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pandas.cut day time: how to set a bin from 22:00 to 6:00?

I'm trying to use pd.cut to divide 24 hours into the following interval:
[6,11),[11,14),[14,17),[17,22),[22,6)
How could I achieve the last bin [22,6)?

Assuming some form of datetime column, try offsetting the datetime by 6 hours so that the lower bound becomes midnight. Then cutting based on those hours instead, with the custom labels:
import pandas as pd
# sample data
df = pd.DataFrame({
'datetime': pd.date_range('2021-01-01', periods=24, freq='H')
})
df['bins'] = pd.cut((df['datetime'] - pd.Timedelta(hours=6)).dt.hour,
bins=[0, 5, 8, 11, 16, 24],
labels=['[6,11)', '[11,14)', '[14,17)',
'[17,22)', '[22,6)'],
right=False)
df:
datetime bins
0 2021-01-01 00:00:00 [22,6)
1 2021-01-01 01:00:00 [22,6)
2 2021-01-01 02:00:00 [22,6)
3 2021-01-01 03:00:00 [22,6)
4 2021-01-01 04:00:00 [22,6)
5 2021-01-01 05:00:00 [22,6)
6 2021-01-01 06:00:00 [6,11)
7 2021-01-01 07:00:00 [6,11)
8 2021-01-01 08:00:00 [6,11)
9 2021-01-01 09:00:00 [6,11)
10 2021-01-01 10:00:00 [6,11)
11 2021-01-01 11:00:00 [11,14)
12 2021-01-01 12:00:00 [11,14)
13 2021-01-01 13:00:00 [11,14)
14 2021-01-01 14:00:00 [14,17)
15 2021-01-01 15:00:00 [14,17)
16 2021-01-01 16:00:00 [14,17)
17 2021-01-01 17:00:00 [17,22)
18 2021-01-01 18:00:00 [17,22)
19 2021-01-01 19:00:00 [17,22)
20 2021-01-01 20:00:00 [17,22)
21 2021-01-01 21:00:00 [17,22)
22 2021-01-01 22:00:00 [22,6)
23 2021-01-01 23:00:00 [22,6)

How do I display a subset of a pandas dataframe?

I have a dataframe df that contains datetimes for every hour of a day between 2003-02-12 to 2017-06-30 and I want to delete all datetimes between 24th Dec and 1st Jan of EVERY year.
An extract of my data frame is:
...
7505,2003-12-23 17:00:00
7506,2003-12-23 18:00:00
7507,2003-12-23 19:00:00
7508,2003-12-23 20:00:00
7509,2003-12-23 21:00:00
7510,2003-12-23 22:00:00
7511,2003-12-23 23:00:00
7512,2003-12-24 00:00:00
7513,2003-12-24 01:00:00
7514,2003-12-24 02:00:00
7515,2003-12-24 03:00:00
7516,2003-12-24 04:00:00
7517,2003-12-24 05:00:00
7518,2003-12-24 06:00:00
...
7723,2004-01-01 19:00:00
7724,2004-01-01 20:00:00
7725,2004-01-01 21:00:00
7726,2004-01-01 22:00:00
7727,2004-01-01 23:00:00
7728,2004-01-02 00:00:00
7729,2004-01-02 01:00:00
7730,2004-01-02 02:00:00
7731,2004-01-02 03:00:00
7732,2004-01-02 04:00:00
7733,2004-01-02 05:00:00
7734,2004-01-02 06:00:00
7735,2004-01-02 07:00:00
...
and my expected output is:
...
7505,2003-12-23 17:00:00
7506,2003-12-23 18:00:00
7507,2003-12-23 19:00:00
7508,2003-12-23 20:00:00
7509,2003-12-23 21:00:00
7510,2003-12-23 22:00:00
7511,2003-12-23 23:00:00
...
7728,2004-01-02 00:00:00
7729,2004-01-02 01:00:00
7730,2004-01-02 02:00:00
7731,2004-01-02 03:00:00
7732,2004-01-02 04:00:00
7733,2004-01-02 05:00:00
7734,2004-01-02 06:00:00
7735,2004-01-02 07:00:00
...

Sample dataframe:
dates
0 2003-12-23 23:00:00
1 2003-12-24 05:00:00
2 2004-12-27 05:00:00
3 2003-12-13 23:00:00
4 2002-12-23 23:00:00
5 2004-01-01 05:00:00
6 2014-12-24 05:00:00
Solution:
If you want it for every year between the following dates excluded, then extract the month and dates first:
df['month'] = df['dates'].dt.month
df['day'] = df['dates'].dt.day
And now put the condition check:
dec_days = [24, 25, 26, 27, 28, 29, 30, 31]
## if the month is dec, then check for these dates
## if the month is jan, then just check for the day to be 1 like below
df = df[~(((df.month == 12) & (df.day.isin(dec_days))) | ((df.month == 1) & (df.day == 1)))]
Sample output:
dates month day
0 2003-12-23 23:00:00 12 23
3 2003-12-13 23:00:00 12 13
4 2002-12-23 23:00:00 12 23

This takes advantage of the fact that datetime-strings in the form mm-dd are sortable. Read everything in from the CSV file then filter for the dates you want:
df = pd.read_csv('...', parse_dates=['DateTime'])
s = df['DateTime'].dt.strftime('%m-%d')
excluded = (s == '01-01') | (s >= '12-24') # Jan 1 or >= Dec 24
df[~excluded]

You can try dropping on conditionals. Maybe with a pattern match to the date string or parsing the date as a number (like in Java) and conditionally removing.
datesIdontLike = df[df['colname'] == <stringPattern>].index
newDF = df.drop(datesIdontLike, inplace=True)
Check this out: https://thispointer.com/python-pandas-how-to-drop-rows-in-dataframe-by-conditions-on-column-values/
(If you have issues, let me know.)

You can use pandas and boolean filtering with strftime
# version 0.23.4
import pandas as pd
# make df
df = pd.DataFrame(pd.date_range('20181223', '20190103', freq='H'), columns=['date'])
# string format the date to only include the month and day
# then set it strictly less than '12-24' AND greater than or equal to `01-02`
df = df.loc[
(df.date.dt.strftime('%m-%d') < '12-24') &
(df.date.dt.strftime('%m-%d') >= '01-02')
].copy()
print(df)
date
0 2018-12-23 00:00:00
1 2018-12-23 01:00:00
2 2018-12-23 02:00:00
3 2018-12-23 03:00:00
4 2018-12-23 04:00:00
5 2018-12-23 05:00:00
6 2018-12-23 06:00:00
7 2018-12-23 07:00:00
8 2018-12-23 08:00:00
9 2018-12-23 09:00:00
10 2018-12-23 10:00:00
11 2018-12-23 11:00:00
12 2018-12-23 12:00:00
13 2018-12-23 13:00:00
14 2018-12-23 14:00:00
15 2018-12-23 15:00:00
16 2018-12-23 16:00:00
17 2018-12-23 17:00:00
18 2018-12-23 18:00:00
19 2018-12-23 19:00:00
20 2018-12-23 20:00:00
21 2018-12-23 21:00:00
22 2018-12-23 22:00:00
23 2018-12-23 23:00:00
240 2019-01-02 00:00:00
241 2019-01-02 01:00:00
242 2019-01-02 02:00:00
243 2019-01-02 03:00:00
244 2019-01-02 04:00:00
245 2019-01-02 05:00:00
246 2019-01-02 06:00:00
247 2019-01-02 07:00:00
248 2019-01-02 08:00:00
249 2019-01-02 09:00:00
250 2019-01-02 10:00:00
251 2019-01-02 11:00:00
252 2019-01-02 12:00:00
253 2019-01-02 13:00:00
254 2019-01-02 14:00:00
255 2019-01-02 15:00:00
256 2019-01-02 16:00:00
257 2019-01-02 17:00:00
258 2019-01-02 18:00:00
259 2019-01-02 19:00:00
260 2019-01-02 20:00:00
261 2019-01-02 21:00:00
262 2019-01-02 22:00:00
263 2019-01-02 23:00:00
264 2019-01-03 00:00:00
This will work with multiple years because we are only filtering on the month and day.
# change range to include 2017
df = pd.DataFrame(pd.date_range('20171223', '20190103', freq='H'), columns=['date'])
df = df.loc[
(df.date.dt.strftime('%m-%d') < '12-24') &
(df.date.dt.strftime('%m-%d') >= '01-02')
].copy()
print(df)
date
0 2017-12-23 00:00:00
1 2017-12-23 01:00:00
2 2017-12-23 02:00:00
3 2017-12-23 03:00:00
4 2017-12-23 04:00:00
5 2017-12-23 05:00:00
6 2017-12-23 06:00:00
7 2017-12-23 07:00:00
8 2017-12-23 08:00:00
9 2017-12-23 09:00:00
10 2017-12-23 10:00:00
11 2017-12-23 11:00:00
12 2017-12-23 12:00:00
13 2017-12-23 13:00:00
14 2017-12-23 14:00:00
15 2017-12-23 15:00:00
16 2017-12-23 16:00:00
17 2017-12-23 17:00:00
18 2017-12-23 18:00:00
19 2017-12-23 19:00:00
20 2017-12-23 20:00:00
21 2017-12-23 21:00:00
22 2017-12-23 22:00:00
23 2017-12-23 23:00:00
240 2018-01-02 00:00:00
241 2018-01-02 01:00:00
242 2018-01-02 02:00:00
243 2018-01-02 03:00:00
244 2018-01-02 04:00:00
245 2018-01-02 05:00:00
... ...
8779 2018-12-23 19:00:00
8780 2018-12-23 20:00:00
8781 2018-12-23 21:00:00
8782 2018-12-23 22:00:00
8783 2018-12-23 23:00:00
9000 2019-01-02 00:00:00
9001 2019-01-02 01:00:00
9002 2019-01-02 02:00:00
9003 2019-01-02 03:00:00
9004 2019-01-02 04:00:00
9005 2019-01-02 05:00:00
9006 2019-01-02 06:00:00
9007 2019-01-02 07:00:00
9008 2019-01-02 08:00:00
9009 2019-01-02 09:00:00
9010 2019-01-02 10:00:00
9011 2019-01-02 11:00:00
9012 2019-01-02 12:00:00
9013 2019-01-02 13:00:00
9014 2019-01-02 14:00:00
9015 2019-01-02 15:00:00
9016 2019-01-02 16:00:00
9017 2019-01-02 17:00:00
9018 2019-01-02 18:00:00
9019 2019-01-02 19:00:00
9020 2019-01-02 20:00:00
9021 2019-01-02 21:00:00
9022 2019-01-02 22:00:00
9023 2019-01-02 23:00:00
9024 2019-01-03 00:00:00

Since you want this to happen for every year, we can first define a series that where we replace the year by a static value (2000 for example). Let date be the column that stores the date, we can generate such column as:
dt = pd.to_datetime({'year': 2000, 'month': df['date'].dt.month, 'day': df['date'].dt.day})
For the given sample data, we get:
>>> dt
0 2000-12-23
1 2000-12-23
2 2000-12-23
3 2000-12-23
4 2000-12-23
5 2000-12-23
6 2000-12-23
7 2000-12-24
8 2000-12-24
9 2000-12-24
10 2000-12-24
11 2000-12-24
12 2000-12-24
13 2000-12-24
14 2000-01-01
15 2000-01-01
16 2000-01-01
17 2000-01-01
18 2000-01-01
19 2000-01-02
20 2000-01-02
21 2000-01-02
22 2000-01-02
23 2000-01-02
24 2000-01-02
25 2000-01-02
26 2000-01-02
dtype: datetime64[ns]
Next we can filter the rows, like:
from datetime import date
df[(dt >= date(2000,1,2)) & (dt < date(2000,12,24))]
This gives us the following data for your sample data:
>>> df[(dt >= date(2000,1,2)) & (dt < date(2000,12,24))]
id dt
0 7505 2003-12-23 17:00:00
1 7506 2003-12-23 18:00:00
2 7507 2003-12-23 19:00:00
3 7508 2003-12-23 20:00:00
4 7509 2003-12-23 21:00:00
5 7510 2003-12-23 22:00:00
6 7511 2003-12-23 23:00:00
19 7728 2004-01-02 00:00:00
20 7729 2004-01-02 01:00:00
21 7730 2004-01-02 02:00:00
22 7731 2004-01-02 03:00:00
23 7732 2004-01-02 04:00:00
24 7733 2004-01-02 05:00:00
25 7734 2004-01-02 06:00:00
26 7735 2004-01-02 07:00:00
So regardless what the year is, we will only consider dates between the 2nd of January and the 23rd of December (both inclusive).

Indentify cells by condition within the same day

Let's say I have the below Dataframe. How would I do to get an extra column 'flag' with 1's where a day has a age bigger than 90 and only if it happens in 2 consecutive days (48h in this case)? The output should contain 1' on 2 or more days, depending on how many days the condition is met The dataset is much bigger, but I put here just a small portion so you get an idea.
Age
Dates
2019-01-01 00:00:00 29
2019-01-01 01:00:00 56
2019-01-01 02:00:00 82
2019-01-01 03:00:00 13
2019-01-01 04:00:00 35
2019-01-01 05:00:00 53
2019-01-01 06:00:00 25
2019-01-01 07:00:00 23
2019-01-01 08:00:00 21
2019-01-01 09:00:00 12
2019-01-01 10:00:00 15
2019-01-01 11:00:00 9
2019-01-01 12:00:00 13
2019-01-01 13:00:00 87
2019-01-01 14:00:00 9
2019-01-01 15:00:00 63
2019-01-01 16:00:00 62
2019-01-01 17:00:00 52
2019-01-01 18:00:00 43
2019-01-01 19:00:00 77
2019-01-01 20:00:00 95
2019-01-01 21:00:00 79
2019-01-01 22:00:00 77
2019-01-01 23:00:00 5
2019-01-02 00:00:00 78
2019-01-02 01:00:00 41
2019-01-02 02:00:00 10
2019-01-02 03:00:00 10
2019-01-02 04:00:00 88
2019-01-02 05:00:00 19
This would be the desired output:
Dates Age flag
0 2019-01-01 00:00:00 29 1
1 2019-01-01 01:00:00 56 1
2 2019-01-01 02:00:00 82 1
3 2019-01-01 03:00:00 13 1
4 2019-01-01 04:00:00 35 1
5 2019-01-01 05:00:00 53 1
6 2019-01-01 06:00:00 25 1
7 2019-01-01 07:00:00 23 1
8 2019-01-01 08:00:00 21 1
9 2019-01-01 09:00:00 12 1
10 2019-01-01 10:00:00 15 1
11 2019-01-01 11:00:00 9 1
12 2019-01-01 12:00:00 13 1
13 2019-01-01 13:00:00 87 1
14 2019-01-01 14:00:00 9 1
15 2019-01-01 15:00:00 63 1
16 2019-01-01 16:00:00 62 1
17 2019-01-01 17:00:00 52 1
18 2019-01-01 18:00:00 43 1
19 2019-01-01 19:00:00 77 1
20 2019-01-01 20:00:00 95 1
21 2019-01-01 21:00:00 79 1
22 2019-01-01 22:00:00 77 1
23 2019-01-01 23:00:00 5 1
24 2019-01-02 00:00:00 78 0
25 2019-01-02 01:00:00 41 0
26 2019-01-02 02:00:00 10 0
27 2019-01-02 03:00:00 10 0
28 2019-01-02 04:00:00 88 0
29 2019-01-02 05:00:00 19 0
The dates is the index of the dataframe and is incremented by 1h.
thanks

You can first compare column by Series.gt, then grouping by DatetimeIndex.date and ccheck if at least one True per groups by GroupBy.transform with GroupBy.any, last cast mask to integers for True/False to 1/0 mapping, then combinae it with previous answer:
df = pd.DataFrame({'Age': 10}, index=pd.date_range('2019-01-01', freq='5H', periods=24))
#for test 1H timestamp use
#df = pd.DataFrame({'Age': 10}, index=pd.date_range('2019-01-01', freq='H', periods=24 * 5))
df.loc[pd.Timestamp('2019-01-02 01:00:00'), 'Age'] = 95
df.loc[pd.Timestamp('2019-01-03 02:00:00'), 'Age'] = 95
df.loc[pd.Timestamp('2019-01-05 19:00:00'), 'Age'] = 95
#print (df)
#for test 48 consecutive values change N = 48
N = 10
s = df['Age'].gt(90)
s1 = (s.groupby(df.index.date).transform('any'))
g1 = s1.ne(s1.shift()).cumsum()
df['flag'] = (s.groupby(g1).transform('size').ge(N) & s1).astype(int)
print (df)
Age flag
2019-01-01 00:00:00 10 0
2019-01-01 05:00:00 10 0
2019-01-01 10:00:00 10 0
2019-01-01 15:00:00 10 0
2019-01-01 20:00:00 10 0
2019-01-02 01:00:00 95 1
2019-01-02 06:00:00 10 1
2019-01-02 11:00:00 10 1
2019-01-02 16:00:00 10 1
2019-01-02 21:00:00 10 1
2019-01-03 02:00:00 95 1
2019-01-03 07:00:00 10 1
2019-01-03 12:00:00 10 1
2019-01-03 17:00:00 10 1
2019-01-03 22:00:00 10 1
2019-01-04 03:00:00 10 0
2019-01-04 08:00:00 10 0
2019-01-04 13:00:00 10 0
2019-01-04 18:00:00 10 0
2019-01-04 23:00:00 10 0
2019-01-05 04:00:00 10 0
2019-01-05 09:00:00 10 0
2019-01-05 14:00:00 10 0
2019-01-05 19:00:00 95 0

Apparently, this could be a solution to the first version of the question: how to add a column whose row values are 1 if at least one of the rows with the same date (y-m-d) has an Age value greater than 90.
import pandas as pd
df = pd.DataFrame({
'Dates':['2019-01-01 00:00:00',
'2019-01-01 01:00:00',
'2019-01-01 02:00:00',
'2019-01-02 00:00:00',
'2019-01-02 01:00:00',
'2019-01-03 02:00:00',
'2019-01-03 03:00:00',],
'Age':[29, 56, 92, 13, 1, 2, 93],})
df.set_index('Dates', inplace=True)
df.index = pd.to_datetime(df.index)
df['flag'] = pd.DatetimeIndex(df.index).day
df['flag'] = df.flag.isin(df['flag'][df['Age']>90]).astype(int)
It returns:
Age flag
Dates
2019-01-01 00:00:00 29 1
2019-01-01 01:00:00 56 1
2019-01-01 02:00:00 92 1
2019-01-02 00:00:00 13 0
2019-01-02 01:00:00 1 0
2019-01-03 02:00:00 2 1
2019-01-03 03:00:00 93 1

Pandas rolling mean for Series returns NaN

Why do I receive Nan for rolling mean? Here's a code and output for this code. Initially I thought my data's wrong but simple .mean() works OK.
print(df_train.head())
y_hat_avg['mean'] = df_train['pickups'].mean()
print(y_hat_avg.head())
y_hat_avg['moving_avg_forecast'] = df_train['pickups'].rolling(1).mean()
print(y_hat_avg.head())
Added some data:
...................................................................
pickups
date
2014-04-01 00:00:00 12
2014-04-01 01:00:00 5
2014-04-01 02:00:00 2
2014-04-01 03:00:00 4
2014-04-01 04:00:00 3
pickups mean
date
2014-08-01 00:00:00 19 47.25888
2014-08-01 01:00:00 26 47.25888
2014-08-01 02:00:00 9 47.25888
2014-08-01 03:00:00 4 47.25888
2014-08-01 04:00:00 11 47.25888
pickups mean moving_avg_forecast
date
2014-08-01 00:00:00 19 47.25888 NaN
2014-08-01 01:00:00 26 47.25888 NaN
2014-08-01 02:00:00 9 47.25888 NaN
2014-08-01 03:00:00 4 47.25888 NaN
2014-08-01 04:00:00 11 47.25888 NaN

df_train.index = pd.RangeIndex(len(df_train.index)) fixed the problem for me.

Resample python list with pandas

Fairly new to python and pandas here.
I make a query that's giving me back a timeseries. I'm never sure how many data points I receive from the query (run for a single day), but what I do know is that I need to resample them to contain 24 points (one for each hour in the day).
Printing m3hstream gives
[(1479218009000L, 109), (1479287368000L, 84)]
Then I try to make a dataframe df with
df = pd.DataFrame(data = list(m3hstream), columns=['Timestamp', 'Value'])
and this gives me an output of
Timestamp Value
0 1479218009000 109
1 1479287368000 84
Following I do this
daily_summary = pd.DataFrame()
daily_summary['value'] = df['Value'].resample('H').mean()
daily_summary = daily_summary.truncate(before=start, after=end)
print "Now daily summary"
print daily_summary
But this is giving me a TypeError: Only valid with DatetimeIndex, TimedeltaIndex or PeriodIndex, but got an instance of 'RangeIndex'
Could anyone please let me know how to resample it so I have 1 point for each hour in the 24 hour period that I'm querying for?
Thanks.

First thing you need to do is convert that 'Timestamp' to an actual pd.Timestamp. It looks like those are milliseconds
Then resample with the on parameter set to 'Timestamp'
df = df.assign(
Timestamp=pd.to_datetime(df.Timestamp, unit='ms')
).resample('H', on='Timestamp').mean().reset_index()
Timestamp Value
0 2016-11-15 13:00:00 109.0
1 2016-11-15 14:00:00 NaN
2 2016-11-15 15:00:00 NaN
3 2016-11-15 16:00:00 NaN
4 2016-11-15 17:00:00 NaN
5 2016-11-15 18:00:00 NaN
6 2016-11-15 19:00:00 NaN
7 2016-11-15 20:00:00 NaN
8 2016-11-15 21:00:00 NaN
9 2016-11-15 22:00:00 NaN
10 2016-11-15 23:00:00 NaN
11 2016-11-16 00:00:00 NaN
12 2016-11-16 01:00:00 NaN
13 2016-11-16 02:00:00 NaN
14 2016-11-16 03:00:00 NaN
15 2016-11-16 04:00:00 NaN
16 2016-11-16 05:00:00 NaN
17 2016-11-16 06:00:00 NaN
18 2016-11-16 07:00:00 NaN
19 2016-11-16 08:00:00 NaN
20 2016-11-16 09:00:00 84.0
If you want to fill those NaN values, use ffill, bfill, or interpolate
df.assign(
Timestamp=pd.to_datetime(df.Timestamp, unit='ms')
).resample('H', on='Timestamp').mean().reset_index().interpolate()
Timestamp Value
0 2016-11-15 13:00:00 109.00
1 2016-11-15 14:00:00 107.75
2 2016-11-15 15:00:00 106.50
3 2016-11-15 16:00:00 105.25
4 2016-11-15 17:00:00 104.00
5 2016-11-15 18:00:00 102.75
6 2016-11-15 19:00:00 101.50
7 2016-11-15 20:00:00 100.25
8 2016-11-15 21:00:00 99.00
9 2016-11-15 22:00:00 97.75
10 2016-11-15 23:00:00 96.50
11 2016-11-16 00:00:00 95.25
12 2016-11-16 01:00:00 94.00
13 2016-11-16 02:00:00 92.75
14 2016-11-16 03:00:00 91.50
15 2016-11-16 04:00:00 90.25
16 2016-11-16 05:00:00 89.00
17 2016-11-16 06:00:00 87.75
18 2016-11-16 07:00:00 86.50
19 2016-11-16 08:00:00 85.25
20 2016-11-16 09:00:00 84.00

Let's try:
daily_summary = daily_summary.set_index('Timestamp')
daily_summary.index = pd.to_datetime(daily_summary.index, unit='ms')
For once an hour:
daily_summary.resample('H').mean()
or for once a day:
daily_summary.resample('D').mean()