Situation
I have the folowwing pandas timeseries data:
date
predicted1
2001-03-13
0.994756
2005-08-22
0.551661
2000-05-07
0.001396
I need to take into account a case of resampling into bigger interval than a 5 years, for e.g. 10 years:
sample = data.set_index(pd.DatetimeIndex(data['date'])).drop('date', axis=1)['predicted1']
sample.resample('10Y').sum()
I get the following:
date
2000-12-31
0.001396
2010-12-31
1.546418
So resampling function groups data for the first year and separetely for other years.
Question
How to group all data to the 10 year interval? I want to get smth like this:
date
2000-12-31
1.5478132011506138
You can change the reference, closing and label in resample:
sample.resample('10Y', origin=sample.index.min(), closed='left', label='left').sum()
Output:
date
1999-12-31 1.547813
Freq: 10A-DEC, Name: predicted1, dtype: float64
Related
I have a dataframe with daily market data (OHLCV) and am resampling it to weekly.
My specific requirement is that the weekly dataframe's index labels must be the index labels of the first day of that week, whose data is present in the daily dataframe.
For example, in July 2022, the trading week beginning 4th July (for US stocks) should be labelled 5th July, since 4th July was a holiday and not found in the daily dataframe, and the first date in that week found in the daily dataframe is 5th July.
The usual weekly resampling offset aliases and anchored offsets do not seem to have such an option.
I can achieve my requirement specifically for US stocks by importing USFederalHolidayCalendar from pandas.tseries.holiday and then using
bday_us = pd.offsets.CustomBusinessDay(calendar=USFederalHolidayCalendar())
dfw.index = dfw.index.map(lambda idx: bday_us.rollforward(idx))
where dfw is the already resampled weekly dataframe with W-MON as option.
However, this would mean that I'd have to use different trading calendars for each different exchange/market, which I'd very much like to avoid.
Any pointers on how to do this simply so that the index label in the weekly dataframe is the index label of the first day of that week available in the daily dataframe would be much appreciated.
You want to group all days by calendar week (Mon-Sun), then aggregate the data, and use the first observed date as the index, correct?
If so, W-MON is not applicable because you will group dates from Tuesday through Monday. Using W-SUN instead, you group by the calendar week where the index is the Sunday. However, you can use method first on the date column to obtain the first observed date in this week and replace the index with this result.
This is possible with either groupby or resample:
import numpy as np
import pandas as pd
# simulate daily data, drop a monday
date_range = pd.bdate_range(start='2022-06-06',end='2022-07-31')
date_range = date_range[~(date_range=='2022-07-04')]
# simulate data
df = pd.DataFrame(data = {
'date': date_range,
'return': np.random.random(size=len(date_range))
})
# resample with groupby
g = df.groupby([pd.Grouper(key='date', freq='W-SUN')])
result_groupby = g[['return']].mean() # example aggregation method
result_groupby['date_first_observed'] = g['date'].first()
result_groupby['date_last_observed'] = g['date'].last()
result_groupby.set_index('date_first_observed', inplace=True)
# resample with resample
df.index = df['date']
g = df.resample('W-SUN')
result_resample = g[['return']].mean() # example aggregation method
result_resample['date_first_observed'] = g['date'].first()
result_resample['date_last_observed'] = g['date'].last()
result_resample.set_index('date_first_observed', inplace=True)
This gives
>>> result_groupby
return date_last_observed
date_first_observed
2022-06-06 0.704949 2022-06-10
2022-06-13 0.460946 2022-06-17
2022-06-20 0.578682 2022-06-24
2022-06-27 0.361004 2022-07-01
2022-07-05 0.692309 2022-07-08
2022-07-11 0.569810 2022-07-15
2022-07-18 0.435222 2022-07-22
2022-07-25 0.454765 2022-07-29
>>> result_resample
return date_last_observed
date_first_observed
2022-06-06 0.704949 2022-06-10
2022-06-13 0.460946 2022-06-17
2022-06-20 0.578682 2022-06-24
2022-06-27 0.361004 2022-07-01
2022-07-05 0.692309 2022-07-08
2022-07-11 0.569810 2022-07-15
2022-07-18 0.435222 2022-07-22
2022-07-25 0.454765 2022-07-29
One row shows 2022-07-05 (Tuesday) instead of 2022-07-04 (Monday).
I have a hypothetical time series data frame, which is with some missing observations (assumption is that the data frame shall include all dates and corresponding values and for all the dates in the year). As we can see in the head and tail information, there are certain dates and corresponding values are missing (30th Jan & 29th Dec). There would be many more such in the data frame, sometimes missing observations for more than one consecutive date.
Is there a way that missing dates are detected and inserted into the data frame and corresponding values are filled with a rolling average with one week window (this would naturally increase the number of rows of the data frame)? Appreciate inputs.
df.head(3)
date value
0 2020-01-28 25
1 2020-01-29 32
2 2020-01-31 45
df.tail(3)
date value
3 2020-12-28 24
4 2020-12-30 35
5 2020-12-31 37
df.dtypes
date object
value int64
dtype: object
Create DaetimeIndex, then use DataFrame.asfreq with rolling and mean:
df['date'] = pd.to_datetime(df['date'])
df = df.set_index('date').asfreq('d').rolling('7D').mean()
If need all values by year use:
df['date'] = pd.to_datetime(df['date'])
idx = pd.date_range('2020-01-01','2020-12-31')
df = df.set_index('date').reindex(idx).rolling('7D').mean()
I was trying out time series analysis with pandas data frames and found that there were easy ways to select specific columns like all the rows of an year, between two dates, etc.
For example, consider
ind = pd.date_range('2004-01-01', '2019-08-13')
data = np.random.randn(len(ind))
df = pd.DataFrame(d, index=ind)
Here, we can select all the rows between and including the dates '2014-01-23' and '2014-06-18' with
df['2014-01-23':'2014-06-18']
and all the rows of the year '2015' with just
df['2015']
Is there a similar way to select all the rows belonging to a specific month but for all years?
I found ways to get all the rows of a particular month and a particular year with syntax like
df['01-2015'] #all rows of January 2015
I was hoping pandas would have a way with simple syntax to get all rows of a month irrespective of the year. Does such a way exist?
Use DatetimeIndex.month, compare and filter by with boolean indexing:
print (df[df.index.month == 1])
0
2004-01-01 2.398676
2004-01-02 2.074744
2004-01-03 0.106972
2004-01-04 0.294587
2004-01-05 0.243768
...
2019-01-27 -1.623171
2019-01-28 -0.043810
2019-01-29 -0.999764
2019-01-30 -0.928471
2019-01-31 -0.304730
[496 rows x 1 columns]
I am working on a large datasets that looks like this:
Time, Value
01.01.2018 00:00:00.000, 5.1398
01.01.2018 00:01:00.000, 5.1298
01.01.2018 00:02:00.000, 5.1438
01.01.2018 00:03:00.000, 5.1228
01.01.2018 00:04:00.000, 5.1168
.... , ,,,,
31.12.2018 23:59:59.000, 6.3498
The data is a minute data from the first day of the year to the last day of the year
I want to use Pandas to find the average of every 5 days.
For example:
Average from 01.01.2018 00:00:00.000 to 05.01.2018 23:59:59.000 is average for 05.01.2018
The next average will be from 02.01.2018 00:00:00.000 to 6.01.2018 23:59:59.000 is average for 06.01.2018
The next average will be from 03.01.2018 00:00:00.000 to 7.01.2018 23:59:59.000 is average for 07.01.2018
and so on... We are incrementing day by 1 but calculating an average from the day to past 5days, including the current date.
For a given day, there are 24hours * 60minutes = 1440 data points. So I need to get the average of 1440 data points * 5 days = 7200 data points.
The final DataFrame will look like this, time format [DD.MM.YYYY] (without hh:mm:ss) and the Value is the average of 5 data including the current date:
Time, Value
05.01.2018, 5.1398
06.01.2018, 5.1298
07.01.2018, 5.1438
.... , ,,,,
31.12.2018, 6.3498
The bottom line is to calculate the average of data from today to the past 5 days and the average value is shown as above.
I tried to iterate through Python loop but I wanted something better than we can do from Pandas.
Perhaps this will work?
import numpy as np
# Create one year of random data spaced evenly in 1 minute intervals.
np.random.seed(0) # So that others can reproduce the same result given the random numbers.
time_idx = pd.date_range(start='2018-01-01', end='2018-12-31', freq='min')
df = pd.DataFrame({'Time': time_idx, 'Value': abs(np.random.randn(len(time_idx))) + 5})
>>> df.shape
(524161, 2)
Given the dataframe with 1 minute intervals, you can take a rolling average over the past five days (5 days * 24 hours/day * 60 minutes/hour = 7200 minutes) and assign the result to a new column named rolling_5d_avg. You can then group on the original timestamps using the dt accessor method to grab the date, and then take the last rolling_5d_avg value for each date.
df = (
df
.assign(rolling_5d_avg=df.rolling(window=5*24*60)['Value'].mean())
.groupby(df['Time'].dt.date)['rolling_5d_avg']
.last()
)
>>> df.head(10)
Time
2018-01-01 NaN
2018-01-02 NaN
2018-01-03 NaN
2018-01-04 NaN
2018-01-05 5.786603
2018-01-06 5.784011
2018-01-07 5.790133
2018-01-08 5.786967
2018-01-09 5.789944
2018-01-10 5.789299
Name: rolling_5d_avg, dtype: float64
I am converting low-frequency data to a higher frequency with pandas (for instance monthly to daily). When making this conversion, I would like the resulting higher-frequency index to span the entire low-frequency window. For example, suppose I have a monthly series, like so:
import numpy as np
from pandas import *
data = np.random.randn(2)
s = Series(data, index=date_range('2012-01-01', periods=len(data), freq='M'))
s
2012-01-31 0
2012-02-29 1
Now, I convert it to daily frequency:
s.resample('D')
2012-01-31 0
2012-02-01 NaN
2012-02-02 NaN
2012-02-03 NaN
...
2012-02-27 NaN
2012-02-28 NaN
2012-02-29 1
Notice how the resulting output goes from 2012-01-31 to 2012-02-29. But what I really want is days from 2011-01-01 to 2012-02-29, so that the daily index "fills" the entire January month, even if 2012-01-31 is still the only non-NaN observation in that month.
I'm also curious if there are built-in methods that give more control over how the higher-frequency period is filled with the lower frequency values. In the monthly to daily example, the default is to fill in just the last day of each month; if I use a PeriodIndex to index my series I can also s.resample('D', convention='start') to have only the first observation filled in. However, I also would like options to fill every day in the month with the monthly value, and to fill every day with the daily average (the monthly value divided by the number of days in the month).
Note that basic backfill and forward fill would not be sufficient to fill every daily observation in the month with the monthly value. For example, if the monthly series runs from January to March but the February value is NaN, then a forward fill would carry the January values into February, which is not desired.
How about this?
s.reindex(DatetimeIndex(start=s.index[0].replace(day=1), end=s.index[-1], freq='D'))