如何連接在某一范圍內(nèi)的列值的兩個(gè)數(shù)據(jù)格式？給定兩個(gè)數(shù)據(jù)df_1和df_2，如何加入其中的datetime列。df_1在兩者之間start和end以數(shù)據(jù)為單位df_2:print df_1 timestamp A B0 2016-05-14 10:54:33 0.020228 0.0265721 2016-05-14 10:54:34 0.057780 0.1754992 2016-05-14 10:54:35 0.098808 0.6209863 2016-05-14 10:54:36 0.158789 1.0148194 2016-05-14 10:54:39 0.038129 2.384590print df_2 start end event 0 2016-05-14 10:54:31 2016-05-14 10:54:33 E11 2016-05-14 10:54:34 2016-05-14 10:54:37 E22 2016-05-14 10:54:38 2016-05-14 10:54:42 E3得到相應(yīng)的event哪里df1.timestamp介于df_2.start和df2.end timestamp A B event0 2016-05-14 10:54:33 0.020228 0.026572 E11 2016-05-14 10:54:34 0.057780 0.175499 E22 2016-05-14 10:54:35 0.098808 0.620986 E23 2016-05-14 10:54:36 0.158789 1.014819 E24 2016-05-14 10:54:39 0.038129 2.384590 E3
3 回答
慕碼人2483693
TA貢獻(xiàn)1860條經(jīng)驗(yàn) 獲得超9個(gè)贊
interval indexstart and endclosed = bothget_loc
df_2.index = pd.IntervalIndex.from_arrays(df_2['start'],df_2['end'],closed='both')df_1['event'] = df_1['timestamp']. apply(lambda x : df_2.iloc[df_2.index.get_loc(x)]['event'])
timestamp A B event 0 2016-05-14 10:54:33 0.020228 0.026572 E1 1 2016-05-14 10:54:34 0.057780 0.175499 E2 2 2016-05-14 10:54:35 0.098808 0.620986 E2 3 2016-05-14 10:54:36 0.158789 1.014819 E2 4 2016-05-14 10:54:39 0.038129 2.384590 E3
慕慕森
TA貢獻(xiàn)1856條經(jīng)驗(yàn) 獲得超17個(gè)贊
idx = pd.IntervalIndex.from_arrays(df_2['start'], df_2['end'], closed='both')
event = df_2.loc[idx.get_indexer(df_1.timestamp), 'event']
event
0 E1
1 E2
1 E2
1 E2
2 E3
Name: event, dtype: object
df_1['event'] = event.values
df_1
timestamp A B event
0 2016-05-14 10:54:33 0.020228 0.026572 E1
1 2016-05-14 10:54:34 0.057780 0.175499 E2
2 2016-05-14 10:54:35 0.098808 0.620986 E2
3 2016-05-14 10:54:36 0.158789 1.014819 E2
4 2016-05-14 10:54:39 0.038129 2.384590 E3
IntervalIndex.get_indexer.
慕哥9229398
TA貢獻(xiàn)1877條經(jīng)驗(yàn) 獲得超6個(gè)贊
備選案文1
idx = pd.IntervalIndex.from_arrays(df_2['start'], df_2['end'], closed='both')
df_2.index=idx
df_1['event']=df_2.loc[df_1.timestamp,'event'].values
備選案文2
df_2['timestamp']=df_2['end']
pd.merge_asof(df_1,df_2[['timestamp','event']],on='timestamp',direction ='forward',allow_exact_matches =True)
Out[405]:
timestamp A B event
0 2016-05-14 10:54:33 0.020228 0.026572 E1
1 2016-05-14 10:54:34 0.057780 0.175499 E2
2 2016-05-14 10:54:35 0.098808 0.620986 E2
3 2016-05-14 10:54:36 0.158789 1.014819 E2
4 2016-05-14 10:54:39 0.038129 2.384590 E3
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