Python 熊猫：向每个组添加行，直到满足条件

我有一个具有以下结构的时间序列数据帧：

| ID | second | speaker1 | speaker2 | company | ... |
|----|--------|----------|----------|---------|-----|
|  A |    1   |     1    |     1    |  name1  |     |
|  A |    2   |     1    |     1    |  name1  |     |
|  A |    3   |     1    |     1    |  name1  |     |
|  B |    1   |     1    |     1    |  name2  |     |
|  B |    2   |     1    |     1    |  name2  |     |
|  B |    3   |     1    |     1    |  name2  |     |
|  B |    4   |     1    |     1    |  name2  |     |
|  C |    1   |     1    |     1    |  name3  |     |
|  C |    2   |     1    |     1    |  name3  |     |

*注意speaker1和speaker2可以是0或1，为了清晰起见，我在这里将all设置为1

我想向每个组添加行，直到每个组的行数相同。（其中行数=行数最多的ID）

对于每一个新行，我希望用0填充speaker1和speaker2列，同时保持其他列中该ID的值相同

因此，输出应为：

| ID | second | speaker1 | speaker2 | company | ... |
|:--:|:------:|:--------:|:--------:|:-------:|:---:|
|  A |    1   |     1    |     1    |  name1  |     |
|  A |    2   |     1    |     1    |  name1  |     |
|  A |    3   |     1    |     1    |  name1  |     |
|  A |    4   |     0    |     0    |  name1  |     |
|  B |    1   |     1    |     1    |  name2  |     |
|  B |    2   |     1    |     1    |  name2  |     |
|  B |    3   |     1    |     1    |  name2  |     |
|  B |    4   |     1    |     1    |  name2  |     |
|  C |    1   |     1    |     1    |  name3  |     |
|  C |    2   |     1    |     1    |  name3  |     |
|  C |    3   |     0    |     0    |  name3  |     |
|  C |    4   |     0    |     0    |  name3  |     |

到目前为止，我已经尝试了groupby和apply，但是发现它非常慢，因为我在这个数据框架中有许多行和列

def add_rows_sec(w):
    'input: dataframe for grouped by ID, output: dataframe with added rows until max call length'
    
    while w['second'].max() < clean_data['second'].max(): # if duration is less than max duration in full data set
        last_row = w.iloc[-1]
        last_row['second'] += 1
        last_row['speaker1'] = 0
        last_row['speaker2'] = 0
        return w.append(last_row)
    return w

df.groupby('ID').apply(add_rows_sec).reset_index(drop=True)

def添加行数秒（w）：
'输入：按ID分组的数据帧，输出：添加行的数据帧，直到最大调用长度'
而w['second'].max（）

有没有办法用numpy做到这一点？差不多

condition = w['second'].max() < df['second'].max()
choice = pd.Series([w.ID, w.second + 1, 0, 0, w.company...])
df = np.select(condition, choice, default = np.nan)

condition=w['second'].max（）

---

非常感谢您的帮助

与熊猫不同的方法

构造一个数据帧，它是

ID

和

second

外部将其连接回原始数据帧

根据您的等级库填充缺少的值

无

groupby（）

无循环

df = pd.DataFrame({"ID":["A","A","A","B","B","B","B","C","C"],"second":["1","2","3","1","2","3","4","1","2"],"speaker1":["1","1","1","1","1","1","1","1","1"],"speaker2":["1","1","1","1","1","1","1","1","1"],"company":["name1","name1","name1","name2","name2","name2","name2","name3","name3"]})

df2 = pd.DataFrame({"ID":df["ID"].unique()}).assign(foo=1).merge(\
    pd.DataFrame({"second":df["second"].unique()}).assign(foo=1)).drop("foo", 1)\
    .merge(df, on=["ID","second"], how="outer")

df2["company"] = df2["company"].fillna(method="ffill")
df2.fillna(0)

输出

    ID  second  speaker1    speaker2    company
0   A   1   1   1   name1
1   A   2   1   1   name1
2   A   3   1   1   name1
3   A   4   0   0   name1
4   B   1   1   1   name2
5   B   2   1   1   name2
6   B   3   1   1   name2
7   B   4   1   1   name2
8   C   1   1   1   name3
9   C   2   1   1   name3
10  C   3   0   0   name3
11  C   4   0   0   name3

---

与熊猫不同的方法

构造一个数据帧，它是

ID

和

second

外部将其连接回原始数据帧

根据您的等级库填充缺少的值

无

groupby（）

无循环

df = pd.DataFrame({"ID":["A","A","A","B","B","B","B","C","C"],"second":["1","2","3","1","2","3","4","1","2"],"speaker1":["1","1","1","1","1","1","1","1","1"],"speaker2":["1","1","1","1","1","1","1","1","1"],"company":["name1","name1","name1","name2","name2","name2","name2","name3","name3"]})

df2 = pd.DataFrame({"ID":df["ID"].unique()}).assign(foo=1).merge(\
    pd.DataFrame({"second":df["second"].unique()}).assign(foo=1)).drop("foo", 1)\
    .merge(df, on=["ID","second"], how="outer")

df2["company"] = df2["company"].fillna(method="ffill")
df2.fillna(0)

输出

    ID  second  speaker1    speaker2    company
0   A   1   1   1   name1
1   A   2   1   1   name1
2   A   3   1   1   name1
3   A   4   0   0   name1
4   B   1   1   1   name2
5   B   2   1   1   name2
6   B   3   1   1   name2
7   B   4   1   1   name2
8   C   1   1   1   name3
9   C   2   1   1   name3
10  C   3   0   0   name3
11  C   4   0   0   name3

---

这是什么意思<代码>我想为每个唯一ID添加行，直到每个ID的行数等于行数最多的ID。基本上只是向每个组添加行，直到每个组的行数相同。（其中行数=行数最多的ID）这意味着什么<代码>我想为每个唯一ID添加行，直到每个ID的行数等于行数最多的ID。基本上只是向每个组添加行，直到每个组的行数相同。（其中行数=行数最多的ID）