IIUC，在 groupby 中，您可以通过"ticker"进行操作，也可以使用 pd.NamedAgg ，其中 first 用于打开， last 用于关闭， max 表示高， min 表示低.

IIUC, in the groupby you can do it by 'ticker' but also using pd.Grouper on 'time' with a frequence of 5 minutes. In the agg method, you can use since pandas>0.25 pd.NamedAgg with first for open, last for close, max for high and min for low.

# dummy variables
np.random.seed(0)
df = pd.DataFrame({'time':pd.date_range('2020-04-01 9:30:00', freq='20s', periods=50).tolist()*2, 
                   'ticker': ['ticker1']*50 + ['ticker2']*50, 
                   'price':np.random.randint(30, 50, 100)})

# groupby and agg, then reset_index
df_f = df.groupby(['ticker', pd.Grouper(key='time', freq='5T')])\
         .agg(open=pd.NamedAgg(column='price', aggfunc='first'), 
              close=pd.NamedAgg(column='price', aggfunc='last'), 
              high=pd.NamedAgg(column='price', aggfunc='max'), 
              low=pd.NamedAgg(column='price', aggfunc='min'))\
         .reset_index()

print (df_f)
    ticker                time  open  close  high  low
0  ticker1 2020-04-01 09:30:00    42     37    49   30
1  ticker1 2020-04-01 09:35:00    44     33    49   30
2  ticker1 2020-04-01 09:40:00    47     32    49   30
3  ticker1 2020-04-01 09:45:00    30     36    36   30
4  ticker2 2020-04-01 09:30:00    38     48    48   31
5  ticker2 2020-04-01 09:35:00    30     44    45   30
6  ticker2 2020-04-01 09:40:00    45     34    48   30
7  ticker2 2020-04-01 09:45:00    32     40    46   32

使用plotly绘制烛台，您可以执行以下操作:

for plotting candlestick with plotly, you can do:

import plotly.figure_factory

def plot_tick(data, ticker):
    ts = data[data["ticker"]==ticker].reset_index(drop=True)
    fig = plotly.figure_factory.create_candlestick(ts.open, ts.high, ts.low, 
                                                   ts.close, dates=ts.time)
    fig.show()

plot_tick(df_f, 'ticker1')