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如何使用 Pandas 合并具有重復(fù)時(shí)間戳的元素

如何使用 Pandas 合并具有重復(fù)時(shí)間戳的元素

慕田峪4524236 2023-07-18 16:42:01
我有以下代表加密交易的數(shù)據(jù)：1599177600000,381.52,1.425,s1599177600000,381.49,0.828,s1599177600000,381.48,0.747,s1599177600212,381.53,3.225,s1599177600560,381.53,0.226,s1599177600560,381.45,0.637,s1599177600560,381.44,11.431,s1599177600560,381.38,2.153,s1599177600560,381.37,0.569,s1599177600560,381.35,150,s1599177600560,381.33,1.056,s1599177600560,381.32,8.581,s1599177600560,381.31,16.947,s1599177600560,381.29,15.877,s1599177600590,381.45,2.586,s1599177600652,381.54,0.03,b1599177600826,381.39,0.5,s1599177601166,381.39,0.139,s1599177601304,381.39,1.445,s1599177601306,381.35,2.555,s1599177601624,381.3,1.552,s1599177601706,381.29,2,s1599177601868,381.31,0.262,s1599177602108,381.29,0.092,s1599177602242,381.3,0.05,b1599177602296,381.31,2.228,b1599177602312,381.32,0.05,b1599177602386,381.33,0.639,b1599177602388,381.29,7.901,s1599177602388,381.25,12.099,s這些列是：unix 時(shí)間戳（毫秒）、價(jià)格、數(shù)量和代表交易是買入還是賣出事件的字母（b 或 s）。使用 Pandas，如何將具有相同時(shí)間戳的行合并在一起，同時(shí)添加額外的列？合并規(guī)則為：new quantity = sum quantity for all rowsnew price = sum (quantity * price) for all rows / new quantity例外的是：if there is a duplicate timestamp with different letters, the one with the letter 'b' has to be pushed ahead by 1ms額外的列是：if a row is a result of a merge, the extra columns needs to have a bool True in it然后使用該時(shí)間戳作為索引？我不確定這是否可以一次性完成，但我也不太熟悉 Pandas 的語法來弄清楚如何做到這一點(diǎn)，所以任何帶有解釋的答案都會(huì)很棒。
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楊魅力

TA貢獻(xiàn)1811條經(jīng)驗(yàn) 獲得超6個(gè)贊

這是一種方法：


w_avg = df.groupby("time").apply(lambda d: sum(d["price"]*d["volume"])/d["volume"].sum())

s = df.loc[df["time"].duplicated(keep=False),"time"].unique()

df = df.groupby("time", as_index=False).agg({"volume": "sum"})


print (df.assign(w_avg=df["time"].map(w_avg), boolean=df["time"].isin(s)))


             time   volume       w_avg  boolean

0   1599177600000    3.000  381.501760     True

1   1599177600212    3.225  381.530000    False

2   1599177600560  207.477  381.346627     True

3   1599177600590    2.586  381.450000    False

4   1599177600652    0.030  381.540000    False

5   1599177600826    0.500  381.390000    False

6   1599177601166    0.139  381.390000    False

7   1599177601304    1.445  381.390000    False

8   1599177601306    2.555  381.350000    False

9   1599177601624    1.552  381.300000    False

10  1599177601706    2.000  381.290000    False

11  1599177601868    0.262  381.310000    False

12  1599177602108    0.092  381.290000    False

13  1599177602242    0.050  381.300000    False

14  1599177602296    2.228  381.310000    False

15  1599177602312    0.050  381.320000    False

16  1599177602386    0.639  381.330000    False

17  1599177602388   20.000  381.265802     True


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反對(duì) 回復(fù) 2023-07-18
?
30秒到達(dá)戰(zhàn)場(chǎng)

TA貢獻(xiàn)1828條經(jīng)驗(yàn) 獲得超6個(gè)贊

IIUC，這是一種包含業(yè)務(wù)邏輯的方法（使用加權(quán)平均值；如果同時(shí)有買入和賣出，則向前移動(dòng)一毫秒的時(shí)間戳）：


# create data frame

df = pd.read_csv(StringIO(data), sep=',')

#df['timestamp'] -= df['timestamp'].min()


# find buy, sell timestamps

buy_timestamps = df.loc[ df['buy_sell'] == 'b', 'timestamp']

sell_timestamps = df.loc[ df['buy_sell'] == 's', 'timestamp']

bs_timestamps = set(buy_timestamps) & set(sell_timestamps)


# adjust timestamps

df.loc[ df['timestamp'].isin(bs_timestamps), 'timestamp' ] += 1


# helper columns

df['price_quantity'] = df['price'] * df['quantity']

df['multiple_trades'] = df.groupby('timestamp')['buy_sell'].transform('count')

現(xiàn)在執(zhí)行聚合計(jì)算：


g = df.groupby(['timestamp', 'buy_sell'])


t = (pd.concat([

    (g['price_quantity'].sum() / g['quantity'].sum()).rename('price'),

    g['quantity'].sum().rename('quantity'),

    g['timestamp'].count().apply(lambda x: True if x > 1 else False).rename('trade_count')], 

    axis=1)

     .reset_index()

     .filter(['timestamp', 'price', 'buy_sell', 'trade_count'])

    )

print(t)

結(jié)果是：


        timestamp       price buy_sell  trade_count

0   1599177600000  381.501760        s         True

1   1599177600212  381.530000        s        False

2   1599177600560  381.346627        s         True

3   1599177600590  381.450000        s        False

4   1599177600652  381.540000        b        False

5   1599177600826  381.390000        s        False

6   1599177601166  381.390000        s        False

7   1599177601304  381.390000        s        False

8   1599177601306  381.350000        s        False

9   1599177601624  381.300000        s        False

10  1599177601706  381.290000        s        False

11  1599177601868  381.310000        s        False

12  1599177602108  381.290000        s        False

13  1599177602242  381.300000        b        False

14  1599177602296  381.310000        b        False

15  1599177602312  381.320000        b        False

16  1599177602386  381.330000        b        False

17  1599177602388  381.265802        s         True


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反對(duì) 回復(fù) 2023-07-18
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