Python 如何根据带条件的列值在数据框中插入行？

我有一个约20k行的数据帧，看起来是这样的：

import pandas as pd
import numpy as np
df = pd.DataFrame({'Car_ID': ['B332', 'B332', 'B332', 'C315', 'C315', 'C315', 'C315', 'C315', 'F310', 'F310'], \
                    'Date': ['2018-03-12', '2018-03-14', '2018-03-15', '2018-03-17', '2018-03-13', '2018-03-15', \
                             '2018-03-18', '2018-03-21', '2018-03-10', '2018-03-13'], \
                    'Driver': ['Alex', 'Alex', 'Mick', 'Sara', 'Sara', 'Jean', 'Sara', 'Sara', 'Franck','Michel']})
df

Out:    
    Car_ID  Date        Driver
0   B332    2018-03-12  Alex
1   B332    2018-03-14  Alex
2   B332    2018-03-15  Mick
3   C315    2018-03-17  Sara
4   C315    2018-03-13  Sara
5   C315    2018-03-15  Jean
6   C315    2018-03-18  Sara
7   C315    2018-03-21  Sara
8   F310    2018-03-10  Franck
9   F310    2018-03-13  Michel

我为数据帧中的每个事件创建一个新列，如下所示：

df["Event"] = np.where(df.Car_ID.str.contains('B', case=True, na=False), 'Rent_Car_B', \
                    np.where(df.Car_ID.str.contains('C', case=True, na=False), 'Rent_Car_C', \
                    np.where(df.Car_ID.str.contains('F', case=True, na=False), 'Rent_Car_F', df.Car_ID))) 
df

Out:
    Car_ID  Date        Driver  Event
0   B332    2018-03-12  Alex    Rent_Car_B
1   B332    2018-03-14  Alex    Rent_Car_B
2   B332    2018-03-15  Mick    Rent_Car_B
3   C315    2018-03-17  Sara    Rent_Car_C
4   C315    2018-03-13  Sara    Rent_Car_C
5   C315    2018-03-15  Jean    Rent_Car_C
6   C315    2018-03-18  Sara    Rent_Car_C
7   C315    2018-03-21  Sara    Rent_Car_C
8   F310    2018-03-10  Franck  Rent_Car_F
9   F310    2018-03-13  Michel  Rent_Car_F

Out:
    Car_ID  Date        Driver  Event
0   B332    2018-03-12  Alex    Rent_Car_B
1   B332    2018-03-14  Alex    Rent_Car_B
2   B332    2018-03-15  Mick    Rent_Car_B
3   B332    2018-03-15          Alex to Mick
4   C315    2018-03-17  Sara    Rent_Car_C
5   C315    2018-03-13  Sara    Rent_Car_C
6   C315    2018-03-15  Jean    Rent_Car_C
7   C315    2018-03-15          Sara to Jean
8   C315    2018-03-18  Sara    Rent_Car_C
9   C315    2018-03-18          Jean to Sara
10  C315    2018-03-21  Sara    Rent_Car_C
11  F310    2018-03-10  Franck  Rent_Car_F
12  F310    2018-03-13  Michel  Rent_Car_F
13  F310    2018-03-13          Franck to Mike

对于我的

事件

列，我想为每个驱动程序更改添加新行，如下所示：

df["Event"] = np.where(df.Car_ID.str.contains('B', case=True, na=False), 'Rent_Car_B', \
                    np.where(df.Car_ID.str.contains('C', case=True, na=False), 'Rent_Car_C', \
                    np.where(df.Car_ID.str.contains('F', case=True, na=False), 'Rent_Car_F', df.Car_ID))) 
df

Out:
    Car_ID  Date        Driver  Event
0   B332    2018-03-12  Alex    Rent_Car_B
1   B332    2018-03-14  Alex    Rent_Car_B
2   B332    2018-03-15  Mick    Rent_Car_B
3   C315    2018-03-17  Sara    Rent_Car_C
4   C315    2018-03-13  Sara    Rent_Car_C
5   C315    2018-03-15  Jean    Rent_Car_C
6   C315    2018-03-18  Sara    Rent_Car_C
7   C315    2018-03-21  Sara    Rent_Car_C
8   F310    2018-03-10  Franck  Rent_Car_F
9   F310    2018-03-13  Michel  Rent_Car_F

Out:
    Car_ID  Date        Driver  Event
0   B332    2018-03-12  Alex    Rent_Car_B
1   B332    2018-03-14  Alex    Rent_Car_B
2   B332    2018-03-15  Mick    Rent_Car_B
3   B332    2018-03-15          Alex to Mick
4   C315    2018-03-17  Sara    Rent_Car_C
5   C315    2018-03-13  Sara    Rent_Car_C
6   C315    2018-03-15  Jean    Rent_Car_C
7   C315    2018-03-15          Sara to Jean
8   C315    2018-03-18  Sara    Rent_Car_C
9   C315    2018-03-18          Jean to Sara
10  C315    2018-03-21  Sara    Rent_Car_C
11  F310    2018-03-10  Franck  Rent_Car_F
12  F310    2018-03-13  Michel  Rent_Car_F
13  F310    2018-03-13          Franck to Mike

我不确定是否有什么诀窍可以实现这项工作。

---

我将非常感谢你的建议

这是一个相当复杂的问题，我的看法是：

# Add the Driver columns by shifting grouped by the Event
df['new'] =  df.groupby('Event').apply(lambda x : x['Driver'].shift(1) +'to'+ x['Driver']).values
# Split them by 'to'
df['new'] =df['new'].str.split('to').bfill()
# Check if both of them are equal
m = df['new'].str[0] != df['new'].str[1]
# Based on the condition create a new dataframe
new_df = df.loc[m].copy().iloc[:-1]
# Convert the list to the format you desired 
new_df['new'] = new_df['new'].str[0] + ' to ' + new_df['new'].str[1]
# Concat new dataframe and old dataframe 
mdf = pd.concat([df.drop('new',1) , new_df.drop(['Driver','Event'],1) \
               .rename(columns = {'new':'Event'})])


     Car_ID        Date  Driver             Event
0   B332  2018-03-12    Alex        Rent_Car_B
1   B332  2018-03-14    Alex        Rent_Car_B
2   B332  2018-03-15    Mick        Rent_Car_B
3   C315  2018-03-17    Sara        Rent_Car_C
4   C315  2018-03-13    Sara        Rent_Car_C
5   C315  2018-03-15    Jean        Rent_Car_C
6   C315  2018-03-18    Sara        Rent_Car_C
7   C315  2018-03-21    Sara        Rent_Car_C
8   F310  2018-03-10  Franck        Rent_Car_F
9   F310  2018-03-13  Michel        Rent_Car_F
2   B332  2018-03-15     NaN      Alex to Mick
5   C315  2018-03-15     NaN      Sara to Jean
6   C315  2018-03-18     NaN      Jean to Sara
8   F310  2018-03-10     NaN  Franck to Michel

如果您需要顺序，则对索引进行排序，即

mdf = mdf.sort_index()

---

使用

shift

方法，首先用它创建一列，我们将在以下步骤后使用该列：

df['Driver_shift'] = df['Driver'].shift()

选择您实际更改驾驶员和相同车辆ID的行（使用掩码）：

mask = (df['Driver'] != df['Driver_shift'])&(df['Car_ID'] == df['Car_ID'].shift())
df_change = df[mask]

现在，通过添加0.5来更改索引，以便以后进行连接和排序，并更改两列的值：

df_change = df_change.set_index(df_change.index+0.5)
df_change.loc[:,'Event'] = df_change['Driver_shift'] + ' to ' + df_change['Driver']
df_change['Driver'] = '' # to replace the value

现在，您可以连接、排序、重置索引和删除：

pd.concat([df,df_change]).sort_index().reset_index(drop=True).drop('Driver_shift',1)

你会得到：

   Car_ID        Date  Driver             Event
0    B332  2018-03-12    Alex        Rent_Car_B
1    B332  2018-03-14    Alex        Rent_Car_B
2    B332  2018-03-15    Mick        Rent_Car_B
3    B332  2018-03-15              Alex to Mick
4    C315  2018-03-17    Sara        Rent_Car_C
5    C315  2018-03-13    Sara        Rent_Car_C
6    C315  2018-03-15    Jean        Rent_Car_C
7    C315  2018-03-15              Sara to Jean
8    C315  2018-03-18    Sara        Rent_Car_C
9    C315  2018-03-18              Jean to Sara
10   C315  2018-03-21    Sara        Rent_Car_C
11   F310  2018-03-10  Franck        Rent_Car_F
12   F310  2018-03-13  Michel        Rent_Car_F
13   F310  2018-03-13          Franck to Michel

编辑：在每个驱动程序和日期前添加一行

df1 = df.copy()
df1.index = df1.index +0.5
df2 = pd.concat([df.drop('Event',1),df1]).sort_index().reset_index(drop=True)
df2['Event'] = df2['Event'].fillna(df2['Driver'])

---

结果是在df2中

只需几次换班，您就可以很好地完成这项工作！您还可以使用此方法正确获取索引，并将它们添加到您想要的位置

添加

事件后，执行
数据帧
：

import pandas as pd

# Modify the index so we can later append to the correct rows
df.index= df.index*2

# Determine when switches occur
mask = (df.Driver != df.Driver.shift(1)) & (df.Car_ID == df.Car_ID.shift(1))
sw_from = df[mask.shift(-1).fillna(False)].copy()
sw_to = df[mask].copy()

# Make the switching rows have the correct information
sw_to['Event'] = sw_from.Driver.values + ' to ' + sw_to.Driver.values
sw_to['Driver'] = ''

# Modify the switching indices so they get added to the proper position
sw_to.index = sw_to.index+1

# Add them to df
df = df.append(sw_to).sort_index().reset_index(drop=True)


输出：

   Car_ID        Date  Driver             Event
0    B332  2018-03-12    Alex        Rent_Car_B
1    B332  2018-03-14    Alex        Rent_Car_B
2    B332  2018-03-15    Mick        Rent_Car_B
3    B332  2018-03-15              Alex to Mick
4    C315  2018-03-17    Sara        Rent_Car_C
5    C315  2018-03-13    Sara        Rent_Car_C
6    C315  2018-03-15    Jean        Rent_Car_C
7    C315  2018-03-15              Sara to Jean
8    C315  2018-03-18    Sara        Rent_Car_C
9    C315  2018-03-18              Jean to Sara
10   C315  2018-03-21    Sara        Rent_Car_C
11   F310  2018-03-10  Franck        Rent_Car_F
12   F310  2018-03-13  Michel        Rent_Car_F
13   F310  2018-03-13          Franck to Michel

“Alex”出现在第0行和第1行是什么意思？第一部分-你可以做
df['Event']='Rent\u Car\uu'+df['Car\u ID'].str[0]
@Dillon这意味着他驾驶同一辆车两天。谢谢你的帮助@DarkHi@Ben.T如果最新日期在datafrmae的顶部，我怎么能做同样的工作。如下：
Car\u ID Date Driver Event 0 F310 2018-03-13 Michel Rent\u Car\u F 1 F310 2018-03-10 Franck Rent\u Car\u F 2 C315 2018-03-21 Sara Rent\u Car\u c3 C315 2018-03-18 Sara Rent\u Car\u c4 C315 2018-03-15 Jean Rent\u Car\u c5 C315 2018-03-13 Sara Rent\u Car c6 C315 2018-03-17 Sara出租汽车C 7 B332 2018-03-15米克出租汽车B 8 B332 2018-03-14亚历克斯出租汽车B 9 B332 2018-03-12亚历克斯出租汽车B
@M-M你想在哪里增加一排？例如弗兰克对米歇尔（或米歇尔对弗兰克）：弗兰克之后，米歇尔之前，两者之间？我解决了这个问题。但是我想为每个唯一的
Car\u ID
添加一个新行，并在事件列中获取驱动程序的名称并添加日期。类似这样的
Car\u ID Date Driver Event 0 F310 2018-03-13 Michel Michel 1 F310 2018-03-13 Michel Rent\u Car\u F
我忘了给你贴标签：）@Ben。T@M-I’我不确定我是否理解你在决赛中想要什么。我明白了，对于Michel，您希望在事件列中添加一行驱动程序名称，但是对于Franck，您也希望添加一行吗？