I have a timeseries data for a full year for every minute.
timestamp day hour min somedata
2010-01-01 00:00:00 1 0 0 x
2010-01-01 00:01:00 1 0 1 x
2010-01-01 00:02:00 1 0 2 x
2010-01-01 00:03:00 1 0 3 x
2010-01-01 00:04:00 1 0 4 x
... ...
2010-12-31 23:55:00 365 23 55
2010-12-31 23:56:00 365 23 56
2010-12-31 23:57:00 365 23 57
2010-12-31 23:58:00 365 23 58
2010-12-31 23:59:00 365 23 59
I want to group-by the data based on the day, i.e 2010-01-01 data should be one group, 2010-01-02 should be another upto 2010-12-31.
I used daily_groupby = dataframe.groupby(pd.to_datetime(dataframe.index.day, unit='D', origin=pd.Timestamp('2009-12-31'))). This creates the group based on the days so all jan, feb upto dec 01 day are in one group. But I want to also group by using month so that jan, feb .. does not get mixed up.
I am a beginner in pandas.
if timestamp is the index use DatetimeIndex.date
df.groupby(pd.to_datetime(df.index).date)
else Series.dt.date
df.groupby(pd.to_datetime(df['timestamp']).dt.date)
If you don't want group by year use:
time_index = pd.to_datetime(df.index)
df.groupby([time_index.month,time_index.day])
Related
I have the following table:
Hora_Retiro count_uses
0 00:00:18 1
1 00:00:34 1
2 00:02:27 1
3 00:03:13 1
4 00:06:45 1
... ... ...
748700 23:58:47 1
748701 23:58:49 1
748702 23:59:11 1
748703 23:59:47 1
748704 23:59:56 1
And I want to group all values within each hour, so I can see the total number of uses per hour (00:00:00 - 23:00:00)
I have the following code:
hora_pico_aug= hora_pico.groupby(pd.Grouper(key="Hora_Retiro",freq='H')).count()
Hora_Retiro column is of timedelta64[ns] type
Which gives the following output:
count_uses
Hora_Retiro
00:00:02 2566
01:00:02 602
02:00:02 295
03:00:02 5
04:00:02 10
05:00:02 4002
06:00:02 16075
07:00:02 39410
08:00:02 76272
09:00:02 56721
10:00:02 36036
11:00:02 32011
12:00:02 33725
13:00:02 41032
14:00:02 50747
15:00:02 50338
16:00:02 42347
17:00:02 54674
18:00:02 76056
19:00:02 57958
20:00:02 34286
21:00:02 22509
22:00:02 13894
23:00:02 7134
However, the index column starts at 00:00:02, and I want it to start at 00:00:00, and then go from one hour intervals. Something like this:
count_uses
Hora_Retiro
00:00:00 2565
01:00:00 603
02:00:00 295
03:00:00 5
04:00:00 10
05:00:00 4002
06:00:00 16075
07:00:00 39410
08:00:00 76272
09:00:00 56721
10:00:00 36036
11:00:00 32011
12:00:00 33725
13:00:00 41032
14:00:00 50747
15:00:00 50338
16:00:00 42347
17:00:00 54674
18:00:00 76056
19:00:00 57958
20:00:00 34286
21:00:00 22509
22:00:00 13894
23:00:00 7134
How can i make it to start at 00:00:00??
Thanks for the help!
You can create an hour column from Hora_Retiro column.
df['hour'] = df['Hora_Retiro'].dt.hour
And then groupby on the basis of hour
gpby_df = df.groupby('hour')['count_uses'].sum().reset_index()
gpby_df['hour'] = pd.to_datetime(gpby_df['hour'], format='%H').dt.time
gpby_df.columns = ['Hora_Retiro', 'sum_count_uses']
gpby_df
gives
Hora_Retiro sum_count_uses
0 00:00:00 14
1 09:00:00 1
2 10:00:00 2
3 20:00:00 2
I assume that Hora_Retiro column in your DataFrame is of
Timedelta type. It is not datetime, as in this case there
would be printed also the date part.
Indeed, your code creates groups starting at the minute / second
taken from the first row.
To group by "full hours":
round each element in this column to hour,
then group (just by this rounded value).
The code to do it is:
hora_pico.groupby(hora_pico.Hora_Retiro.apply(
lambda tt: tt.round('H'))).count_uses.count()
However I advise you to make up your mind, what do you want to count:
rows or values in count_uses column.
In the second case replace count function with sum.
I have a timeseries data for a full year for every minute.
timestamp day hour min rainfall_rate
2010-01-01 00:00:00 1 0 0 x
2010-01-01 00:01:00 1 0 1 x
2010-01-01 00:02:00 1 0 2 x
2010-01-01 00:03:00 1 0 3 x
2010-01-01 00:04:00 1 0 4 x
... ...
2010-12-31 23:55:00 365 23 55
2010-12-31 23:56:00 365 23 56
2010-12-31 23:57:00 365 23 57
2010-12-31 23:58:00 365 23 58
2010-12-31 23:59:00 365 23 59
I want to combine the timestamps such that I can get the combined rainfall_rate for every month, i.e I want to use group-by to combine them based on the date and also the plot them with the axis as timestamp for further analysis.
How can I perform this using pandas?
I used -
daily_groups = rainfall_df.groupby(rainfall_df.index.date) then
daily_groups.get_group(pd.Timestamp(2010,1,1))['rainfall_rate'].sum() but of course I could not plot them because they are of different shape.
Use pd.Grouper with freq="M":
print (df.groupby(pd.Grouper(freq="M"))["rainfall_rate"].count())
#
timestamp
2010-01-31 5
2010-02-28 0
2010-03-31 0
2010-04-30 0
2010-05-31 0
2010-06-30 0
2010-07-31 0
2010-08-31 0
2010-09-30 0
2010-10-31 0
2010-11-30 0
2010-12-31 0
I have a time data in a column and trying to figure out how can I get it in datetime format
2000
2100
2300
2355
0
1
5
10
100
105
330
My question is how can I get these in datetime format:
output should be:
20:00:00
21:00:00
23:00:00
23:55:00
00:00:00
00:01:00
00:05:00
00:10:00
01:00:00
01:05:00
03:30:00
tried:
1. da = pd.to_datetime(330, format='%H%M')
output: '03:30:00'
2. d= str(datetime.timedelta(minutes = 55 ))
output : '0:55:00'
But if I apply 1. to 100 it gives 10 hrs.
eg: da = pd.to_datetime(100, format='%H%M')
output: '10:00:00'
Try,
pd.to_datetime(df['time'].astype(str).str.zfill(4), format = '%H%M').dt.time
0 20:00:00
1 21:00:00
2 23:00:00
3 23:55:00
4 00:00:00
5 00:01:00
6 00:05:00
7 00:10:00
8 01:00:00
9 01:05:00
10 03:30:00
IIUC str.rjust
pd.to_datetime(s.astype(str).str.rjust(4,'0'),format='%H%M').dt.time
Out[41]:
0 20:00:00
1 21:00:00
2 23:00:00
3 23:55:00
4 00:00:00
5 00:01:00
6 00:05:00
7 00:10:00
8 01:00:00
9 01:05:00
10 03:30:00
Name: x, dtype: object
Since novice code, I am making the things more explicit and adding the formatting letters %H and %M info:
df['cname'] = pd.to_datetime(df['cname'].astype(str).str.zfill(4), format = '%H%M').dt.time
print(df['cname'])
# %H Hour (24-hour clock) as a zero-padded decimal number. 07
# %M Minute as a zero-padded decimal number. 06
I have DataFrame in following format:
Date Open High Low Close
0 2015-06-19 20:00:00 1201.60 1202.84 1201.55 1202.13
1 2015-06-19 21:00:00 1202.13 1202.50 1200.84 1200.88
2 2015-06-19 22:00:00 1200.88 1201.55 1200.61 1201.06
3 2015-06-19 23:00:00 1201.06 1201.26 1200.02 1200.57
4 2015-06-22 01:00:00 1200.57 1201.48 1197.04 1198.94
5 2015-06-22 02:00:00 1198.94 1199.79 1198.49 1199.34
6 2015-06-22 03:00:00 1199.34 1200.05 1198.64 1199.74
7 2015-06-22 04:00:00 1199.74 1200.34 1199.14 1199.66
I am trying to split this DataFrame by dates and after that i am trying to split dates in eveery 4 hours. Here is how i select DataFrame by date:
i = 0
this_date = df["Date"][i:i+1].values[0].split(" ")[0]
today = df[df["Date"].apply(lambda x: x.split(" ")[0]) == this_date]
Now i need to split today dataframe in every 4 hours. The last size will be 3 in total as it ends at 23:00
How can i do this? Are there any easy way or do i need to map over DataFrame and do it manually?
I have a group of dates. I would like to subtract them from their forward neighbor to get the delta between them. My code look like this:
import pandas, numpy, StringIO
txt = '''ID,DATE
002691c9cec109e64558848f1358ac16,2003-08-13 00:00:00
002691c9cec109e64558848f1358ac16,2003-08-13 00:00:00
0088f218a1f00e0fe1b94919dc68ec33,2006-05-07 00:00:00
0088f218a1f00e0fe1b94919dc68ec33,2006-06-03 00:00:00
00d34668025906d55ae2e529615f530a,2006-03-09 00:00:00
00d34668025906d55ae2e529615f530a,2006-03-09 00:00:00
0101d3286dfbd58642a7527ecbddb92e,2007-10-13 00:00:00
0101d3286dfbd58642a7527ecbddb92e,2007-10-27 00:00:00
0103bd73af66e5a44f7867c0bb2203cc,2001-02-01 00:00:00
0103bd73af66e5a44f7867c0bb2203cc,2008-01-20 00:00:00
'''
df = pandas.read_csv(StringIO.StringIO(txt))
df = df.sort('DATE')
df.DATE = pandas.to_datetime(df.DATE)
grouped = df.groupby('ID')
df['X_SEQUENCE_GAP'] = pandas.concat([g['DATE'].sub(g['DATE'].shift(), fill_value=0) for title,g in grouped])
I am getting pretty incomprehensible results. So, I am going to go with I have a logic error.
The results I get are as follows:
ID DATE X_SEQUENCE_GAP
0 002691c9cec109e64558848f1358ac16 2003-08-13 00:00:00 12277 days, 00:00:00
1 002691c9cec109e64558848f1358ac16 2003-08-13 00:00:00 00:00:00
3 0088f218a1f00e0fe1b94919dc68ec33 2006-06-03 00:00:00 27 days, 00:00:00
2 0088f218a1f00e0fe1b94919dc68ec33 2006-05-07 00:00:00 13275 days, 00:00:00
5 00d34668025906d55ae2e529615f530a 2006-03-09 00:00:00 13216 days, 00:00:00
4 00d34668025906d55ae2e529615f530a 2006-03-09 00:00:00 00:00:00
6 0101d3286dfbd58642a7527ecbddb92e 2007-10-13 00:00:00 13799 days, 00:00:00
7 0101d3286dfbd58642a7527ecbddb92e 2007-10-27 00:00:00 14 days, 00:00:00
9 0103bd73af66e5a44f7867c0bb2203cc 2008-01-20 00:00:00 2544 days, 00:00:00
8 0103bd73af66e5a44f7867c0bb2203cc 2001-02-01 00:00:00 11354 days, 00:00:00
I was expecting for exapme that 0 and 1 would have both a 0 result. Any help is most appreciated.
This is in 0.11rc1 (I don't think will work on a prior version)
When you shift dates the first one is a NaT (like a nan, but for datetimes/timedeltas)
In [27]: df['X_SEQUENCE_GAP'] = grouped.apply(lambda g: g['DATE']-g['DATE'].shift())
In [30]: df.sort()
Out[30]:
ID DATE X_SEQUENCE_GAP
0 002691c9cec109e64558848f1358ac16 2003-08-13 00:00:00 NaT
1 002691c9cec109e64558848f1358ac16 2003-08-13 00:00:00 00:00:00
2 0088f218a1f00e0fe1b94919dc68ec33 2006-05-07 00:00:00 NaT
3 0088f218a1f00e0fe1b94919dc68ec33 2006-06-03 00:00:00 27 days, 00:00:00
4 00d34668025906d55ae2e529615f530a 2006-03-09 00:00:00 NaT
5 00d34668025906d55ae2e529615f530a 2006-03-09 00:00:00 00:00:00
6 0101d3286dfbd58642a7527ecbddb92e 2007-10-13 00:00:00 NaT
7 0101d3286dfbd58642a7527ecbddb92e 2007-10-27 00:00:00 14 days, 00:00:00
8 0103bd73af66e5a44f7867c0bb2203cc 2001-02-01 00:00:00 NaT
9 0103bd73af66e5a44f7867c0bb2203cc 2008-01-20 00:00:00 2544 days, 00:00:00
You can then fillna (but you have to do this ackward type conversion becuase of a numpy bug, will get fixed in 0.12).
In [57]: df['X_SEQUENCE_GAP'].sort_index().astype('timedelta64[ns]').fillna(0)
Out[57]:
0 00:00:00
1 00:00:00
2 00:00:00
3 27 days, 00:00:00
4 00:00:00
5 00:00:00
6 00:00:00
7 14 days, 00:00:00
8 00:00:00
9 2544 days, 00:00:00
Name: X_SEQUENCE_GAP, dtype: timedelta64[ns]