Question

我遇到了这个问题：我需要在用户第一次点击电子邮件（变量发送）时找到它，并在发生时在相应的行中放置一个。

数据集有数千名用户（哈希），他们在简报中点击了电子邮件的一部分。我尝试通过发送，哈希对它们进行分组，然后找到最早的日期，但无法使其工作。

所以我选择了一个令人讨厌的解决方案，然而这回归奇怪的事情：

我的数据集（相关变量）：

>>> clicks[['datetime','hash','sending']].head()

             datetime                              hash  sending
0 2016-11-01 19:13:34  0b1f4745df5925dfb1c8f53a56c43995        5
1 2016-11-01 10:47:14  0a73d5953ebf5826fbb7f3935bad026d        5
2 2016-10-31 19:09:21  605cebbabe0ba1b4248b3c54c280b477        5
3 2016-10-31 13:42:36  d26d61fb10c834292803b247a05b6cb7        5
4 2016-10-31 10:46:30  48f8ab83e8790d80af628e391f3325ad        5

有6轮发送，datetime为datetime64[ns]。

我这样做的方法如下：

clicks['first'] = 0

for hash in clicks['hash'].unique():
    t = clicks.ix[clicks.hash==hash, ['hash','datetime','sending']]
    part = t['sending'].unique()

    for i in part:
        temp = t.ix[t.sending == i,'datetime']
        clicks.ix[t[t.datetime == np.min(temp)].index.values,'first']=1

首先，我不认为它是非常pythonic，并且非常慢。但大多数情况下它会返回奇怪的类型！有0.0和1.0值，但我无法使用它们：

    >>> type(clicks.first)
    <type 'instancemethod'>

>>> clicks.loc[clicks.first==1]
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/Users/air/anaconda/lib/python2.7/site-packages/pandas/core/indexing.py", line 1296, in __getitem__
    return self._getitem_axis(key, axis=0)
  File "/Users/air/anaconda/lib/python2.7/site-packages/pandas/core/indexing.py", line 1467, in _getitem_axis
    return self._get_label(key, axis=axis)
  File "/Users/air/anaconda/lib/python2.7/site-packages/pandas/core/indexing.py", line 93, in _get_label
    return self.obj._xs(label, axis=axis)
  File "/Users/air/anaconda/lib/python2.7/site-packages/pandas/core/generic.py", line 1749, in xs
    loc = self.index.get_loc(key)
  File "/Users/air/anaconda/lib/python2.7/site-packages/pandas/indexes/base.py", line 1947, in get_loc
    return self._engine.get_loc(self._maybe_cast_indexer(key))
  File "pandas/index.pyx", line 137, in pandas.index.IndexEngine.get_loc (pandas/index.c:4154)
  File "pandas/index.pyx", line 156, in pandas.index.IndexEngine.get_loc (pandas/index.c:3977)
  File "pandas/index.pyx", line 373, in pandas.index.Int64Engine._check_type (pandas/index.c:7634)
KeyError: False

-----更新：------

  INSTALLED VERSIONS
    ------------------
    commit: None
    python: 2.7.12.final.0
    python-bits: 64
    OS: Darwin
    OS-release: 15.6.0
    machine: x86_64
    processor: i386
    byteorder: little
    LC_ALL: None
    LANG: en_US.UTF-8

    pandas: 0.18.1

Answer 1

我认为您需要groupby apply，其中minimal和输出的比较值是布尔值 - 需要转换为int 0和{{1} } astype：

clicks = pd.DataFrame({'hash': {0: '0b1f4745df5925dfb1c8f53a56c43995', 1: '0a73d5953ebf5826fbb7f3935bad026d', 2: '605cebbabe0ba1b4248b3c54c280b477', 3: '0b1f4745df5925dfb1c8f53a56c43995', 4: '0a73d5953ebf5826fbb7f3935bad026d', 5: '605cebbabe0ba1b4248b3c54c280b477', 6: 'd26d61fb10c834292803b247a05b6cb7', 7: '48f8ab83e8790d80af628e391f3325ad'}, 'sending': {0: 5, 1: 5, 2: 5, 3: 5, 4: 5, 5: 5, 6: 5, 7: 5}, 'datetime': {0: pd.Timestamp('2016-11-01 19:13:34'), 1: pd.Timestamp('2016-11-01 10:47:14'), 2: pd.Timestamp('2016-10-31 19:09:21'), 3: pd.Timestamp('2016-11-01 19:13:34'), 4: pd.Timestamp('2016-11-01 11:47:14'), 5: pd.Timestamp('2016-10-31 19:09:20'), 6: pd.Timestamp('2016-10-31 13:42:36'), 7: pd.Timestamp('2016-10-31 10:46:30')}})
print (clicks)
             datetime                              hash  sending
0 2016-11-01 19:13:34  0b1f4745df5925dfb1c8f53a56c43995        5
1 2016-11-01 10:47:14  0a73d5953ebf5826fbb7f3935bad026d        5
2 2016-10-31 19:09:21  605cebbabe0ba1b4248b3c54c280b477        5
3 2016-11-01 19:13:34  0b1f4745df5925dfb1c8f53a56c43995        5
4 2016-11-01 11:47:14  0a73d5953ebf5826fbb7f3935bad026d        5
5 2016-10-31 19:09:20  605cebbabe0ba1b4248b3c54c280b477        5
6 2016-10-31 13:42:36  d26d61fb10c834292803b247a05b6cb7        5
7 2016-10-31 10:46:30  48f8ab83e8790d80af628e391f3325ad        5

-----更新：------

#if column dtype of column datetime is not datetime (with this sample not necessary)
clicks.datetime = pd.to_datetime(clicks.datetime)
clicks['first'] = clicks.groupby(['hash','sending'])['datetime'] \
                        .apply(lambda x: x == x.min()) \
                        .astype(int)
print (clicks)
             datetime                              hash  sending  first
0 2016-11-01 19:13:34  0b1f4745df5925dfb1c8f53a56c43995        5      1
1 2016-11-01 10:47:14  0a73d5953ebf5826fbb7f3935bad026d        5      1
2 2016-10-31 19:09:21  605cebbabe0ba1b4248b3c54c280b477        5      0
3 2016-11-01 19:13:34  0b1f4745df5925dfb1c8f53a56c43995        5      1
4 2016-11-01 11:47:14  0a73d5953ebf5826fbb7f3935bad026d        5      0
5 2016-10-31 19:09:20  605cebbabe0ba1b4248b3c54c280b477        5      1
6 2016-10-31 13:42:36  d26d61fb10c834292803b247a05b6cb7        5      1
7 2016-10-31 10:46:30  48f8ab83e8790d80af628e391f3325ad        5      1

Answer 2

注意：我不熟悉pandas模块，但我经常使用python（它是系统工程）

为什么不使用datetime模块？您可以根据时间戳轻松对其进行排序。例如：

{ 
    "TableName": "Comments",
    "Item": {
        "commentId": {
            "S": "$context.requestId"
        },
        "pageId": {
            "S": "$input.path('$.pageId')"
            },
        "userName": {
            "S": "$input.path('$.userName')"
        },
        "message": {
            "S": "$input.path('$.message')"
        }
    }
}

如您所见，使用日期时间模块对日期进行排序很容易。编写比较函数似乎很简单，并根据日期对它们进行排序以找到最早的事件。

在Python

2 个答案: