Python 基于另一个数据帧的内容向数据帧添加列
我有两个数据帧Python 基于另一个数据帧的内容向数据帧添加列,python,pandas,dataframe,Python,Pandas,Dataframe,我有两个数据帧df和时间，分别表示维护记录和每月时间。我想根据df中的数据在times中添加一列： #df represents car maintenance records data = {"07-18-2012": ["replaced wheels", 45, 200], "09-12-2014": ["changed oil", 30, 40], "09-18-2015": ["fixed dent", 92, 0]} df = pd.DataFrame.from_dict(data,
df时间dftimes#df represents car maintenance records
data = {"07-18-2012": ["replaced wheels", 45, 200], "09-12-2014": ["changed oil", 30, 40], "09-18-2015": ["fixed dent", 92, 0]}
df = pd.DataFrame.from_dict(data, orient = "index")
df.index = pd.to_datetime(df.index)
df.sort_index(inplace = True)
df.columns = ["description", "mins_spent", "cost"]
#times represents monthly periods
rng = pd.date_range(start = '12/31/2013', end = '1/1/2015', freq='M')
ts = pd.Series(rng)
times = ts.to_frame(name = "months")
days\u-since\u-maintenancetimesdf发生最近一次维护以来的天数
我尝试过使用df.ix[]
，迭代for loop
，以及searchsorted（）
df
：
description mins_spent cost
2012-07-18 replaced wheels 45 200
2014-09-12 changed oil 30 40
2015-09-18 fixed dent 92 0
months
0 2013-12-31
1 2014-01-31
2 2014-02-28
3 2014-03-31
4 2014-04-30
5 2014-05-31
6 2014-06-30
7 2014-07-31
8 2014-08-31
9 2014-09-30
10 2014-10-31
11 2014-11-30
12 2014-12-31
months days_since_maintenance
0 2013-12-31 531 days
1 2014-01-31 562 days
2 2014-02-28 ...
3 2014-03-31 ...
4 2014-04-30 ...
5 2014-05-31 ...
6 2014-06-30 ...
7 2014-07-31 ...
8 2014-08-31 774 days
9 2014-09-30 18 days
10 2014-10-31 ...
11 2014-11-30 ...
12 2014-12-31 ...
时代
：
description mins_spent cost
2012-07-18 replaced wheels 45 200
2014-09-12 changed oil 30 40
2015-09-18 fixed dent 92 0
months
0 2013-12-31
1 2014-01-31
2 2014-02-28
3 2014-03-31
4 2014-04-30
5 2014-05-31
6 2014-06-30
7 2014-07-31
8 2014-08-31
9 2014-09-30
10 2014-10-31
11 2014-11-30
12 2014-12-31
months days_since_maintenance
0 2013-12-31 531 days
1 2014-01-31 562 days
2 2014-02-28 ...
3 2014-03-31 ...
4 2014-04-30 ...
5 2014-05-31 ...
6 2014-06-30 ...
7 2014-07-31 ...
8 2014-08-31 774 days
9 2014-09-30 18 days
10 2014-10-31 ...
11 2014-11-30 ...
12 2014-12-31 ...
所需数据帧：
description mins_spent cost
2012-07-18 replaced wheels 45 200
2014-09-12 changed oil 30 40
2015-09-18 fixed dent 92 0
months
0 2013-12-31
1 2014-01-31
2 2014-02-28
3 2014-03-31
4 2014-04-30
5 2014-05-31
6 2014-06-30
7 2014-07-31
8 2014-08-31
9 2014-09-30
10 2014-10-31
11 2014-11-30
12 2014-12-31
months days_since_maintenance
0 2013-12-31 531 days
1 2014-01-31 562 days
2 2014-02-28 ...
3 2014-03-31 ...
4 2014-04-30 ...
5 2014-05-31 ...
6 2014-06-30 ...
7 2014-07-31 ...
8 2014-08-31 774 days
9 2014-09-30 18 days
10 2014-10-31 ...
11 2014-11-30 ...
12 2014-12-31 ...
嗯，这肯定不是最好的解决方案，因为它会循环通过df.index
：
for d in df.index:
times.ix[times['months'] >= d, 'days_since_maintenance'] = times['months'] - d
In [123]: times
Out[123]:
months days_since_maintenance
0 2013-12-31 531 days
1 2014-01-31 562 days
2 2014-02-28 590 days
3 2014-03-31 621 days
4 2014-04-30 651 days
5 2014-05-31 682 days
6 2014-06-30 712 days
7 2014-07-31 743 days
8 2014-08-31 774 days
9 2014-09-30 18 days
10 2014-10-31 49 days
11 2014-11-30 79 days
12 2014-12-31 110 days
df['dates']=df.index
距离最近的定义天数（x，df）：
最近的=df[df['dates']
[13行x 2列]应用
有效，但利用索引
而不是为日期添加新列
：
def days_since_x(row, df):
'''returns days between the row date
and the most recent maintenance date in df'''
#filter records
all_maint_prior = df[(df.index <= row)]
if all_maint_prior.empty:
return float('NaN')
else:
#get last row of filtered results
most_recent = all_maint_prior.iloc[-1]
#return difference in dates
return row-most_recent.name
times["days_since_maintenance"] = times["months"].apply(lambda row: days_since_x (row,df))
def天数（自第x行起）：
''返回行日期之间的天数
以及df“”中的最近维护日期
#过滤记录
所有维护之前=df[（df.index哦，我知道你需要实际查找值。我可以修改。但是apply可能是你的朋友。这是有效的——不确定它是否比MaxU-answer更有效，MaxU-answer也有效。谢谢！这可能取决于日期列表的长度。我的假设是，如果列表较长，apply会更有效。。。。也许其他人知道这是有效的——不确定它是否比同样有效的GMarsh answer效率更高或更低。谢谢！！