[英]Pandas groupby and get two columns
這是數據:作為命令
{'date': {2: Timestamp('2019-04-29 00:00:00'), 3: Timestamp('2019-04-29 00:00:00'), 4: Timestamp('2019-04-29 00:00:00'), 5: Timestamp('2019-04-29 00:00:00'), 6: Timestamp('2019-04-30 00:00:00'), 7: Timestamp('2019-04-30 00:00:00'), 8: Timestamp('2019-04-30 00:00:00'), 9: Timestamp('2019-04-30 00:00:00')}, 'tickers': {2: 'SOGO', 3: 'CHGG', 4: 'GOOG', 5: 'GOOGL', 6: 'ARLO', 7: 'MTLS', 8: 'MSTR', 9: 'CVLT'}, 'market_cap': {2: 2109999999.9999998, 3: 4520000000.0, 4: 873150000000.0, 5: 875970000000.0, 6: 293310000.0, 7: 890760000.0, 8: 1530000000.0, 9: 2830000000.0}, 'bin': {2: '1', 3: '0', 4: '0', 5: '0', 6: '0', 7: '1', 8: '0', 9: '1'}}
數據幀:
date ticker market_cap bin
2 2019-04-29 SOGO 2.110000e+09 1
3 2019-04-29 CHGG 4.520000e+09 0
4 2019-04-29 GOOG 8.731500e+11 0
5 2019-04-29 GOOGL 8.759700e+11 0
6 2019-04-30 ARLO 2.933100e+08 0
7 2019-04-30 MTLS 8.907600e+08 1
8 2019-04-30 MSTR 1.530000e+09 0
9 2019-04-30 CVLT 2.830000e+09 1
我想按date
和bin
marketcap
,並通過marketcap
以及相應的ticker
獲得nlargest(2)
這會執行所有操作,除了向我顯示股票代碼,而且我無法與market_cap
上的原始df合並,因為多個market_cap
tickers
可以具有相同的market_cap
df.groupby(['expected_date', 'bin'])['market_cap'].nlargest(2)
2019-04-29 0 5 8.759700e+11
4 8.731500e+11
1 2 2.110000e+09
2019-04-30 0 8 1.530000e+09
6 2.933100e+08
1 9 2.830000e+09
7 8.907600e+08
理想的答案應該是MultiIndex ['date','bin']和column market_cap
, ticker
嘗試使用( 請根據提供的示例更改列名稱 ):
df[df.groupby(['date', 'time'])['market_cap'].rank(method='dense',ascending=False)<=2]
date tickers market_cap time
2 2019-04-29 SOGO 2.110000e+09 1
4 2019-04-29 GOOG 8.731500e+11 0
5 2019-04-29 GOOGL 8.759700e+11 0
6 2019-04-30 ARLO 2.933100e+08 0
7 2019-04-30 MTLS 8.907600e+08 1
8 2019-04-30 MSTR 1.530000e+09 0
9 2019-04-30 CVLT 2.830000e+09 1
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