：\n[　　https://pypi.python.org/pypi/scikit-learn/](https://pypi.python.org/pypi/scikit-learn/)\n[　　http://scikit-learn.org/](http://scikit-learn.org/)\n[　　https://github.com/scikit-learn/scikit-learn](https://github.com/scikit-learn/scikit-learn)\n2. **NLTK**\nNLTK(Natural Language Toolkit)是Python的自然语言处理模块，包括一系列的字符处理和语言统计模型。NLTK 常用于学术研究和教学，应用的领域有语言学、认知科学、人工智能、信息检索、机器学习等。 NLTK提供超过50个语料库和词典资源，文本处理库包括分类、分词、词干提取、解析、语义推理。可稳定运行在Windows, Mac OS X和Linux平台上. \n　　项目主页：\n[　　http://sourceforge.net/projects/nltk/](http://sourceforge.net/projects/nltk/)\n[　　https://pypi.python.org/pypi/nltk/](https://pypi.python.org/pypi/nltk/)\n[　　http://nltk.org/](http://nltk.org/)\n3. **Mlpy**\nMlpy是基于NumPy/SciPy的Python机器学习模块，它是Cython的扩展应用。\n　　项目主页：\n[　　http://sourceforge.net/projects/mlpy](http://sourceforge.net/projects/mlpy)\n[　　https://mlpy.fbk.eu/](https://mlpy.fbk.eu/)\n更多的机器学习库可通过[https://pypi.python.org/pypi](https://pypi.python.org/pypi)查找。\n\n这是几个主流的机器学习库，当然现在更火的是深度学习，用Python编程的深度学习框架就有[Theano](http://deeplearning.net/software/theano/)、[Keras](http://keras.io/)、[Lasagne](https://github.com/Lasagne/Lasagne)和Google的开源框架[Tensorflow](https://www.tensorflow.org/)。\n\n绘图和可视化依赖于[Matplotlib](http://matplotlib.org/)库，附上我用Matplotlib画的一张图：\n\n![近20年O'Reilly上编程书籍的销量变化](http://upload-images.jianshu.io/upload_images/5027777-831fa6032b8b0e7d.png?imageMogr2/auto-orient/strip%7CimageView2/2/w/1240)\n\n再一次说明Python的流行度，哈哈！\n\n**PS**: 深度学习框架以后也会介绍，还有一些个人学习的笔记，有兴趣的同学记得关注我的简书。这个系列会详细介绍scikit-learn这个应用最广泛的机器学习框架，有兴趣的同学多多支持一下。\n\n**Ref**：http://www.scipy-lectures.org/intro/intro.html\n\n**Thanks for reading!**",
      "json_metadata": "{\"tags\":[\"deeplearning\",\"python\",\"scratch\",\"programming\",\"language\"],\"image\":[\"http://upload-images.jianshu.io/upload_images/5027777-a1f418f12b9937d0.png?imageMogr2/auto-orient/strip%7CimageView2/2/w/1240\",\"http://upload-images.jianshu.io/upload_images/5027777-831fa6032b8b0e7d.png?imageMogr2/auto-orient/strip%7CimageView2/2/w/1240\"],\"links\":[\"https://steemit.com/@whytin\",\"http://www.numpy.org/\",\"http://www.scipy.org/\",\"https://pypi.python.org/pypi/scikit-learn/\",\"http://scikit-learn.org/\",\"https://github.com/scikit-learn/scikit-learn\",\"http://sourceforge.net/projects/nltk/\",\"https://pypi.python.org/pypi/nltk/\",\"http://nltk.org/\",\"http://sourceforge.net/projects/mlpy\",\"https://mlpy.fbk.eu/\",\"https://pypi.python.org/pypi\",\"http://deeplearning.net/software/theano/\",\"http://keras.io/\",\"https://github.com/Lasagne/Lasagne\",\"https://www.tensorflow.org/\",\"http://matplotlib.org/\",\"http://www.scipy-lectures.org/intro/intro.html\"],\"app\":\"steemit/0.1\",\"format\":\"markdown\"}"
    }
  ]
}

{
  "trx_id": "a24978d26f789e28f898c26a3102fd36a54c15e2",
  "block": 17986865,
  "trx_in_block": 11,
  "op_in_trx": 0,
  "virtual_op": 0,
  "timestamp": "2017-12-11T08:13:18",
  "op": [
    "vote",
    {
      "voter": "whytin",
      "author": "whytin",
      "permlink": "re-sandywhy-final-death-the-mexican-culture-by-reflacting-by-movie-coco-20171211t081259736z",
      "weight": 10000
    }
  ]
}

{
  "trx_id": "962f7eb6b44aaad0ebfa518db074171c9b8ed29b",
  "block": 17986859,
  "trx_in_block": 12,
  "op_in_trx": 0,
  "virtual_op": 0,
  "timestamp": "2017-12-11T08:13:00",
  "op": [
    "comment",
    {
      "parent_author": "sandywhy",
      "parent_permlink": "final-death-the-mexican-culture-by-reflacting-by-movie-coco",
      "author": "whytin",
      "permlink": "re-sandywhy-final-death-the-mexican-culture-by-reflacting-by-movie-coco-20171211t081259736z",
      "title": "",
      "body": "Fantastic movie!",
      "json_metadata": "{\"tags\":[\"movie-coco\"],\"app\":\"steemit/0.1\"}"
    }
  ]
}

{
  "trx_id": "f2f90d7ccb2dc29362cca7c10ebf06ee089ce4c0",
  "block": 17986825,
  "trx_in_block": 10,
  "op_in_trx": 0,
  "virtual_op": 0,
  "timestamp": "2017-12-11T08:11:18",
  "op": [
    "custom_json",
    {
      "required_auths": [],
      "required_posting_auths": [
        "whytin"
      ],
      "id": "follow",
      "json": "[\"follow\",{\"follower\":\"whytin\",\"following\":\"sandywhy\",\"what\":[\"blog\"]}]"
    }
  ]
}

{
  "trx_id": "fe15efed28f63e77077dc4e3284b12f8e8dceab8",
  "block": 17562531,
  "trx_in_block": 13,
  "op_in_trx": 0,
  "virtual_op": 0,
  "timestamp": "2017-11-26T14:25:42",
  "op": [
    "custom_json",
    {
      "required_auths": [],
      "required_posting_auths": [
        "whytin"
      ],
      "id": "follow",
      "json": "[\"follow\",{\"follower\":\"whytin\",\"following\":\"cryptoriddler\",\"what\":[\"blog\"]}]"
    }
  ]
}

{
  "trx_id": "8d0b03d189969ab2a4971897d7999f8d0c8c0f9d",
  "block": 17562208,
  "trx_in_block": 18,
  "op_in_trx": 0,
  "virtual_op": 0,
  "timestamp": "2017-11-26T14:09:33",
  "op": [
    "custom_json",
    {
      "required_auths": [],
      "required_posting_auths": [
        "whytin"
      ],
      "id": "follow",
      "json": "[\"follow\",{\"follower\":\"whytin\",\"following\":\"steemitboard\",\"what\":[\"blog\"]}]"
    }
  ]
}

{
  "trx_id": "5853e718a5421e02a457552e7ebe4c3145e6dace",
  "block": 17528591,
  "trx_in_block": 19,
  "op_in_trx": 0,
  "virtual_op": 0,
  "timestamp": "2017-11-25T10:08:36",
  "op": [
    "comment",
    {
      "parent_author": "whytin",
      "parent_permlink": "shock-python-has-ranked-the-first-programming-language-scratch-from-o-reilly",
      "author": "steemitboard",
      "permlink": "steemitboard-notify-whytin-20171125t100838000z",
      "title": "",
      "body": "Congratulations @whytin! You have completed some achievement on Steemit and have been rewarded with new badge(s) :\n\n[![](https://steemitimages.com/70x80/http://steemitboard.com/notifications/firstpost.png)](http://steemitboard.com/@whytin) You published your First Post\n[![](https://steemitimages.com/70x80/http://steemitboard.com/notifications/firstvote.png)](http://steemitboard.com/@whytin) You made your First Vote\n[![](https://steemitimages.com/70x80/http://steemitboard.com/notifications/firstcomment.png)](http://steemitboard.com/@whytin) You made your First Comment\n[![](https://steemitimages.com/70x80/http://steemitboard.com/notifications/firstvoted.png)](http://steemitboard.com/@whytin) You got a First Vote\n[![](https://steemitimages.com/70x80/http://steemitboard.com/notifications/voted.png)](http://steemitboard.com/@whytin) Award for the number of upvotes received\n\nClick on any badge to view your own Board of Honor on SteemitBoard.\nFor more information about SteemitBoard, click [here](https://steemit.com/@steemitboard)\n\nIf you no longer want to receive notifications, reply to this comment with the word `STOP`\n\n> By upvoting this notification, you can help all Steemit users. Learn how [here](https://steemit.com/steemitboard/@steemitboard/http-i-cubeupload-com-7ciqeo-png)!",
      "json_metadata": "{\"image\":[\"https://steemitboard.com/img/notifications.png\"]}"
    }
  ]
}

{
  "trx_id": "563d7972df6f6e30aedbb23c34049f3cae5f57e2",
  "block": 17499357,
  "trx_in_block": 0,
  "op_in_trx": 0,
  "virtual_op": 0,
  "timestamp": "2017-11-24T09:46:42",
  "op": [
    "vote",
    {
      "voter": "gbutr",
      "author": "whytin",
      "permlink": "shock-python-has-ranked-the-first-programming-language-scratch-from-o-reilly",
      "weight": 10000
    }
  ]
}

{
  "trx_id": "2c797bb5bfaa0dd901ed264e0021027979d03318",
  "block": 17499349,
  "trx_in_block": 6,
  "op_in_trx": 0,
  "virtual_op": 0,
  "timestamp": "2017-11-24T09:46:18",
  "op": [
    "vote",
    {
      "voter": "liyabal",
      "author": "whytin",
      "permlink": "shock-python-has-ranked-the-first-programming-language-scratch-from-o-reilly",
      "weight": 10000
    }
  ]
}

{
  "trx_id": "e44033857f8308b3685626c2ab39bb8fa338f26e",
  "block": 17499348,
  "trx_in_block": 15,
  "op_in_trx": 0,
  "virtual_op": 0,
  "timestamp": "2017-11-24T09:46:15",
  "op": [
    "vote",
    {
      "voter": "kornejmak",
      "author": "whytin",
      "permlink": "shock-python-has-ranked-the-first-programming-language-scratch-from-o-reilly",
      "weight": 10000
    }
  ]
}

{
  "trx_id": "64bd090a076722bfe8c3fe94fff701db5935993f",
  "block": 17499348,
  "trx_in_block": 8,
  "op_in_trx": 0,
  "virtual_op": 0,
  "timestamp": "2017-11-24T09:46:15",
  "op": [
    "vote",
    {
      "voter": "fotakebas",
      "author": "whytin",
      "permlink": "shock-python-has-ranked-the-first-programming-language-scratch-from-o-reilly",
      "weight": 10000
    }
  ]
}

{
  "trx_id": "fa2dbe135043d919bf0162ecfdc825c6e481c714",
  "block": 17499348,
  "trx_in_block": 2,
  "op_in_trx": 0,
  "virtual_op": 0,
  "timestamp": "2017-11-24T09:46:15",
  "op": [
    "vote",
    {
      "voter": "azatbadrani",
      "author": "whytin",
      "permlink": "shock-python-has-ranked-the-first-programming-language-scratch-from-o-reilly",
      "weight": 10000
    }
  ]
}

{
  "trx_id": "eb9be9d7b6028b2bc28b4b51038684762e4e02e2",
  "block": 17499347,
  "trx_in_block": 9,
  "op_in_trx": 0,
  "virtual_op": 0,
  "timestamp": "2017-11-24T09:46:12",
  "op": [
    "vote",
    {
      "voter": "nasolncz",
      "author": "whytin",
      "permlink": "shock-python-has-ranked-the-first-programming-language-scratch-from-o-reilly",
      "weight": 10000
    }
  ]
}

{
  "trx_id": "77354c69e97a885891894615d2739d3ec71a69d3",
  "block": 17497568,
  "trx_in_block": 12,
  "op_in_trx": 0,
  "virtual_op": 0,
  "timestamp": "2017-11-24T08:17:12",
  "op": [
    "vote",
    {
      "voter": "gamedevers",
      "author": "whytin",
      "permlink": "shock-python-has-ranked-the-first-programming-language-scratch-from-o-reilly",
      "weight": 6000
    }
  ]
}

{
  "trx_id": "95ed61f61ffd2bc1cadc1ebc9cffab7b2aa911bf",
  "block": 17496160,
  "trx_in_block": 4,
  "op_in_trx": 0,
  "virtual_op": 0,
  "timestamp": "2017-11-24T07:06:48",
  "op": [
    "vote",
    {
      "voter": "whytin",
      "author": "saluton",
      "permlink": "7qebenvpguwhracffn6uia",
      "weight": 10000
    }
  ]
}

{
  "trx_id": "b1deb8098afcf9c69cdddd0562c0cd4c8d6452fa",
  "block": 17496108,
  "trx_in_block": 15,
  "op_in_trx": 0,
  "virtual_op": 0,
  "timestamp": "2017-11-24T07:04:12",
  "op": [
    "comment",
    {
      "parent_author": "whytin",
      "parent_permlink": "shock-python-has-ranked-the-first-programming-language-scratch-from-o-reilly",
      "author": "saluton",
      "permlink": "7qebenvpguwhracffn6uia",
      "title": "",
      "body": "Hello!",
      "json_metadata": "{\"tags\": [\"\"]}"
    }
  ]
}

{
  "trx_id": "238c63815b8f1a4b4e4f991edbd50e6d6cf86ac5",
  "block": 17496101,
  "trx_in_block": 17,
  "op_in_trx": 0,
  "virtual_op": 0,
  "timestamp": "2017-11-24T07:03:51",
  "op": [
    "vote",
    {
      "voter": "saluton",
      "author": "whytin",
      "permlink": "shock-python-has-ranked-the-first-programming-language-scratch-from-o-reilly",
      "weight": 500
    }
  ]
}

{
  "trx_id": "98fca84aa4774f3f2982ca03c337e9516bde0571",
  "block": 17496091,
  "trx_in_block": 4,
  "op_in_trx": 0,
  "virtual_op": 0,
  "timestamp": "2017-11-24T07:03:21",
  "op": [
    "vote",
    {
      "voter": "whytin",
      "author": "whytin",
      "permlink": "shock-python-has-ranked-the-first-programming-language-scratch-from-o-reilly",
      "weight": 10000
    }
  ]
}

{
  "trx_id": "c54b91f278c97657f48c37561d712cb17f4472d6",
  "block": 17496033,
  "trx_in_block": 3,
  "op_in_trx": 0,
  "virtual_op": 0,
  "timestamp": "2017-11-24T07:00:27",
  "op": [
    "comment",
    {
      "parent_author": "",
      "parent_permlink": "deeplearning",
      "author": "whytin",
      "permlink": "shock-python-has-ranked-the-first-programming-language-scratch-from-o-reilly",
      "title": "Shock! Python has ranked the first programming language?(Scratch from O'Reilly)",
      "body": "**Self-intro:** I am a graduate student at an unnamed institution in China :) The main focus is Computer vision using deep learning. I will update some of notes about deep learning at steemit. Hope that like-minded friends follow me, and we discuss and support with each other. Thanks for reading! [@whytin](https://steemit.com/@whytin)\n\nPython has ranked the first programming language?(who know, I don't know)\n So, let's explore it!\n# Finding the most popular category of books in O'Reilly\n## Overview\nRefer to *\"Data Science From Scratch\"*,  I want to explore which category of books is the largest quantity, and I will recommend you to start studying which kind of books when you are still confused with your future.\n\n![programming_book.png](http://upload-images.jianshu.io/upload_images/5027777-f4ccf1f10ac838fb.png?imageMogr2/auto-orient/strip%7CimageView2/2/w/1240)\n\n![book.png](http://upload-images.jianshu.io/upload_images/5027777-f331001c8f6980a9.png?imageMogr2/auto-orient/strip%7CimageView2/2/w/1240)\n\nYou can fork the project by Github:\n**Github:** *http://github.com/whytin/book_scratch*\n\n## Preparation\n### Tool\n+ **BeautifulSoup4**(a python library designed for dissecting a doucument into a parse tree, we can extract what we need esaily);\nRefer to : *http://www.crummy.com/software/BeautifulSoup/*\n+ **htmll5lib**(a popular Python parser to handle the HTML format);\n+  **requests**(make a HTTP request)\n\n### Environment\n+ Linux Mint 18.1 (Unlimited)\n+ Python 2.7\n+ Sqlite3\n\n### Foundation\n* Python\n* HTML\n* Matplotlib\n* SQL\n\n## Start\n### Scratch admited\nBefore you start the project, make sure your target is open to scratch.\nLike O'Reilly: *[http://oreilly.com/terms/](http://oreilly.com/terms/)*\nGlance over the page I have not found some issues with banning the scratch.\nThen we look over the robots.txt file. *[http://shop.oreilly.com/robots.txt](http://shop.oreilly.com/robots.txt)*\nWe found that :\n```\nCrawl-delay: 30\nRequest-rate: 1/30\n```\nIt means that we should delay 30s between two requests.\n### Parsing the page\nIf you know well with HTML, it is easy for you to find out the tags.\n**First**, you can select category of data through *[Browse Subjects](http://shop.oreilly.com/category/browse-subjects/data.do)*\n\n**Second**, use the developer tools.\n![Paste_Image.png](http://upload-images.jianshu.io/upload_images/5027777-e1943a4936451607.png?imageMogr2/auto-orient/strip%7CimageView2/2/w/1240)\n\nIt is wise to use the button of **Select an element in the page to inspect it** ,and then find out the tag <td class=\"thumbtext\">\nWe can extract the title, authors, date, isbn, price of the book.\n**Do yourself , you will fall in curiousity.**\n\n### Coding\n```\nfrom bs4 import BeautifulSoup\nimport requests\n```\n```\n# Making a request of url and send to BeautifulSoup parsing with html5lib.\nurl = \"http://shop.oreilly.com/category/browse-subjects/data.do?sortby=publicationDate&page=1\"\nsoup = BeautifulSoup(requests.get(url).text, 'html5lib')\ntds = soup('td', 'thumbtext')\n```\n\nWe found book's title involved the a tag of <div class=\"thumbheader\">, and extract it.\n```\ntitles = [td.find(\"div\", \"thumbheader\").a.text for td in tds]\n``` \nAnd we can build the function of book_info()\n```\n# In order to extract the book information like title, authors, isbn, date, price. Return a dict.\ndef book_info(td):\n    title = td.find(\"div\", \"thumbheader\").a.text\n    authors = td.find('div', 'AuthorName').text\n    isbn_link = td.find(\"div\", \"thumbheader\").a.get(\"href\")\n    isbn = re.match(\"/product/(.*)\\.do\", isbn_link).group(1)\n    date = td.find(\"span\", \"directorydate\").text.strip()\n    price = td('span', 'pricelabel')[0].find('span', 'price')\n    \n    return {\n            \"tilte\": title,\n            \"authors\": authors,\n             \"isbn\": isbn,\n             \"date\":date,\n             \"price\":price  }\n```\nScratching:\n```\nfrom bs4 import BeautifulSoup\nimport requests\nimport re\nfrom time import sleep\nbase_url = \"http://shop.oreilly.com/category/browse-subjects/data.do?sortby=publicationDate&page=\"\nbooks=[]\nNUM_PAGES = 44\n\nfor page_num in range(1, NUM_PAGES + 1):\n    url = baseurl + str(page_num)\n    soup = BeautifulSoup(requests.get(url).text, 'html5lib')\n    for td in soup('td', 'thumbtext'):\n        books.append(book_info(td))\n    sleep(30)\n```\nVisualization:\n```\nimport matplotlib as plt\ndef get_year(book):\n    return int(book[\"date\"].split()[1])\n\n# Counter(): dict subclass for counting hashable objects\nyears_counts = Counter(get_year(book) for book in books if get_year(book) <= 2016)\nyears = sorted(years_counts)\nbook_counts = [year_counts[year] for year in years]\nplt.plot(years, book_counts)\nplt.show()\n```\n\n## Summary\nIt is the brief induction of usage of python scratching, using BeautifulSoup and Matplotlib. You can also scratching Amazon website or whatever you want to obtain. Remember you are risk in data scratching, square up your behavior in Internet.\n\n**Thanks for reading!**",
      "json_metadata": "{\"tags\":[\"deeplearning\",\"python\",\"scratch\",\"programming\",\"language\"],\"image\":[\"http://upload-images.jianshu.io/upload_images/5027777-f4ccf1f10ac838fb.png?imageMogr2/auto-orient/strip%7CimageView2/2/w/1240\",\"http://upload-images.jianshu.io/upload_images/5027777-f331001c8f6980a9.png?imageMogr2/auto-orient/strip%7CimageView2/2/w/1240\",\"http://upload-images.jianshu.io/upload_images/5027777-e1943a4936451607.png?imageMogr2/auto-orient/strip%7CimageView2/2/w/1240\"],\"links\":[\"https://steemit.com/@whytin\",\"http://github.com/whytin/book_scratch\",\"http://www.crummy.com/software/BeautifulSoup/\",\"http://oreilly.com/terms/\",\"http://shop.oreilly.com/robots.txt\",\"http://shop.oreilly.com/category/browse-subjects/data.do\"],\"app\":\"steemit/0.1\",\"format\":\"markdown\"}"
    }
  ]
}

{
  "trx_id": "afe64d7f8acb663e2952e2bf811c827e790e7dca",
  "block": 17495462,
  "trx_in_block": 2,
  "op_in_trx": 0,
  "virtual_op": 0,
  "timestamp": "2017-11-24T06:31:54",
  "op": [
    "vote",
    {
      "voter": "whytin",
      "author": "mreko",
      "permlink": "cardano-what-is-cardano",
      "weight": 10000
    }
  ]
}

{
  "trx_id": "886e7d8b6c926586fd2d0fc36f3ffa8b5bae05fe",
  "block": 17495456,
  "trx_in_block": 13,
  "op_in_trx": 0,
  "virtual_op": 0,
  "timestamp": "2017-11-24T06:31:36",
  "op": [
    "vote",
    {
      "voter": "whytin",
      "author": "mreko",
      "permlink": "tezos-decred-missed-tezos-ico-buy-some-decred",
      "weight": 10000
    }
  ]
}

{
  "trx_id": "df3f4d901ad55078ac67695758c8b050f4b9e2cf",
  "block": 17495450,
  "trx_in_block": 15,
  "op_in_trx": 0,
  "virtual_op": 0,
  "timestamp": "2017-11-24T06:31:18",
  "op": [
    "vote",
    {
      "voter": "whytin",
      "author": "mreko",
      "permlink": "ripple-1-cny-10-ripple",
      "weight": 10000
    }
  ]
}

{
  "trx_id": "d534d9d148aa2322d714d2eb183e6c03d1a277ff",
  "block": 17495422,
  "trx_in_block": 15,
  "op_in_trx": 0,
  "virtual_op": 0,
  "timestamp": "2017-11-24T06:29:54",
  "op": [
    "account_update",
    {
      "account": "whytin",
      "memo_key": "STM8Sp56BHWTKN4iLq28THf45Dq7H4NQKtHhjFt6YZS69UQ3BQQK2",
      "json_metadata": "{\"profile\":{\"profile_image\":\"https://s3.amazonaws.com/re-work-production/post_images/27/original/original.jpg?1446179738\",\"name\":\"whytin\"}}"
    }
  ]
}

{
  "trx_id": "dd08a3eaa411a383f43027a1b347da0c79a09d18",
  "block": 17495341,
  "trx_in_block": 3,
  "op_in_trx": 0,
  "virtual_op": 0,
  "timestamp": "2017-11-24T06:25:51",
  "op": [
    "vote",
    {
      "voter": "whytin",
      "author": "whytin",
      "permlink": "re-mreko-dtfxa-20171124t062540773z",
      "weight": 10000
    }
  ]
}

{
  "trx_id": "be65a04d20ed030acf694eb4bf6bf3a30cd58add",
  "block": 17495340,
  "trx_in_block": 19,
  "op_in_trx": 0,
  "virtual_op": 0,
  "timestamp": "2017-11-24T06:25:48",
  "op": [
    "vote",
    {
      "voter": "whytin",
      "author": "fighting",
      "permlink": "re-mreko-dtfxa-20171022t162205978z",
      "weight": 10000
    }
  ]
}

{
  "trx_id": "ecd663b64a5a56484926fb5bd54dfd481b03da04",
  "block": 17495340,
  "trx_in_block": 10,
  "op_in_trx": 0,
  "virtual_op": 0,
  "timestamp": "2017-11-24T06:25:48",
  "op": [
    "vote",
    {
      "voter": "whytin",
      "author": "ohmeitounao",
      "permlink": "re-mreko-dtfxa-20171022t040856541z",
      "weight": 10000
    }
  ]
}

{
  "trx_id": "faff38aa84b99329cbbfbfb44a6190a130db8f31",
  "block": 17495337,
  "trx_in_block": 17,
  "op_in_trx": 0,
  "virtual_op": 0,
  "timestamp": "2017-11-24T06:25:39",
  "op": [
    "comment",
    {
      "parent_author": "mreko",
      "parent_permlink": "dtfxa",
      "author": "whytin",
      "permlink": "re-mreko-dtfxa-20171124t062540773z",
      "title": "",
      "body": "Hello everybody! 我偶然在3个月前邂逅了希多，开始接触Blockchain。我是国内不知名院校的在读研究生，主攻方向是Computer vision （深度学习），将来会在steemit上更新一些相关笔记，希望志同道合的朋友可以关注，大家一起探讨，互相支持，哈哈。",
      "json_metadata": "{\"tags\":[\"cn\"],\"app\":\"steemit/0.1\"}"
    }
  ]
}

{
  "trx_id": "c8a6631b0cc9f447eba36bd1177f4c70a45c2c00",
  "block": 17495203,
  "trx_in_block": 47,
  "op_in_trx": 0,
  "virtual_op": 0,
  "timestamp": "2017-11-24T06:18:57",
  "op": [
    "vote",
    {
      "voter": "whytin",
      "author": "mreko",
      "permlink": "dtfxa",
      "weight": 10000
    }
  ]
}

{
  "trx_id": "94a8422fab314313d4f05a3021c3eeb63706f5cb",
  "block": 17495146,
  "trx_in_block": 11,
  "op_in_trx": 0,
  "virtual_op": 0,
  "timestamp": "2017-11-24T06:16:06",
  "op": [
    "custom_json",
    {
      "required_auths": [],
      "required_posting_auths": [
        "whytin"
      ],
      "id": "follow",
      "json": "[\"follow\",{\"follower\":\"whytin\",\"following\":\"mreko\",\"what\":[\"blog\"]}]"
    }
  ]
}

{
  "trx_id": "9112fc9246c0dde6d65f39fef1d01fccc2e4e735",
  "block": 17476904,
  "trx_in_block": 2,
  "op_in_trx": 0,
  "virtual_op": 0,
  "timestamp": "2017-11-23T15:03:45",
  "op": [
    "account_create_with_delegation",
    {
      "fee": "0.500 STEEM",
      "delegation": "57000.000000 VESTS",
      "creator": "steem",
      "new_account_name": "whytin",
      "owner": {
        "weight_threshold": 1,
        "account_auths": [],
        "key_auths": [
          [
            "STM5ijxyRdtSw958iyFua6FyHAF7g8arJcy5BVtAPUhQkKzK6qfo1",
            1
          ]
        ]
      },
      "active": {
        "weight_threshold": 1,
        "account_auths": [],
        "key_auths": [
          [
            "STM6C4YKapQBdX2xWk1VRddHt3My9E8i5n3RqNsf6HMzFVt3LZFVs",
            1
          ]
        ]
      },
      "posting": {
        "weight_threshold": 1,
        "account_auths": [],
        "key_auths": [
          [
            "STM7rQEwyAqZ6zXA3qhumSAw4pmsRyhNiAXFaBT3CmA1xfqBVRtmM",
            1
          ]
        ]
      },
      "memo_key": "STM8Sp56BHWTKN4iLq28THf45Dq7H4NQKtHhjFt6YZS69UQ3BQQK2",
      "json_metadata": "",
      "extensions": []
    }
  ]
}