[英]finding first and last available days of a month in pandas
I have a pandas dataframe from 2007 to 2017. The data is like this:我有一个从 2007 年到 2017 年的熊猫数据框。数据是这样的:
date closing_price
2007-12-03 728.73
2007-12-04 728.83
2007-12-05 728.83
2007-12-07 728.93
2007-12-10 728.22
2007-12-11 728.50
2007-12-12 728.51
2007-12-13 728.65
2007-12-14 728.65
2007-12-17 728.70
2007-12-18 728.73
2007-12-19 728.73
2007-12-20 728.73
2007-12-21 728.52
2007-12-24 728.52
2007-12-26 728.90
2007-12-27 728.90
2007-12-28 728.91
2008-01-05 728.88
2008-01-08 728.86
2008-01-09 728.84
2008-01-10 728.85
2008-01-11 728.85
2008-01-15 728.86
2008-01-16 728.89
As you can see, some days are missing for each month.如您所见,每个月都缺少一些日子。 I want to take the first and last 'available' days of each month, and calculate the difference of their closing_price, and put the results in a new dataframe.我想取每个月的第一个和最后一个“可用”天数,并计算它们的收盘价的差异,并将结果放入一个新的数据框中。 For example for the first month, the days will be 2007-12-03 and 2007-12-28, and the closing prices would be 728.73 and 728.91, so the result would be 0.18.例如,第一个月的天数为 2007-12-03 和 2007-12-28,收盘价为 728.73 和 728.91,因此结果为 0.18。 How can I do this?我怎样才能做到这一点?
you can group df by month and apply a function to do it.您可以按月对 df 进行分组并应用一个函数来完成它。 Notice the to_period , this function convert DataFrame from DatetimeIndex to PeriodIndex with desired frequency.注意to_period ,此函数将 DataFrame 从 DatetimeIndex 转换为具有所需频率的 PeriodIndex 。
def calculate(x):
start_closing_price = x.loc[x.index.min(), "closing_price"]
end_closing_price = x.loc[x.index.max(), "closing_price"]
return end_closing_price-start_closing_price
result = df.groupby(df["date"].dt.to_period("M")).apply(calculate)
# result
date
2007-12 0.18
2008-01 0.01
Freq: M, dtype: float64
First make sure they are datetime
and sorted:首先确保它们是datetime
并已排序:
import pandas as pd
df['date'] = pd.to_datetime(df.date)
df = df.sort_values('date')
gp = df.groupby([df.date.dt.year.rename('year'), df.date.dt.month.rename('month')])
gp.closing_price.last() - gp.closing_price.first()
#year month
#2007 12 0.18
#2008 1 0.01
#Name: closing_price, dtype: float64
or或者
gp = df.groupby(pd.Grouper(key='date', freq='1M'))
gp.last() - gp.first()
# closing_price
#date
#2007-12-31 0.18
#2008-01-31 0.01
gp = df.set_index('date').resample('1M')
gp.last() - gp.first()
# closing_price
#date
#2007-12-31 0.18
#2008-01-31 0.01
Problem : Get first or last date of indexed dataframe问题:获取索引数据帧的第一个或最后一个日期
Solution : Resample the index and then extract the data.解决方案:重新采样索引,然后提取数据。
lom = pd.Series(x.index, index = x.index).resample('m').last()
xlast = x[x.index.isin(lom)] # .resample('m').last() to get monthly freq
fom = pd.Series(x.index, index = x.index).resample('m').first()
xfirst = x[x.index.isin(fom)]
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