我有一个超过1亿行的df。我需要识别与时间段相关的任何重叠行,并将这些重叠行放入新的df(df2)中,然后从初始df中删除它们。我的代码工作正常,并提供了@PeterLeimbigler的有用建议的最后两个df。然而,这是获得答案的一种资源密集型方法,因为每次迭代都需要再次遍历整个df。177K行大约需要11分20秒(这因迭代的长度不同而有所不同)。你知道吗
df = pd.DataFrame({'Key': ['10003', '10003', '10003', '10003', '10003','10003','10034', '10034'],
'Num1': [12,13,13,12,13,13,16,13],
'Num2': [121,122,122,124,125,126,127,128],
'admit': [20120506, 20120508, 20120510,20121010,20121010,20121110,20120516,20120520],
'discharge': [20120515, 20120508, 20120512,20121016,20121023,20121111,20120520,20120522]})
df['admit'] = pd.to_datetime(df['admit'], format='%Y%m%d')
df['discharge'] = pd.to_datetime(df['discharge'], format='%Y%m%d')
初始测向:
Key Num1 Num2 admit discharge
0 10003 12 121 2012-05-06 2012-05-15
1 10003 13 122 2012-05-08 2012-05-08
2 10003 13 122 2012-05-10 2012-05-12
3 10003 12 124 2012-10-10 2012-10-16
4 10003 13 125 2012-10-10 2012-10-23
5 10003 13 126 2012-11-10 2012-11-11
6 10034 16 127 2012-05-16 2012-05-20
7 10034 13 128 2012-05-20 2012-05-22
此df表示最终将移动的列:
Key Num1 Num2 admit discharge flag
0 10003 12 121 2012-05-06 2012-05-15 move
1 10003 13 122 2012-05-08 2012-05-08 move
2 10003 13 122 2012-05-10 2012-05-12 move
3 10003 12 124 2012-10-10 2012-10-16 move
4 10003 13 125 2012-10-10 2012-10-23 move
5 10003 13 126 2012-11-10 2012-11-11
6 10034 16 127 2012-05-16 2012-05-20
7 10034 13 128 2012-05-20 2012-05-22
脚本:
#create new df
df2 = pd.DataFrame()
# Step 1: first find all 'Initial_overlap' rows,
df.loc[df.groupby(['Key']).apply(lambda x : (x['admit']<=x['admit'].shift(-1))&(x['discharge'] > x['admit'].shift(-1))).values,'flag']='Initial_Overlap'
#Step 2: then find first set of overlaps, then move those overlaps to df2, then drop from df1, iterate to the max number of rows with a group (since it could be the case that all rows are overlaps)
for item in range(df.groupby('Key')['Key'].count().max()):
df.loc[df.groupby(['Key']).apply(lambda x : (x['admit']>=x['admit'].shift(1))&(x['admit'] < x['discharge'].shift(1))).values,'flag']='Overlap'
moved_rows = df.loc[df['flag']=='Overlap',:]
df2 = df2.append(moved_rows)
df.drop(moved_rows.index, inplace=True)
#Step 3: after all iterations to find overlaps, then the INITIAL overlaps are moved to df2 and dropped from df1
moved_initial = df.loc[df['flag']=='Initial_Overlap',:]
df2 = df2.append(moved_initial)
df.drop(moved_initial.index, inplace=True)
循环在删除第一个已识别的重叠之后再次分析所有行,但我的一部分认为有一种资源密集度较低的方法来识别这些行。有没有一种方法可以在迭代过程中移动/删除一行,这样代码就不必多次过滤数据帧(就像我原来的代码那样)?你知道吗
最后两个预期结果:
df1
Key Num1 Num2 admit discharge flag
5 10003 13 126 2012-11-10 2012-11-11 0
6 10034 16 127 2012-05-16 2012-05-20 0
7 10034 13 128 2012-05-20 2012-05-22 0
df2
Key Num1 Num2 admit discharge flag
0 10003 12 121 2012-05-06 2012-05-15 Initial_overlap
1 10003 13 122 2012-05-08 2012-05-08 Overlap
2 10003 13 122 2012-05-10 2012-05-12 Overlap
3 10003 12 124 2012-10-10 2012-10-16 Initial_overlap
4 10003 13 125 2012-10-10 2012-10-23 Overlap
编辑: 更新的DF
df = pd.DataFrame({'Key': ['10003', '10003', '10003', '10003', '10003','10003','10003', '10003','2003'],
'Num1': [12,13,13,12,13,13,16,13,4],
'Num2': [121,122,122,124,125,126,127,128,128],
'admit': [20150119, 20150124, 20150206,20150211,20150220,20150304,20150407,20150422,20150407],
'discharge': [20150123, 20150202, 20150211,20150220,20150304,20150422,20120410,20120523,20150410]})
df['admit'] = pd.to_datetime(df['admit'], format='%Y%m%d')
df['discharge'] = pd.to_datetime(df['discharge'], format='%Y%m%d')
预期结果:
Key Num1 Num2 admit discharge flag
0 10003 12 121 2015-01-19 2015-01-23 NaN
1 10003 13 122 2015-01-24 2015-02-02 NaN
2 10003 13 122 2015-02-06 2015-02-11 NaN
3 10003 12 124 2015-02-11 2015-02-20 NaN
4 10003 13 125 2015-02-20 2015-03-04 NaN
5 10003 13 126 2015-03-04 2015-04-22 Move
6 10003 16 127 2015-04-07 2012-04-10 Move
7 10003 13 128 2015-04-22 2012-05-23 NaN
8 2003 4 128 2015-04-07 2015-04-10 NaN
让我们看看这是否有效:
您的设置:
让我们填写“重叠”,检查“承认”是否在前一个“初始重叠”的日期之间。使用
cumsum
将“初始重叠”记录分组到下一个“初始重叠”:输出:
编辑:将标志更改为标志
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