Pandas groupby 多條件

Question

我正在嘗試創建通話記錄摘要。 有4個案例

1. 一部電話只有一個通話記錄記錄並且它有結果，我們選擇它的持續時間、狀態和結果記錄值
2. 同一部電話的多個通話記錄有結果，我們選擇通話記錄的摘要，持續時間和結果記錄，最長持續時間
3. 一部電話只有一個通話記錄記錄並且沒有結果，我們選擇其持續時間和狀態的值。 結果記錄將為無
4. 同一部電話的多個通話記錄沒有結果，我們選擇通話記錄的摘要和持續時間，最長持續時間。 結果記錄將為無

我嘗試的是循環組。 但是在處理大量數據時速度非常慢。 我想我需要使用 pandas 方法而不是循環。 如何使用 pandas 方法來實現相同的，具有多個條件。 謝謝。

import pandas as pd
def get_summarized_call_logs_df(df):
    data_list = []
    phone_groups = df.groupby('phone')
    unique_phones = df.phone.unique()
    for ph in unique_phones:
        row_data = {"phone": ph}
        group = phone_groups.get_group(ph)
        group_len = len(group)
        if True in group['outcome'].to_list():
            outcome = group.loc[group['outcome'] == True]
            row_data.update({"has_outcome": True})
            if outcome.phone.count() == 1:
                # Cases where there is outcome for single calls
                row_data.update({"status": outcome.status.iloc[0],
                                 "duration": outcome.duration.iloc[0],
                                 "outcome_record": outcome.id.iloc[0]})
            else:
                # Cases where there is outcome for multiple calls
                # We choose the status and duration of outcome record with maximum duration
                out_rec = outcome.loc[outcome['duration'] == outcome['duration'].max()]
                row_data.update({"status": out_rec.status.iloc[0],
                                 "duration": out_rec.duration.iloc[0],
                                 "outcome_record": out_rec.id.iloc[0]})
        else:
            row_data.update({"has_outcome": False, "outcome_record": None})
            if group_len == 1:
                # Cases where there is no outcome for single calls
                row_data.update({"status": group.status.iloc[0], "duration": group.duration.iloc[0]})
            else:
                # Cases where there is no outcome for multiple calls
                # We choose the status and duration of the record with maximum duration
                row_data.update({"status": group.loc[group['duration'] == group['duration'].max()].status.iloc[0],
                                "duration": group.loc[group['duration'] == group['duration'].max()].duration.iloc[0]})
        data_list.append(row_data)
    new_df = pd.DataFrame(data_list)
    return new_df

if __name__ == "__main__":
    data = [
    {"id": 1, "phone": "123", "outcome": True, "status": "sale", "duration": 1550},
    {"id": 2, "phone": "123", "outcome": False, "status": "failed", "duration": 3},
    {"id": 3, "phone": "123", "outcome": False, "status": "no_ring", "duration": 5},
    {"id": 4, "phone": "456", "outcome": True, "status": "call_back", "duration": 550},
    {"id": 5, "phone": "456", "outcome": True, "status": "sale", "duration": 2500},
    {"id": 6, "phone": "456", "outcome": False, "status": "no_ring", "duration": 5},
    {"id": 7, "phone": "789", "outcome": False, "status": "no_pick", "duration": 4},
    {"id": 8, "phone": "741", "outcome": False, "status": "try_again", "duration": 25},
    {"id": 9, "phone": "741", "outcome": False, "status": "try_again", "duration": 10},
    {"id": 10, "phone": "741", "outcome": False, "status": "no_ring", "duration": 5},
    ]
    df = pd.DataFrame(data)
    new_df = get_summarized_call_logs_df(df)
    print(new_df)

它應該產生一個 output

  phone  has_outcome     status  duration  outcome_record
0   123         True       sale      1550             1.0
1   456         True       sale      2500             5.0
2   789        False    no_pick         4             NaN
3   741        False  try_again        25             NaN

Answer 1

我認為您可以簡化邏輯。 如果您主要按“結果”和“持續時間”對值進行排序，則只需刪除重復項並保留每個排序組的最后一行，如下所示：

cols = ['phone', 'outcome', 'duration']
new_df = df.sort_values(cols).drop_duplicates('phone', keep='last')
print(new_df)

# Output:
   id phone  outcome     status  duration
0   1   123     True       sale      1550
4   5   456     True       sale      2500
7   8   741    False  try_again        25
6   7   789    False    no_pick         4

Answer 2

只是給出一個替代選項，基於convtools ：

from convtools import conversion as c

# fmt: off
data = [
    {"id": 1, "phone": "123", "outcome": True, "status": "sale", "duration": 1550},
    {"id": 2, "phone": "123", "outcome": False, "status": "failed", "duration": 3},
    {"id": 3, "phone": "123", "outcome": False, "status": "no_ring", "duration": 5},
    {"id": 4, "phone": "456", "outcome": True, "status": "call_back", "duration": 550},
    {"id": 5, "phone": "456", "outcome": True, "status": "sale", "duration": 2500},
    {"id": 6, "phone": "456", "outcome": False, "status": "no_ring", "duration": 5},
    {"id": 7, "phone": "789", "outcome": False, "status": "no_pick", "duration": 4},
    {"id": 8, "phone": "741", "outcome": False, "status": "try_again", "duration": 25},
    {"id": 9, "phone": "741", "outcome": False, "status": "try_again", "duration": 10},
    {"id": 10, "phone": "741", "outcome": False, "status": "no_ring", "duration": 5},
]
# fmt: on

# you are interested in rows with max duration
max_duration_call_log = c.ReduceFuncs.MaxRow(c.item("duration"))

# you need to know whether there's been an outcome
has_outcome = c.ReduceFuncs.Count(where=c.item("outcome")) > 0

converter = (
    c.group_by(c.item("phone"))
    .aggregate(
        {
            "phone": c.item("phone"),
            "has_outcome": has_outcome,
            "status": max_duration_call_log.item("status"),
            "duration": max_duration_call_log.item("duration"),
            "outcome_record": c.if_(
                has_outcome,
                max_duration_call_log.item("id"),
                None,
            ),
        }
    )
    # this step generates and compiles ad hoc function
    .gen_converter()
)

# fmt: off
assert converter(data) == [
    {'phone': '123', 'has_outcome': True, 'status': 'sale', 'duration': 1550, 'outcome_record': 1},
    {'phone': '456', 'has_outcome': True, 'status': 'sale', 'duration': 2500, 'outcome_record': 5},
    {'phone': '789', 'has_outcome': False, 'status': 'no_pick', 'duration': 4, 'outcome_record': None},
    {'phone': '741', 'has_outcome': False, 'status': 'try_again', 'duration': 25, 'outcome_record': None},
]
# fmt: on