Question

我想从我的数据中获取原始价格并将其与尺寸匹配（如下所示）我有以下代码：

import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import csv
headers = ['ticker', 'size', 'price', 'unix','type','time']
dtypes = {'ticker': 'str', 'size': 'float', 'price': 'float', 'unix': 'float','type': 'str','time': 'str'}
parse_dates = ['time']
btcnow = pd.read_csv('new 113-115.csv', header=None, names=headers, dtype=dtypes, parse_dates=parse_dates)
now3 = pd.DataFrame(btcnow, columns=['size','time','unix','price'])

from datetime import datetime, timedelta


time_interval = timedelta(minutes = 5)
df = now3[[ 'time', 'size', 'price']]

# extract time size for merge
df_time_size= df[['time', 'size']]
df_time_size.loc[:, 'time'] = df_time_size.loc[:, 'time'] + time_interval

# inner join dataframe by size&time
df = df_time_size.merge(df[['time', 'size', 'price']], how = 'inner')
df['orig_time'] = df['time'] - time_interval
df=df.groupby('time').last().reset_index()

df1= df.loc[df["size"] == 4, "price"]
df2= df.loc[df["size"] == 4, "time"]
df3= df.loc[df["size"] == 4, "size"]
df4=df.loc[df["size"] == 4, "orig_time"]
frames = [df3,df1,df2,df4]
result = pd.concat(frames, axis=1, sort=True)
print(result)

它打印出以下（帧）：

      size    price            time           orig_time
4      4.0   9300  2019-11-03 02:47:00 2019-11-03 02:42:00
116    4.0  9244.5 2019-11-03 04:43:00 2019-11-03 04:38:00
170    4.0  9240.5 2019-11-03 05:37:00 2019-11-03 05:32:00
297    4.0  9176.5 2019-11-03 07:44:00 2019-11-03 07:39:00
351    4.0  9149.5 2019-11-03 08:38:00 2019-11-03 08:33:00
etc.

我也希望能够捕获原始价格并将其添加到数据框中。从now3数据帧开始：

           size                time          unix   price
0           4.0 2019-11-03 02:42:00  1.570000e+12  9288.5
1           4.0 2019-11-03 02:42:00  1.570000e+12  9288.5
2           4.0 2019-11-03 02:42:00  1.570000e+12  9288.5
3           4.0 2019-11-03 02:42:00  1.570000e+12  9288.5
4           4.0 2019-11-03 02:42:00  1.570000e+12  9288.5

例如，上面的代码为我提供了2:47的价格的最后价格，该价格是2:42的5分钟，但是如何将原始价格也捕获到第一张桌子（框架）中？所以我的输出应如下所示：

       size   price     orig_price          time           orig_time
116    4.0    9300      9288.5  2019-11-03 2:47:00     2019-11-03 2:42:00
170    4.0    9240.5    9500    2019-11-03 05:37:00    2019-11-03 05:32:00

Answer 1

我知道了。我先做了

now4=now3[['time','price','size']]
df6= now4.loc[now4["size"] == 4,'size']
df7= now4.loc[now4["size"] == 4, "time"]
df8= now4.loc[now4["size"] == 4, "price"]
frames1 = [df6,df7,df8]
result1 = pd.concat(frames1, axis=1, sort=True)
result1.columns = ['size','orig_time','price']
df10=result1.groupby('orig_time').last().reset_index()
df10 = df10[['size','orig_time','price']]

设置类似的数据框，然后将其与上面的数据框合并。

我想从数据框中获取原始价格

1 个答案: