我有一個帶有索引日期時間的 pandas 數據框，我想按秒分組，結果得到列“a_ABS”中具有最大值的行，但我只得到每列的最大值。import pandas as pddata = {'lat':[4.2471, 4.2646,4.2945, 4.2819,4.2635,4.2616,4.2731,4.2555], 'lng':[-76.7504,-76.7198,-76.7069,-76.7251,-76.726,-76.7196,-76.715,-767.118], 'a':[208.999,-894.0,-171.0,108.999,-162.0,-29.0,-143.999,-133.0], 'e':[0.105,0.209,0.934,0.150,0.158,0.347,0.333,0.089]}df = pd.DataFrame(data)df = pd.DataFrame(data, index =['2020-01-01 16:32:14.105000-05:00', '2020-01-01 16:32:14.112000-05:00', '2020-01-01 16:32:14.175000-05:00', '2020-01-01 16:32:14.176000-05:00', '2020-01-01 16:32:14.211000-05:00','2020-01-01 16:32:14.220000-05:00', '2020-01-01 16:32:14.310000-05:00','2020-01-01 16:32:14.327000-05:00'])df.index = pd.to_datetime(df.index)a=dfa['a_ABS']=a['a'].abs()aa=a.groupby([a.index.floor('s')], as_index=True).max()
2 回答
牛魔王的故事
TA貢獻1830條經驗 獲得超3個贊
您快到了。使用 排序后選擇第一行a.iloc[:1]。完整代碼：
import pandas as pd
data = {'lat':[4.2471, 4.2646,4.2945, 4.2819,4.2635,4.2616,4.2731,4.2555],
'lng':[-76.7504,-76.7198,-76.7069,-76.7251,-76.726,-76.7196,-76.715,-767.118],
'a':[208.999,-894.0,-171.0,108.999,-162.0,-29.0,-143.999,-133.0],
'e':[0.105,0.209,0.934,0.150,0.158,0.347,0.333,0.089]}
df = pd.DataFrame(data)
df = pd.DataFrame(data, index =['2020-01-01 16:32:14.105000-05:00', '2020-01-01 16:32:14.112000-05:00',
'2020-01-01 16:32:14.175000-05:00', '2020-01-01 16:32:14.176000-05:00',
'2020-01-01 16:32:14.211000-05:00','2020-01-01 16:32:14.220000-05:00',
'2020-01-01 16:32:14.310000-05:00','2020-01-01 16:32:14.327000-05:00'])
df.index = pd.to_datetime(df.index)
a=df
a['a_ABS']=a['a'].abs()
a=a.sort_values(by="a_ABS", ascending=False)
first_df=a.iloc[:1]
print(first_df)
繁華開滿天機
TA貢獻1816條經驗 獲得超4個贊
像這樣的東西會起作用：
import pandas as pd
# create dataframe:
df = pd.DataFrame({
'lat':[4.2471, 4.2646,4.2945, 4.2819,4.2635,4.2616,4.2731,4.2555],
'lng':[-76.7504,-76.7198,-76.7069,-76.7251,-76.726,-76.7196,-76.715,-767.118],
'a':[208.999,-894.0,-171.0,108.999,-162.0,-29.0,-143.999,-133.0],
'e':[0.105,0.209,0.934,0.150,0.158,0.347,0.333,0.089]
})
# set index:
df.index = pd.to_datetime([
'2020-01-01 16:32:14.105000-05:00', '2020-01-01 16:32:14.112000-05:00',
'2020-01-01 16:32:14.175000-05:00', '2020-01-01 16:32:14.176000-05:00',
'2020-01-01 16:32:14.211000-05:00', '2020-01-01 16:32:15.220000-05:00',
'2020-01-01 16:32:14.310000-05:00', '2020-01-01 16:32:15.327000-05:00',
])
# create absolute column:
df['a_ABS'] = df['a'].abs()
# create seconds column:
df['seconds'] = df.index.second
# group columns by seconds:
df_grouped = df.groupby(['seconds']).max()
# extract only the 'a_ABS' column:
df_grouped = df_grouped['a_ABS']
# reset index:
df_grouped = df_grouped.reset_index()
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