My data looks like this, it is a minute based data for 2 years.
2017-04-02 00:00:00
2017-04-02 00:01:00
2017-04-02 00:02:00
2017-04-02 00:03:00
2017-04-02 00:04:00
....
2017-04-02 23:59:00
...
2019-02-01 22:54:00
2019-02-01 22:55:00
2019-02-01 22:56:00
2019-02-01 22:57:00
2019-02-01 22:58:00
2019-02-01 22:59:00
2019-02-01 23:00:00
I want to access all the data rows between the end of the workday to the beginning of the next. Example between 2018-04-02 18:00:00 2018-04-03 05:00:00 for all the days in my data frame. Please help
If you use a DatetimeIndex then you can use .between_time
import pandas as pd
df = pd.DataFrame({'date': pd.date_range('2017-04-02', freq='90min', periods=100)})
df = df.set_index('date')
df.between_time('18:00', '5:00')
#date
#2017-04-02 00:00:00
#2017-04-02 01:30:00
#2017-04-02 03:00:00
#2017-04-02 04:30:00
#2017-04-02 18:00:00
#2017-04-02 19:30:00
#2017-04-02 21:00:00
#2017-04-02 22:30:00
#....
One approach is boolean indexing based on conditions on the datetime column or index. Assuming your DataFrame is named df and it has a DatetimeIndex equal to the example data you've posted, try this:
df[(df.index.hour >= 18) | (df.index.hour <= 5)]
Related
Suppose, I have a pandas Series with daily observations:
pd_series = pd.Series(np.random.rand(26281), index = pd.date_range('2022-01-01', '2024-12-31', freq = 'H'))
pd_series
2022-01-01 00:00:00 0.933746
2022-01-01 01:00:00 0.588907
2022-01-01 02:00:00 0.229040
2022-01-01 03:00:00 0.557752
2022-01-01 04:00:00 0.798649
2024-12-30 20:00:00 0.314143
2024-12-30 21:00:00 0.670485
2024-12-30 22:00:00 0.300531
2024-12-30 23:00:00 0.075403
2024-12-31 00:00:00 0.716685
What I want is to replace every observation by the monthly average. I know that the average can be calculated as
pd_series.resample('MS').mean()
But how do I put the observations to the respective observations?
Use Resampler.transform:
print (pd_series.resample('MS').transform('mean'))
2022-01-01 00:00:00 0.495015
2022-01-01 01:00:00 0.495015
2022-01-01 02:00:00 0.495015
2022-01-01 03:00:00 0.495015
2022-01-01 04:00:00 0.495015
2024-12-30 20:00:00 0.508646
2024-12-30 21:00:00 0.508646
2024-12-30 22:00:00 0.508646
2024-12-30 23:00:00 0.508646
2024-12-31 00:00:00 0.508646
Freq: H, Length: 26281, dtype: float64
I got two pandas dataframes as following:
ts1
Out[50]:
soil_moisture_ids41
date_time
2007-01-07 05:00:00 0.1830
2007-01-07 06:00:00 0.1825
2007-01-07 07:00:00 0.1825
2007-01-07 08:00:00 0.1825
2007-01-07 09:00:00 0.1825
... ...
2017-10-10 20:00:00 0.0650
2017-10-10 21:00:00 0.0650
2017-10-10 22:00:00 0.0650
2017-10-10 23:00:00 0.0650
2017-10-11 00:00:00 0.0650
[94316 rows x 3 columns]
and the other one is
ts2
Out[51]:
soil_moisture_ids42
date_time
2016-07-20 00:00:00 0.147
2016-07-20 01:00:00 0.148
2016-07-20 02:00:00 0.149
2016-07-20 03:00:00 0.150
2016-07-20 04:00:00 0.152
... ...
2019-12-31 19:00:00 0.216
2019-12-31 20:00:00 0.216
2019-12-31 21:00:00 0.215
2019-12-31 22:00:00 0.215
2019-12-31 23:00:00 0.215
[30240 rows x 3 columns]
You could see that, from 2007-01-07 to 2016-07-19, only ts1 has the data points. And from 2016-07-20 to 2017-10-11 there are some overlapped time series. Now I want to combine these two data frames. During the overlapped period, I want to get the mean values over ts1 and ts2. During the non-overlapped period, (2007-01-07 to 2016-07-19 and 2017-10-12 to 2019-12-31), the values at each time stamp is set as the value from ts1 or ts2. So how can I do it?
Thanks!
Use concat with aggregate mean, if only one value get same ouput, if multiple get mean. Also finally DatatimeIndex is sorted:
s = pd.concat([ts1, ts2]).groupby(level=0).mean()
Just store the concatenated series first and then apply the mean. i.e. merged_ts = pd.concat([ts1, ts2]) and then mean_ts = merged_ts.group_by(level=0).mean()
I have a dataframe like this:
df11 = pd.DataFrame(
{
"Start_date": ["2018-01-31 12:00:00", "2018-02-28 16:00:00", "2018-02-27 22:00:00"],
"End_date": ["2019-01-31 21:45:00", "2019-03-24 22:00:00", "2018-02-28 01:00:00"],
}
)
Start_date End_date
0 2018-01-31 12:00:00 2019-01-31 21:45:00
1 2018-02-28 16:00:00 2019-03-24 22:00:00
2 2018-02-27 22:00:00 2018-02-28 01:00:00
I need to check the overlap time duration in specific periods in seconds. My expected results are like this:
Start_date End_date 12h-16h 16h-22h 22h-00h 00h-02h30
0 2018-01-31 12:00:00 2019-01-31 21:45:00 14400 20700 0 0
1 2018-02-28 16:00:00 2019-03-24 22:00:00 0 21600 0 0
2 2018-02-27 22:00:00 2018-02-28 01:00:00 0 0 7200 3600
I know it`s completely wrong and I´ve tried other solutions. This is one of my attempts:
df11['12h-16h']=np.where(df11['Start_date']<timedelta(hours=16, minutes=0, seconds=0) & df11['End_date']>timedelta(hours=12, minutes=0, seconds=0),(np.minimum(df11['End_date'],timedelta(hours=16, minutes=0, seconds=0)))-(np.maximum(df11['Start_date'],timedelta(hours=12, minutes=0, seconds=0)))
I've have the following Multiindex and I am trying to get the last time entry for a slice of the MultiIndex.
df.loc['AUDCAD'][-1]
would return 2019-04-30 00:00:00
and
df.loc['USDCHF'][-1]
would return 2021-03-05 23:55:00
open high low close
AUDCAD 2018-12-31 00:00:00 0.95708 0.96276 0.95649 0.95979
2019-01-31 00:00:00 0.96039 0.96309 0.92200 0.94895
2019-02-28 00:00:00 0.94849 0.95800 0.93185 0.93655
2019-03-31 00:00:00 0.93718 0.95632 0.93160 0.94745
2019-04-30 00:00:00 0.94998 0.96147 0.94150 0.94750
USDCHF 2021-03-05 23:35:00 0.93109 0.93119 0.93108 0.93116
2021-03-05 23:40:00 0.93116 0.93150 0.93116 0.93143
2021-03-05 23:45:00 0.93143 0.93147 0.93127 0.93128
2021-03-05 23:50:00 0.93129 0.93134 0.93117 0.93126
2021-03-05 23:55:00 0.93126 0.93141 0.93114 0.93118```
I guess you're looking for :
df.loc[block_name].index[-1]
I have 2 columns of data in a pandas DF that looks like this with the "DateTime" column in format YYYY-MM-DD HH:MM:SS - this is first 24 hrs but the df is for one full year or 8784 x 2.
BAFFIN BAY DateTime
8759 8.112838 2016-01-01 00:00:00
8760 7.977169 2016-01-01 01:00:00
8761 8.420204 2016-01-01 02:00:00
8762 9.515370 2016-01-01 03:00:00
8763 9.222840 2016-01-01 04:00:00
8764 8.872423 2016-01-01 05:00:00
8765 8.776145 2016-01-01 06:00:00
8766 9.030668 2016-01-01 07:00:00
8767 8.394983 2016-01-01 08:00:00
8768 8.092915 2016-01-01 09:00:00
8769 8.946967 2016-01-01 10:00:00
8770 9.620883 2016-01-01 11:00:00
8771 9.535951 2016-01-01 12:00:00
8772 8.861761 2016-01-01 13:00:00
8773 9.077692 2016-01-01 14:00:00
8774 9.116074 2016-01-01 15:00:00
8775 8.724343 2016-01-01 16:00:00
8776 8.916940 2016-01-01 17:00:00
8777 8.920438 2016-01-01 18:00:00
8778 8.926278 2016-01-01 19:00:00
8779 8.817666 2016-01-01 20:00:00
8780 8.704014 2016-01-01 21:00:00
8781 8.496358 2016-01-01 22:00:00
8782 8.434297 2016-01-01 23:00:00
I am trying to calculate daily averages of the "BAFFIN BAY" and I've tried these approaches:
davg_df2 = df2.groupby(pd.Grouper(freq='D', key='DateTime')).mean()
davg_df2 = df2.groupby(pd.Grouper(freq='1D', key='DateTime')).mean()
davg_df2 = df2.groupby(by=df2['DateTime'].dt.date).mean()
All of these approaches yields the same answer as shown below :
BAFFIN BAY
DateTime
2016-01-01 6.008044
However, if you do the math, the correct average for 2016-01-01 is 8.813134 Thank you kindly for your help. I'm assuming the grouping is just by day or 24hrs to make consecutive DAILY averages but the 3 approaches above clearly is looking at other data in my 8784 x 2 DF.
I just ran your df with this code and i get 8.813134:
df['DateTime'] = pd.to_datetime(df['DateTime'])
df = df.groupby(by=pd.Grouper(freq='D', key='DateTime')).mean()
print(df)
Output:
BAFFIN BAY
DateTime
2016-01-01 8.813134