我想在pr上做一个pandas groupby。股票代码组。在下面的代码中。为了计算不同的KPI,pr。股票清单中的股票代码。在这里,我只是展示了col'差异'从前一天开始。显然我不希望不同代码之间的区别 - 这没有意义 - 因此是groupby。但它没有按预期工作。
输出文件中出现问题 正如你在下面的输出中所看到的那样,实际的groupby并没有按照预期的那样做,即col'差异'超越并跨越groupby中的不同组(自动收报机)。因此,它计算第一组中最后一个自动收报行与第二组中第一个自动收报行之间的差异。这不是预期的。这一行应该是NaN作为第一行...
这是“差异”的结果。 col in df差异 日期
2015-04-09 NaN
2015-04-10 1.180000
2015-04-13 3.150000
2015-04-14 -0.980000
2015-04-15 1.280000
2015-04-16 -8.280000
2015-04-17 -8.770000
2015-04-09 -139.859995 This is not correct. The groupby does not separate the tickers as it should. This should be a NaN... not the diff between 2 different tickers!
2015-04-10 0.899994
2015-04-13 -1.130005
2015-04-14 -0.589996
2015-04-15 1.000000
2015-04-16 0.350006
2015-04-09 -139.859995
任何有关“差异”原因的想法。 col在我的代码中不应该像分组一样分开吗?
import pandas as pd
import time
from io import StringIO
text = """Date Ticker Open High Low Adj_Close Volume
2015-04-09 vws.co 315.000000 316.100000 312.500000 311.520000 1686800
2015-04-10 vws.co 317.000000 319.700000 316.400000 312.700000 1396500
2015-04-13 vws.co 317.900000 321.500000 315.200000 315.850000 1564500
2015-04-14 vws.co 320.000000 322.400000 318.700000 314.870000 1370600
2015-04-15 vws.co 320.000000 321.500000 319.200000 316.150000 945000
2015-04-16 vws.co 319.000000 320.200000 310.400000 307.870000 2236100
2015-04-17 vws.co 309.900000 310.000000 302.500000 299.100000 2711900
2015-04-20 vws.co 303.000000 312.000000 303.000000 306.490000 1629700
2015-04-09 mmm 166.750000 167.500000 166.500000 166.630005 1762800
2015-04-10 mmm 165.630005 167.740005 164.789993 167.529999 1993700
2015-04-13 mmm 167.110001 167.490005 165.919998 166.399994 2022800
2015-04-14 mmm 165.179993 166.550003 164.649994 165.809998 1610300
2015-04-15 mmm 165.339996 167.080002 164.839996 166.809998 2092200
2015-04-16 mmm 165.880005 167.229996 165.250000 167.160004 2721900"""
df = pd.read_csv(StringIO(text), delim_whitespace=1, parse_dates=[0], index_col=0)
def Screener(group):
def diff_calc(group):
df['Difference'] = df['Adj_Close'].diff()
return df['Difference']
df['Difference'] = diff_calc(group)
return df
if __name__ == '__main__':
### groupby screeener (filtering to only rel ticker group)
grouped = df.groupby('Ticker', as_index=False) # Now doing the groupby outside the iteration...
for name, group in grouped:
# Testing/showing the groups...
print ('(group)\n',name,'\n')
print ('(group (ticker) in df)\n',group.head(10),'\n')
df = Screener(group)
print(60 * '=')
# Test the first 3 rows of each group for 'Difference' col transgress groups...
df_test = df.groupby('Ticker').head(3).reset_index().set_index('Date')
print ('df_test (summary from df) (Output)\n',df_test,'\n')
显然我的groupby按预期工作,但预期的差异' col在我的测试输出中表现得不正常:
(group)
mmm
(group (ticker) in df)
Ticker Open High Low Adj_Close Volume
Date
2015-04-09 mmm 166.750000 167.500000 166.500000 166.630005 1762800
2015-04-10 mmm 165.630005 167.740005 164.789993 167.529999 1993700
2015-04-13 mmm 167.110001 167.490005 165.919998 166.399994 2022800
2015-04-14 mmm 165.179993 166.550003 164.649994 165.809998 1610300
2015-04-15 mmm 165.339996 167.080002 164.839996 166.809998 2092200
2015-04-16 mmm 165.880005 167.229996 165.250000 167.160004 2721900
============================================================
(group)
vws.co
(group (ticker) in df)
Ticker Open High Low Adj_Close Volume
Date
2015-04-09 vws.co 315.0 316.1 312.5 311.52 1686800
2015-04-10 vws.co 317.0 319.7 316.4 312.70 1396500
2015-04-13 vws.co 317.9 321.5 315.2 315.85 1564500
2015-04-14 vws.co 320.0 322.4 318.7 314.87 1370600
2015-04-15 vws.co 320.0 321.5 319.2 316.15 945000
2015-04-16 vws.co 319.0 320.2 310.4 307.87 2236100
2015-04-17 vws.co 309.9 310.0 302.5 299.10 2711900
2015-04-20 vws.co 303.0 312.0 303.0 306.49 1629700
============================================================
df_test (summary from df) (Output)
Ticker Open High Low Adj_Close Volume Date
2015-04-09 vws.co 315.000000 316.100000 312.500000 311.520000 1686800
2015-04-10 vws.co 317.000000 319.700000 316.400000 312.700000 1396500
2015-04-13 vws.co 317.900000 321.500000 315.200000 315.850000 1564500
2015-04-09 mmm 166.750000 167.500000 166.500000 166.630005 1762800
2015-04-10 mmm 165.630005 167.740005 164.789993 167.529999 1993700
2015-04-13 mmm 167.110001 167.490005 165.919998 166.399994 2022800
Difference
Date
2015-04-09 NaN
2015-04-10 1.180000
2015-04-13 3.150000
2015-04-09 -139.859995 This is not correct!!! This should be NaN...
2015-04-10 0.899994
2015-04-13 -1.130005