Python 按一月的第一天筛选熊猫股票行情数据帧
抱歉,我对Python很陌生 我有当前代码:Python 按一月的第一天筛选熊猫股票行情数据帧,python,pandas,group-by,ticker,quandl,split-apply-combine,Python,Pandas,Group By,Ticker,Quandl,Split Apply Combine,抱歉,我对Python很陌生 我有当前代码: # Put data into a dataframe df = pd.DataFrame(ZACKSP_raw_data) """ Reformat dataframe data """ # Change exchange from NSDQ to NASDAQ df['exchange'] = df['exchange'].str.replace('NSDQ','NASDAQ') # Change date format to DD/
# Put data into a dataframe
df = pd.DataFrame(ZACKSP_raw_data)
""" Reformat dataframe data """
# Change exchange from NSDQ to NASDAQ
df['exchange'] = df['exchange'].str.replace('NSDQ','NASDAQ')
# Change date format to DD/MM/YYYY
df['date'] = df['date'].dt.strftime('%d/%m/%Y')
# Round closing share price to 2 digits
df['close'] = df['close'].round(2)
# Filter data for Jan
ZACKSP_data_StartOfJanYearMinus1 = df[df['date'] == '05/01/%s' % CurrentYearMinus1]
# Test
print(ZACKSP_data_StartOfJanYearMinus1.head())
以以下格式返回数据:
现在,我希望数组只保留1月份第一次记录的收盘价和12月份最后一次记录的收盘价(对于每个股票)。我曾想过尝试在一天中使用通配符,然后使用head()或tail()之类的符号来实现这一点,但我正在努力。有什么想法吗?解决方案如果所有日期时间都已排序: 我想您需要为每一个
行情器的第一行和最后一行添加
此外,还需要为年添加新的列,为带有标记的年的第一个和最后一个值添加新的列
df['year'] = pd.to_datetime(df['date']).dt.year
df1 = pd.concat([df.drop_duplicates(['ticker', 'year']),
df.drop_duplicates(['ticker', 'year'], keep='last')])
使用未排序的datetime
s的更通用的解决方案:
c = ['ticker','exchange','date','close']
df = pd.DataFrame({'date':pd.to_datetime(['2017-01-04','2017-01-12',
'2017-01-05',
'2018-01-02','2018-12-27','2017-12-27',
'2018-01-05','2018-01-12','2017-01-05',
'2017-01-12','2018-12-22','2017-12-22']),
'close':[4.56,5.45,4.32,5.67,5.23,4.78,7.43,8.67,
9.32,4.73,2.42,3.45],
'ticker':['BA','BA','BA','BA','BA','BA',
'AAPL','AAPL','AAPL','AAPL','AAPL','AAPL'],
'exchange':['NYSE'] * 6 + ['NSDQ'] * 6}, columns=c)
print (df)
ticker exchange date close
0 BA NYSE 2017-01-04 4.56
1 BA NYSE 2017-01-12 5.45
2 BA NYSE 2017-01-05 4.32
3 BA NYSE 2018-01-02 5.67
4 BA NYSE 2018-12-27 5.23
5 BA NYSE 2017-12-27 4.78
6 AAPL NSDQ 2018-01-05 7.43
7 AAPL NSDQ 2018-01-12 8.67
8 AAPL NSDQ 2017-01-05 9.32
9 AAPL NSDQ 2017-01-12 4.73
10 AAPL NSDQ 2018-12-22 2.42
11 AAPL NSDQ 2017-12-22 3.45
另一个具有不同数据输出的解决方案是聚合第一个
和最后一个
:
""" Reformat dataframe data """
# Change exchange from NSDQ to NASDAQ
df['exchange'] = df['exchange'].str.replace('NSDQ','NASDAQ')
# Round closing share price to 2 digits
df['close'] = df['close'].round(2)
#sorting dates for first date per ticker is first day in Jan and last day in Dec
df = df.sort_values('date')
#extract years from dates
df['year'] = pd.to_datetime(df['date']).dt.year
df = (df.groupby(['ticker','year'])['close']
.agg(['first','last'])
.reset_index())
print (df)
ticker year first last
0 AAPL 2017 9.32 3.45
1 AAPL 2018 7.43 2.42
2 BA 2017 4.56 4.78
3 BA 2018 5.67 5.23
您需要df.groupby('ticker')
,然后按月分组,过滤月份=='Dec',并取tail()
,过滤月份=='Jan'并取head(),然后解组()
(如果您发布可复制的数据,我将发布执行此操作的代码。)
阅读熊猫博士关于
范式,数据科学的关键范式之一。有关SO的示例,请参见标记。OK,这与我预期的不同,但我喜欢生成的输出。我有两个后续问题:1。我如何过滤这些年?我想包括大于已定义变量的所有年份,或者包括数组中匹配的所有年份,其中包含5年。1。您可以通过df['year']=pd.to_datetime(df['date']).dt.year
进行过滤,然后df[df['year']>2016]
-称为.2。我想为每年的第一个和最后一个列命名,如2017年第一个、2017年最后一个、2018年第一个、2018年最后一个,我将使用什么方法来实现这一点?年份定义如下:#立即查找当前年份=datetime.datetime.now()当前_year=str(now.year)2。对于解决方案1的输出df1=df.drop_duplicates(['ticker','year'])
adddf1['year']=df1['year'])。astype(str)+'first'
和类似的df2
#join DataFrames together and sorting if necessary
df = pd.concat([df1, df2]).sort_values(['ticker','date'])
print (df)
ticker exchange date close year
8 AAPL NASDAQ 2017-01-05 9.32 2017
11 AAPL NASDAQ 2017-12-22 3.45 2017
6 AAPL NASDAQ 2018-01-05 7.43 2018
10 AAPL NASDAQ 2018-12-22 2.42 2018
0 BA NYSE 2017-01-04 4.56 2017
5 BA NYSE 2017-12-27 4.78 2017
3 BA NYSE 2018-01-02 5.67 2018
4 BA NYSE 2018-12-27 5.23 2018
""" Reformat dataframe data """
# Change exchange from NSDQ to NASDAQ
df['exchange'] = df['exchange'].str.replace('NSDQ','NASDAQ')
# Round closing share price to 2 digits
df['close'] = df['close'].round(2)
#sorting dates for first date per ticker is first day in Jan and last day in Dec
df = df.sort_values('date')
#extract years from dates
df['year'] = pd.to_datetime(df['date']).dt.year
df = (df.groupby(['ticker','year'])['close']
.agg(['first','last'])
.reset_index())
print (df)
ticker year first last
0 AAPL 2017 9.32 3.45
1 AAPL 2018 7.43 2.42
2 BA 2017 4.56 4.78
3 BA 2018 5.67 5.23