我在这里尝试了get_click_rate函数，它返回的结果似乎与您编写的第二种方法相同。我遇到的唯一问题是，当您试图计算没有'pageview'事件的linkid组的点击率时。因此，我对get_click_rate函数做了一些小修改：

import pandas as pd

def get_click_rate(data: pd.DataFrame) -> pd.Series:
    """Calculate the click rate for a given ``linkid`` group.

    Parameters
    ----------
    data : pd.DataFrame
        A dataframe representing values from a given ``linkid`` group,
        containing the column 'event'.

    Returns
    -------
    pd.Series
        A series containing the click count, view count, and click rate.
        The click rate is calculated as the ratio of the click count to
        the view counts. If the view count is zero, click rate gets set to zero.

    Examples
    --------
    >>> df = pd.DataFrame(
    >>>     {'event': ['click', 'pageview', 'click', 'some_other_value'],
    >>>              'linkid': [1, 1, 2, 2]}
    >>> )
    >>> df.groupby('linkid').apply(get_click_rate).reset_index()
       linkid  click count  view count  click_rate
    0       1          1.0         1.0         1.0
    1       2          1.0         0.0         0.0

    Notes
    -----
    This function returns a pandas Series regardless of whether the
    ``linkid`` group contains any view count or not. Therefore, if you want
    only the ``linkid``s' that have click rates, you can use the following code:

    .. code-block:: python

        (
            df.groupby('linkid')
            .apply(get_click_rate)
            .reset_index()
            .loc[lambda xdf: xdf['click_rate'] > 0, :]
        )

    """

    click_count: int = data[data['event'] == 'click'].shape[0]
    view_count: int = data[data['event'] == 'pageview'].shape[0]
    click_rate: float = 0

    # Only compute the click rate when `view_count` is greater than zero.
    if view_count > 0:
        click_rate = round(click_count / view_count, 2)

    return pd.Series({'click count': click_count,
                      'view count': view_count,
                      'click_rate': click_rate})

测试get_click_rate函数

import pandas as pd
import numpy as np

def get_click_rate(data: pd.DataFrame) -> pd.Series:
    """Calculate the click rate for a given ``linkid`` group.

    Parameters
    ----------
    data : pd.DataFrame
        A dataframe representing values from a given ``linkid`` group,
        containing the column 'event'.

    Returns
    -------
    pd.Series
        A series containing the click count, view count, and click rate.
        The click rate is calculated as the ratio of the click count to
        the view counts. If the view count is zero, click rate gets set to zero.

    Examples
    --------
    >>> df = pd.DataFrame(
    >>>     {'event': ['click', 'pageview', 'click', 'some_other_value'],
    >>>              'linkid': [1, 1, 2, 2]}
    >>> )
    >>> df.groupby('linkid').apply(get_click_rate).reset_index()
       linkid  click count  view count  click_rate
    0       1          1.0         1.0         1.0
    1       2          1.0         0.0         0.0

    Notes
    -----
    This function returns a pandas Series regardless of whether the
    ``linkid`` group contains any view count or not. Therefore, if you want
    only the ``linkid``s' that have click rates, you can use the following code:

    .. code-block:: python

        (
            df.groupby('linkid')
            .apply(get_click_rate)
            .reset_index()
            .loc[lambda xdf: xdf['click_rate'] > 0, :]
        )

    """

    click_count: int = data[data['event'] == 'click'].shape[0]
    view_count: int = data[data['event'] == 'pageview'].shape[0]
    click_rate: float = 0

    # Only compute the click rate when `view_count` is greater than zero.
    if view_count > 0:
        click_rate = round(click_count / view_count, 2)

    return pd.Series({'click count': click_count,
                      'view count': view_count,
                      'click_rate': click_rate})

event_choices = ['click', 'pageview', 'some_other_value']
linkid_choices = ['1', '2', '3']
nrows = 30

# -- Generating a Dummy DataFrame for Testing ------------------------------
df = pd.concat(
    [
        pd.DataFrame(
            {
                'event': np.random.choice(event_choices, nrows),
                'linkid': np.random.choice(linkid_choices, nrows),
            }
        ),
        pd.DataFrame({'event': ['some_other_value'] * 3, 'linkid': '4'})
    ], ignore_index=True
)

(
    # Group dataframe by column `linkid`
    df.groupby('linkid')
    # Apply function `get_click_rate` that returns a pandas Series with three
    # columns ('click count', 'view count' and 'click_rate') for every 'linkid' value.
    .apply(get_click_rate)
    .reset_index()
    # Convert the data type of 'click count', and 'view count' column to integers
    .astype({'click count': int, 'view count': int})
    # Filter for `linkid`s' that have a click rate greater than zero.
    .loc[lambda xdf: xdf['click_rate'] > 0, :]
)
# Returns:
#
#   linkid  click count  view count  click_rate
# 0      1            2           3        0.67
# 1      2            3           3        1.00
# 2      3            3           6        0.50

输出：