I have a datafarme with a few lines listed below. I wanted to group the dataframe by month on the column labeled Date which spans from 1/1/1970 to 1/1/2011. Then compute some statistics for each month. The date column is datetime.datetime type. I used the following w/o success.
DataFrame.groupby(pd.Grouper(key='Date',freq='M')
I converted the datetime.datetime to Timestamp and tried DatetimeIndex but neither worked.
But i got the following error "Only valid with DatetimeIndex, TimedeltaIndex or PeriodIndex, but got an instance of 'Index'"
Date Name Time I D Pre Height
1 2011-01-01 OldFathful 9.55 01:39:00 4:15 09:41:00 130.0
2 2011-01-01 OldFathful 11.33 01:38:00 3:59 11:20:00 130.0
3 2011-01-01 OldFathful 13:00 01:27:00 4:00 13:00:00 140.0
4 2011-01-01 OldFathful 14:42 01:42:00 3:44 14:29:00 150.0
5 2011-01-01 OldFathful 16:08 01:26:00 4:00 16:02:00 140.0
Thanks in advance
EK
Related
I have a dataset with missing values and a Datetimeindex. I would like to fill this values with the mean values of other values reported at the same month, day and hour. If there is no values reported at this specific month/day/hour for all years I would like to get the interpolated value mean values of the nearest hour reported. How can I achieve this? Right now my approach is this:
df_Na = df_Na[df_Na['Generation'].isna()]
df_raw = df_raw[~df_raw['Generation'].isna()]
# reduce to month
same_month = df_raw[df_raw.index.month.isin(df_Na.index.month)]
# reduce to same day
same_day = same_month[same_month.index.day.isin(df_Na.index.day)]
# reduce to hour
same_hour = same_day[same_day.index.hour.isin(df_Na.index.hour)]
df_Na are all missing values I liked to fill and df_raw are all reported values from which I liked to get the mean value. I have a huge dataset which is why I would like to avoid a for loop at all cost.
My Data looks like this:
df_Na
Generation
2017-12-02 19:00:00 NaN
2021-01-12 00:00:00 NaN
2021-01-12 01:00:00 NaN
..............................
2021-02-12 20:00:00 NaN
2021-02-12 21:00:00 NaN
2021-02-12 22:00:00 NaN
df_raw
Generation
2015-09-12 00:00:00 0.0
2015-09-12 01:00:00 19.0
2015-09-12 02:00:00 0.0
..............................
2021-12-11 21:00:00 0.0
2021-12-11 22:00:00 180.0
2021-12-11 23:00:00 0.0
Use GroupBy.transform with mean for averages per MM-DD HH and replace missing values by DataFrame.fillna:
df = df.fillna(df.groupby(df.index.strftime('%m-%d %H')).transform('mean'))
And then if necessary add DataFrame.interpolate:
df = df.interpolate(method='nearest')
I have the following three dataframes:
df1:
date_time system_load
01-01-2017 00:00:00 208111
01-01-2017 01:00:00 208311
01-01-2017 02:00:00 208311
01-01-2017 03:00:00 208011
............... ...
31-12-2017 20:00:00 208611
31-12-2017 21:00:00 208411
31-12-2017 22:00:00 208111
31-12-2017 23:00:00 208911
The system load values of df1 has no problem.
df2:
date_time system_load
01-01-2018 00:00:00 208111
01-01-2018 01:00:00 208311
01-01-2018 02:00:00 208311
01-01-2018 03:00:00 208011
............... ...
31-12-2018 20:00:00 209611
31-12-2018 21:00:00 209411
31-12-2018 22:00:00 209111
31-12-2018 23:00:00 209911
The system load values of df2 is missed starting from 06-03-2018 20:00:00 till up to 24-10-2018 22:00:00.
df3:
date_time system_load
01-01-2019 00:00:00 309119
01-01-2019 01:00:00 309391
01-01-2019 02:00:00 309811
01-01-2019 03:00:00 309711
............... ...
31-12-2019 20:00:00 309611
31-12-2019 21:00:00 309411
31-12-2019 22:00:00 309111
31-12-2019 23:00:00 309911
The system load values of df3 has no problem.
What I want is to interpolate in suitable way the missed hourly records in df2 using the corresponding df1 and df3 hourly records (06-03-2017 20:00:00 till up to 24-10-2017 22:00:00 and 06-03-2019 20:00:00 till up to 24-10-2019 22:00:00 respectively). Based on "Pierre D"'s valuable comment I attached my scaled data.
Here is a very basic strategy that just takes data from neighboring years to fill the missing values. The offset is chosen to be precisely 52 weeks, so as to reflect possible weekly seasonality.
# get the whole series together, and resample to have missing data as NaN:
s = pd.concat([df1, df2, df3])['system_load'].resample('H').asfreq()
offset = 52 * 7 * 24 # 52 weeks, 7 days/week, 24 hours/day
filler = pd.concat([s.shift(offset), s.shift(-offset)], axis=1).mean(axis=1)
out = s.where(~s.isna(), filler)
# optional: make a new df2 with the filled values
df2mod = out.truncate(
before='2018',
after=pd.Timestamp('2019') - pd.Timedelta(1)
).to_frame('system_load')
Notes:
out contains the "filled" series for the whole system_load using neighboring years.
we use pandas.DataFrame.mean() to build the filler series as the mean of the two neighboring years, in a way that takes care of NaN (e.g. if one year or the other has NaN, then the mean is the only non-NaN value).
this is one of the most basic ways of filling the missing data, and likely won't fool a careful observer. Depending on the intended usage of the reconstructed data, a more elaborate strategy should be considered. Data reconstruction is an active field of research, and there are sophisticated methods in the literature. For example, one could use a GAN to build a resulting series that would be very hard to discriminate from real data.
I have a dataframe that looks like this
which contains every minute of a year.
I need to simplify it on hourly base and to get only hours of the year and then maximum of Reserved and Used columns for the respective hours.
I made this, which works, but not totally for my purposes
df = df.assign(date=df.date.dt.round('H'))
df1 = df.groupby('date').agg({'Reserved': ['max'], 'Used': ['max'] }).droplevel(1, axis=1).reset_index()
which just groups the minutes into hours.
date Reserved Used
0 2020-01-01 00:00:00 2176 0.0
1 2020-01-01 01:00:00 2176 0.0
2 2020-01-01 02:00:00 2176 0.0
3 2020-01-01 03:00:00 2176 0.0
4 2020-01-01 04:00:00 2176 0.0
... ... ... ...
8780 2020-12-31 20:00:00 3450 50.0
8781 2020-12-31 21:00:00 3450 0.0
8782 2020-12-31 22:00:00 3450 0.0
8783 2020-12-31 23:00:00 3450 0.0
8784 2021-01-01 00:00:00 3450 0.0
Now I need group it more to plot several curves, containing only 24 points (for every hour) based on several criteria
average used and reserved for the whole year (so to group together every 00 hour, every 01 hour, etc.)
average used and reserved for every month (so to group every 00 hour, 01 hour etc for each month individually)
average used and reserved for weekdays and for weekends
I know this is only the similar groupby as before, but I somehow miss the logic of doing it.
Could anybody help?
Thanks.
I have a time series that is very irregular. The difference in time, between two records can be 1s or 10 days.
I want to resample the data every 1h, but only when the sequential records are less than 1h.
How to approach this, without making too many loops?
In the example above, I would like to resample only rows 5-6 (delta difference is 10s) and rows 6-7 (delta difference is 50min).
The others should remain as they are.
tmp=vals[['datumtijd','filter data']]
datumtijd filter data
0 1970-11-01 00:00:00 129.0
1 1970-12-01 00:00:00 143.0
2 1971-01-05 00:00:00 151.0
3 1971-02-01 00:00:00 151.0
4 1971-03-01 00:00:00 163.0
5 1971-03-01 00:00:10 163.0
6 1971-03-01 00:00:20 163.0
7 1971-03-01 00:01:10 163.0
8 1971-03-01 00:04:10 163.0
.. ... ...
244 1981-08-19 00:00:00 102.0
245 1981-09-02 00:00:00 98.0
246 1981-09-17 00:00:00 92.0
247 1981-10-01 00:00:00 89.0
248 1981-10-19 00:00:00 92.0
You can be a little explicit about this by using groupby on the hour-floor of the time stamps:
grouped = df.groupby(df['datumtijd'].dt.floor('1H')).mean()
This is explicitly looking for the hour of each existing data point and grouping the matching ones.
But you can also just do the resample and then filter out the empty data, as pandas can still do this pretty quickly:
resampled = df.resample('1H', on='datumtijd').mean().dropna()
In either case, you get the following (note that I changed the last time stamp just so that the console would show the hours):
filter data
datumtijd
1970-11-01 00:00:00 129.0
1970-12-01 00:00:00 143.0
1971-01-05 00:00:00 151.0
1971-02-01 00:00:00 151.0
1971-03-01 00:00:00 163.0
1981-08-19 00:00:00 102.0
1981-09-02 00:00:00 98.0
1981-09-17 00:00:00 92.0
1981-10-01 00:00:00 89.0
1981-10-19 03:00:00 92.0
One quick clarification also. In your example, rows 5-8 all occur within the same hour, so they all get grouped together (hour:minute:second)!.
Also, see this related post.
I have an 'hour' column in a pandas dataframe that is simply a list of numbers from 0 to 23 representing hours. How can I convert them to an hour format such as 01:00 when the numbers are single digit ( like 1 ) and double digit (like 18)? The single digit numbers need to have a leading zero, a colon and two trailing zeros. The double digit numbers need only a colon and two trailing zeros. How can this be accomplished in a dataframe? Also, I have a 'date' column that needs to merge with the hour column after the hour column is converted.
e.g. date hour
2018-07-01 0
2018-07-01 1
2018-07-01 3
...
2018-07-01 21
2018-07-01 22
2018-07-01 23
Needs to look like:
date
2018-07-01 01:00
...
2018-07-01 23:00
The source of the data is a .csv file.
Thanks for your consideration. I'm new to pandas and I can't find in their documentation how to do this considering the single and double digit numbers.
Convert hours to timedeltas by to_timedelta and add to datetimes converted by to_datetime if necessary:
df['date'] = pd.to_datetime(df['date']) + pd.to_timedelta(df['hour'], unit='h')
print (df)
date hour
0 2018-07-01 00:00:00 0
1 2018-07-01 01:00:00 1
2 2018-07-01 03:00:00 3
3 2018-07-01 21:00:00 21
4 2018-07-01 22:00:00 22
5 2018-07-01 23:00:00 23
If need also remove hour column use DataFrame.pop
df['date'] = pd.to_datetime(df['date']) + pd.to_timedelta(df.pop('hour'), unit='h')
print (df)
date
0 2018-07-01 00:00:00
1 2018-07-01 01:00:00
2 2018-07-01 03:00:00
3 2018-07-01 21:00:00
4 2018-07-01 22:00:00
5 2018-07-01 23:00:00